chore(release): update version to 0.2.1

The previous fix made retain_recent_legal_suffix return the actual dropped
message list, but already_consolidated was still computed with
min(before_last_consolidated, len(dropped)), which assumes dropped messages
are always a prefix. In the else branch (tail has no user messages), dropped
may include messages from after the consolidated prefix, causing
already_consolidated to skip too many and leaving tail messages neither
retained nor raw-archived.

Fix by having retain_recent_legal_suffix return (dropped,
already_consolidated_count) where already_consolidated_count is computed
against original message indices. Also fix last_consolidated update to count
how many retained messages were inside the old consolidated prefix.

.agent/gotchas.md

@@ -31,10 +31,6 @@ Tool descriptions, skills, and replayed session history also shape model behavio
 
 Anything written into memory, session history, or prompt inputs can be replayed into future LLM calls. Metadata such as timestamps, local media paths, tool-call echoes, and raw fallback dumps must be bounded and sanitized before they become examples for the model to imitate.
 
-## Heartbeat Virtual Tool Call
-
-The heartbeat service (`heartbeat/service.py`) does not parse free-text LLM output. Instead, it injects a virtual `heartbeat` tool with `action: skip | run` into the conversation. Phase 1 is a structured decision; Phase 2 executes only on `run`. When adding new periodic background checks, follow this virtual-tool-call pattern rather than string matching.
-
 ## Skills as Extension Point
 
 Built-in skills live in `nanobot/skills/` (markdown + YAML frontmatter format). Agent capabilities that are "know-how" rather than code should be added as skills, not hardcoded into the agent loop. External skills can be published to and installed from ClawHub.

.dockerignore

@@ -5,6 +5,7 @@ __pycache__
 *.egg-info
 dist/
 build/
+nanobot/web/dist/
 .git
 .env
 .assets

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -49,7 +49,7 @@ body:
     attributes:
       label: nanobot Version
       description: Run `nanobot --version` or `pip show nanobot-ai`
-      placeholder: e.g., 0.1.5
+      placeholder: e.g., 0.2.0
     validations:
       required: true
 

.github/workflows/ci.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: ${{ github.event_name == 'pull_request' && fromJSON('["ubuntu-latest"]') || fromJSON('["ubuntu-latest","windows-latest"]') }}
+        os: ${{ fromJSON('["ubuntu-latest","windows-latest"]') }}
         # CI concentrates on newer runtimes (3.11/3.12 still supported per pyproject requires-python).
         python-version: ${{ fromJSON('["3.13","3.14"]') }}
 

.gitignore

@@ -6,6 +6,8 @@
 .env
 .web
 .orion
+nanobot-desktop/
+desktop/
 
 # Claude / AI assistant artifacts
 docs/superpowers/
@@ -97,3 +99,5 @@ logs/
 tmp/
 temp/
 *.tmp
+exp/
+.playwright-mcp/

AGENTS.md

@@ -0,0 +1,82 @@
+This file provides guidance to AI coding agents working with this repository.
+
+## Project Overview
+
+nanobot is a lightweight, open-source AI agent framework written in Python with a React/TypeScript WebUI. It centers around a small agent loop that receives messages from chat channels, invokes an LLM provider, executes tools, and manages session memory.
+
+## Development Commands
+
+```bash
+# Python: run single test / lint
+pytest tests/test_openai_api.py::test_function -v
+ruff check nanobot/
+
+# WebUI: dev server (proxies API/WS to gateway :8765), build, test
+# Build outputs to ../nanobot/web/dist (bundled into the Python wheel)
+cd webui && bun run dev      # or NANOBOT_API_URL=... bun run dev
+cd webui && bun run build
+cd webui && bun run test
+
+# Gateway
+nanobot gateway
+```
+
+## High-Level Architecture
+
+### Core Data Flow
+
+Messages flow through an async `MessageBus` (`nanobot/bus/queue.py`) that decouples chat channels from the agent core:
+
+1. **Channels** (`nanobot/channels/`) receive messages from external platforms and publish `InboundMessage` events to the bus.
+2. **`AgentLoop`** (`nanobot/agent/loop.py`) consumes inbound messages, builds context, and coordinates the turn.
+3. **`AgentRunner`** (`nanobot/agent/runner.py`) handles the actual LLM conversation loop: send messages to the provider, receive tool calls, execute tools, and stream responses.
+4. Responses are published as `OutboundMessage` events back to the appropriate channel.
+
+### Key Subsystems
+
+- **Agent Loop** (`nanobot/agent/loop.py`, `runner.py`): The core processing engine. `AgentLoop` manages session keys, hooks, and context building. `AgentRunner` executes the multi-turn LLM conversation with tool execution.
+- **LLM Providers** (`nanobot/providers/`): Provider implementations (Anthropic, OpenAI-compatible, OpenAI Responses API, Azure, Bedrock, GitHub Copilot, OpenAI Codex, etc.) built on a common base (`base.py`). Includes image generation (`image_generation.py`) and audio transcription (`transcription.py`). `factory.py` and `registry.py` handle instantiation and model discovery.
+- **Channels** (`nanobot/channels/`): Platform integrations (Telegram, Discord, Slack, Feishu, Matrix, WhatsApp, QQ, WeChat, WeCom, DingTalk, Email, MoChat, MS Teams, WebSocket). `manager.py` discovers and coordinates them. Channels are auto-discovered via `pkgutil` scan + entry-point plugins.
+- **Tools** (`nanobot/agent/tools/`): Agent capabilities exposed to the LLM: filesystem (read/write/edit/list), shell execution (with sandbox backends), web search/fetch, MCP servers, cron, notebook editing, subagent spawning, long-running tasks / sustained goals (`long_task.py`), image generation, and self-modification. Tools are auto-discovered via `pkgutil` scan + entry-point plugins.
+- **Memory** (`nanobot/agent/memory.py`): Session history persistence with Dream two-phase memory consolidation. Uses atomic writes with fsync for durability.
+- **Session Management** (`nanobot/session/`): Per-session history, context compaction, TTL-based auto-compaction (`manager.py`), and sustained goal state tracking (`goal_state.py`).
+- **Config** (`nanobot/config/schema.py`, `loader.py`): Pydantic-based configuration loaded from `~/.nanobot/config.json`. Supports camelCase aliases for JSON compatibility.
+- **Bridge** (`bridge/`): TypeScript services (e.g. WhatsApp bridge) bundled into the wheel via `pyproject.toml` `force-include`.
+- **WebUI** (`webui/`): Vite-based React SPA that talks to the gateway over a WebSocket multiplex protocol. The dev server proxies `/api`, `/webui`, `/auth`, and WebSocket traffic to the gateway.
+- **API Server** (`nanobot/api/server.py`): OpenAI-compatible HTTP API (`/v1/chat/completions`, `/v1/models`) for programmatic access.
+- **Command Router** (`nanobot/command/`): Slash command routing and built-in command handlers.
+- **Heartbeat** (`nanobot/templates/HEARTBEAT.md`): Periodic task list checked via `cron` jobs (legacy dedicated service removed).
+- **Pairing** (`nanobot/pairing/`): DM sender approval store with persistent pairing codes per channel.
+- **Skills** (`nanobot/skills/`): Built-in skill definitions (long-goal, cron, github, image-generation, etc.) loaded into agent context.
+- **Security** (`nanobot/security/`): PTH file guard and other security measures activated at CLI entry.
+
+### Entry Points
+
+- **CLI**: `nanobot/cli/commands.py`
+- **Python SDK**: `nanobot/nanobot.py`
+
+## Project-Specific Notes
+
+- Architecture constraints: [`.agent/design.md`](.agent/design.md)
+- Security boundaries: [`.agent/security.md`](.agent/security.md)
+- Common gotchas: [`.agent/gotchas.md`](.agent/gotchas.md)
+
+## Branching Strategy
+
+See [`CONTRIBUTING.md`](./CONTRIBUTING.md) for the full two-branch model (`main` vs `nightly`) and PR guidelines.
+
+## Code Style
+
+- Python 3.11+, asyncio throughout.
+- Line length: 100.
+- Linting: `ruff` with rules E, F, I, N, W (E501 ignored).
+- pytest with `asyncio_mode = "auto"`.
+
+## Common File Locations
+
+- Config schema: `nanobot/config/schema.py`
+- Provider base / new provider template: `nanobot/providers/base.py`
+- Channel base / new channel template: `nanobot/channels/base.py`
+- Tool registry: `nanobot/agent/tools/registry.py`
+- WebUI dev proxy config: `webui/vite.config.ts`
+- Tests mirror the `nanobot/` package structure.

CLAUDE.md

@@ -1,84 +1 @@
-# CLAUDE.md
+@AGENTS.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Project Overview
-
-nanobot is a lightweight, open-source AI agent framework written in Python with a React/TypeScript WebUI. It centers around a small agent loop that receives messages from chat channels, invokes an LLM provider, executes tools, and manages session memory.
-
-## Development Commands
-
-```bash
-# Python: run single test / lint
-pytest tests/test_openai_api.py::test_function -v
-ruff check nanobot/
-
-# WebUI: dev server (proxies API/WS to gateway :8765), build, test
-# Build outputs to ../nanobot/web/dist (bundled into the Python wheel)
-cd webui && bun run dev      # or NANOBOT_API_URL=... bun run dev
-cd webui && bun run build
-cd webui && bun run test
-
-# Gateway
-nanobot gateway
-```
-
-## High-Level Architecture
-
-### Core Data Flow
-
-Messages flow through an async `MessageBus` (`nanobot/bus/queue.py`) that decouples chat channels from the agent core:
-
-1. **Channels** (`nanobot/channels/`) receive messages from external platforms and publish `InboundMessage` events to the bus.
-2. **`AgentLoop`** (`nanobot/agent/loop.py`) consumes inbound messages, builds context, and coordinates the turn.
-3. **`AgentRunner`** (`nanobot/agent/runner.py`) handles the actual LLM conversation loop: send messages to the provider, receive tool calls, execute tools, and stream responses.
-4. Responses are published as `OutboundMessage` events back to the appropriate channel.
-
-### Key Subsystems
-
-- **Agent Loop** (`nanobot/agent/loop.py`, `runner.py`): The core processing engine. `AgentLoop` manages session keys, hooks, and context building. `AgentRunner` executes the multi-turn LLM conversation with tool execution.
-- **LLM Providers** (`nanobot/providers/`): Provider implementations (Anthropic, OpenAI-compatible, OpenAI Responses API, Azure, Bedrock, GitHub Copilot, OpenAI Codex, etc.) built on a common base (`base.py`). Includes image generation (`image_generation.py`) and audio transcription (`transcription.py`). `factory.py` and `registry.py` handle instantiation and model discovery.
-- **Channels** (`nanobot/channels/`): Platform integrations (Telegram, Discord, Slack, Feishu, Matrix, WhatsApp, QQ, WeChat, WeCom, DingTalk, Email, MoChat, MS Teams, WebSocket). `manager.py` discovers and coordinates them. Channels are auto-discovered via `pkgutil` scan + entry-point plugins.
-- **Tools** (`nanobot/agent/tools/`): Agent capabilities exposed to the LLM: filesystem (read/write/edit/list), shell execution (with sandbox backends), web search/fetch, MCP servers, cron, notebook editing, subagent spawning, long-running tasks / sustained goals (`long_task.py`), image generation, and self-modification. Tools are auto-discovered via `pkgutil` scan + entry-point plugins.
-- **Memory** (`nanobot/agent/memory.py`): Session history persistence with Dream two-phase memory consolidation. Uses atomic writes with fsync for durability.
-- **Session Management** (`nanobot/session/`): Per-session history, context compaction, TTL-based auto-compaction (`manager.py`), and sustained goal state tracking (`goal_state.py`).
-- **Config** (`nanobot/config/schema.py`, `loader.py`): Pydantic-based configuration loaded from `~/.nanobot/config.json`. Supports camelCase aliases for JSON compatibility.
-- **Bridge** (`bridge/`): TypeScript services (e.g. WhatsApp bridge) bundled into the wheel via `pyproject.toml` `force-include`.
-- **WebUI** (`webui/`): Vite-based React SPA that talks to the gateway over a WebSocket multiplex protocol. The dev server proxies `/api`, `/webui`, `/auth`, and WebSocket traffic to the gateway.
-- **API Server** (`nanobot/api/server.py`): OpenAI-compatible HTTP API (`/v1/chat/completions`, `/v1/models`) for programmatic access.
-- **Command Router** (`nanobot/command/`): Slash command routing and built-in command handlers.
-- **Heartbeat** (`nanobot/heartbeat/`): Periodic agent wake-up service for scheduled task checking.
-- **Pairing** (`nanobot/pairing/`): DM sender approval store with persistent pairing codes per channel.
-- **Skills** (`nanobot/skills/`): Built-in skill definitions (long-goal, cron, github, image-generation, etc.) loaded into agent context.
-- **Security** (`nanobot/security/`): PTH file guard and other security measures activated at CLI entry.
-
-### Entry Points
-
-- **CLI**: `nanobot/cli/commands.py`
-- **Python SDK**: `nanobot/nanobot.py`
-
-## Project-Specific Notes
-
-- Architecture constraints: [`.agent/design.md`](.agent/design.md)
-- Security boundaries: [`.agent/security.md`](.agent/security.md)
-- Common gotchas: [`.agent/gotchas.md`](.agent/gotchas.md)
-
-## Branching Strategy
-
-See [`CONTRIBUTING.md`](./CONTRIBUTING.md) for the full two-branch model (`main` vs `nightly`) and PR guidelines.
-
-## Code Style
-
-- Python 3.11+, asyncio throughout.
-- Line length: 100.
-- Linting: `ruff` with rules E, F, I, N, W (E501 ignored).
-- pytest with `asyncio_mode = "auto"`.
-
-## Common File Locations
-
-- Config schema: `nanobot/config/schema.py`
-- Provider base / new provider template: `nanobot/providers/base.py`
-- Channel base / new channel template: `nanobot/channels/base.py`
-- Tool registry: `nanobot/agent/tools/registry.py`
-- WebUI dev proxy config: `webui/vite.config.ts`
-- Tests mirror the `nanobot/` package structure.

CONTRIBUTING.md

@@ -12,6 +12,8 @@ software together: with care, clarity, and respect for the next person reading t
 
 ## Maintainers
 
+Maintainers are community stewards who help review, organize, and maintain the project. The list below describes each maintainer's current open-source project responsibilities.
+
 | Maintainer | Focus |
 |------------|-------|
 | [@re-bin](https://github.com/re-bin) | Project lead, `main` branch |

Dockerfile

@@ -14,8 +14,9 @@ RUN apt-get update && \
 
 WORKDIR /app
 
-# Install Python dependencies first (cached layer)
+# Install Python dependencies first (cached layer). Hatch reads the custom build
-COPY pyproject.toml README.md LICENSE ./
+# hook from hatch_build.py even for this metadata-only install.
+COPY pyproject.toml README.md LICENSE THIRD_PARTY_NOTICES.md hatch_build.py ./
 RUN mkdir -p nanobot bridge && touch nanobot/__init__.py && \
     uv pip install --system --no-cache . && \
     rm -rf nanobot bridge
@@ -23,7 +24,8 @@ RUN mkdir -p nanobot bridge && touch nanobot/__init__.py && \
 # Copy the full source and install
 COPY nanobot/ nanobot/
 COPY bridge/ bridge/
-RUN uv pip install --system --no-cache .
+COPY webui/ webui/
+RUN NANOBOT_FORCE_WEBUI_BUILD=1 uv pip install --system --no-cache .
 
 # Build the WhatsApp bridge
 WORKDIR /app/bridge
@@ -43,8 +45,8 @@ RUN sed -i 's/\r$//' /usr/local/bin/entrypoint.sh && chmod +x /usr/local/bin/ent
 USER nanobot
 ENV HOME=/home/nanobot
 
-# Gateway default port
+# Gateway health endpoint and optional WebUI/WebSocket channel ports
-EXPOSE 18790
+EXPOSE 18790 8765
 
 ENTRYPOINT ["entrypoint.sh"]
 CMD ["status"]

README.md

@@ -1,6 +1,18 @@
-![cover-v5-optimized](./images/GitHub_README.png)
+![nanobot README cover](./images/readme-cover.png)
 
 <div align="center">
+  <p>
+    <a href="https://nanobot.wiki/docs/latest/getting-started/nanobot-overview">English</a> |
+    <a href="https://nanobot.wiki/cn/docs/latest/getting-started/nanobot-overview">简体中文</a> |
+    <a href="https://nanobot.wiki/zh-Hant/docs/latest/getting-started/nanobot-overview">繁體中文</a> |
+    <a href="https://nanobot.wiki/es/docs/latest/getting-started/nanobot-overview">Español</a> |
+    <a href="https://nanobot.wiki/fr/docs/latest/getting-started/nanobot-overview">Français</a> |
+    <a href="https://nanobot.wiki/id/docs/latest/getting-started/nanobot-overview">Bahasa Indonesia</a> |
+    <a href="https://nanobot.wiki/ja/docs/latest/getting-started/nanobot-overview">日本語</a> |
+    <a href="https://nanobot.wiki/ko/docs/latest/getting-started/nanobot-overview">한국어</a> |
+    <a href="https://nanobot.wiki/ru/docs/latest/getting-started/nanobot-overview">Русский</a> |
+    <a href="https://nanobot.wiki/vi/docs/latest/getting-started/nanobot-overview">Tiếng Việt</a>
+  </p>
   <p>
     <a href="https://pypi.org/project/nanobot-ai/"><img src="https://img.shields.io/pypi/v/nanobot-ai" alt="PyPI"></a>
     <a href="https://pepy.tech/project/nanobot-ai"><img src="https://static.pepy.tech/badge/nanobot-ai" alt="Downloads"></a>
@@ -19,10 +31,30 @@
   </p>
 </div>
 
-🐈 **nanobot** is an open-source and ultra-lightweight AI agent in the spirit of [OpenClaw](https://github.com/openclaw/openclaw), [Claude Code](https://www.anthropic.com/claude-code), and [Codex](https://www.openai.com/codex/). It keeps the core agent loop small and readable while still supporting chat channels, memory, MCP and practical deployment paths, so you can go from local setup to a long-running personal agent with minimal overhead.
+🐈 **nanobot** is an open-source, ultra-lightweight agent runtime for people who want to own their AI agent stack. It gives you a small, readable core plus the practical pieces for real long-running agents: WebUI, chat channels, tools, memory, MCP, model routing, and deployment.
 
 ## 📢 News
 
+- **2026-05-30** 🔐 Safer Matrix verification, bounded media downloads, clearer WebUI model timeline.
+- **2026-05-29** 🧩 Extension registry, context-window tuning, document extraction controls.
+- **2026-05-28** 🗂️ Project workspaces, access controls, steadier goals and streaming.
+- **2026-05-27** ⏱️ Codex streams respect idle timeouts during long runs.
+- **2026-05-26** 📡 Telegram webhooks, refreshed Kagi search, cleaner transport errors.
+- **2026-05-25** 🔌 Unified CLI Apps and MCP, Step Plan support, steadier sustained goals.
+- **2026-05-24** 🧰 MCP presets, richer slash actions, configurable OpenAI-compatible requests.
+- **2026-05-23** 🖼️ Zhipu image generation, longer exec windows, cleaner transcription config.
+- **2026-05-22** 🛠️ CLI Apps, more image providers, safer web redirects and edits.
+- **2026-05-21** ⚡ Novita provider, faster sidebar, smoother coding tools and Weixin replies.
+
+<details>
+<summary>Earlier news</summary>
+
+- **2026-05-20** 📶 Signal channel, faster gateway startup, multilingual README links.
+- **2026-05-19** 🎨 Image provider registry, StepFun and Skywork, stronger WebUI controls.
+- **2026-05-18** 🖌️ Gemini and MiniMax images, Ant Ling, live file-edit activity.
+- **2026-05-17** 🌊 Smoother WebUI streaming, AutoCompact fixes, buffered CLI reasoning.
+- **2026-05-16** 🧠 Atomic Chat provider, goal-aware timeouts, safer exec URL handling.
+- **2026-05-15** 🚀 Released **v0.2.0** — **`/goal`** holds sustained objectives across turns, WebUI now ships inside the wheel, image generation end to end, 5 new providers with `fallback_models`, and a real agent-loop refactor. Please see [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.2.0) for details.
 - **2026-05-14** 🎯 **`/goal`** for long-term objectives, visible multi-step progress, long-horizon missions in chat.
 - **2026-05-13** 🧠 Streaming reasoning before answers, automatic backup models, smoother plug-in reconnects.
 - **2026-05-12** 🎛️ Saved model presets with WebUI badge, simpler plug-in tools, quieter Feishu topic threads.
@@ -32,10 +64,6 @@
 - **2026-05-07** 📜 Locale-aware slash palette in WebUI, LAN login, faithful HTTP streaming responses.
 - **2026-05-06** 🧩 Tunable tool hint, steadier voice and plug-in startups, schedules and reminders that stick.
 - **2026-05-05** 🛡️ Quiet deny for unknown Telegram chats, Dream cleanup, fuller automation summaries.
-
-<details>
-<summary>Earlier news</summary>
-
 - **2026-05-04** 🔐 Safer DingTalk outbound media links, durable cron persistence, DeepSeek polish.
 - **2026-05-03** ⚙️ Predictable shell allow-list behavior, isolated chats mid-reply, cleaner interactive retries.
 - **2026-05-02** 🐈 LongCat support, smarter token sizing hints, clearer bundled upgrade guidance.
@@ -60,7 +88,7 @@
 - **2026-04-13** 🛡️ Agent turn hardened — user messages persisted early, auto-compact skips active tasks.
 - **2026-04-12** 🔒 Lark global domain support, Dream learns discovered skills, shell sandbox tightened.
 - **2026-04-11** ⚡ Context compact shrinks sessions on the fly; Kagi web search; QQ & WeCom full media.
-- **2026-04-10** 📓 Notebook editing tool, multiple MCP servers, Feishu streaming & done-emoji.
+- **2026-04-10** 📓 Multiple MCP servers, Feishu streaming & done-emoji.
 - **2026-04-09** 🔌 WebSocket channel, unified cross-channel session, `disabled_skills` config.
 - **2026-04-08** 📤 API file uploads, OpenAI reasoning auto-routing with Responses fallback.
 - **2026-04-07** 🧠 Anthropic adaptive thinking, MCP resources & prompts exposed as tools.
@@ -132,12 +160,13 @@
 </details>
 
 
-## 💡 Key Features of nanobot
+## 💡 Why nanobot
 
-- **Ultra-lightweight**: stable long-running agent behavior with a small, readable core.
+- **Persistent workflows**: goals, memory, tools, and chat context survive long-running work.
-- **Research-ready**: the codebase is intentionally simple enough to study, modify, and extend.
+- **Chat-native reach**: WebUI, API, Telegram, Feishu, Slack, Discord, Teams, and email.
-- **Practical**: chat channels, API, memory, MCP, and deployment paths are already built in.
+- **Model freedom**: OpenAI-compatible APIs, local LLMs, image generation, search, and fallbacks.
-- **Hackable**: you can start fast, then go deeper through repo docs instead of a monolithic landing page.
+- **Small core**: readable internals with MCP, memory, deployment, and automation built in.
+- **Own your stack**: inspect, customize, self-host, and extend without a giant platform.
 
 ## 📦 Install
 
@@ -211,13 +240,13 @@ nanobot agent
 
 
 - Want different LLM providers, web search, MCP, security settings, or more config options? See [Configuration](./docs/configuration.md)
+- Want to run locally? Use [Atomic Chat](./docs/configuration.md#atomic-chat-local), [vLLM](./docs/configuration.md#vllm-local-openai-compatible), [Ollama](./docs/configuration.md#ollama-local), and [others](./docs/configuration.md#local-providers).
 - Want to run nanobot in chat apps like Telegram, Discord, WeChat or Feishu? See [Chat Apps](./docs/chat-apps.md)
 - Want Docker or Linux service deployment? See [Deployment](./docs/deployment.md)
 
-## 🧪 WebUI (Development)
+## 🌐 WebUI
 
-> [!NOTE]
+The WebUI ships **inside the published wheel** — no extra build step. Just enable the WebSocket channel and open it in your browser.
-> The WebUI development workflow currently requires a source checkout and is not yet shipped together with the official packaged release. See [WebUI Document](./webui/README.md) for full WebUI development docs and build steps.
 
 <p align="center">
   <img src="images/nanobot_webui.png" alt="nanobot webui preview" width="900">
@@ -235,13 +264,12 @@ nanobot agent
 nanobot gateway
 ```
 
-**3. Start the webui dev server**
+**3. Open the WebUI**
 
-```bash
+Visit [`http://127.0.0.1:8765`](http://127.0.0.1:8765) in your browser. To open it from another device on your LAN, see [WebUI docs → LAN access](./webui/README.md#access-from-another-device-lan).
-cd webui
+
-bun install
+> [!TIP]
-bun run dev
+> Working on the WebUI itself? Check out [`webui/README.md`](./webui/README.md) for the Vite dev server (HMR) workflow.
-```
 
 ## 🏗️ Architecture
 
@@ -330,4 +358,4 @@ This project was started by [Xubin Ren](https://github.com/re-bin) as a personal
 <p align="center">
   <em> Thanks for visiting ✨ nanobot!</em><br><br>
   <img src="https://visitor-badge.laobi.icu/badge?page_id=HKUDS.nanobot&style=for-the-badge&color=00d4ff" alt="Views">
-</p>
+</p>

core_agent_lines.sh

@@ -46,17 +46,15 @@ core_agent=$(count_top_level_py_lines "nanobot/agent")
 core_bus=$(count_top_level_py_lines "nanobot/bus")
 core_config=$(count_top_level_py_lines "nanobot/config")
 core_cron=$(count_top_level_py_lines "nanobot/cron")
-core_heartbeat=$(count_top_level_py_lines "nanobot/heartbeat")
 core_session=$(count_top_level_py_lines "nanobot/session")
 
 print_row "agent/" "$core_agent"
 print_row "bus/" "$core_bus"
 print_row "config/" "$core_config"
 print_row "cron/" "$core_cron"
-print_row "heartbeat/" "$core_heartbeat"
 print_row "session/" "$core_session"
 
-core_total=$((core_agent + core_bus + core_config + core_cron + core_heartbeat + core_session))
+core_total=$((core_agent + core_bus + core_config + core_cron + core_session))
 
 echo ""
 echo "Separate buckets"

docker-compose.yml

@@ -20,6 +20,7 @@ services:
     restart: unless-stopped
     ports:
       - 18790:18790
+      - 8765:8765
     deploy:
       resources:
         limits:

docs/README.md

@@ -15,6 +15,7 @@ Start here for setup, everyday usage, and deployment.
 | Agent social network | [`agent-social-network.md`](./agent-social-network.md) | Join external agent communities from nanobot |
 | Configuration | [`configuration.md`](./configuration.md) | Providers, tools, channels, MCP, and runtime settings |
 | Image generation | [`image-generation.md`](./image-generation.md) | Configure image providers, WebUI image mode, and generated artifacts |
+| WebUI | [`../webui/README.md`](../webui/README.md) | Open the bundled browser UI; LAN access; Vite dev server for contributors |
 | Multiple instances | [`multiple-instances.md`](./multiple-instances.md) | Run isolated bots with separate configs and workspaces |
 | CLI reference | [`cli-reference.md`](./cli-reference.md) | Core CLI commands and common entrypoints |
 | In-chat commands | [`chat-commands.md`](./chat-commands.md) | Slash commands and periodic task behavior |

docs/chat-apps.md

@@ -17,6 +17,7 @@ Connect nanobot to your favorite chat platform. Want to build your own? See the
 | **Wecom** | Bot ID + Bot Secret |
 | **Microsoft Teams** | App ID + App Password + public HTTPS endpoint |
 | **Mochat** | Claw token (auto-setup available) |
+| **Signal** | signal-cli daemon + phone number |
 
 <details>
 <summary><b>Telegram</b> (Recommended)</summary>
@@ -50,6 +51,43 @@ Connect nanobot to your favorite chat platform. Want to build your own? See the
 nanobot gateway
 ```
 
+**Webhook mode (optional)**
+
+Telegram uses long polling by default. To receive updates through a webhook, expose
+a public HTTPS URL that forwards to nanobot's local listener and set `mode` to
+`webhook`:
+
+```json
+{
+  "channels": {
+    "telegram": {
+      "enabled": true,
+      "token": "YOUR_BOT_TOKEN",
+      "mode": "webhook",
+      "webhookUrl": "https://example.com/telegram",
+      "webhookListenHost": "127.0.0.1",
+      "webhookListenPort": 8081,
+      "webhookPath": "/telegram",
+      "webhookSecretToken": "CHANGE_ME_RANDOM_SECRET",
+      "webhookMaxConnections": 4,
+      "allowFrom": ["YOUR_USER_ID"]
+    }
+  }
+}
+```
+
+> `webhookSecretToken` is required in webhook mode. Do not expose the local
+> webhook listener directly to the public internet without a reverse proxy or
+> tunnel in front of it. TLS/Host policy is handled by your proxy; nanobot only
+> listens on `webhookListenHost:webhookListenPort` and validates Telegram's
+> webhook secret token. `webhookMaxConnections` defaults to `4`; nanobot
+> still serializes Telegram updates per conversation before forwarding them to
+> the agent.
+>
+> `webhookUrl` is the public HTTPS URL registered with Telegram.
+> `webhookPath` is the local path nanobot listens on. They often use the same
+> path, but may differ when a reverse proxy or tunnel rewrites the request path.
+
 </details>
 
 <details>
@@ -206,6 +244,7 @@ for reliable encryption, password login is recommended instead. If the
       "userId": "@nanobot:matrix.org",
       "password": "mypasswordhere",
       "e2eeEnabled": true,
+      "sasVerification": true,
       "allowFrom": ["@your_user:matrix.org"],
       "groupPolicy": "open",
       "groupAllowFrom": [],
@@ -225,6 +264,7 @@ for reliable encryption, password login is recommended instead. If the
 | `groupAllowFrom` | Room allowlist (used when policy is `allowlist`). |
 | `allowRoomMentions` | Accept `@room` mentions in mention mode. |
 | `e2eeEnabled` | E2EE support (default `true`). Set `false` for plaintext-only. |
+| `sasVerification` | Auto-complete SAS device verification requests from allowed users (default `false`). Useful for Element X, which does not expose manual trust for third-party devices. |
 | `maxMediaBytes` | Max attachment size (default `20MB`). Set `0` to block all media. |
 
 
@@ -669,3 +709,69 @@ nanobot gateway
 ```
 
 </details>
+
+<details>
+<summary><b>Signal</b></summary>
+
+Uses **signal-cli** daemon in HTTP mode — receive messages via SSE, send via JSON-RPC.
+
+**1. Install signal-cli**
+
+Install [signal-cli](https://github.com/AsamK/signal-cli) and register a phone number:
+
+```bash
+signal-cli -u +1234567890 register
+signal-cli -u +1234567890 verify <CODE>
+```
+
+Start the daemon:
+
+```bash
+signal-cli -a +1234567890 daemon --http localhost:8080
+```
+
+**2. Configure**
+
+```json
+{
+  "channels": {
+    "signal": {
+      "enabled": true,
+      "phoneNumber": "+1234567890",
+      "daemonHost": "localhost",
+      "daemonPort": 8080,
+      "dm": {
+        "enabled": true,
+        "policy": "open"
+      },
+      "group": {
+        "enabled": true,
+        "policy": "open",
+        "requireMention": true
+      }
+    }
+  }
+}
+```
+
+> - `phoneNumber`: Your registered Signal phone number.
+> - `daemonHost` / `daemonPort`: Where signal-cli daemon is listening (default `localhost:8080`).
+> - `dm.policy`: `"open"` (anyone can DM) or `"allowlist"` (only listed numbers/UUIDs). When `"allowlist"`, unlisted DM senders receive a pairing code.
+> - `dm.allowFrom`: List of allowed phone numbers or UUIDs (used when policy is `"allowlist"`).
+> - `group.policy`: `"open"` (all groups) or `"allowlist"` (only listed group IDs).
+> - `group.requireMention`: When `true` (default), the bot only responds in groups when @mentioned.
+> - `group.allowFrom`: List of allowed group IDs (used when group policy is `"allowlist"`).
+> - `attachmentsDir`: Override the directory where signal-cli stores inbound attachments. Defaults to `~/.local/share/signal-cli/attachments` (the Linux default). Set this if signal-cli runs with a custom `XDG_DATA_HOME` or on macOS/Windows.
+> - `groupMessageBufferSize`: Number of recent group messages kept for context (default `20`, must be > 0).
+
+**3. Run**
+
+```bash
+nanobot gateway
+```
+
+> [!TIP]
+> The channel automatically reconnects to the signal-cli daemon with exponential backoff if the connection drops.
+> Markdown in bot replies is automatically converted to Signal text styles (bold, italic, code, etc.).
+
+</details>

docs/chat-commands.md

@@ -56,17 +56,17 @@ Preset names come from the top-level `modelPresets` config. Switching is runtime
 
 ## Periodic Tasks
 
-The gateway wakes up every 30 minutes and checks `HEARTBEAT.md` in your workspace (`~/.nanobot/workspace/HEARTBEAT.md`). If the file has tasks, the agent executes them and delivers results to your most recently active chat channel.
+The gateway wakes up every 30 minutes and checks `HEARTBEAT.md` in your workspace (`~/.nanobot/workspace/HEARTBEAT.md`). If the file has tasks under `## Active Tasks`, the agent executes them and delivers results to your most recently active chat channel. If there are no active tasks, the heartbeat is skipped silently.
 
 **Setup:** edit `~/.nanobot/workspace/HEARTBEAT.md` (created automatically by `nanobot onboard`):
 
 ```markdown
-## Periodic Tasks
+## Active Tasks
 
 - [ ] Check weather forecast and send a summary
 - [ ] Scan inbox for urgent emails
 ```
 
-The agent can also manage this file itself — ask it to "add a periodic task" and it will update `HEARTBEAT.md` for you.
+The agent can also manage this file itself — ask it to "add a periodic task" and it will update `HEARTBEAT.md` for you. Completed tasks should be deleted from the file, not moved to another section.
 
 > **Note:** The gateway must be running (`nanobot gateway`) and you must have chatted with the bot at least once so it knows which channel to deliver to.

docs/configuration.md

@@ -26,7 +26,52 @@ Instead of storing secrets directly in `config.json`, you can use `${VAR_NAME}`
 }
 ```
 
-For **systemd** deployments, use `EnvironmentFile=` in the service unit to load variables from a file that only the deploying user can read:
+Any string value in `config.json` can use `${VAR_NAME}`. Resolution runs once at startup, in memory only — resolved values are never written back to disk, so editing config through `nanobot onboard` or the WebUI preserves the placeholder.
+
+If a referenced variable is unset, nanobot fails fast at startup with `ValueError: Environment variable 'NAME' referenced in config is not set`.
+
+### More examples
+
+**MCP servers** — both stdio `env` and HTTP `headers`:
+
+```json
+{
+  "tools": {
+    "mcpServers": {
+      "github": {
+        "command": "npx",
+        "args": ["-y", "@modelcontextprotocol/server-github"],
+        "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}" }
+      },
+      "remote": {
+        "url": "https://example.com/mcp/",
+        "headers": { "Authorization": "Bearer ${REMOTE_MCP_TOKEN}" }
+      }
+    }
+  }
+}
+```
+
+**Web search providers:**
+
+```json
+{
+  "tools": {
+    "web": {
+      "search": {
+        "provider": "brave",
+        "apiKey": "${BRAVE_API_KEY}"
+      }
+    }
+  }
+}
+```
+
+### Loading variables at startup
+
+Pick whatever fits your deployment — nanobot only reads `os.environ` at startup, so any mechanism that populates the process environment works.
+
+**systemd** — use `EnvironmentFile=` in the service unit to load variables from a file that only the deploying user can read:
 
 ```ini
 # /etc/systemd/system/nanobot.service (excerpt)
@@ -42,6 +87,35 @@ TELEGRAM_TOKEN=your-token-here
 IMAP_PASSWORD=your-password-here
 ```
 
+**Docker** — pass an env file to the locally built image (one `KEY=VALUE` per line), or use `-e KEY=value`:
+
+```bash
+docker run --rm --env-file=./nanobot.env \
+  -v ~/.nanobot:/home/nanobot/.nanobot \
+  nanobot agent -m "Hello"
+```
+
+**direnv** — drop a `.envrc` in your working directory and run `direnv allow`:
+
+```bash
+# .envrc (auto-loaded by direnv)
+export TELEGRAM_TOKEN=your-token-here
+export ANTHROPIC_API_KEY=...
+```
+
+**Secret managers (1Password, Bitwarden, pass)** — wrap the process so secrets only exist as env vars for the lifetime of the run, never on disk:
+
+```bash
+# 1Password — references in .env.tpl look like `op://Vault/Item/field`
+op run --env-file=.env.tpl -- nanobot agent
+
+# pass (passwordstore.org)
+ANTHROPIC_API_KEY="$(pass show api/anthropic)" nanobot agent
+
+# Bitwarden
+ANTHROPIC_API_KEY="$(bw get password api/anthropic)" nanobot agent
+```
+
 ## Providers
 
 > [!TIP]
@@ -52,14 +126,17 @@ IMAP_PASSWORD=your-password-here
 > - **VolcEngine / BytePlus Coding Plan**: Use dedicated providers `volcengineCodingPlan` or `byteplusCodingPlan` instead of the pay-per-use `volcengine` / `byteplus` providers.
 > - **Zhipu Coding Plan**: If you're on Zhipu's coding plan, set `"apiBase": "https://open.bigmodel.cn/api/coding/paas/v4"` in your zhipu provider config.
 > - **Alibaba Cloud BaiLian**: If you're using Alibaba Cloud BaiLian's OpenAI-compatible endpoint, set `"apiBase": "https://dashscope.aliyuncs.com/compatible-mode/v1"` in your dashscope provider config.
+> - **StepFun Step Plan**: If you're on StepFun's Step Plan subscription, set `"apiBase": "https://api.stepfun.com/step_plan/v1"` in your stepfun provider config. Supported models include `step-3.5-flash`, `step-3.5-flash-2603`, and `step-router-v1`.
 > - **Step Fun (Mainland China)**: If your API key is from Step Fun's mainland China platform (stepfun.com), set `"apiBase": "https://api.stepfun.com/v1"` in your stepfun provider config.
 > - **Xiaomi MiMo thinking mode**: MiMo models (e.g. `mimo-v2.5-pro`) default to enabled thinking. Use `agents.defaults.reasoningEffort: "none"` to disable it, or `"low"` / `"medium"` / `"high"` to keep it on. Omitting the field preserves the provider's per-model default.
+> - **Xiaomi MiMo Token Plan**: If you're on MiMo's token plan, set `"apiBase": "https://token-plan-sgp.xiaomimimo.com/v1"` in your xiaomi_mimo provider config.
 
 | Provider | Purpose | Get API Key |
 |----------|---------|-------------|
 | `custom` | Any OpenAI-compatible endpoint | — |
 | `openrouter` | LLM (recommended, access to all models) | [openrouter.ai](https://openrouter.ai) |
 | `huggingface` | LLM (Hugging Face Inference Providers) | [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens) |
+| `skywork` | LLM (Skywork / APIFree API gateway) | [apifree.ai](https://www.apifree.ai) |
 | `volcengine` | LLM (VolcEngine, pay-per-use) | [Coding Plan](https://www.volcengine.com/activity/codingplan?utm_campaign=nanobot&utm_content=nanobot&utm_medium=devrel&utm_source=OWO&utm_term=nanobot) · [volcengine.com](https://www.volcengine.com) |
 | `byteplus` | LLM (VolcEngine international, pay-per-use) | [Coding Plan](https://www.byteplus.com/en/activity/codingplan?utm_campaign=nanobot&utm_content=nanobot&utm_medium=devrel&utm_source=OWO&utm_term=nanobot) · [byteplus.com](https://www.byteplus.com) |
 | `anthropic` | LLM (Claude direct) | [console.anthropic.com](https://console.anthropic.com) |
@@ -73,11 +150,13 @@ IMAP_PASSWORD=your-password-here
 | `gemini` | LLM (Gemini direct) | [aistudio.google.com](https://aistudio.google.com) |
 | `aihubmix` | LLM (API gateway, access to all models) | [aihubmix.com](https://aihubmix.com) |
 | `siliconflow` | LLM (SiliconFlow/硅基流动) | [siliconflow.cn](https://siliconflow.cn) |
+| `novita` | LLM (Novita AI OpenAI-compatible gateway) | [novita.ai](https://novita.ai) |
 | `dashscope` | LLM (Qwen) | [dashscope.console.aliyun.com](https://dashscope.console.aliyun.com) |
 | `moonshot` | LLM (Moonshot/Kimi) | [platform.moonshot.cn](https://platform.moonshot.cn) |
 | `zhipu` | LLM (Zhipu GLM) | [open.bigmodel.cn](https://open.bigmodel.cn) |
 | `mimo` | LLM (MiMo) | [platform.xiaomimimo.com](https://platform.xiaomimimo.com) |
 | `longcat` | LLM (LongCat) | [longcat.chat](https://longcat.chat/platform/docs/zh/) |
+| `ant_ling` | LLM (Ant Ling / 蚂蚁百灵) | [developer.ant-ling.com](https://developer.ant-ling.com/en/docs/api-reference/openai/) |
 | `ollama` | LLM (local, Ollama) | — |
 | `lm_studio` | LLM (local, LM Studio) | — |
 | `atomic_chat` | LLM (local, [Atomic Chat](https://atomic.chat/)) | — |
@@ -89,6 +168,73 @@ IMAP_PASSWORD=your-password-here
 | `github_copilot` | LLM (GitHub Copilot, OAuth) | `nanobot provider login github-copilot` |
 | `qianfan` | LLM (Baidu Qianfan) | [cloud.baidu.com](https://cloud.baidu.com/doc/qianfan/s/Hmh4suq26) |
 
+<details>
+<summary><b>OpenAI</b></summary>
+
+By default, OpenAI uses `apiType: "auto"`: nanobot calls Chat Completions normally and routes GPT-5/o-series or explicit `reasoningEffort` requests through the Responses API when useful. You can force a specific API surface:
+
+```json
+{
+  "providers": {
+    "openai": {
+      "apiKey": "${OPENAI_API_KEY}",
+      "apiType": "chat_completions"
+    }
+  }
+}
+```
+
+Valid `apiType` values are exactly `auto`, `chat_completions`, and `responses`.
+
+`extraBody` follows the selected OpenAI API surface. With Chat Completions, nanobot passes it through as the SDK `extra_body` value. With Responses, configure it in Responses API body shape; nanobot merges ordinary top-level fields into the Responses request body, appends `extraBody.tools` after generated function tools, and merges `extraBody.include` without duplicates:
+
+```json
+{
+  "providers": {
+    "openai": {
+      "apiKey": "${OPENAI_API_KEY}",
+      "apiType": "responses",
+      "extraBody": {
+        "tools": [{ "type": "web_search" }],
+        "include": ["web_search_call.action.sources"]
+      }
+    }
+  }
+}
+```
+
+</details>
+
+<details>
+<summary><b>Skywork / APIFree</b></summary>
+
+Skywork uses APIFree's OpenAI-compatible Agent API endpoint. Configure the provider
+once, then use Skywork model IDs such as `skywork-ai/skyclaw-v1`.
+
+```json
+{
+  "providers": {
+    "skywork": {
+      "apiKey": "${SKYWORK_API_KEY}",
+      "apiBase": "https://api.apifree.ai/agent/v1"
+    }
+  },
+  "agents": {
+    "defaults": {
+      "provider": "skywork",
+      "model": "skywork-ai/skyclaw-v1",
+      "maxTokens": 32768,
+      "contextWindowTokens": 131072
+    }
+  }
+}
+```
+
+You can also reference `${APIFREE_API_KEY}` in `apiKey` if that is how your
+environment names the credential.
+
+</details>
+
 <details>
 <summary><b>AWS Bedrock (Converse API)</b></summary>
 
@@ -370,6 +516,96 @@ Official model names include `LongCat-Flash-Chat`, `LongCat-Flash-Thinking`,
 
 </details>
 
+<details>
+<summary><b>Xiaomi MiMo</b></summary>
+
+Xiaomi MiMo models are automatically detected by the `xiaomi_mimo` provider when
+the model name contains `mimo`. The default API base is
+`https://api.xiaomimimo.com/v1`.
+
+> **Token Plan**: If you're using MiMo's token plan, override `apiBase` with the
+> dedicated endpoint:
+>
+> ```json
+> {
+>   "providers": {
+>     "xiaomi_mimo": {
+>       "apiKey": "${XIAOMIMIMO_API_KEY}",
+>       "apiBase": "https://token-plan-sgp.xiaomimimo.com/v1"
+>     }
+>   },
+>   "agents": {
+>     "defaults": {
+>       "model": "xiaomi/mimo-v2.5-pro"
+>     }
+>   }
+> }
+> ```
+>
+> No need to set `provider` explicitly — the model name contains `mimo`, which
+> auto-matches to the `xiaomi_mimo` provider spec. Use an API key from the MiMo
+> token plan console and check the MiMo platform for the latest supported model
+> names.
+
+</details>
+
+<details>
+<summary><b>StepFun Step Plan (subscription)</b></summary>
+
+Step Plan is StepFun's subscription-based service for high-frequency AI developers.
+If you're on a Step Plan subscription, override `apiBase` in the existing `stepfun`
+provider config to point to the dedicated Step Plan endpoint.
+
+```json
+{
+  "providers": {
+    "stepfun": {
+      "apiKey": "${STEPFUN_API_KEY}",
+      "apiBase": "https://api.stepfun.com/step_plan/v1"
+    }
+  },
+  "agents": {
+    "defaults": {
+      "provider": "stepfun",
+      "model": "step-3.5-flash"
+    }
+  }
+}
+```
+
+Supported models include `step-3.5-flash`, `step-3.5-flash-2603`, and
+`step-router-v1`.
+
+</details>
+
+<details>
+<summary><b>Ant Ling (OpenAI-compatible)</b></summary>
+
+Ant Ling is available through nanobot's built-in OpenAI-compatible provider flow.
+The default API base points to `https://api.ant-ling.com/v1`, so you usually
+only need to set `apiKey`.
+
+```json
+{
+  "providers": {
+    "antLing": {
+      "apiKey": "${ANT_LING_API_KEY}"
+    }
+  },
+  "agents": {
+    "defaults": {
+      "provider": "ant_ling",
+      "model": "Ling-2.6-flash"
+    }
+  }
+}
+```
+
+Official OpenAI-compatible model names include `Ling-2.6-1T`,
+`Ling-2.6-flash`, `Ling-2.5-1T`, `Ling-1T`, `Ring-2.5-1T`, and `Ring-1T`.
+
+</details>
+
 <details>
 <summary><b>Custom Provider (Any OpenAI-compatible API)</b></summary>
 
@@ -438,6 +674,8 @@ Some OpenAI-compatible gateways expose request-body extensions such as vLLM guid
 
 </details>
 
+<a id="local-providers"></a>
+<a id="ollama-local"></a>
 <details>
 <summary><b>Ollama (local)</b></summary>
 
@@ -503,12 +741,19 @@ ollama run llama3.2
 
 </details>
 
+<a id="atomic-chat-local"></a>
 <details>
 <summary><b>Atomic Chat (local)</b></summary>
 
-[Atomic Chat](https://atomic.chat/) is a local-first desktop app that exposes an **OpenAI-compatible** HTTP API (default `http://localhost:1337/v1`). Start Atomic Chat and enable the local API server, then point nanobot at it.
+[Atomic Chat](https://atomic.chat/) is a local-first desktop app that exposes an **OpenAI-compatible** HTTP API (default `http://localhost:1337/v1`). Use it when you want to run nanobot against a model on your own machine instead of a hosted API provider.
 
-**1. Add to config** (partial — merge into `~/.nanobot/config.json`):
+**1. Start Atomic Chat**
+
+- Install [Atomic Chat](https://atomic.chat/) on your machine.
+- Open Atomic Chat, download a model, and keep the app running. The local API is enabled by default.
+- Copy the model ID exposed by the local API. For example, the model ID for `Qwen 3 32B` might be `qwen3-32b`.
+
+**2. Add to config** (partial — merge into `~/.nanobot/config.json`):
 
 ```json
 {
@@ -521,13 +766,13 @@ ollama run llama3.2
   "agents": {
     "defaults": {
       "provider": "atomic_chat",
-      "model": "your-model-id-from-atomic-chat"
+      "model": "qwen3-32b"
     }
   }
 }
 ```
 
-> **Note:** Set `apiKey` to `null` if your Atomic Chat server does not require a key. If it does, set `apiKey` (or the `ATOMIC_CHAT_API_KEY` environment variable) to the value Atomic Chat expects. The `model` string must match the model id Atomic Chat exposes on its OpenAI-compatible endpoint.
+> **Note:** Replace `qwen3-32b` with the model ID from Atomic Chat. Set `apiKey` to `null` if your Atomic Chat server does not require a key. If it does, set `apiKey` (or the `ATOMIC_CHAT_API_KEY` environment variable) to the value Atomic Chat expects.
 
 > `provider: "auto"` also works when `providers.atomic_chat.apiBase` is configured, but setting `"provider": "atomic_chat"` is the clearest option.
 
@@ -608,6 +853,7 @@ docker run -d \
 > See the [official OVMS docs](https://docs.openvino.ai/2026/model-server/ovms_docs_llm_quickstart.html) for more details.
 </details>
 
+<a id="vllm-local-openai-compatible"></a>
 <details>
 <summary><b>vLLM (local / OpenAI-compatible)</b></summary>
 
@@ -797,6 +1043,7 @@ Global settings that apply to all channels. Configure under the `channels` secti
   "channels": {
     "sendProgress": true,
     "sendToolHints": false,
+    "extractDocumentText": true,
     "sendMaxRetries": 3,
     "transcriptionProvider": "groq",
     "transcriptionLanguage": null,
@@ -810,8 +1057,9 @@ Global settings that apply to all channels. Configure under the `channels` secti
 | `sendProgress` | `true` | Stream agent's text progress to the channel |
 | `sendToolHints` | `false` | Stream tool-call hints (e.g. `read_file("…")`) |
 | `showReasoning` | `true` | Allow channels to surface model reasoning/thinking content (DeepSeek-R1 `reasoning_content`, Anthropic `thinking_blocks`, inline `<think>` tags). Reasoning flows as a dedicated stream with `_reasoning_delta` / `_reasoning_end` markers — channels override `send_reasoning_delta` / `send_reasoning_end` to render in-place updates. Even with `true`, channels without those overrides stay no-op silently. Currently surfaced on CLI and WebSocket/WebUI (italic shimmer header, auto-collapses after the stream ends); Telegram / Slack / Discord / Feishu / WeChat / Matrix keep the base no-op until their bubble UI is adapted. Independent of `sendProgress`. |
+| `extractDocumentText` | `true` | Extract supported document/text attachments into the model prompt. Set to `false` to keep document content out of the prompt and include attachment path references instead. |
 | `sendMaxRetries` | `3` | Max delivery attempts per outbound message, including the initial send (0-10 configured, minimum 1 actual attempt) |
-| `transcriptionProvider` | `"groq"` | Voice transcription backend: `"groq"` (free tier, default) or `"openai"`. API key is auto-resolved from the matching provider config. |
+| `transcriptionProvider` | `"groq"` | Voice transcription backend: `"groq"` (free tier, default) or `"openai"`. API key and optional `apiBase` are auto-resolved from the matching provider config. Chat-style bases such as `https://api.groq.com/openai/v1` are normalized to the audio transcription endpoint. |
 | `transcriptionLanguage` | `null` | Optional ISO-639-1 language hint for audio transcription, e.g. `"en"`, `"ko"`, `"ja"`. |
 
 `sendProgress` and `sendToolHints` can also be overridden per channel. The
@@ -917,7 +1165,7 @@ By default, web search uses `duckduckgo`, and it works out of the box without an
     "web": {
       "search": {
         "provider": "brave",
-        "apiKey": "BSA..."
+        "apiKey": "${BRAVE_API_KEY}"
       }
     }
   }
@@ -931,7 +1179,7 @@ By default, web search uses `duckduckgo`, and it works out of the box without an
     "web": {
       "search": {
         "provider": "tavily",
-        "apiKey": "tvly-..."
+        "apiKey": "${TAVILY_API_KEY}"
       }
     }
   }
@@ -945,7 +1193,7 @@ By default, web search uses `duckduckgo`, and it works out of the box without an
     "web": {
       "search": {
         "provider": "jina",
-        "apiKey": "jina_..."
+        "apiKey": "${JINA_API_KEY}"
       }
     }
   }
@@ -959,7 +1207,7 @@ By default, web search uses `duckduckgo`, and it works out of the box without an
     "web": {
       "search": {
         "provider": "kagi",
-        "apiKey": "your-kagi-api-key"
+        "apiKey": "${KAGI_API_KEY}"
       }
     }
   }
@@ -973,7 +1221,7 @@ By default, web search uses `duckduckgo`, and it works out of the box without an
     "web": {
       "search": {
         "provider": "olostep",
-        "apiKey": "YOUR_OLOSTEP_API_KEY"
+        "apiKey": "${OLOSTEP_API_KEY}"
       }
     }
   }
@@ -1050,7 +1298,7 @@ If you want to always use the local conversion, you can force it using:
 
 ## Image Generation
 
-Image generation is configured under `tools.imageGeneration` and uses provider credentials from `providers.openrouter` or `providers.aihubmix`.
+Image generation is configured under `tools.imageGeneration` and uses credentials from the selected provider's `providers.<name>` block.
 
 See [Image Generation](./image-generation.md) for WebUI usage, provider examples, artifact storage, and troubleshooting.
 
@@ -1136,11 +1384,14 @@ MCP tools are automatically discovered and registered on startup. The LLM can us
 > [!TIP]
 > For production deployments, set `"restrictToWorkspace": true` and `"tools.exec.sandbox": "bwrap"` in your config to sandbox the agent.
 
+For API keys, tokens, and other secrets, see [Environment Variables for Secrets](#environment-variables-for-secrets) — avoid storing them directly in `config.json`.
+
 | Option | Default | Description |
 |--------|---------|-------------|
 | `tools.restrictToWorkspace` | `false` | When `true`, restricts **all** agent tools (shell, file read/write/edit, list) to the workspace directory. Prevents path traversal and out-of-scope access. |
 | `tools.exec.sandbox` | `""` | Sandbox backend for shell commands. Set to `"bwrap"` to wrap exec calls in a [bubblewrap](https://github.com/containers/bubblewrap) sandbox — the process can only see the workspace (read-write) and media directory (read-only); config files and API keys are hidden. Automatically enables `restrictToWorkspace` for file tools. **Linux only** — requires `bwrap` installed (`apt install bubblewrap`; pre-installed in the Docker image). Not available on macOS or Windows (bwrap depends on Linux kernel namespaces). |
 | `tools.exec.enable` | `true` | When `false`, the shell `exec` tool is not registered at all. Use this to completely disable shell command execution. |
+| `tools.exec.timeout` | `60` | Default hard timeout in seconds for shell commands. Config values may exceed the per-call tool cap; set `0` to disable the hard timeout for trusted long-running commands. |
 | `tools.exec.pathAppend` | `""` | Extra directories to append to `PATH` when running shell commands (e.g. `/usr/sbin` for `ufw`). |
 | `channels.*.allowFrom` | omitted | Access control per channel. Omit to use pairing-only mode; set `["*"]` to allow everyone; or list specific user IDs. See [Pairing](#pairing) for details. |
 
@@ -1283,7 +1534,7 @@ By default, nanobot uses `UTC` for runtime time context. If you want the agent t
 }
 ```
 
-This affects runtime time strings shown to the model, such as runtime context and heartbeat prompts. It also becomes the default timezone for cron schedules when a cron expression omits `tz`, and for one-shot `at` times when the ISO datetime has no explicit offset.
+This affects runtime time strings shown to the model, such as runtime context. It also becomes the default timezone for cron schedules when a cron expression omits `tz`, and for one-shot `at` times when the ISO datetime has no explicit offset.
 
 Common examples: `UTC`, `America/New_York`, `America/Los_Angeles`, `Europe/London`, `Europe/Berlin`, `Asia/Tokyo`, `Asia/Shanghai`, `Asia/Singapore`, `Australia/Sydney`.
 

docs/deployment.md

@@ -10,6 +10,25 @@
 > [!IMPORTANT]
 > Official Docker usage currently means building from this repository with the included `Dockerfile`. Docker Hub images under third-party namespaces are not maintained or verified by HKUDS/nanobot; do not mount API keys or bot tokens into them unless you trust the publisher.
 
+> [!IMPORTANT]
+> The gateway and WebSocket channel default to `host: "127.0.0.1"` in `config.json` (set in `nanobot/config/schema.py`). Docker `-p` port forwarding cannot reach a container's loopback interface, so for the host or LAN to reach the exposed ports you must set both binds to `0.0.0.0` in `~/.nanobot/config.json` before starting the container. To serve the bundled WebUI from Docker, enable the WebSocket channel and protect bootstrap with a secret:
+>
+> ```json
+> {
+>   "gateway": { "host": "0.0.0.0" },
+>   "channels": {
+>     "websocket": {
+>       "enabled": true,
+>       "host": "0.0.0.0",
+>       "port": 8765,
+>       "tokenIssueSecret": "your-secret-here"
+>     }
+>   }
+> }
+> ```
+>
+> When the WebSocket `host` is `0.0.0.0`, the channel refuses to start unless `token` or `tokenIssueSecret` is also configured — see [`webui/README.md`](../webui/README.md) for details.
+
 ### Docker Compose
 
 ```bash
@@ -36,8 +55,20 @@ docker run -v ~/.nanobot:/home/nanobot/.nanobot --rm nanobot onboard
 # Edit config on host to add API keys
 vim ~/.nanobot/config.json
 
-# Run gateway (connects to enabled channels, e.g. Telegram/Discord/Mochat)
+# Run gateway (connects to enabled channels, e.g. Telegram/Discord/Mochat).
-docker run -v ~/.nanobot:/home/nanobot/.nanobot -p 18790:18790 nanobot gateway
+# Mirrors the security caps and port mappings declared in docker-compose.yml:
+#   - `--cap-drop ALL --cap-add SYS_ADMIN` + unconfined apparmor/seccomp are required
+#     when `tools.exec.sandbox: "bwrap"` is enabled (bwrap needs CAP_SYS_ADMIN for
+#     user namespaces). Without them, `bwrap` exits with `clone3: Operation not permitted`.
+#   - `-p 8765:8765` exposes the WebSocket channel / WebUI alongside the gateway health
+#     endpoint on 18790.
+docker run \
+  --cap-drop ALL --cap-add SYS_ADMIN \
+  --security-opt apparmor=unconfined \
+  --security-opt seccomp=unconfined \
+  -v ~/.nanobot:/home/nanobot/.nanobot \
+  -p 18790:18790 -p 8765:8765 \
+  nanobot gateway
 
 # Or run a single command
 docker run -v ~/.nanobot:/home/nanobot/.nanobot --rm nanobot agent -m "Hello!"

docs/image-generation.md

@@ -6,8 +6,6 @@ The feature is disabled by default. Enable it in `~/.nanobot/config.json`, confi
 
 ## Quick Setup
 
-OpenRouter example:
-
 ```json
 {
   "providers": {
@@ -19,34 +17,13 @@ OpenRouter example:
     "imageGeneration": {
       "enabled": true,
       "provider": "openrouter",
-      "model": "openai/gpt-5.4-image-2",
+      "model": "openai/gpt-5.4-image-2"
-      "defaultAspectRatio": "1:1",
-      "defaultImageSize": "1K"
     }
   }
 }
 ```
 
-AIHubMix example:
+See [Provider Notes](#provider-notes) for AIHubMix, MiniMax, Gemini, Ollama, StepFun, and Zhipu configuration examples.
-
-```json
-{
-  "providers": {
-    "aihubmix": {
-      "apiKey": "${AIHUBMIX_API_KEY}"
-    }
-  },
-  "tools": {
-    "imageGeneration": {
-      "enabled": true,
-      "provider": "aihubmix",
-      "model": "gpt-image-2-free",
-      "defaultAspectRatio": "1:1",
-      "defaultImageSize": "1K"
-    }
-  }
-}
-```
 
 > [!TIP]
 > Prefer environment variables for API keys. nanobot resolves `${VAR_NAME}` values from the environment at startup.
@@ -69,7 +46,7 @@ The WebUI hides provider storage details from the user. The agent sees the saved
 | Option | Type | Default | Description |
 |--------|------|---------|-------------|
 | `tools.imageGeneration.enabled` | boolean | `false` | Register the `generate_image` tool |
-| `tools.imageGeneration.provider` | string | `"openrouter"` | Image provider name. Currently `openrouter` and `aihubmix` are supported |
+| `tools.imageGeneration.provider` | string | `"openrouter"` | Image provider name. Supported values: `openrouter`, `aihubmix`, `minimax`, `gemini`, `ollama`, `stepfun`, `zhipu` |
 | `tools.imageGeneration.model` | string | `"openai/gpt-5.4-image-2"` | Provider model name |
 | `tools.imageGeneration.defaultAspectRatio` | string | `"1:1"` | Default ratio when the prompt/tool call does not specify one |
 | `tools.imageGeneration.defaultImageSize` | string | `"1K"` | Default size hint, for example `1K`, `2K`, `4K`, or `1024x1024` |
@@ -139,6 +116,160 @@ Configure:
 
 `quality: low` is optional. It can make free image models faster and less likely to time out, but it is not required for correctness.
 
+### MiniMax
+
+MiniMax `image-01` supports text-to-image and reference-image (subject reference) edits. Supported aspect ratios are `1:1`, `16:9`, `4:3`, `3:2`, `2:3`, `3:4`, `9:16`, and `21:9`.
+
+```json
+{
+  "providers": {
+    "minimax": {
+      "apiKey": "${MINIMAX_API_KEY}"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "minimax",
+      "model": "image-01",
+      "defaultAspectRatio": "1:1"
+    }
+  }
+}
+```
+
+### Gemini
+
+nanobot supports two Gemini image generation model families via Google's Generative Language API:
+
+| Model | Endpoint | Reference images |
+|-------|----------|-----------------|
+| `imagen-4.0-generate-001` | `:predict` | Not supported by this integration |
+| `gemini-2.5-flash-image` | `:generateContent` | Supported |
+
+For reference-image edits, use a Gemini Flash image model:
+
+```json
+{
+  "providers": {
+    "gemini": {
+      "apiKey": "${GEMINI_API_KEY}"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "gemini",
+      "model": "gemini-2.5-flash-image"
+    }
+  }
+}
+```
+
+Imagen 4 supports the aspect ratios `1:1`, `9:16`, `16:9`, `3:4`, and `4:3`. Unsupported ratios are ignored and the model uses its default. The `defaultImageSize` setting has no effect on Gemini models; sizing is controlled by `defaultAspectRatio` only. Reference images passed with an Imagen model are ignored (with a warning logged).
+
+### Ollama
+
+Ollama's experimental native image generation API works with local servers and hosted ollama.com models. Local access at `http://localhost:11434/api` does not require an API key; set `providers.ollama.apiKey` only when targeting `https://ollama.com/api`.
+
+```json
+{
+  "providers": {
+    "ollama": {
+      "apiBase": "http://localhost:11434/api"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "ollama",
+      "model": "x/z-image-turbo",
+      "defaultAspectRatio": "16:9",
+      "defaultImageSize": "2K"
+    }
+  }
+}
+```
+
+Ollama maps `defaultAspectRatio` and `defaultImageSize` to native `width` and `height` values. Reference images are not supported by this integration.
+
+### StepFun
+
+StepFun (阶跃星辰) `step-image-edit-2` supports text-to-image generation.  The `step-1x-medium` variant additionally supports **style-reference** image edits, where a reference image guides the visual style of the output.
+
+Supported aspect ratios: `1:1`, `16:9`, `9:16`, `3:4`, `4:3`.  Sizes are specified as `WIDTHxHEIGHT` (e.g. `1024x1024`, `1280x800`, `800x1280`).
+
+```json
+{
+  "providers": {
+    "stepfun": {
+      "apiKey": "${STEPFUN_API_KEY}"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "stepfun",
+      "model": "step-image-edit-2"
+    }
+  }
+}
+```
+
+> [!NOTE]
+> The StepFun provider reuses the existing `providers.stepfun` config block (the same one used for StepFun's LLM API).  Set `providers.stepfun.apiKey` once and it is shared between text and image generation.
+>
+> When `step-image-edit-2` is used, `reference_images` are ignored (the model does not support style reference).  Switch to `step-1x-medium` to use reference-image-guided generation.
+
+#### StepPlan (Subscription)
+
+StepPlan is StepFun's subscription tier and uses a different API base URL. The image generation endpoint path is the same — just override `apiBase`:
+
+```json
+{
+  "providers": {
+    "stepfun": {
+      "apiKey": "${STEPFUN_API_KEY}",
+      "apiBase": "https://api.stepfun.com/step_plan/v1"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "stepfun",
+      "model": "step-image-edit-2"
+    }
+  }
+}
+```
+
+`apiBase` takes precedence over the registry default, so with the StepPlan base URL configured, image requests are sent to `https://api.stepfun.com/step_plan/v1/images/generations` — the same path prefix used for LLM calls. The API key is shared with the standard StepFun provider.
+
+### Zhipu
+
+Zhipu (智谱) `glm-image` model supports text-to-image generation. The API returns temporary image URLs (valid for 30 days); nanobot downloads and re-encodes them as base64 data URLs.
+
+Supported aspect ratios: `1:1`, `16:9`, `9:16`, `3:4`, `4:3`. Sizes can be specified as `WIDTHxHEIGHT` (e.g. `1280x1280`, `1728x960`) or using aspect ratio presets.
+
+```json
+{
+  "providers": {
+    "zhipu": {
+      "apiKey": "${ZAI_API_KEY}"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "zhipu",
+      "model": "glm-image"
+    }
+  }
+}
+```
+
+Other supported models: `cogview-4`, `cogview-4-250304`, `cogview-3-flash`. Reference images are not supported by this integration.
+
 ## Artifacts
 
 Generated images are stored under the active nanobot instance's media directory:
@@ -193,8 +324,7 @@ Use the reference image. Keep the same robot and composition, change the palette
 |---------|-------|
 | `generate_image` is not available | Set `tools.imageGeneration.enabled` to `true` and restart the gateway |
 | Missing API key error | Configure `providers.<provider>.apiKey`; if using `${VAR_NAME}`, confirm the environment variable is visible to the gateway process |
-| `unsupported image generation provider` | Use `openrouter` or `aihubmix` |
+| `unsupported image generation provider` | Use `openrouter`, `aihubmix`, `minimax`, `gemini`, `ollama`, `stepfun`, or `zhipu` |
 | AIHubMix says `Incorrect model ID` | Use `model: "gpt-image-2-free"`; nanobot expands it to the required `openai/gpt-image-2-free` model path internally |
 | Generation times out | Try a smaller/default image size, set AIHubMix `extraBody.quality` to `"low"`, or retry later |
 | Reference image rejected | Reference image paths must be inside the workspace or nanobot media directory and must be valid image files |
-

hatch_build.py

@@ -0,0 +1,101 @@
+"""Hatch build hook that bundles the webui (Vite) into nanobot/web/dist.
+
+Triggered automatically by `python -m build` (and any other hatch-driven build)
+so published wheels and sdists ship a fresh webui without requiring developers
+to remember `cd webui && bun run build` beforehand.
+
+Behaviour:
+
+- Skips for editable installs (`pip install -e .`). Editable mode is for Python
+  development; webui contributors use `cd webui && bun run dev` (Vite HMR) and
+  do not need a packaged `dist/`.
+- No-op when `webui/package.json` is absent (e.g. installing from an sdist that
+  already contains a prebuilt `nanobot/web/dist/`).
+- Skips when `NANOBOT_SKIP_WEBUI_BUILD=1` is set.
+- Skips when `nanobot/web/dist/index.html` already exists, unless
+  `NANOBOT_FORCE_WEBUI_BUILD=1` is set.
+- Uses `bun` when available, otherwise falls back to `npm`. The chosen tool
+  performs `install` followed by `run build`.
+"""
+
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+from pathlib import Path
+
+from hatchling.builders.hooks.plugin.interface import BuildHookInterface
+
+
+class WebUIBuildHook(BuildHookInterface):
+    PLUGIN_NAME = "webui-build"
+
+    def initialize(self, version: str, build_data: dict) -> None:  # noqa: D401
+        root = Path(self.root)
+        webui_dir = root / "webui"
+        package_json = webui_dir / "package.json"
+        dist_dir = root / "nanobot" / "web" / "dist"
+        index_html = dist_dir / "index.html"
+
+        # `pip install -e .` builds an editable wheel; skip the (slow) webui
+        # bundle since editable installs target Python development and webui
+        # work uses `bun run dev` instead.
+        if self.target_name == "wheel" and version == "editable":
+            self.app.display_info(
+                "[webui-build] skipped for editable install "
+                "(use `cd webui && bun run build` to bundle webui manually)"
+            )
+            return
+
+        if os.environ.get("NANOBOT_SKIP_WEBUI_BUILD") == "1":
+            self.app.display_info("[webui-build] skipped via NANOBOT_SKIP_WEBUI_BUILD=1")
+            return
+
+        if not package_json.is_file():
+            self.app.display_info(
+                "[webui-build] no webui/ source tree, assuming prebuilt nanobot/web/dist/"
+            )
+            return
+
+        force = os.environ.get("NANOBOT_FORCE_WEBUI_BUILD") == "1"
+        if index_html.is_file() and not force:
+            self.app.display_info(
+                f"[webui-build] reusing existing build at {dist_dir} "
+                "(set NANOBOT_FORCE_WEBUI_BUILD=1 to rebuild)"
+            )
+            return
+
+        runner = self._pick_runner()
+        if runner is None:
+            raise RuntimeError(
+                "[webui-build] neither `bun` nor `npm` is available on PATH; "
+                "install one or set NANOBOT_SKIP_WEBUI_BUILD=1 to bypass."
+            )
+
+        self.app.display_info(f"[webui-build] using {runner} to build webui")
+        self._run([runner, "install"], cwd=webui_dir)
+        self._run([runner, "run", "build"], cwd=webui_dir)
+
+        if not index_html.is_file():
+            raise RuntimeError(
+                f"[webui-build] build finished but {index_html} is missing; "
+                "check webui/vite.config.ts outDir."
+            )
+        self.app.display_info(f"[webui-build] webui ready at {dist_dir}")
+
+    @staticmethod
+    def _pick_runner() -> str | None:
+        for candidate in ("bun", "npm"):
+            if shutil.which(candidate):
+                return candidate
+        return None
+
+    def _run(self, cmd: list[str], *, cwd: Path) -> None:
+        self.app.display_info(f"[webui-build] $ {' '.join(cmd)} (cwd={cwd})")
+        try:
+            subprocess.run(cmd, cwd=cwd, check=True)
+        except subprocess.CalledProcessError as exc:
+            raise RuntimeError(
+                f"[webui-build] command failed ({exc.returncode}): {' '.join(cmd)}"
+            ) from exc

nanobot/__init__.py

@@ -2,9 +2,10 @@
 nanobot - A lightweight AI agent framework
 """
 
-from importlib.metadata import PackageNotFoundError, version as _pkg_version
-from pathlib import Path
 import tomllib
+from importlib.metadata import PackageNotFoundError
+from importlib.metadata import version as _pkg_version
+from pathlib import Path
 
 
 def _read_pyproject_version() -> str | None:
@@ -21,12 +22,27 @@ def _resolve_version() -> str:
         return _pkg_version("nanobot-ai")
     except PackageNotFoundError:
         # Source checkouts often import nanobot without installed dist-info.
-        return _read_pyproject_version() or "0.1.5.post3"
+        return _read_pyproject_version() or "0.2.1"
 
 
 __version__ = _resolve_version()
 __logo__ = "🐈"
 
-from nanobot.nanobot import Nanobot, RunResult
+_LAZY_EXPORTS = {
+    "Nanobot": ".nanobot",
+    "RunResult": ".nanobot",
+}
+
+
+def __getattr__(name: str):
+    module_path = _LAZY_EXPORTS.get(name)
+    if module_path is None:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    from importlib import import_module
+    mod = import_module(module_path, __name__)
+    val = getattr(mod, name)
+    globals()[name] = val
+    return val
+
 
 __all__ = ["Nanobot", "RunResult"]

nanobot/agent/autocompact.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from collections.abc import Collection
 from datetime import datetime
-from typing import TYPE_CHECKING, Any, Callable, Coroutine
+from typing import TYPE_CHECKING, Callable, Coroutine
 
 from loguru import logger
 
@@ -37,27 +37,6 @@ class AutoCompact:
     def _format_summary(text: str, last_active: datetime) -> str:
         return f"Previous conversation summary (last active {last_active.isoformat()}):\n{text}"
 
-    def _split_unconsolidated(
-        self, session: Session,
-    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
-        """Split live session tail into archiveable prefix and retained recent suffix."""
-        tail = list(session.messages[session.last_consolidated:])
-        if not tail:
-            return [], []
-
-        probe = Session(
-            key=session.key,
-            messages=tail.copy(),
-            created_at=session.created_at,
-            updated_at=session.updated_at,
-            metadata={},
-            last_consolidated=0,
-        )
-        probe.retain_recent_legal_suffix(self._RECENT_SUFFIX_MESSAGES)
-        kept = probe.messages
-        cut = len(tail) - len(kept)
-        return tail[:cut], kept
-
     def check_expired(self, schedule_background: Callable[[Coroutine], None],
                       active_session_keys: Collection[str] = ()) -> None:
         """Schedule archival for idle sessions, skipping those with in-flight agent tasks."""
@@ -74,33 +53,17 @@ class AutoCompact:
 
     async def _archive(self, key: str) -> None:
         try:
-            self.sessions.invalidate(key)
+            summary = await self.consolidator.compact_idle_session(
-            session = self.sessions.get_or_create(key)
+                key, self._RECENT_SUFFIX_MESSAGES,
-            archive_msgs, kept_msgs = self._split_unconsolidated(session)
+            )
-            if not archive_msgs and not kept_msgs:
-                session.updated_at = datetime.now()
-                self.sessions.save(session)
-                return
-
-            last_active = session.updated_at
-            summary = ""
-            if archive_msgs:
-                summary = await self.consolidator.archive(archive_msgs) or ""
             if summary and summary != "(nothing)":
-                self._summaries[key] = (summary, last_active)
+                session = self.sessions.get_or_create(key)
-                session.metadata["_last_summary"] = {"text": summary, "last_active": last_active.isoformat()}
+                meta = session.metadata.get("_last_summary")
-            session.messages = kept_msgs
+                if isinstance(meta, dict):
-            session.last_consolidated = 0
+                    self._summaries[key] = (
-            session.updated_at = datetime.now()
+                        meta["text"],
-            self.sessions.save(session)
+                        datetime.fromisoformat(meta["last_active"]),
-            if archive_msgs:
+                    )
-                logger.info(
-                    "Auto-compact: archived {} (archived={}, kept={}, summary={})",
-                    key,
-                    len(archive_msgs),
-                    len(kept_msgs),
-                    bool(summary),
-                )
         except Exception:
             logger.exception("Auto-compact: failed for {}", key)
         finally:

nanobot/agent/context.py

@@ -3,26 +3,55 @@
 import base64
 import mimetypes
 import platform
-from contextlib import suppress
-from importlib.resources import files as pkg_files
 from pathlib import Path
 from typing import Any, Mapping, Sequence
 
 from nanobot.agent.memory import MemoryStore
 from nanobot.agent.skills import SkillsLoader
+from nanobot.agent.tools import mcp as mcp_tools
+from nanobot.agent.tools.registry import ToolRegistry
+from nanobot.apps.cli import utils as cli_app_utils
+from nanobot.bus.events import InboundMessage
 from nanobot.session.goal_state import goal_state_runtime_lines
 from nanobot.utils.helpers import (
     current_time_str,
     detect_image_mime,
+    load_bundled_template,
     truncate_text,
 )
 from nanobot.utils.prompt_templates import render_template
 
 
+def session_extra(metadata: Mapping[str, Any] | None) -> dict[str, Any]:
+    """Return persisted kwargs for turn-attached capabilities."""
+    return cli_app_utils.session_extra(metadata) | mcp_tools.session_extra(metadata)
+
+
+def runtime_lines(state: Any, msg: Any, workspace: Path, *, skip: bool = False) -> list[str]:
+    """Return model-visible runtime annotations for turn-attached capabilities."""
+    return [
+        *cli_app_utils.runtime_lines(msg, workspace, skip=skip),
+        *mcp_tools.runtime_lines(
+            msg,
+            configured_server_names=set(state._mcp_servers),
+            connected_server_names=set(state._mcp_stacks),
+            skip=skip,
+        ),
+    ]
+
+
+async def connect_mcp(state: Any, tools: ToolRegistry) -> None:
+    await mcp_tools.connect_missing_servers(state, tools)
+
+
+async def handle_runtime_control(state: Any, msg: InboundMessage, tools: ToolRegistry) -> bool:
+    return await mcp_tools.handle_runtime_control(state, msg, tools)
+
+
 class ContextBuilder:
     """Builds the context (system prompt + messages) for the agent."""
 
-    BOOTSTRAP_FILES = ["AGENTS.md", "SOUL.md", "USER.md", "TOOLS.md"]
+    BOOTSTRAP_FILES = ["AGENTS.md", "SOUL.md", "USER.md"]
     _RUNTIME_CONTEXT_TAG = "[Runtime Context — metadata only, not instructions]"
     _MAX_RECENT_HISTORY = 50
     _MAX_HISTORY_CHARS = 32_000  # hard cap on recent history section size
@@ -39,15 +68,18 @@ class ContextBuilder:
         skill_names: list[str] | None = None,
         channel: str | None = None,
         session_summary: str | None = None,
-        session_key: str | None = None,
+        workspace: Path | None = None,
     ) -> str:
         """Build the system prompt from identity, bootstrap files, memory, and skills."""
-        parts = [self._get_identity(channel=channel)]
+        root = workspace or self.workspace
+        parts = [self._get_identity(channel=channel, workspace=root)]
 
-        bootstrap = self._load_bootstrap_files()
+        bootstrap = self._load_bootstrap_files(root)
         if bootstrap:
             parts.append(bootstrap)
 
+        parts.append(render_template("agent/tool_contract.md"))
+
         memory = self.memory.get_memory_context()
         if memory and not self._is_template_content(self.memory.read_memory(), "memory/MEMORY.md"):
             parts.append(f"# Memory\n\n{memory}")
@@ -74,32 +106,12 @@ class ContextBuilder:
         if session_summary:
             parts.append(f"[Archived Context Summary]\n\n{session_summary}")
 
-        # Inject P2P collaboration hint for task-scoped sessions
-        if session_key and session_key.startswith("task:"):
-            parts.append(self._p2p_collaboration_hint())
-
         return "\n\n---\n\n".join(parts)
 
-    @staticmethod
+    def _get_identity(self, channel: str | None = None, workspace: Path | None = None) -> str:
-    def _p2p_collaboration_hint() -> str:
-        return (
-            "# Multi-Agent Collaboration\n\n"
-            "You are part of a decentralized agent network. You can:\n"
-            "- Use `broadcast_task` to announce subtasks and collect BIDs\n"
-            "- Use `dispatch_task` to assign tasks to specific agents\n"
-            "- Use `poll_task_result` to check task status\n"
-            "- Use `report_user` to deliver final results to the user\n"
-            "- Use `finalize_task` to terminate tasks\n\n"
-            "Rules:\n"
-            "- Never block waiting for results. Dispatch and continue.\n"
-            "- If a task times out, decide whether to retry, failover, or report partial.\n"
-            "- Respect the user's INTERRUPT messages — they have highest priority.\n"
-            "- You are currently in a task-scoped session; focus on the delegated task."
-        )
-
-    def _get_identity(self, channel: str | None = None) -> str:
         """Get the core identity section."""
-        workspace_path = str(self.workspace.expanduser().resolve())
+        root = workspace or self.workspace
+        workspace_path = str(root.expanduser().resolve())
         system = platform.system()
         runtime = f"{'macOS' if system == 'Darwin' else system} {platform.machine()}, Python {platform.python_version()}"
 
@@ -143,12 +155,13 @@ class ContextBuilder:
 
         return _to_blocks(left) + _to_blocks(right)
 
-    def _load_bootstrap_files(self) -> str:
+    def _load_bootstrap_files(self, workspace: Path | None = None) -> str:
         """Load all bootstrap files from workspace."""
         parts = []
+        root = workspace or self.workspace
 
         for filename in self.BOOTSTRAP_FILES:
-            file_path = self.workspace / filename
+            file_path = root / filename
             if file_path.exists():
                 content = file_path.read_text(encoding="utf-8")
                 parts.append(f"## {filename}\n\n{content}")
@@ -158,10 +171,9 @@ class ContextBuilder:
     @staticmethod
     def _is_template_content(content: str, template_path: str) -> bool:
         """Check if *content* is identical to the bundled template (user hasn't customized it)."""
-        with suppress(Exception):
+        tpl = load_bundled_template(template_path)
-            tpl = pkg_files("nanobot") / "templates" / template_path
+        if tpl is not None:
-            if tpl.is_file():
+            return content.strip() == tpl.strip()
-                return content.strip() == tpl.read_text(encoding="utf-8").strip()
         return False
 
     def build_messages(
@@ -176,10 +188,21 @@ class ContextBuilder:
         sender_id: str | None = None,
         session_summary: str | None = None,
         session_metadata: Mapping[str, Any] | None = None,
-        session_key: str | None = None,
+        current_runtime_lines: Sequence[str] | None = None,
+        workspace: Path | None = None,
+        runtime_state: Any | None = None,
+        inbound_message: Any | None = None,
+        skip_runtime_lines: bool = False,
     ) -> list[dict[str, Any]]:
         """Build the complete message list for an LLM call."""
-        extra = goal_state_runtime_lines(session_metadata)
+        root = workspace or self.workspace
+        extra = [
+            *goal_state_runtime_lines(session_metadata),
+        ]
+        if runtime_state is not None and inbound_message is not None:
+            extra.extend(runtime_lines(runtime_state, inbound_message, root, skip=skip_runtime_lines))
+        if current_runtime_lines:
+            extra.extend(line for line in current_runtime_lines if line)
         runtime_ctx = self._build_runtime_context(
             channel,
             chat_id,
@@ -198,7 +221,15 @@ class ContextBuilder:
         else:
             merged = user_content + [{"type": "text", "text": runtime_ctx}]
         messages = [
-            {"role": "system", "content": self.build_system_prompt(skill_names, channel=channel, session_summary=session_summary, session_key=session_key)},
+            {
+                "role": "system",
+                "content": self.build_system_prompt(
+                    skill_names,
+                    channel=channel,
+                    session_summary=session_summary,
+                    workspace=root,
+                ),
+            },
             *history,
         ]
         if messages[-1].get("role") == current_role:
@@ -233,4 +264,3 @@ class ContextBuilder:
         if not images:
             return text
         return images + [{"type": "text", "text": text}]
-

nanobot/agent/loop.py

@@ -14,6 +14,7 @@ from typing import TYPE_CHECKING, Any, Awaitable, Callable
 
 from loguru import logger
 
+from nanobot.agent import context as agent_context
 from nanobot.agent import model_presets as preset_helpers
 from nanobot.agent.autocompact import AutoCompact
 from nanobot.agent.context import ContextBuilder
@@ -22,16 +23,9 @@ from nanobot.agent.memory import Consolidator, Dream
 from nanobot.agent.progress_hook import AgentProgressHook
 from nanobot.agent.runner import _MAX_INJECTIONS_PER_TURN, AgentRunner, AgentRunSpec
 from nanobot.agent.subagent import SubagentManager
+from nanobot.agent.tools.context import RequestContext, bind_request_context, reset_request_context
 from nanobot.agent.tools.file_state import FileStateStore, bind_file_states, reset_file_states
 from nanobot.agent.tools.message import MessageTool
-from nanobot.agent.tools.p2p import (
-    BroadcastTaskTool,
-    CheckAggregationTool,
-    DispatchTaskTool,
-    FinalizeTaskTool,
-    PollTaskResultTool,
-    ReportUserTool,
-)
 from nanobot.agent.tools.registry import ToolRegistry
 from nanobot.agent.tools.self import MyTool
 from nanobot.bus.events import InboundMessage, OutboundMessage
@@ -40,20 +34,32 @@ from nanobot.command import CommandContext, CommandRouter, register_builtin_comm
 from nanobot.config.schema import AgentDefaults, ModelPresetConfig
 from nanobot.providers.base import LLMProvider
 from nanobot.providers.factory import ProviderSnapshot
+from nanobot.security.workspace_access import (
+    WorkspaceScopeResolver,
+    bind_workspace_scope,
+    reset_workspace_scope,
+)
 from nanobot.session.goal_state import (
-    goal_state_ws_blob,
+    goal_state_runtime_lines,
     runner_wall_llm_timeout_s,
+    sustained_goal_active,
 )
 from nanobot.session.manager import Session, SessionManager
-from nanobot.utils.artifacts import generated_image_paths_from_messages
+from nanobot.session import turn_continuation
-from nanobot.utils.document import extract_documents
+from nanobot.session.webui_turns import (
+    WebuiTurnCoordinator,
+    build_bus_progress_callback,
+    mark_webui_session,
+)
+from nanobot.utils.document import extract_documents, reference_non_image_attachments
 from nanobot.utils.helpers import image_placeholder_text
 from nanobot.utils.helpers import truncate_text as truncate_text_fn
 from nanobot.utils.image_generation_intent import image_generation_prompt
-from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE
+from nanobot.utils.llm_runtime import LLMRuntime
-from nanobot.utils.session_attachments import merge_turn_media_into_last_assistant
+from nanobot.utils.runtime import (
-from nanobot.utils.webui_titles import mark_webui_session, maybe_generate_webui_title_after_turn
+    EMPTY_FINAL_RESPONSE_MESSAGE,
-from nanobot.utils.webui_turn_helpers import publish_turn_run_status
+    SUSTAINED_GOAL_CONTINUE_PROMPT,
+)
 
 if TYPE_CHECKING:
     from nanobot.config.schema import (
@@ -66,7 +72,6 @@ if TYPE_CHECKING:
 
 UNIFIED_SESSION_KEY = "unified:default"
 
-
 class TurnState(Enum):
     RESTORE = auto()
     COMPACT = auto()
@@ -108,7 +113,7 @@ class TurnContext:
     save_skip: int = 0
 
     outbound: OutboundMessage | None = None
-    generated_media: list[str] = field(default_factory=list)
+    suppress_response: bool = False
 
     on_progress: Callable[..., Awaitable[None]] | None = None
     on_stream: Callable[[str], Awaitable[None]] | None = None
@@ -117,8 +122,8 @@ class TurnContext:
 
     pending_queue: asyncio.Queue | None = None
     pending_summary: str | None = None
-
     turn_wall_started_at: float = field(default_factory=time.time)
+    visible_run_started_at: float | None = None
     turn_latency_ms: int | None = None
 
     trace: list[StateTraceEntry] = field(default_factory=list)
@@ -144,6 +149,11 @@ class AgentLoop:
     def tool_names(self) -> list[str]:
         return self.tools.tool_names
 
+    def llm_runtime(self) -> LLMRuntime:
+        """Return the current provider/model pair owned by this loop."""
+        self._refresh_provider_snapshot()
+        return LLMRuntime(self.provider, self.model)
+
     _RUNTIME_CHECKPOINT_KEY = "runtime_checkpoint"
     _PENDING_USER_TURN_KEY = "pending_user_turn"
 
@@ -167,6 +177,7 @@ class AgentLoop:
         workspace: Path,
         model: str | None = None,
         max_iterations: int | None = None,
+        max_concurrent_subagents: int | None = None,
         context_window_tokens: int | None = None,
         context_block_limit: int | None = None,
         max_tool_result_chars: int | None = None,
@@ -193,7 +204,6 @@ class AgentLoop:
         model_preset: str | None = None,
         preset_snapshot_loader: preset_helpers.PresetSnapshotLoader | None = None,
         runtime_model_publisher: Callable[[str, str | None], None] | None = None,
-        p2p_shell: Any | None = None,
     ):
         from nanobot.config.schema import ToolsConfig
 
@@ -201,7 +211,6 @@ class AgentLoop:
         defaults = AgentDefaults()
         self.bus = bus
         self.channels_config = channels_config
-        self.p2p_shell = p2p_shell
         self.provider = provider
         self._provider_snapshot_loader = provider_snapshot_loader
         self._preset_snapshot_loader = preset_snapshot_loader
@@ -240,6 +249,10 @@ class AgentLoop:
             self._image_generation_provider_configs["openrouter"] = image_generation_provider_config
         self.cron_service = cron_service
         self.restrict_to_workspace = restrict_to_workspace
+        self.workspace_scopes = WorkspaceScopeResolver(
+            default_workspace=workspace,
+            default_restrict_to_workspace=restrict_to_workspace,
+        )
         self._start_time = time.time()
         self._last_usage: dict[str, int] = {}
         self._pending_turn_latency_ms: dict[str, int] = {}
@@ -247,6 +260,11 @@ class AgentLoop:
 
         self.context = ContextBuilder(workspace, timezone=timezone, disabled_skills=disabled_skills)
         self.sessions = session_manager or SessionManager(workspace)
+        self._webui_turns = WebuiTurnCoordinator(
+            bus=self.bus,
+            sessions=self.sessions,
+            schedule_background=lambda coro: self._schedule_background(coro),
+        )
         self.tools = ToolRegistry()
         # One file-read/write tracker per logical session. The tool registry is
         # shared by this loop, so tools resolve the active state via contextvars.
@@ -262,6 +280,7 @@ class AgentLoop:
             restrict_to_workspace=restrict_to_workspace,
             disabled_skills=disabled_skills,
             max_iterations=self.max_iterations,
+            max_concurrent_subagents=max_concurrent_subagents,
             llm_wall_timeout_for_session=lambda sk: runner_wall_llm_timeout_s(self.sessions, sk),
         )
         self._unified_session = unified_session
@@ -347,6 +366,7 @@ class AgentLoop:
             workspace=config.workspace_path,
             model=model,
             max_iterations=defaults.max_tool_iterations,
+            max_concurrent_subagents=defaults.max_concurrent_subagents,
             context_window_tokens=context_window_tokens,
             context_block_limit=defaults.context_block_limit,
             max_tool_result_chars=defaults.max_tool_result_chars,
@@ -462,6 +482,7 @@ class AgentLoop:
             provider_snapshot_loader=self._provider_snapshot_loader,
             image_generation_provider_configs=self._image_generation_provider_configs,
             timezone=self.context.timezone or "UTC",
+            workspace_sandbox=self.workspace_scopes.sandbox_status,
         )
         loader = ToolLoader()
         registered = loader.load(ctx, self.tools)
@@ -473,45 +494,11 @@ class AgentLoop:
             )
             registered.append("my")
 
-        # Register P2P tools if enabled
-        if self.p2p_shell:
-            self.tools.register(DispatchTaskTool(shell=self.p2p_shell))
-            self.tools.register(PollTaskResultTool(shell=self.p2p_shell))
-            self.tools.register(BroadcastTaskTool(shell=self.p2p_shell))
-            self.tools.register(CheckAggregationTool(shell=self.p2p_shell))
-            self.tools.register(
-                ReportUserTool(
-                    send_callback=self.bus.publish_outbound,
-                    default_channel=getattr(self.channels_config, "default_channel", ""),
-                    default_chat_id=getattr(self.channels_config, "default_chat_id", ""),
-                )
-            )
-            self.tools.register(FinalizeTaskTool(shell=self.p2p_shell, session_manager=self.sessions))
-            registered.append("p2p")
-
         logger.info("Registered {} tools: {}", len(registered), registered)
 
     async def _connect_mcp(self) -> None:
-        """Connect to configured MCP servers (one-time, lazy)."""
+        """Connect configured MCP servers."""
-        if self._mcp_connected or self._mcp_connecting or not self._mcp_servers:
+        await agent_context.connect_mcp(self, self.tools)
-            return
-        self._mcp_connecting = True
-        from nanobot.agent.tools.mcp import connect_mcp_servers
-
-        try:
-            self._mcp_stacks = await connect_mcp_servers(self._mcp_servers, self.tools)
-            if self._mcp_stacks:
-                self._mcp_connected = True
-            else:
-                logger.warning("No MCP servers connected successfully (will retry next message)")
-        except asyncio.CancelledError:
-            logger.warning("MCP connection cancelled (will retry next message)")
-            self._mcp_stacks.clear()
-        except BaseException as e:
-            logger.warning("Failed to connect MCP servers (will retry next message): {}", e)
-            self._mcp_stacks.clear()
-        finally:
-            self._mcp_connecting = False
 
     def _set_tool_context(
         self, channel: str, chat_id: str,
@@ -519,7 +506,7 @@ class AgentLoop:
         session_key: str | None = None,
     ) -> None:
         """Update context for all tools that need routing info."""
-        from nanobot.agent.tools.context import ContextAware, RequestContext
+        from nanobot.agent.tools.context import ContextAware
 
         if session_key is not None:
             effective_key = session_key
@@ -550,34 +537,7 @@ class AgentLoop:
         self, msg: InboundMessage
     ) -> Callable[..., Awaitable[None]]:
         """Build a progress callback that publishes to the message bus."""
-
+        return build_bus_progress_callback(self.bus, msg)
-        async def _bus_progress(
-            content: str,
-            *,
-            tool_hint: bool = False,
-            tool_events: list[dict[str, Any]] | None = None,
-            reasoning: bool = False,
-            reasoning_end: bool = False,
-        ) -> None:
-            meta = dict(msg.metadata or {})
-            meta["_progress"] = True
-            meta["_tool_hint"] = tool_hint
-            if reasoning:
-                meta["_reasoning_delta"] = True
-            if reasoning_end:
-                meta["_reasoning_end"] = True
-            if tool_events:
-                meta["_tool_events"] = tool_events
-            await self.bus.publish_outbound(
-                OutboundMessage(
-                    channel=msg.channel,
-                    chat_id=msg.chat_id,
-                    content=content,
-                    metadata=meta,
-                )
-            )
-
-        return _bus_progress
 
     async def _build_retry_wait_callback(
         self, msg: InboundMessage
@@ -608,10 +568,12 @@ class AgentLoop:
 
         Returns True if the message was persisted.
         """
+        if not turn_continuation.should_persist_user_message(msg.metadata):
+            return False
         media_paths = [p for p in (msg.media or []) if isinstance(p, str) and p]
         has_text = isinstance(msg.content, str) and msg.content.strip()
         if has_text or media_paths:
-            extra: dict[str, Any] = {"media": list(media_paths)} if media_paths else {}
+            extra: dict[str, Any] = ({"media": list(media_paths)} if media_paths else {}) | agent_context.session_extra(msg.metadata)
             extra.update(kwargs)
             text = msg.content if isinstance(msg.content, str) else ""
             session.add_message("user", text, **extra)
@@ -628,6 +590,7 @@ class AgentLoop:
         pending_summary: str | None,
     ) -> list[dict[str, Any]]:
         """Build the initial message list for the LLM turn."""
+        scope = self.workspace_scopes.for_message(msg, session.metadata)
         return self.context.build_messages(
             history=history,
             current_message=image_generation_prompt(msg.content, msg.metadata),
@@ -637,6 +600,9 @@ class AgentLoop:
             sender_id=msg.sender_id,
             session_summary=pending_summary,
             session_metadata=session.metadata,
+            workspace=scope.project_path,
+            runtime_state=self,
+            inbound_message=msg,
         )
 
     async def _dispatch_command_inline(
@@ -750,7 +716,7 @@ class AgentLoop:
                 content = pending_msg.content
                 media = pending_msg.media if pending_msg.media else None
                 if media:
-                    content, media = extract_documents(content, media)
+                    content, media = self._prepare_message_media(content, media)
                     media = media or None
                 user_content = self.context._build_user_content(content, media)
                 return {"role": "user", "content": user_content}
@@ -786,7 +752,31 @@ class AgentLoop:
             return items
 
         active_session_key = session.key if session else session_key
+        effective_scope = self.workspace_scopes.for_turn(
+            channel=channel,
+            message_metadata=metadata,
+            session_metadata=session.metadata if session is not None else None,
+        )
+        request_ctx = RequestContext(
+            channel=channel,
+            chat_id=chat_id,
+            message_id=message_id,
+            session_key=active_session_key,
+            metadata=dict(metadata or {}),
+        )
         file_state_token = bind_file_states(self._file_state_store.for_session(active_session_key))
+        request_token = bind_request_context(request_ctx)
+        workspace_token = bind_workspace_scope(effective_scope)
+        # Build continuation message that embeds the active goal objective so
+        # the LLM can see it even if earlier Runtime Context was truncated.
+        _goal_lines = goal_state_runtime_lines(session.metadata if session is not None else None)
+        _goal_continue = (
+            "You have an active sustained goal:\n\n"
+            + "\n".join(_goal_lines)
+            + "\n\nPlease continue working toward the objective using your tools, "
+            "or call complete_goal if the work is truly finished."
+        ) if _goal_lines else SUSTAINED_GOAL_CONTINUE_PROMPT
+        session_metadata = session.metadata if session is not None else None
         try:
             result = await self.runner.run(AgentRunSpec(
                 initial_messages=initial_messages,
@@ -797,7 +787,7 @@ class AgentLoop:
                 hook=hook,
                 error_message="Sorry, I encountered an error calling the AI model.",
                 concurrent_tools=True,
-                workspace=self.workspace,
+                workspace=effective_scope.project_path,
                 session_key=session.key if session else None,
                 context_window_tokens=self.context_window_tokens,
                 context_block_limit=self.context_block_limit,
@@ -812,17 +802,28 @@ class AgentLoop:
                 llm_timeout_s=runner_wall_llm_timeout_s(
                     self.sessions,
                     session.key if session is not None else session_key,
-                    metadata=(session.metadata if session is not None else None),
+                    metadata=session_metadata,
+                    message_metadata=metadata,
                 ),
+                goal_active_predicate=lambda: sustained_goal_active(session.metadata) if session is not None else False,
+                goal_continue_message=_goal_continue,
             ))
         finally:
+            reset_workspace_scope(workspace_token)
+            reset_request_context(request_token)
             reset_file_states(file_state_token)
         self._last_usage = result.usage
         if result.stop_reason == "max_iterations":
             logger.warning("Max iterations ({}) reached", self.max_iterations)
+            should_stream = turn_continuation.should_stream_budget_response(
+                stop_reason=result.stop_reason,
+                pending_queue_available=pending_queue is not None and session is not None,
+                session_metadata=session_metadata,
+                message_metadata=metadata,
+            )
             # Push final content through stream so streaming channels (e.g. Feishu)
             # update the card instead of leaving it empty.
-            if on_stream and on_stream_end:
+            if on_stream and on_stream_end and should_stream:
                 await on_stream(result.final_content or "")
                 await on_stream_end(resuming=False)
         elif result.stop_reason == "error":
@@ -855,13 +856,15 @@ class AgentLoop:
                 continue
 
             raw = msg.content.strip()
+            effective_key = self._effective_session_key(msg)
+            if await agent_context.handle_runtime_control(self, msg, self.tools):
+                continue
             if self.commands.is_priority(raw):
                 await self._dispatch_command_inline(
-                    msg, msg.session_key, raw,
+                    msg, effective_key, raw,
                     self.commands.dispatch_priority,
                 )
                 continue
-            effective_key = self._effective_session_key(msg)
             # If this session already has an active pending queue (i.e. a task
             # is processing this session), route the message there for mid-turn
             # injection instead of creating a competing task.
@@ -912,13 +915,13 @@ class AgentLoop:
         lock = self._session_locks.setdefault(session_key, asyncio.Lock())
         gate = self._concurrency_gate or nullcontext()
 
-        # Register a pending queue so follow-up messages for this session are
+        pending: asyncio.Queue | None = None
-        # routed here (mid-turn injection) instead of spawning a new task.
-        pending = asyncio.Queue(maxsize=20)
-        self._pending_queues[session_key] = pending
-
         try:
             async with lock, gate:
+                # Only the task that owns the session lock may publish the
+                # active mid-turn injection queue for this session.
+                pending = asyncio.Queue(maxsize=20)
+                self._pending_queues[session_key] = pending
                 try:
                     on_stream = on_stream_end = None
                     if msg.metadata.get("_wants_stream"):
@@ -963,39 +966,14 @@ class AgentLoop:
                             channel=msg.channel, chat_id=msg.chat_id,
                             content="", metadata=msg.metadata or {},
                         ))
-                    if msg.channel == "websocket":
+                    continuing = turn_continuation.internal_continuation_pending(msg.metadata)
-                        # Signal that the turn is fully complete (all tools executed,
+                    if msg.channel == "websocket" and not continuing:
-                        # final text streamed).  This lets WS clients know when to
-                        # definitively stop the loading indicator.
                         turn_lat = self._pending_turn_latency_ms.pop(session_key, None)
-                        turn_metadata: dict[str, Any] = {**msg.metadata, "_turn_end": True}
+                        await self._webui_turns.handle_turn_end(
-                        if turn_lat is not None:
+                            msg,
-                            turn_metadata["latency_ms"] = int(turn_lat)
+                            session_key=session_key,
-                        sess_turn = self.sessions.get_or_create(session_key)
+                            latency_ms=turn_lat,
-                        turn_metadata["goal_state"] = goal_state_ws_blob(sess_turn.metadata)
+                        )
-                        await self.bus.publish_outbound(OutboundMessage(
-                            channel=msg.channel, chat_id=msg.chat_id,
-                            content="", metadata=turn_metadata,
-                        ))
-                        if msg.metadata.get("webui") is True:
-                            async def _generate_title_and_notify() -> None:
-                                generated = await maybe_generate_webui_title_after_turn(
-                                    channel=msg.channel,
-                                    metadata=msg.metadata,
-                                    sessions=self.sessions,
-                                    session_key=session_key,
-                                    provider=self.provider,
-                                    model=self.model,
-                                )
-                                if generated:
-                                    await self.bus.publish_outbound(OutboundMessage(
-                                        channel=msg.channel,
-                                        chat_id=msg.chat_id,
-                                        content="",
-                                        metadata={**msg.metadata, "_session_updated": True},
-                                    ))
-
-                            self._schedule_background(_generate_title_and_notify())
                 except asyncio.CancelledError:
                     logger.info("Task cancelled for session {}", session_key)
                     # Preserve partial context from the interrupted turn so
@@ -1028,27 +1006,40 @@ class AgentLoop:
                         channel=msg.channel, chat_id=msg.chat_id,
                         content="Sorry, I encountered an error.",
                     ))
+                finally:
+                    # Drain any messages still in the pending queue and re-publish
+                    # them to the bus so they are processed as fresh inbound messages
+                    # rather than silently lost.  Only remove our own queue; a
+                    # later task waiting on the lock must not be able to steal
+                    # cleanup ownership.
+                    queue = None
+                    if self._pending_queues.get(session_key) is pending:
+                        queue = self._pending_queues.pop(session_key, None)
+                    else:
+                        queue = pending
+                    if queue is not None:
+                        leftover = 0
+                        while True:
+                            try:
+                                item = queue.get_nowait()
+                            except asyncio.QueueEmpty:
+                                break
+                            await self.bus.publish_inbound(item)
+                            leftover += 1
+                        if leftover:
+                            logger.info(
+                                "Re-published {} leftover message(s) to bus for session {}",
+                                leftover, session_key,
+                            )
+                    if not turn_continuation.internal_continuation_pending(msg.metadata):
+                        await self._webui_turns.publish_run_status(msg, "idle")
+                        self._pending_turn_latency_ms.pop(session_key, None)
+                        self._webui_turns.discard(session_key)
         finally:
-            # Drain any messages still in the pending queue and re-publish
+            if pending is None:
-            # them to the bus so they are processed as fresh inbound messages
+                await self._webui_turns.publish_run_status(msg, "idle")
-            # rather than silently lost.
+                self._pending_turn_latency_ms.pop(session_key, None)
-            queue = self._pending_queues.pop(session_key, None)
+                self._webui_turns.discard(session_key)
-            if queue is not None:
-                leftover = 0
-                while True:
-                    try:
-                        item = queue.get_nowait()
-                    except asyncio.QueueEmpty:
-                        break
-                    await self.bus.publish_inbound(item)
-                    leftover += 1
-                if leftover:
-                    logger.info(
-                        "Re-published {} leftover message(s) to bus for session {}",
-                        leftover, session_key,
-                    )
-            await publish_turn_run_status(self.bus, msg, "idle")
-            self._pending_turn_latency_ms.pop(session_key, None)
 
     async def close_mcp(self) -> None:
         """Drain pending background archives, then close MCP connections."""
@@ -1117,6 +1108,7 @@ class AgentLoop:
         }
         history = session.get_history(**_hist_kwargs)
         current_role = "assistant" if is_subagent else "user"
+        workspace_scope = self.workspace_scopes.for_message(msg, session.metadata)
 
         messages = self.context.build_messages(
             history=history,
@@ -1127,6 +1119,10 @@ class AgentLoop:
             sender_id=msg.sender_id,
             session_summary=pending,
             session_metadata=session.metadata,
+            workspace=workspace_scope.project_path,
+            runtime_state=self,
+            inbound_message=msg,
+            skip_runtime_lines=is_subagent,
         )
         t_wall = time.time()
         final_content, _, all_msgs, stop_reason, _ = await self._run_agent_loop(
@@ -1186,12 +1182,17 @@ class AgentLoop:
             )
 
         key = session_key or msg.session_key
+        t0 = time.time()
         ctx = TurnContext(
             msg=msg,
             session=None,
             session_key=key,
             state=TurnState.RESTORE,
             turn_id=f"{key}:{time.time_ns()}",
+            turn_wall_started_at=t0,
+            visible_run_started_at=turn_continuation.internal_continuation_run_started_at(
+                msg.metadata,
+            ),
             on_progress=on_progress,
             on_stream=on_stream,
             on_stream_end=on_stream_end,
@@ -1259,7 +1260,6 @@ class AgentLoop:
         all_msgs: list[dict[str, Any]],
         stop_reason: str,
         had_injections: bool,
-        generated_media: list[str],
         on_stream: Callable[[str], Awaitable[None]] | None,
         *,
         turn_latency_ms: int | None = None,
@@ -1283,7 +1283,6 @@ class AgentLoop:
             channel=msg.channel,
             chat_id=msg.chat_id,
             content=final_content,
-            media=generated_media,
             metadata=meta,
         )
 
@@ -1292,7 +1291,7 @@ class AgentLoop:
         msg = ctx.msg
 
         if msg.media:
-            new_content, image_only = extract_documents(msg.content, msg.media)
+            new_content, image_only = self._prepare_message_media(msg.content, msg.media)
             ctx.msg = dataclasses.replace(msg, content=new_content, media=image_only)
             msg = ctx.msg
 
@@ -1304,6 +1303,7 @@ class AgentLoop:
         if ctx.session is None:
             ctx.session = self.sessions.get_or_create(ctx.session_key)
         mark_webui_session(ctx.session, msg.metadata)
+        self.workspace_scopes.persist_message_scope(ctx.session, msg)
 
         if self._restore_runtime_checkpoint(ctx.session):
             self.sessions.save(ctx.session)
@@ -1312,6 +1312,16 @@ class AgentLoop:
 
         return "ok"
 
+    def _prepare_message_media(self, content: str, media: list[str]) -> tuple[str, list[str]]:
+        if self._should_extract_document_text():
+            return extract_documents(content, media)
+        return reference_non_image_attachments(content, media)
+
+    def _should_extract_document_text(self) -> bool:
+        if self.channels_config is None:
+            return True
+        return self.channels_config.extract_document_text
+
     async def _state_compact(self, ctx: TurnContext) -> str:
         ctx.session, pending = self.auto_compact.prepare_session(ctx.session, ctx.session_key)
         ctx.pending_summary = pending
@@ -1364,9 +1374,17 @@ class AgentLoop:
             "include_timestamps": True,
         }
         ctx.history = ctx.session.get_history(**_hist_kwargs)
+        self._webui_turns.capture_title_context(
+            ctx.session_key,
+            ctx.msg,
+            self.llm_runtime(),
+        )
 
         ctx.initial_messages = self._build_initial_messages(
-            ctx.msg, ctx.session, ctx.history, ctx.pending_summary
+            ctx.msg,
+            ctx.session,
+            ctx.history,
+            ctx.pending_summary,
         )
         ctx.user_persisted_early = self._persist_user_message_early(
             ctx.msg, ctx.session
@@ -1380,7 +1398,13 @@ class AgentLoop:
         return "ok"
 
     async def _state_run(self, ctx: TurnContext) -> str:
-        await publish_turn_run_status(self.bus, ctx.msg, "running")
+        if ctx.visible_run_started_at is None:
+            ctx.visible_run_started_at = time.time()
+        await self._webui_turns.publish_run_status(
+            ctx.msg,
+            "running",
+            started_at=ctx.visible_run_started_at,
+        )
         result = await self._run_agent_loop(
             ctx.initial_messages,
             on_progress=ctx.on_progress,
@@ -1401,20 +1425,25 @@ class AgentLoop:
         ctx.all_messages = all_msgs
         ctx.stop_reason = stop_reason
         ctx.had_injections = had_injections
+        await turn_continuation.maybe_continue_turn(ctx)
         return "ok"
 
     async def _state_save(self, ctx: TurnContext) -> str:
-        if ctx.final_content is None or not ctx.final_content.strip():
+        turn_continuation.prepare_save_boundary(ctx)
+
+        if (
+            (ctx.final_content is None or not ctx.final_content.strip())
+            and not ctx.suppress_response
+        ):
             ctx.final_content = EMPTY_FINAL_RESPONSE_MESSAGE
 
-        ctx.save_skip = 1 + len(ctx.history) + (1 if ctx.user_persisted_early else 0)
+        latency_started_at = (
-        skip_msgs = ctx.all_messages[ctx.save_skip:]
+            ctx.visible_run_started_at
-        ctx.generated_media = generated_image_paths_from_messages(skip_msgs)
+            if turn_continuation.internal_continuation_inbound(ctx.msg.metadata)
-        mt = self.tools.get("message")
+            and ctx.visible_run_started_at is not None
-        extra = getattr(mt, "turn_delivered_media_paths", lambda: [])() if mt else []
+            else ctx.turn_wall_started_at
-        merge_turn_media_into_last_assistant(ctx.all_messages, ctx.generated_media, extra)
+        )
-
+        ctx.turn_latency_ms = max(0, int((time.time() - latency_started_at) * 1000))
-        ctx.turn_latency_ms = max(0, int((time.time() - ctx.turn_wall_started_at) * 1000))
         self._save_turn(
             ctx.session, ctx.all_messages, ctx.save_skip,
             turn_latency_ms=ctx.turn_latency_ms,
@@ -1434,13 +1463,15 @@ class AgentLoop:
         return "ok"
 
     async def _state_respond(self, ctx: TurnContext) -> str:
+        if ctx.suppress_response:
+            ctx.outbound = None
+            return "ok"
         ctx.outbound = self._assemble_outbound(
             ctx.msg,
             ctx.final_content,
             ctx.all_messages,
             ctx.stop_reason,
             ctx.had_injections,
-            ctx.generated_media,
             ctx.on_stream,
             turn_latency_ms=ctx.turn_latency_ms,
         )
@@ -1675,10 +1706,19 @@ class AgentLoop:
             channel=channel, sender_id="user", chat_id=chat_id,
             content=content, media=media or [],
         )
-        return await self._process_message(
+        # Share the dispatch lock so direct calls serialize with bus turns.
-            msg,
+        lock = self._session_locks.setdefault(session_key, asyncio.Lock())
-            session_key=session_key,
+        try:
-            on_progress=on_progress,
+            async with lock:
-            on_stream=on_stream,
+                return await self._process_message(
-            on_stream_end=on_stream_end,
+                    msg,
-        )
+                    session_key=session_key,
+                    on_progress=on_progress,
+                    on_stream=on_stream,
+                    on_stream_end=on_stream_end,
+                )
+        finally:
+            if channel == "websocket":
+                await self._webui_turns.publish_run_status(msg, "idle")
+                self._pending_turn_latency_ms.pop(session_key, None)
+                self._webui_turns.discard(session_key)

nanobot/agent/memory.py

@@ -678,11 +678,18 @@ class Consolidator:
         The budget reserves space for completion tokens and a safety buffer
         so the LLM request never exceeds the context window.
         """
-        if not session.messages or self.context_window_tokens <= 0:
+        if self.context_window_tokens <= 0:
             return
 
         lock = self.get_lock(session.key)
         async with lock:
+            # Refresh session reference: AutoCompact may have replaced it.
+            fresh = self.sessions.get_or_create(session.key)
+            if fresh is not session:
+                session = fresh
+            if not session.messages:
+                return
+
             budget = self._input_token_budget
             target = int(budget * self.consolidation_ratio)
             last_summary = await self._consolidate_replay_overflow(
@@ -769,6 +776,73 @@ class Consolidator:
             # the summary injection strategy with AutoCompact._archive().
             self._persist_last_summary(session, last_summary)
 
+    async def compact_idle_session(
+        self,
+        session_key: str,
+        max_suffix: int = 8,
+    ) -> str | None:
+        """Hard-truncate an idle session under the consolidation lock.
+
+        Used by AutoCompact so all session mutation goes through a single
+        lock-protected path.  Returns the summary text on success, ``None``
+        if the LLM failed (raw_archive fallback), or ``""`` if there was
+        nothing to archive.
+        """
+        lock = self.get_lock(session_key)
+        async with lock:
+            self.sessions.invalidate(session_key)
+            session = self.sessions.get_or_create(session_key)
+
+            tail = list(session.messages[session.last_consolidated:])
+            if not tail:
+                session.updated_at = datetime.now()
+                self.sessions.save(session)
+                return ""
+
+            probe = Session(
+                key=session.key,
+                messages=tail.copy(),
+                created_at=session.created_at,
+                updated_at=session.updated_at,
+                metadata={},
+                last_consolidated=0,
+            )
+            dropped, already_consolidated = probe.retain_recent_legal_suffix(max_suffix)
+            kept = probe.messages
+            archive_msgs = dropped[already_consolidated:]
+
+            if not archive_msgs and not kept:
+                session.updated_at = datetime.now()
+                self.sessions.save(session)
+                return ""
+
+            last_active = session.updated_at
+            summary: str | None = ""
+            if archive_msgs:
+                summary = await self.archive(archive_msgs)
+
+            if summary and summary != "(nothing)":
+                session.metadata["_last_summary"] = {
+                    "text": summary,
+                    "last_active": last_active.isoformat(),
+                }
+
+            session.messages = kept
+            session.last_consolidated = 0
+            session.updated_at = datetime.now()
+            self.sessions.save(session)
+
+            if archive_msgs:
+                logger.info(
+                    "Idle-session compact for {}: archived={}, kept={}, summary={}",
+                    session_key,
+                    len(archive_msgs),
+                    len(kept),
+                    bool(summary),
+                )
+
+            return summary
+
 
 # ---------------------------------------------------------------------------
 # Dream — heavyweight cron-scheduled memory consolidation

nanobot/agent/runner.py

@@ -8,13 +8,23 @@ import os
 from contextlib import suppress
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any
+from typing import Any, Callable
 
 from loguru import logger
 
 from nanobot.agent.hook import AgentHook, AgentHookContext
 from nanobot.agent.tools.registry import ToolRegistry
 from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
+from nanobot.utils.file_edit_events import (
+    StreamingFileEditTracker,
+    build_file_edit_end_event,
+    build_file_edit_error_event,
+    build_file_edit_start_event,
+    prepare_file_edit_trackers,
+)
+from nanobot.utils.file_edit_events import (
+    prepare_file_edit_tracker as _prepare_file_edit_tracker,
+)
 from nanobot.utils.helpers import (
     IncrementalThinkExtractor,
     build_assistant_message,
@@ -26,10 +36,15 @@ from nanobot.utils.helpers import (
     strip_think,
     truncate_text,
 )
+from nanobot.utils.progress_events import (
+    invoke_file_edit_progress,
+    on_progress_accepts_file_edit_events,
+)
 from nanobot.utils.prompt_templates import render_template
 from nanobot.utils.runtime import (
     EMPTY_FINAL_RESPONSE_MESSAGE,
     build_finalization_retry_message,
+    build_goal_continue_message,
     build_length_recovery_message,
     ensure_nonempty_tool_result,
     is_blank_text,
@@ -38,6 +53,10 @@ from nanobot.utils.runtime import (
 )
 
 _DEFAULT_ERROR_MESSAGE = "Sorry, I encountered an error calling the AI model."
+_ARREARAGE_ERROR_MESSAGE = (
+    "The AI provider rejected the request because the API key is out of quota or the "
+    "account is in arrears. Please top up / check the billing status of your API key and try again."
+)
 _PERSISTED_MODEL_ERROR_PLACEHOLDER = "[Assistant reply unavailable due to model error.]"
 _MAX_EMPTY_RETRIES = 2
 _MAX_LENGTH_RECOVERIES = 3
@@ -47,11 +66,14 @@ _SNIP_SAFETY_BUFFER = 1024
 _MICROCOMPACT_KEEP_RECENT = 10
 _MICROCOMPACT_MIN_CHARS = 500
 _COMPACTABLE_TOOLS = frozenset({
-    "read_file", "exec", "grep",
+    "read_file", "exec", "grep", "find_files",
-    "web_search", "web_fetch", "list_dir",
+    "web_search", "web_fetch", "list_dir", "list_exec_sessions",
 })
 _BACKFILL_CONTENT = "[Tool result unavailable — call was interrupted or lost]"
 
+# Backward-compatible module attribute for tests/extensions that monkeypatch
+# the former single-file tracker hook. Runtime uses prepare_file_edit_trackers.
+prepare_file_edit_tracker = _prepare_file_edit_tracker
 
 
 @dataclass(slots=True)
@@ -82,6 +104,8 @@ class AgentRunSpec:
     checkpoint_callback: Any | None = None
     injection_callback: Any | None = None
     llm_timeout_s: float | None = None
+    goal_active_predicate: Callable[[], bool] | None = None
+    goal_continue_message: str | None = None
 
 
 @dataclass(slots=True)
@@ -152,6 +176,7 @@ class AgentRunner:
         *,
         phase: str = "after error",
         iteration: int | None = None,
+        allow_goal_continue: bool = False,
     ) -> tuple[bool, int]:
         """Drain pending injections. Returns (should_continue, updated_cycles).
 
@@ -160,12 +185,19 @@ class AgentRunner:
         and *iteration* are both provided) and return (True, cycles+1) so the
         caller continues the iteration loop.  Otherwise return (False, cycles).
         """
-        if injection_cycles >= _MAX_INJECTION_CYCLES:
+        injections: list[dict[str, Any]] = []
-            return False, injection_cycles
+        real_injection = False
-        injections = await self._drain_injections(spec)
+        if injection_cycles < _MAX_INJECTION_CYCLES:
+            injections = await self._drain_injections(spec)
+            real_injection = bool(injections)
+        if not injections and allow_goal_continue and assistant_message is not None:
+            predicate = spec.goal_active_predicate
+            if predicate is not None and predicate():
+                injections = [build_goal_continue_message(spec.goal_continue_message)]
         if not injections:
             return False, injection_cycles
-        injection_cycles += 1
+        if real_injection:
+            injection_cycles += 1
         if assistant_message is not None:
             messages.append(assistant_message)
             if iteration is not None:
@@ -181,10 +213,13 @@ class AgentRunner:
                     },
                 )
         self._append_injected_messages(messages, injections)
-        logger.info(
+        if real_injection:
-            "Injected {} follow-up message(s) {} ({}/{})",
+            logger.info(
-            len(injections), phase, injection_cycles, _MAX_INJECTION_CYCLES,
+                "Injected {} follow-up message(s) {} ({}/{})",
-        )
+                len(injections), phase, injection_cycles, _MAX_INJECTION_CYCLES,
+            )
+        else:
+            logger.info("Injected sustained-goal continuation {}", phase)
         return True, injection_cycles
 
     async def _drain_injections(self, spec: AgentRunSpec) -> list[dict[str, Any]]:
@@ -460,6 +495,7 @@ class AgentRunner:
                 spec, messages, assistant_message, injection_cycles,
                 phase="after final response",
                 iteration=iteration,
+                allow_goal_continue=True,
             )
             if should_continue:
                 had_injections = True
@@ -472,7 +508,10 @@ class AgentRunner:
                 continue
 
             if response.finish_reason == "error":
-                final_content = clean or spec.error_message or _DEFAULT_ERROR_MESSAGE
+                if LLMProvider.is_arrearage_response(response):
+                    final_content = _ARREARAGE_ERROR_MESSAGE
+                else:
+                    final_content = clean or spec.error_message or _DEFAULT_ERROR_MESSAGE
                 stop_reason = "error"
                 error = final_content
                 self._append_model_error_placeholder(messages)
@@ -619,6 +658,24 @@ class AgentRunner:
         )
 
         progress_state: dict[str, bool] | None = None
+        live_file_edits: StreamingFileEditTracker | None = None
+
+        if (
+            spec.progress_callback is not None
+            and on_progress_accepts_file_edit_events(spec.progress_callback)
+        ):
+            async def _emit_live_file_edits(events: list[dict[str, Any]]) -> None:
+                await invoke_file_edit_progress(spec.progress_callback, events)
+
+            live_file_edits = StreamingFileEditTracker(
+                workspace=spec.workspace,
+                tools=spec.tools,
+                emit=_emit_live_file_edits,
+            )
+
+        async def _tool_call_delta(delta: dict[str, Any]) -> None:
+            if live_file_edits is not None:
+                await live_file_edits.update(delta)
 
         if wants_streaming:
             async def _stream(delta: str) -> None:
@@ -636,6 +693,7 @@ class AgentRunner:
                 **kwargs,
                 on_content_delta=_stream,
                 on_thinking_delta=_thinking,
+                on_tool_call_delta=_tool_call_delta if live_file_edits is not None else None,
             )
         elif wants_progress_streaming:
             stream_buf = ""
@@ -665,6 +723,7 @@ class AgentRunner:
             coro = self.provider.chat_stream_with_retry(
                 **kwargs,
                 on_content_delta=_stream_progress,
+                on_tool_call_delta=_tool_call_delta if live_file_edits is not None else None,
             )
         else:
             coro = self.provider.chat_with_retry(**kwargs)
@@ -679,6 +738,14 @@ class AgentRunner:
                 await coro if outer_timeout_s is None
                 else await asyncio.wait_for(coro, timeout=outer_timeout_s)
             )
+            if live_file_edits is not None:
+                await live_file_edits.flush()
+                if response.should_execute_tools:
+                    live_file_edits.apply_final_call_ids(response.tool_calls)
+                await live_file_edits.error_unmatched(
+                    response.tool_calls if response.should_execute_tools else [],
+                    "Tool call did not complete.",
+                )
         except asyncio.TimeoutError:
             if outer_timeout_s is None:
                 return LLMResponse(
@@ -813,6 +880,30 @@ class AgentRunner:
             return prep_error + hint, event, (
                 RuntimeError(prep_error) if spec.fail_on_tool_error else None
             )
+        emit_file_edit_events = (
+            spec.progress_callback is not None
+            and on_progress_accepts_file_edit_events(spec.progress_callback)
+        )
+        progress_callback = spec.progress_callback if emit_file_edit_events else None
+        file_edit_trackers = (
+            prepare_file_edit_trackers(
+                call_id=tool_call.id,
+                tool_name=tool_call.name,
+                tool=tool,
+                workspace=spec.workspace,
+                params=params if isinstance(params, dict) else None,
+            )
+            if progress_callback is not None
+            else None
+        )
+        if file_edit_trackers and progress_callback is not None:
+            await invoke_file_edit_progress(
+                progress_callback,
+                [build_file_edit_start_event(
+                    file_edit_tracker,
+                    params if isinstance(params, dict) else None,
+                ) for file_edit_tracker in file_edit_trackers],
+            )
         try:
             if tool is not None:
                 result = await tool.execute(**params)
@@ -821,6 +912,14 @@ class AgentRunner:
         except asyncio.CancelledError:
             raise
         except BaseException as exc:
+            if file_edit_trackers and progress_callback is not None:
+                await invoke_file_edit_progress(
+                    progress_callback,
+                    [
+                        build_file_edit_error_event(file_edit_tracker, str(exc))
+                        for file_edit_tracker in file_edit_trackers
+                    ],
+                )
             event = {
                 "name": tool_call.name,
                 "status": "error",
@@ -842,6 +941,14 @@ class AgentRunner:
             return payload, event, None
 
         if isinstance(result, str) and result.startswith("Error"):
+            if file_edit_trackers and progress_callback is not None:
+                await invoke_file_edit_progress(
+                    progress_callback,
+                    [
+                        build_file_edit_error_event(file_edit_tracker, result)
+                        for file_edit_tracker in file_edit_trackers
+                    ],
+                )
             event = {
                 "name": tool_call.name,
                 "status": "error",
@@ -860,6 +967,15 @@ class AgentRunner:
                 return result + hint, event, RuntimeError(result)
             return result + hint, event, None
 
+        if file_edit_trackers and progress_callback is not None:
+            await invoke_file_edit_progress(
+                progress_callback,
+                [build_file_edit_end_event(
+                    file_edit_tracker,
+                    params if isinstance(params, dict) else None,
+                ) for file_edit_tracker in file_edit_trackers],
+            )
+
         detail = "" if result is None else str(result)
         detail = detail.replace("\n", " ").strip()
         if not detail:
@@ -1164,7 +1280,13 @@ class AgentRunner:
             return messages
 
         system_tokens = sum(estimate_message_tokens(msg) for msg in system_messages)
-        remaining_budget = max(128, budget - system_tokens)
+        fixed_tokens, _ = estimate_prompt_tokens_chain(
+            self.provider,
+            spec.model,
+            system_messages,
+            spec.tools.get_definitions(),
+        )
+        remaining_budget = max(0, budget - max(system_tokens, fixed_tokens))
         kept: list[dict[str, Any]] = []
         kept_tokens = 0
         for message in reversed(non_system):

nanobot/agent/subagent.py

@@ -16,6 +16,12 @@ from nanobot.agent.tools.context import ToolContext
 from nanobot.agent.tools.file_state import FileStates
 from nanobot.agent.tools.loader import ToolLoader
 from nanobot.agent.tools.registry import ToolRegistry
+from nanobot.security.workspace_access import (
+    WorkspaceScope,
+    bind_workspace_scope,
+    reset_workspace_scope,
+    workspace_sandbox_status,
+)
 from nanobot.bus.events import InboundMessage
 from nanobot.bus.queue import MessageBus
 from nanobot.config.schema import AgentDefaults, ToolsConfig
@@ -79,6 +85,7 @@ class SubagentManager:
         restrict_to_workspace: bool = False,
         disabled_skills: list[str] | None = None,
         max_iterations: int | None = None,
+        max_concurrent_subagents: int | None = None,
         llm_wall_timeout_for_session: Callable[[str | None], float | None] | None = None,
     ):
         defaults = AgentDefaults()
@@ -95,7 +102,11 @@ class SubagentManager:
             if max_iterations is not None
             else defaults.max_tool_iterations
         )
-        self.max_concurrent_subagents = defaults.max_concurrent_subagents
+        self.max_concurrent_subagents = (
+            max_concurrent_subagents
+            if max_concurrent_subagents is not None
+            else defaults.max_concurrent_subagents
+        )
         self.runner = AgentRunner(provider)
         self._llm_wall_timeout_for_session = llm_wall_timeout_for_session
         self._running_tasks: dict[str, asyncio.Task[None]] = {}
@@ -123,6 +134,10 @@ class SubagentManager:
             config=cfg,
             workspace=str(root.resolve()),
             file_state_store=FileStates(),
+            workspace_sandbox=workspace_sandbox_status(
+                restrict_to_workspace=cfg.restrict_to_workspace,
+                workspace=root,
+            ),
         )
         ToolLoader().load(ctx, registry, scope="subagent")
         return registry
@@ -140,6 +155,8 @@ class SubagentManager:
         origin_chat_id: str = "direct",
         session_key: str | None = None,
         origin_message_id: str | None = None,
+        temperature: float | None = None,
+        workspace_scope: WorkspaceScope | None = None,
     ) -> str:
         """Spawn a subagent to execute a task in the background."""
         task_id = str(uuid.uuid4())[:8]
@@ -155,7 +172,16 @@ class SubagentManager:
         self._task_statuses[task_id] = status
 
         bg_task = asyncio.create_task(
-            self._run_subagent(task_id, task, display_label, origin, status, origin_message_id)
+            self._run_subagent(
+                task_id,
+                task,
+                display_label,
+                origin,
+                status,
+                origin_message_id,
+                temperature,
+                workspace_scope,
+            )
         )
         self._running_tasks[task_id] = bg_task
         if session_key:
@@ -182,6 +208,8 @@ class SubagentManager:
         origin: dict[str, str],
         status: SubagentStatus,
         origin_message_id: str | None = None,
+        temperature: float | None = None,
+        workspace_scope: WorkspaceScope | None = None,
     ) -> None:
         """Execute the subagent task and announce the result."""
         logger.info("Subagent [{}] starting task: {}", task_id, label)
@@ -191,8 +219,13 @@ class SubagentManager:
             status.iteration = payload.get("iteration", status.iteration)
 
         try:
-            tools = self._build_tools()
+            root = workspace_scope.project_path if workspace_scope is not None else self.workspace
-            system_prompt = self._build_subagent_prompt()
+            cfg = None
+            if workspace_scope is not None:
+                cfg = self._subagent_tools_config()
+                cfg.restrict_to_workspace = workspace_scope.restrict_to_workspace
+            tools = self._build_tools(workspace=root, tools_config=cfg)
+            system_prompt = self._build_subagent_prompt(workspace=root)
             messages: list[dict[str, Any]] = [
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": task},
@@ -204,20 +237,27 @@ class SubagentManager:
                 if self._llm_wall_timeout_for_session
                 else None
             )
-            result = await self.runner.run(AgentRunSpec(
+            token = bind_workspace_scope(workspace_scope) if workspace_scope is not None else None
-                initial_messages=messages,
+            try:
-                tools=tools,
+                result = await self.runner.run(AgentRunSpec(
-                model=self.model,
+                    initial_messages=messages,
-                max_iterations=self.max_iterations,
+                    tools=tools,
-                max_tool_result_chars=self.max_tool_result_chars,
+                    model=self.model,
-                hook=_SubagentHook(task_id, status),
+                    temperature=temperature,
-                max_iterations_message="Task completed but no final response was generated.",
+                    max_iterations=self.max_iterations,
-                error_message=None,
+                    max_tool_result_chars=self.max_tool_result_chars,
-                fail_on_tool_error=True,
+                    hook=_SubagentHook(task_id, status),
-                checkpoint_callback=_on_checkpoint,
+                    max_iterations_message="Task completed but no final response was generated.",
-                session_key=sess_key,
+                    error_message=None,
-                llm_timeout_s=llm_timeout,
+                    fail_on_tool_error=True,
-            ))
+                    checkpoint_callback=_on_checkpoint,
+                    session_key=sess_key,
+                    workspace=root,
+                    llm_timeout_s=llm_timeout,
+                ))
+            finally:
+                if token is not None:
+                    reset_workspace_scope(token)
             status.phase = "done"
             status.stop_reason = result.stop_reason
 
@@ -311,20 +351,21 @@ class SubagentManager:
             lines.append(f"- {result.error}")
         return "\n".join(lines) or (result.error or "Error: subagent execution failed.")
 
-    def _build_subagent_prompt(self) -> str:
+    def _build_subagent_prompt(self, workspace: Path | None = None) -> str:
         """Build a focused system prompt for the subagent."""
         from nanobot.agent.context import ContextBuilder
         from nanobot.agent.skills import SkillsLoader
 
         time_ctx = ContextBuilder._build_runtime_context(None, None)
+        root = workspace or self.workspace
         skills_summary = SkillsLoader(
-            self.workspace,
+            root,
             disabled_skills=self.disabled_skills,
         ).build_skills_summary()
         return render_template(
             "agent/subagent_system.md",
             time_ctx=time_ctx,
-            workspace=str(self.workspace),
+            workspace=str(root),
             skills_summary=skills_summary or "",
         )
 

nanobot/agent/tools/apply_patch.py

@@ -0,0 +1,290 @@
+"""Apply file edits by providing structured edit instructions."""
+
+from __future__ import annotations
+
+import difflib
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from nanobot.agent.tools.base import tool_parameters
+from nanobot.agent.tools.filesystem import _FsTool
+from nanobot.agent.tools.schema import (
+    ArraySchema,
+    BooleanSchema,
+    ObjectSchema,
+    StringSchema,
+    tool_parameters_schema,
+)
+
+
+@dataclass(slots=True)
+class _PatchSummary:
+    action: str
+    path: str
+    added: int = 0
+    deleted: int = 0
+
+
+class _PatchError(ValueError):
+    pass
+
+
+_ABSOLUTE_WINDOWS_RE = re.compile(r"^[A-Za-z]:[\\/]")
+
+
+def _validate_relative_path(path: str) -> str:
+    normalized = path.strip()
+    if not normalized:
+        raise _PatchError("patch path cannot be empty")
+    if "\0" in normalized:
+        raise _PatchError(f"patch path contains a null byte: {path!r}")
+    if normalized.startswith(("~", "/", "\\")) or _ABSOLUTE_WINDOWS_RE.match(normalized):
+        raise _PatchError(f"patch path must be relative: {path}")
+    if any(part == ".." for part in re.split(r"[\\/]+", normalized)):
+        raise _PatchError(f"patch path must not contain '..': {path}")
+    return normalized
+
+
+def _lines_to_text(lines: list[str]) -> str:
+    if not lines:
+        return ""
+    return "\n".join(lines) + "\n"
+
+
+def _text_line_count(text: str) -> int:
+    if not text:
+        return 0
+    return len(text.splitlines())
+
+
+def _line_diff_stats(before: str, after: str) -> tuple[int, int]:
+    before_lines = before.replace("\r\n", "\n").splitlines()
+    after_lines = after.replace("\r\n", "\n").splitlines()
+    added = 0
+    deleted = 0
+    matcher = difflib.SequenceMatcher(a=before_lines, b=after_lines, autojunk=False)
+    for tag, i1, i2, j1, j2 in matcher.get_opcodes():
+        if tag == "equal":
+            continue
+        if tag in ("replace", "delete"):
+            deleted += i2 - i1
+        if tag in ("replace", "insert"):
+            added += j2 - j1
+    return added, deleted
+
+
+def _format_summary(summary: _PatchSummary) -> str:
+    stats = ""
+    if summary.added or summary.deleted:
+        stats = f" (+{summary.added}/-{summary.deleted})"
+    return f"- {summary.action} {summary.path}{stats}"
+
+
+@tool_parameters(
+    tool_parameters_schema(
+        edits=ArraySchema(
+            items=ObjectSchema(
+                path=StringSchema("Relative path to the file to edit."),
+                action=StringSchema(
+                    "Operation type: replace or add.",
+                    enum=["replace", "add"],
+                ),
+                old_text=StringSchema(
+                    "Exact text to search for in the file. Required for replace.",
+                    nullable=True,
+                ),
+                new_text=StringSchema(
+                    "Text to replace with or append. Required for replace and add.",
+                    nullable=True,
+                ),
+                required=["path", "action"],
+            ),
+            description="List of edits to apply. Each edit specifies a file and the change to make.",
+            min_items=1,
+            max_items=20,
+        ),
+        dry_run=BooleanSchema(
+            description="Validate and summarize the patch without writing files.",
+            default=False,
+        ),
+        required=["edits"],
+    )
+)
+class ApplyPatchTool(_FsTool):
+    """Apply file edits by providing structured edit instructions."""
+    _scopes = {"core", "subagent"}
+
+    @property
+    def name(self) -> str:
+        return "apply_patch"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Default tool for code edits. Supports multi-file changes in a single call. "
+            "Provide a list of structured edits, each specifying a file path, action "
+            "(replace/add), and the exact text to change. "
+            "Paths must be relative. Set dry_run=true to validate and preview without writing files. "
+            "Use edit_file only for small exact replacements on a single file."
+        )
+
+    async def execute(
+        self,
+        edits: list[dict] | None = None,
+        dry_run: bool = False,
+        **kwargs: Any,
+    ) -> str:
+        try:
+            if not edits:
+                raise _PatchError("must provide edits")
+
+            writes: dict[Path, str] = {}
+            summaries: list[_PatchSummary] = []
+
+            for edit in edits:
+                if not isinstance(edit, dict):
+                    raise _PatchError("each edit must be an object")
+                raw_path = edit.get("path")
+                if not isinstance(raw_path, str):
+                    raise _PatchError("path required for edit")
+                path = _validate_relative_path(raw_path)
+                action = edit.get("action")
+                if not isinstance(action, str):
+                    raise _PatchError(f"action required for edit: {path}")
+                source = self._resolve(path)
+
+                if action == "add":
+                    new_text = edit.get("new_text")
+                    if new_text is None:
+                        raise _PatchError(f"new_text required for add: {path}")
+
+                    pending = writes.get(source)
+                    if pending is not None:
+                        content = pending
+                        exists = True
+                    elif source.exists():
+                        raw = source.read_bytes()
+                        try:
+                            content = raw.decode("utf-8")
+                        except UnicodeDecodeError:
+                            raise _PatchError(f"file is not UTF-8 text: {path}")
+                        exists = True
+                    else:
+                        content = ""
+                        exists = False
+
+                    if exists:
+                        uses_crlf = "\r\n" in content
+                        new_norm = content.replace("\r\n", "\n") + new_text.replace("\r\n", "\n")
+                        if new_norm and not new_norm.endswith("\n"):
+                            new_norm += "\n"
+                        if uses_crlf:
+                            new_norm = new_norm.replace("\n", "\r\n")
+                        writes[source] = new_norm
+                        added, deleted = _line_diff_stats(content, new_norm)
+                        action_name = "update"
+                    else:
+                        new_norm = new_text.replace("\r\n", "\n")
+                        if new_norm and not new_norm.endswith("\n"):
+                            new_norm += "\n"
+                        writes[source] = new_norm
+                        added = _text_line_count(new_norm)
+                        deleted = 0
+                        action_name = "add"
+
+                    summaries.append(
+                        _PatchSummary(
+                            action=action_name, path=path, added=added, deleted=deleted
+                        )
+                    )
+
+                elif action == "replace":
+                    old_text = edit.get("old_text") or ""
+                    if not old_text:
+                        raise _PatchError(f"old_text required for replace: {path}")
+                    new_text = edit.get("new_text")
+                    if new_text is None:
+                        raise _PatchError(f"new_text required for replace: {path}")
+
+                    pending = writes.get(source)
+                    if pending is not None:
+                        content = pending
+                    elif source.exists():
+                        raw = source.read_bytes()
+                        try:
+                            content = raw.decode("utf-8")
+                        except UnicodeDecodeError:
+                            raise _PatchError(f"file is not UTF-8 text: {path}")
+                    else:
+                        raise _PatchError(f"file to update does not exist: {path}")
+
+                    if pending is None and not source.is_file():
+                        raise _PatchError(f"path to update is not a file: {path}")
+
+                    uses_crlf = "\r\n" in content
+                    norm_content = content.replace("\r\n", "\n")
+                    norm_old = old_text.replace("\r\n", "\n")
+
+                    pos = norm_content.find(norm_old)
+                    if pos < 0:
+                        raise _PatchError(f"old_text not found in {path}")
+                    if norm_content.find(norm_old, pos + 1) >= 0:
+                        raise _PatchError(f"old_text appears multiple times in {path}")
+
+                    new_norm = (
+                        norm_content[:pos]
+                        + new_text.replace("\r\n", "\n")
+                        + norm_content[pos + len(norm_old) :]
+                    )
+                    if new_norm and not new_norm.endswith("\n"):
+                        new_norm += "\n"
+                    if uses_crlf:
+                        new_norm = new_norm.replace("\n", "\r\n")
+
+                    writes[source] = new_norm
+                    added, deleted = _line_diff_stats(content, new_norm)
+                    summaries.append(
+                        _PatchSummary(
+                            action="update", path=path, added=added, deleted=deleted
+                        )
+                    )
+
+                else:
+                    raise _PatchError(f"unknown action: {action}")
+
+            if dry_run:
+                return "Patch dry-run succeeded:\n" + "\n".join(
+                    _format_summary(summary) for summary in summaries
+                )
+
+            backups: dict[Path, bytes | None] = {}
+            for path in writes:
+                backups[path] = path.read_bytes() if path.exists() else None
+
+            try:
+                for path, content in writes.items():
+                    path.parent.mkdir(parents=True, exist_ok=True)
+                    path.write_text(content, encoding="utf-8", newline="")
+            except Exception:
+                for path, data in backups.items():
+                    if data is None:
+                        if path.exists():
+                            path.unlink()
+                    else:
+                        path.parent.mkdir(parents=True, exist_ok=True)
+                        path.write_bytes(data)
+                raise
+
+            for path in writes:
+                self._file_states.record_write(path)
+            return "Patch applied:\n" + "\n".join(
+                _format_summary(summary) for summary in summaries
+            )
+        except PermissionError as exc:
+            return f"Error: {exc}"
+        except _PatchError as exc:
+            return f"Error applying patch: {exc}"
+        except Exception as exc:
+            return f"Error applying patch: {exc}"

nanobot/agent/tools/cli_apps.py

@@ -0,0 +1,133 @@
+"""Controlled runner for installed CLI Apps."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from pydantic import Field
+
+from nanobot.agent.tools.base import Tool, tool_parameters
+from nanobot.agent.tools.schema import ArraySchema, BooleanSchema, IntegerSchema, StringSchema, tool_parameters_schema
+from nanobot.security.workspace_access import current_tool_workspace
+from nanobot.apps.cli import CliAppError, CliAppManager, CliAppsRuntimeConfig
+from nanobot.config.schema import Base
+
+
+class CliAppsToolConfig(Base):
+    """CLI Apps tool configuration."""
+
+    enable: bool = True
+    install_timeout: int = Field(default=300, ge=1, le=3600)
+    run_timeout: int = Field(default=60, ge=1, le=600)
+    catalog_ttl_seconds: int = Field(default=3600, ge=60, le=86_400)
+
+
+@tool_parameters(
+    tool_parameters_schema(
+        required=["name"],
+        name=StringSchema("Installed CLI app registry name, for example gimp, safari, or obsidian."),
+        args=ArraySchema(
+            StringSchema("One command-line argument."),
+            description="Arguments to pass to the CLI entry point. Do not include the entry point itself.",
+            nullable=True,
+        ),
+        json=BooleanSchema(
+            description="Whether to prepend --json when supported by the CLI.",
+            default=False,
+            nullable=True,
+        ),
+        working_dir=StringSchema("Optional working directory for the CLI call.", nullable=True),
+        timeout=IntegerSchema(
+            description="Timeout in seconds for this CLI call.",
+            minimum=1,
+            maximum=600,
+            nullable=True,
+        ),
+    )
+)
+class CliAppsTool(Tool):
+    """Run an installed CLI-Anything or public CLI app through a controlled argv subprocess."""
+
+    config_key = "cli_apps"
+    _scopes = {"core", "subagent"}
+
+    @classmethod
+    def config_cls(cls):
+        return CliAppsToolConfig
+
+    @classmethod
+    def enabled(cls, ctx: Any) -> bool:
+        return ctx.config.cli_apps.enable
+
+    @classmethod
+    def create(cls, ctx: Any) -> Tool:
+        cfg = ctx.config.cli_apps
+        return cls(
+            workspace=Path(ctx.workspace),
+            restrict_to_workspace=ctx.config.restrict_to_workspace,
+            runtime=CliAppsRuntimeConfig(
+                install_timeout=cfg.install_timeout,
+                run_timeout=cfg.run_timeout,
+                catalog_ttl_seconds=cfg.catalog_ttl_seconds,
+            ),
+        )
+
+    def __init__(
+        self,
+        *,
+        workspace: Path,
+        restrict_to_workspace: bool = False,
+        runtime: CliAppsRuntimeConfig | None = None,
+    ) -> None:
+        self.workspace = workspace
+        self.restrict_to_workspace = restrict_to_workspace
+        self.runtime = runtime or CliAppsRuntimeConfig()
+
+    @property
+    def name(self) -> str:
+        return "run_cli_app"
+
+    @property
+    def description(self) -> str:
+        try:
+            installed = CliAppManager(workspace=self.workspace, runtime=self.runtime).installed_names()
+        except Exception:
+            installed = []
+        installed_note = (
+            f" Installed Settings CLI Apps: {', '.join(installed)}."
+            if installed
+            else " No Settings CLI Apps are currently installed."
+        )
+        return (
+            "Run a CLI App that the user explicitly installed in Settings or attached as @app. "
+            "Do not use this for ordinary system CLIs such as git, gh, python, npm, or brew; "
+            "unknown names are rejected. Execution uses argv, not shell."
+            + installed_note
+        )
+
+    async def execute(
+        self,
+        name: str,
+        args: list[str] | None = None,
+        json: bool | None = False,
+        working_dir: str | None = None,
+        timeout: int | None = None,
+    ) -> str:
+        access = current_tool_workspace(
+            self.workspace,
+            restrict_to_workspace=self.restrict_to_workspace,
+        )
+        workspace = access.project_path or self.workspace
+        manager = CliAppManager(workspace=workspace, runtime=self.runtime)
+        try:
+            return manager.run(
+                name,
+                args=args or [],
+                json_output=bool(json),
+                working_dir=working_dir,
+                timeout=timeout,
+                restrict_to_workspace=access.restrict_to_workspace,
+            )
+        except CliAppError as exc:
+            return f"Error: {exc.message}"

nanobot/agent/tools/context.py

@@ -1,9 +1,15 @@
 """Runtime context for tool construction."""
 from __future__ import annotations
 
+from contextvars import ContextVar, Token
 from dataclasses import dataclass, field
 from typing import Any, Callable, Protocol, runtime_checkable
 
+_CURRENT_REQUEST_CONTEXT: ContextVar["RequestContext | None"] = ContextVar(
+    "nanobot_tool_request_context",
+    default=None,
+)
+
 
 @dataclass(frozen=True)
 class RequestContext:
@@ -21,6 +27,23 @@ class ContextAware(Protocol):
         ...
 
 
+def bind_request_context(ctx: RequestContext) -> Token[RequestContext | None]:
+    return _CURRENT_REQUEST_CONTEXT.set(ctx)
+
+
+def reset_request_context(token: Token[RequestContext | None]) -> None:
+    _CURRENT_REQUEST_CONTEXT.reset(token)
+
+
+def current_request_context() -> RequestContext | None:
+    return _CURRENT_REQUEST_CONTEXT.get()
+
+
+def current_request_session_key() -> str | None:
+    ctx = current_request_context()
+    return ctx.session_key if ctx else None
+
+
 @dataclass
 class ToolContext:
     config: Any
@@ -33,3 +56,4 @@ class ToolContext:
     provider_snapshot_loader: Callable[[], Any] | None = None
     image_generation_provider_configs: dict[str, Any] | None = None
     timezone: str = "UTC"
+    workspace_sandbox: Any | None = None

nanobot/agent/tools/exec_session.py

@@ -0,0 +1,598 @@
+"""Session support for long-running exec workflows."""
+
+from __future__ import annotations
+
+import asyncio
+import time
+import uuid
+from contextlib import suppress
+from dataclasses import dataclass
+from typing import Any
+
+from nanobot.agent.tools.base import Tool, tool_parameters
+from nanobot.agent.tools.context import current_request_session_key
+from nanobot.agent.tools.schema import (
+    BooleanSchema,
+    IntegerSchema,
+    StringSchema,
+    tool_parameters_schema,
+)
+
+DEFAULT_YIELD_MS = 1000
+MAX_YIELD_MS = 30_000
+DEFAULT_WAIT_FOR_MS = 10_000
+MAX_WAIT_FOR_MS = 120_000
+DEFAULT_MAX_OUTPUT_CHARS = 10_000
+MAX_OUTPUT_CHARS = 50_000
+
+
+@dataclass(slots=True)
+class _SessionPoll:
+    output: str
+    done: bool
+    exit_code: int | None
+    elapsed_s: float = 0.0
+    timed_out: bool = False
+    terminated: bool = False
+    stdin_closed: bool = False
+    truncated_chars: int = 0
+
+
+@dataclass(slots=True)
+class ExecSessionInfo:
+    session_id: str
+    command: str
+    cwd: str
+    elapsed_s: float
+    idle_s: float
+    remaining_s: float
+    returncode: int | None
+    owner_session_key: str | None = None
+
+
+class _ExecSession:
+    def __init__(
+        self,
+        *,
+        session_id: str,
+        process: asyncio.subprocess.Process,
+        command: str,
+        cwd: str,
+        timeout: int | None,
+        owner_session_key: str | None = None,
+    ) -> None:
+        self.session_id = session_id
+        self.process = process
+        self.command = command
+        self.cwd = cwd
+        self.owner_session_key = owner_session_key
+        self.started_at = time.monotonic()
+        # timeout None/0 means no limit; an infinite deadline is never reached.
+        self.deadline = time.monotonic() + timeout if timeout else float("inf")
+        self.last_access = time.monotonic()
+        self._chunks: list[str] = []
+        self._lock = asyncio.Lock()
+        self._timed_out = False
+        self._stdout_task = asyncio.create_task(self._read_stream(process.stdout, ""))
+        self._stderr_task = asyncio.create_task(self._read_stream(process.stderr, "STDERR:\n"))
+
+    async def _read_stream(
+        self,
+        stream: asyncio.StreamReader | None,
+        prefix: str,
+    ) -> None:
+        if stream is None:
+            return
+        first = True
+        while True:
+            chunk = await stream.read(4096)
+            if not chunk:
+                break
+            text = chunk.decode("utf-8", errors="replace")
+            if prefix and first:
+                text = prefix + text
+                first = False
+            async with self._lock:
+                self._chunks.append(text)
+
+    async def write(self, chars: str) -> str | None:
+        if self.process.returncode is not None:
+            return "session has already exited"
+        if self.process.stdin is None:
+            return "session stdin is not available"
+        try:
+            self.process.stdin.write(chars.encode("utf-8"))
+            await self.process.stdin.drain()
+        except (BrokenPipeError, ConnectionResetError):
+            return "session stdin is closed"
+        return None
+
+    async def close_stdin(self) -> str | None:
+        if self.process.returncode is not None:
+            return "session has already exited"
+        if self.process.stdin is None:
+            return "session stdin is not available"
+        self.process.stdin.close()
+        with suppress(BrokenPipeError, ConnectionResetError):
+            await self.process.stdin.wait_closed()
+        return None
+
+    async def poll(
+        self,
+        yield_time_ms: int,
+        max_output_chars: int,
+        *,
+        terminated: bool = False,
+        stdin_closed: bool = False,
+    ) -> _SessionPoll:
+        self.last_access = time.monotonic()
+        if yield_time_ms > 0 and self.process.returncode is None:
+            await asyncio.sleep(min(yield_time_ms, MAX_YIELD_MS) / 1000)
+
+        if self.process.returncode is None and time.monotonic() >= self.deadline:
+            self._timed_out = True
+            await self.kill()
+
+        if self.process.returncode is not None:
+            with suppress(asyncio.TimeoutError):
+                await asyncio.wait_for(
+                    asyncio.gather(self._stdout_task, self._stderr_task),
+                    timeout=2.0,
+                )
+
+        async with self._lock:
+            output = "".join(self._chunks)
+            self._chunks.clear()
+
+        output, truncated = _truncate_output(output, max_output_chars)
+        return _SessionPoll(
+            output=output,
+            done=self.process.returncode is not None,
+            exit_code=self.process.returncode,
+            elapsed_s=max(0.0, time.monotonic() - self.started_at),
+            timed_out=self._timed_out,
+            terminated=terminated,
+            stdin_closed=stdin_closed,
+            truncated_chars=truncated,
+        )
+
+    async def kill(self) -> None:
+        if self.process.returncode is not None:
+            return
+        self.process.kill()
+        with suppress(asyncio.TimeoutError):
+            await asyncio.wait_for(self.process.wait(), timeout=5.0)
+
+
+class ExecSessionManager:
+    def __init__(self, *, max_sessions: int = 8, idle_timeout: int = 1800) -> None:
+        self.max_sessions = max_sessions
+        self.idle_timeout = idle_timeout
+        self._sessions: dict[str, _ExecSession] = {}
+        self._lock = asyncio.Lock()
+
+    async def start(
+        self,
+        *,
+        command: str,
+        cwd: str,
+        env: dict[str, str],
+        timeout: int | None,
+        shell_program: str | None,
+        login: bool,
+        yield_time_ms: int,
+        max_output_chars: int,
+        owner_session_key: str | None = None,
+    ) -> tuple[str, _SessionPoll]:
+        async with self._lock:
+            await self._cleanup_locked()
+            if len(self._sessions) >= self.max_sessions:
+                raise RuntimeError(f"maximum exec sessions reached ({self.max_sessions})")
+            process = await self._spawn(command, cwd, env, shell_program, login)
+            session_id = uuid.uuid4().hex[:12]
+            session = _ExecSession(
+                session_id=session_id,
+                process=process,
+                command=command,
+                cwd=cwd,
+                timeout=timeout,
+                owner_session_key=owner_session_key,
+            )
+            self._sessions[session_id] = session
+
+        poll = await session.poll(yield_time_ms, max_output_chars)
+        if poll.done:
+            async with self._lock:
+                self._sessions.pop(session_id, None)
+        return session_id, poll
+
+    async def write(
+        self,
+        *,
+        session_id: str,
+        chars: str | None,
+        close_stdin: bool,
+        terminate: bool,
+        yield_time_ms: int,
+        max_output_chars: int,
+        owner_session_key: str | None = None,
+    ) -> _SessionPoll:
+        async with self._lock:
+            await self._cleanup_locked()
+            session = self._sessions.get(session_id)
+        if session is None:
+            raise KeyError(session_id)
+        if (
+            owner_session_key
+            and session.owner_session_key
+            and session.owner_session_key != owner_session_key
+        ):
+            raise KeyError(session_id)
+
+        if chars:
+            error = await session.write(chars)
+            if error:
+                raise RuntimeError(error)
+        stdin_closed = False
+        if close_stdin:
+            error = await session.close_stdin()
+            if error:
+                raise RuntimeError(error)
+            stdin_closed = True
+        if terminate:
+            await session.kill()
+        poll = await session.poll(
+            yield_time_ms,
+            max_output_chars,
+            terminated=terminate,
+            stdin_closed=stdin_closed,
+        )
+        if poll.done:
+            async with self._lock:
+                self._sessions.pop(session_id, None)
+        return poll
+
+    async def list(self, *, owner_session_key: str | None = None) -> list[ExecSessionInfo]:
+        async with self._lock:
+            await self._cleanup_locked()
+            now = time.monotonic()
+            return [
+                ExecSessionInfo(
+                    session_id=session_id,
+                    command=session.command,
+                    cwd=session.cwd,
+                    elapsed_s=max(0.0, now - session.started_at),
+                    idle_s=max(0.0, now - session.last_access),
+                    remaining_s=max(0.0, session.deadline - now),
+                    returncode=session.process.returncode,
+                    owner_session_key=session.owner_session_key,
+                )
+                for session_id, session in sorted(self._sessions.items())
+                if not owner_session_key
+                or not session.owner_session_key
+                or session.owner_session_key == owner_session_key
+            ]
+
+    async def _cleanup_locked(self) -> None:
+        now = time.monotonic()
+        stale = [
+            session_id
+            for session_id, session in self._sessions.items()
+            if now - session.last_access > self.idle_timeout
+        ]
+        for session_id in stale:
+            session = self._sessions.pop(session_id)
+            await session.kill()
+
+    async def _spawn(
+        self,
+        command: str,
+        cwd: str,
+        env: dict[str, str],
+        shell_program: str | None,
+        login: bool,
+    ) -> asyncio.subprocess.Process:
+        from nanobot.agent.tools.shell import ExecTool
+
+        return await ExecTool._spawn(
+            command, cwd, env, shell_program, login,
+            stdin=asyncio.subprocess.PIPE,
+        )
+
+
+DEFAULT_EXEC_SESSION_MANAGER = ExecSessionManager()
+
+
+def clamp_session_int(value: int | None, default: int, minimum: int, maximum: int) -> int:
+    if value is None:
+        return default
+    return min(max(value, minimum), maximum)
+
+
+def _truncate_output(output: str, max_output_chars: int) -> tuple[str, int]:
+    if len(output) <= max_output_chars:
+        return output, 0
+    half = max_output_chars // 2
+    omitted = len(output) - max_output_chars
+    return (
+        output[:half]
+        + f"\n\n... ({omitted:,} chars truncated) ...\n\n"
+        + output[-half:],
+        omitted,
+    )
+
+
+def format_session_poll(session_id: str, poll: _SessionPoll) -> str:
+    parts = [poll.output] if poll.output else []
+    if poll.truncated_chars:
+        parts.append(f"(output truncated by {poll.truncated_chars:,} chars)")
+    if poll.timed_out:
+        parts.append("Error: Command timed out; session was terminated.")
+    if poll.terminated and not poll.timed_out:
+        parts.append("Session terminated.")
+    if poll.stdin_closed:
+        parts.append("Stdin closed.")
+    if poll.done:
+        parts.append(f"Exit code: {poll.exit_code}")
+    else:
+        parts.append(f"Process running. session_id: {session_id}")
+    parts.append(f"Elapsed: {poll.elapsed_s:.1f}s")
+    return "\n".join(parts) if parts else "(no output yet)"
+
+
+@tool_parameters(
+    tool_parameters_schema(
+        session_id=StringSchema("Session id returned by exec when yield_time_ms is used."),
+        chars=StringSchema(
+            "Bytes/text to write to stdin. Omit or pass an empty string to only poll recent output.",
+            nullable=True,
+        ),
+        close_stdin=BooleanSchema(
+            description="Close stdin after writing chars. Useful for commands waiting for EOF.",
+            default=False,
+        ),
+        terminate=BooleanSchema(
+            description="Terminate the running exec session.",
+            default=False,
+        ),
+        yield_time_ms=IntegerSchema(
+            DEFAULT_YIELD_MS,
+            description="Milliseconds to wait before returning recent output (default 1000, max 30000).",
+            minimum=0,
+            maximum=MAX_YIELD_MS,
+        ),
+        wait_for=StringSchema(
+            "Optional text to wait for in output before returning. "
+            "Useful for interactive commands and dev servers.",
+            nullable=True,
+        ),
+        wait_timeout_ms=IntegerSchema(
+            DEFAULT_WAIT_FOR_MS,
+            description="Maximum milliseconds to wait for wait_for text (default 10000, max 120000).",
+            minimum=0,
+            maximum=MAX_WAIT_FOR_MS,
+            nullable=True,
+        ),
+        max_output_chars=IntegerSchema(
+            DEFAULT_MAX_OUTPUT_CHARS,
+            description="Maximum output characters to return from this poll (default 10000, max 50000).",
+            minimum=1000,
+            maximum=MAX_OUTPUT_CHARS,
+        ),
+        max_output_tokens=IntegerSchema(
+            DEFAULT_MAX_OUTPUT_CHARS,
+            description="Compatibility alias for max_output_chars. The current runtime uses a character budget.",
+            minimum=1000,
+            maximum=MAX_OUTPUT_CHARS,
+            nullable=True,
+        ),
+        required=["session_id"],
+    )
+)
+class WriteStdinTool(Tool):
+    """Write to or poll a running exec session."""
+
+    _scopes = {"core", "subagent"}
+    config_key = "exec"
+
+    @classmethod
+    def config_cls(cls):
+        from nanobot.agent.tools.shell import ExecToolConfig
+
+        return ExecToolConfig
+
+    @classmethod
+    def enabled(cls, ctx: Any) -> bool:
+        return ctx.config.exec.enable
+
+    def __init__(
+        self,
+        *,
+        manager: ExecSessionManager | None = None,
+    ) -> None:
+        self._manager = manager or DEFAULT_EXEC_SESSION_MANAGER
+
+    @classmethod
+    def create(cls, ctx: Any) -> Tool:
+        return cls()
+
+    @property
+    def exclusive(self) -> bool:
+        return True
+
+    @property
+    def name(self) -> str:
+        return "write_stdin"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Interact with a running exec session created by exec with "
+            "yield_time_ms. Use chars='' to poll without writing, chars to send "
+            "stdin, close_stdin=true to send EOF, or terminate=true to stop the "
+            "process. Use wait_for with wait_timeout_ms for dev servers, test "
+            "watchers, and prompts where you need to wait for expected output. "
+            "Do not use this to start new commands; start them with exec."
+        )
+
+    async def execute(
+        self,
+        session_id: str,
+        chars: str | None = None,
+        close_stdin: bool = False,
+        terminate: bool = False,
+        yield_time_ms: int | None = None,
+        wait_for: str | None = None,
+        wait_timeout_ms: int | None = None,
+        max_output_chars: int | None = None,
+        max_output_tokens: int | None = None,
+        **kwargs: Any,
+    ) -> str:
+        try:
+            if max_output_chars is None:
+                max_output_chars = max_output_tokens
+            output_limit = clamp_session_int(
+                max_output_chars,
+                DEFAULT_MAX_OUTPUT_CHARS,
+                1000,
+                MAX_OUTPUT_CHARS,
+            )
+            if wait_for:
+                return await self._wait_for_output(
+                    session_id=session_id,
+                    chars=chars,
+                    close_stdin=close_stdin,
+                    terminate=terminate,
+                    wait_for=wait_for,
+                    wait_timeout_ms=clamp_session_int(
+                        wait_timeout_ms,
+                        DEFAULT_WAIT_FOR_MS,
+                        0,
+                        MAX_WAIT_FOR_MS,
+                    ),
+                    max_output_chars=output_limit,
+                )
+            poll = await self._manager.write(
+                session_id=session_id,
+                chars=chars,
+                close_stdin=close_stdin,
+                terminate=terminate,
+                yield_time_ms=clamp_session_int(yield_time_ms, DEFAULT_YIELD_MS, 0, MAX_YIELD_MS),
+                max_output_chars=output_limit,
+                owner_session_key=current_request_session_key(),
+            )
+            return format_session_poll(session_id, poll)
+        except KeyError:
+            return f"Error: exec session not found: {session_id}"
+        except Exception as exc:
+            return f"Error writing to exec session: {exc}"
+
+    async def _wait_for_output(
+        self,
+        *,
+        session_id: str,
+        chars: str | None,
+        close_stdin: bool,
+        terminate: bool,
+        wait_for: str,
+        wait_timeout_ms: int,
+        max_output_chars: int,
+    ) -> str:
+        deadline = time.monotonic() + (wait_timeout_ms / 1000)
+        aggregate: list[str] = []
+        first = True
+        poll: _SessionPoll | None = None
+
+        while True:
+            remaining_ms = max(0, int((deadline - time.monotonic()) * 1000))
+            step_ms = min(500, remaining_ms)
+            poll = await self._manager.write(
+                session_id=session_id,
+                chars=chars if first else None,
+                close_stdin=close_stdin if first else False,
+                terminate=terminate if first else False,
+                yield_time_ms=step_ms,
+                max_output_chars=max_output_chars,
+                owner_session_key=current_request_session_key(),
+            )
+            first = False
+            if poll.output:
+                aggregate.append(poll.output)
+                joined = "".join(aggregate)
+                if wait_for in joined:
+                    poll.output = joined
+                    return format_session_poll(session_id, poll)
+            if poll.done or remaining_ms <= 0:
+                poll.output = "".join(aggregate)
+                result = format_session_poll(session_id, poll)
+                if wait_for not in poll.output:
+                    result += f"\nWait target not observed: {wait_for!r}"
+                return result
+
+
+@tool_parameters(tool_parameters_schema())
+class ListExecSessionsTool(Tool):
+    """List active exec sessions."""
+
+    _scopes = {"core", "subagent"}
+    config_key = "exec"
+
+    @classmethod
+    def config_cls(cls):
+        from nanobot.agent.tools.shell import ExecToolConfig
+
+        return ExecToolConfig
+
+    @classmethod
+    def enabled(cls, ctx: Any) -> bool:
+        return ctx.config.exec.enable
+
+    def __init__(
+        self,
+        *,
+        manager: ExecSessionManager | None = None,
+    ) -> None:
+        self._manager = manager or DEFAULT_EXEC_SESSION_MANAGER
+
+    @classmethod
+    def create(cls, ctx: Any) -> Tool:
+        return cls()
+
+    @property
+    def name(self) -> str:
+        return "list_exec_sessions"
+
+    @property
+    def description(self) -> str:
+        return (
+            "List active long-running exec sessions, including session_id, cwd, "
+            "elapsed time, idle time, remaining timeout, and command preview. "
+            "Use this to recover a session_id after context shifts before "
+            "polling, writing stdin, or terminating with write_stdin."
+        )
+
+    @property
+    def read_only(self) -> bool:
+        return True
+
+    async def execute(self, **kwargs: Any) -> str:
+        try:
+            sessions = await self._manager.list(
+                owner_session_key=current_request_session_key(),
+            )
+            if not sessions:
+                return "No active exec sessions."
+            lines = []
+            for info in sessions:
+                command = " ".join(info.command.split())
+                if len(command) > 120:
+                    command = command[:119] + "..."
+                status = "exited" if info.returncode is not None else "running"
+                lines.append(
+                    f"{info.session_id} | {status} | elapsed={info.elapsed_s:.1f}s "
+                    f"| idle={info.idle_s:.1f}s | remaining={info.remaining_s:.1f}s "
+                    f"| cwd={info.cwd} | {command}"
+                )
+            return "\n".join(lines)
+        except Exception as exc:
+            return f"Error listing exec sessions: {exc}"

nanobot/agent/tools/filesystem.py

@@ -10,6 +10,7 @@ from typing import Any
 from nanobot.agent.tools.base import Tool, tool_parameters
 from nanobot.agent.tools.file_state import FileStates, _hash_file, current_file_states
 from nanobot.agent.tools.path_utils import resolve_workspace_path
+from nanobot.security.workspace_access import current_tool_workspace
 from nanobot.agent.tools.schema import (
     BooleanSchema,
     IntegerSchema,
@@ -28,10 +29,18 @@ class _FsTool(Tool):
         allowed_dir: Path | None = None,
         extra_allowed_dirs: list[Path] | None = None,
         file_states: FileStates | None = None,
+        restrict_to_workspace: bool | None = None,
+        sandbox_restricts_workspace: bool = False,
     ):
         self._workspace = workspace
         self._allowed_dir = allowed_dir
         self._extra_allowed_dirs = extra_allowed_dirs
+        self._restrict_to_workspace = (
+            bool(restrict_to_workspace)
+            if restrict_to_workspace is not None
+            else allowed_dir is not None
+        )
+        self._sandbox_restricts_workspace = sandbox_restricts_workspace
         # Explicit state is used by isolated runners like Dream/subagents.
         # Main AgentLoop tools leave this unset and resolve state from the
         # current async task, which keeps shared tool instances session-safe.
@@ -46,13 +55,16 @@ class _FsTool(Tool):
             ctx.config.restrict_to_workspace
             or ctx.config.exec.sandbox
         )
+        sandbox_restricts = bool(ctx.config.exec.sandbox)
         allowed_dir = Path(ctx.workspace) if restrict else None
-        extra_read = [BUILTIN_SKILLS_DIR] if allowed_dir else None
+        extra_read = [BUILTIN_SKILLS_DIR]
         return cls(
             workspace=Path(ctx.workspace),
             allowed_dir=allowed_dir,
             extra_allowed_dirs=extra_read,
             file_states=ctx.file_state_store,
+            restrict_to_workspace=ctx.config.restrict_to_workspace,
+            sandbox_restricts_workspace=sandbox_restricts,
         )
 
     @property
@@ -62,13 +74,21 @@ class _FsTool(Tool):
         return current_file_states(self._fallback_file_states)
 
     def _resolve(self, path: str) -> Path:
+        access = current_tool_workspace(
+            self._workspace,
+            restrict_to_workspace=self._restrict_to_workspace,
+            sandbox_restricts_workspace=self._sandbox_restricts_workspace,
+        )
         return resolve_workspace_path(
             path,
-            self._workspace,
+            access.project_path,
-            self._allowed_dir,
+            access.allowed_root,
             self._extra_allowed_dirs,
         )
 
+    def _display_workspace(self) -> Path | None:
+        return current_tool_workspace(self._workspace).project_path
+
 
 # ---------------------------------------------------------------------------
 # read_file
@@ -132,6 +152,10 @@ def _parse_page_range(pages: str, total: int) -> tuple[int, int]:
             minimum=1,
         ),
         pages=StringSchema("Page range for PDF files, e.g. '1-5' (default: all, max 20 pages)"),
+        force=BooleanSchema(
+            description="Bypass same-file read deduplication and return content again.",
+            default=False,
+        ),
         required=["path"],
     )
 )
@@ -154,7 +178,11 @@ class ReadFileTool(_FsTool):
             "Text output format: LINE_NUM|CONTENT. "
             "Images return visual content for analysis. "
             "Supports PDF, DOCX, XLSX, PPTX documents. "
+            "Use find_files/list_dir first when the path is uncertain. "
+            "Read the relevant range before editing so replacements or patches "
+            "are based on current content. "
             "Use offset and limit for large text files. "
+            "Use force=true to re-read content even if unchanged. "
             "Reads exceeding ~128K chars are truncated."
         )
 
@@ -162,7 +190,15 @@ class ReadFileTool(_FsTool):
     def read_only(self) -> bool:
         return True
 
-    async def execute(self, path: str | None = None, offset: int = 1, limit: int | None = None, pages: str | None = None, **kwargs: Any) -> Any:
+    async def execute(
+        self,
+        path: str | None = None,
+        offset: int = 1,
+        limit: int | None = None,
+        pages: str | None = None,
+        force: bool = False,
+        **kwargs: Any,
+    ) -> Any:
         try:
             if not path:
                 return "Error reading file: Unknown path"
@@ -202,7 +238,13 @@ class ReadFileTool(_FsTool):
                 current_mtime = os.path.getmtime(fp)
             except OSError:
                 current_mtime = 0.0
-            if entry and entry.can_dedup and entry.offset == offset and entry.limit == limit:
+            if (
+                not force
+                and entry
+                and entry.can_dedup
+                and entry.offset == offset
+                and entry.limit == limit
+            ):
                 if current_mtime != entry.mtime:
                     # File was modified externally - force full read and mark as not dedupable
                     entry.can_dedup = False
@@ -365,9 +407,10 @@ class WriteFileTool(_FsTool):
     @property
     def description(self) -> str:
         return (
-            "Write content to a file. Overwrites if the file already exists; "
+            "Create a new file or intentionally replace an entire file with "
-            "creates parent directories as needed. "
+            "the provided content. Overwrites existing files and creates parent "
-            "For partial edits, prefer edit_file instead."
+            "directories as needed. For code changes or partial edits, prefer "
+            "apply_patch; use edit_file only for small exact replacements."
         )
 
     async def execute(self, path: str | None = None, content: str | None = None, **kwargs: Any) -> str:
@@ -657,6 +700,24 @@ def _find_match(content: str, old_text: str) -> tuple[str | None, int]:
         old_text=StringSchema("The text to find and replace"),
         new_text=StringSchema("The text to replace with"),
         replace_all=BooleanSchema(description="Replace all occurrences (default false)"),
+        occurrence=IntegerSchema(
+            1,
+            description="Optional 1-based occurrence to replace when old_text appears multiple times.",
+            minimum=1,
+            nullable=True,
+        ),
+        line_hint=IntegerSchema(
+            1,
+            description="Optional 1-based line hint used to choose the nearest match.",
+            minimum=1,
+            nullable=True,
+        ),
+        expected_replacements=IntegerSchema(
+            1,
+            description="Optional guard for the number of replacements that must be made.",
+            minimum=1,
+            nullable=True,
+        ),
         required=["path", "old_text", "new_text"],
     )
 )
@@ -674,10 +735,13 @@ class EditFileTool(_FsTool):
     @property
     def description(self) -> str:
         return (
-            "Edit a file by replacing old_text with new_text. "
+            "Perform a small, exact replacement in one file by replacing "
-            "Tolerates minor whitespace/indentation differences and curly/straight quote mismatches. "
+            "old_text with new_text. Use this for narrow text substitutions "
-            "If old_text matches multiple times, you must provide more context "
+            "with old_text copied from read_file. For multi-file, structural, "
-            "or set replace_all=true. Shows a diff of the closest match on failure."
+            "or generated code edits, prefer apply_patch. If old_text matches "
+            "multiple times, provide more context or set occurrence, line_hint, "
+            "replace_all, and expected_replacements. Shows closest-match "
+            "diagnostics on failure."
         )
 
     @staticmethod
@@ -688,7 +752,8 @@ class EditFileTool(_FsTool):
     async def execute(
         self, path: str | None = None, old_text: str | None = None,
         new_text: str | None = None,
-        replace_all: bool = False, **kwargs: Any,
+        replace_all: bool = False, occurrence: int | None = None,
+        line_hint: int | None = None, expected_replacements: int | None = None, **kwargs: Any,
     ) -> str:
         try:
             if not path:
@@ -697,10 +762,12 @@ class EditFileTool(_FsTool):
                 raise ValueError("Unknown old_text")
             if new_text is None:
                 raise ValueError("Unknown new_text")
-
+            if occurrence is not None and occurrence < 1:
-            # .ipynb detection
+                return "Error: occurrence must be >= 1."
-            if path.endswith(".ipynb"):
+            if line_hint is not None and line_hint < 1:
-                return "Error: This is a Jupyter notebook. Use the notebook_edit tool instead of edit_file."
+                return "Error: line_hint must be >= 1."
+            if expected_replacements is not None and expected_replacements < 1:
+                return "Error: expected_replacements must be >= 1."
 
             fp = self._resolve(path)
 
@@ -743,15 +810,42 @@ class EditFileTool(_FsTool):
             if not matches:
                 return self._not_found_msg(old_text, content, path)
             count = len(matches)
+            if replace_all and occurrence is not None:
+                return "Error: occurrence cannot be used with replace_all=true."
+            if replace_all and line_hint is not None:
+                return "Error: line_hint cannot be used with replace_all=true."
+            if occurrence is not None and line_hint is not None:
+                return "Error: line_hint cannot be used with occurrence."
             if count > 1 and not replace_all:
-                line_numbers = [match.line for match in matches]
+                if occurrence is not None:
-                preview = ", ".join(f"line {n}" for n in line_numbers[:3])
+                    if occurrence > count:
-                if len(line_numbers) > 3:
+                        return (
-                    preview += ", ..."
+                            f"Error: occurrence {occurrence} is out of range; "
-                location_hint = f" at {preview}" if preview else ""
+                            f"old_text appears {count} times."
+                        )
+                elif line_hint is not None:
+                    nearest = min(matches, key=lambda match: abs(match.line - line_hint))
+                    distance = abs(nearest.line - line_hint)
+                    if sum(1 for match in matches if abs(match.line - line_hint) == distance) > 1:
+                        return (
+                            f"Error: line_hint {line_hint} is ambiguous; "
+                            f"old_text appears {count} times."
+                        )
+                else:
+                    line_numbers = [match.line for match in matches]
+                    preview = ", ".join(f"line {n}" for n in line_numbers[:3])
+                    if len(line_numbers) > 3:
+                        preview += ", ..."
+                    location_hint = f" at {preview}" if preview else ""
+                    return (
+                        f"Warning: old_text appears {count} times{location_hint}. "
+                        "Provide more context, set occurrence to choose one match, "
+                        "or set replace_all=true."
+                    )
+            elif occurrence is not None and occurrence > count:
                 return (
-                    f"Warning: old_text appears {count} times{location_hint}. "
+                    f"Error: occurrence {occurrence} is out of range; "
-                    "Provide more context to make it unique, or set replace_all=true."
+                    f"old_text appears {count} time."
                 )
 
             norm_new = new_text.replace("\r\n", "\n")
@@ -760,7 +854,17 @@ class EditFileTool(_FsTool):
             if fp.suffix.lower() not in self._MARKDOWN_EXTS:
                 norm_new = self._strip_trailing_ws(norm_new)
 
-            selected = matches if replace_all else matches[:1]
+            if replace_all:
+                selected = matches
+            elif line_hint is not None:
+                selected = [min(matches, key=lambda match: abs(match.line - line_hint))]
+            else:
+                selected = [matches[occurrence - 1 if occurrence else 0]]
+            if expected_replacements is not None and len(selected) != expected_replacements:
+                return (
+                    f"Error: expected {expected_replacements} replacements but "
+                    f"would make {len(selected)}."
+                )
             new_content = content
             for match in reversed(selected):
                 replacement = _preserve_quote_style(norm_old, match.text, norm_new)

nanobot/agent/tools/image_generation.py

@@ -14,13 +14,15 @@ from nanobot.agent.tools.schema import (
     StringSchema,
     tool_parameters_schema,
 )
+from nanobot.security.workspace_access import current_tool_workspace
 from nanobot.config.paths import get_media_dir
 from nanobot.config.schema import Base
 from nanobot.providers.image_generation import (
-    AIHubMixImageGenerationClient,
     ImageGenerationError,
-    OpenRouterImageGenerationClient,
+    ImageGenerationProvider,
+    get_image_gen_provider,
 )
+from nanobot.security.workspace_policy import WorkspaceBoundaryError, resolve_allowed_path
 from nanobot.utils.artifacts import (
     ArtifactError,
     generated_image_tool_result,
@@ -117,41 +119,36 @@ class ImageGenerationTool(Tool):
     def _provider_config(self) -> ProviderConfig | None:
         return self.provider_configs.get(self.config.provider)
 
-    def _provider_client(self) -> OpenRouterImageGenerationClient | AIHubMixImageGenerationClient | None:
+    def _provider_client(self) -> ImageGenerationProvider | None:
         provider = self._provider_config()
+        cls = get_image_gen_provider(self.config.provider)
+        if cls is None:
+            return None
         kwargs = {
             "api_key": provider.api_key if provider else None,
             "api_base": provider.api_base if provider else None,
             "extra_headers": provider.extra_headers if provider else None,
             "extra_body": provider.extra_body if provider else None,
         }
-        if self.config.provider == "openrouter":
+        return cls(**kwargs)
-            return OpenRouterImageGenerationClient(**kwargs)
-        if self.config.provider == "aihubmix":
-            return AIHubMixImageGenerationClient(**kwargs)
-        return None
-
-    def _missing_api_key_error(self) -> str:
-        provider = self.config.provider
-        if provider == "openrouter":
-            return "Error: OpenRouter API key is not configured. Set providers.openrouter.apiKey."
-        if provider == "aihubmix":
-            return "Error: AIHubMix API key is not configured. Set providers.aihubmix.apiKey."
-        return f"Error: {provider} API key is not configured."
 
     def _resolve_reference_image(self, value: str) -> str:
-        raw_path = Path(value).expanduser()
+        access = current_tool_workspace(self.workspace, restrict_to_workspace=True)
-        path = raw_path if raw_path.is_absolute() else self.workspace / raw_path
+        workspace = access.project_path or self.workspace
         try:
-            resolved = path.resolve(strict=True)
+            resolved = resolve_allowed_path(
-        except OSError as exc:
+                value,
-            raise ImageGenerationError(f"reference image not found: {value}") from exc
+                workspace=workspace,
-
+                allowed_root=access.allowed_root,
-        allowed_roots = [self.workspace.resolve(), get_media_dir().resolve()]
+                extra_allowed_roots=[get_media_dir()] if access.allowed_root is not None else None,
-        if not any(_is_relative_to(resolved, root) for root in allowed_roots):
+                strict=True,
+            )
+        except WorkspaceBoundaryError as exc:
             raise ImageGenerationError(
                 "reference_images must be inside the workspace or nanobot media directory"
-            )
+            ) from exc
+        except OSError as exc:
+            raise ImageGenerationError(f"reference image not found: {value}") from exc
         if not resolved.is_file():
             raise ImageGenerationError(f"reference image is not a file: {value}")
         raw = resolved.read_bytes()
@@ -176,9 +173,6 @@ class ImageGenerationTool(Tool):
         client = self._provider_client()
         if client is None:
             return f"Error: unsupported image generation provider '{self.config.provider}'"
-        provider = self._provider_config()
-        if not provider or not provider.api_key:
-            return self._missing_api_key_error()
 
         requested = count or 1
         if requested > self.config.max_images_per_turn:
@@ -213,11 +207,3 @@ class ImageGenerationTool(Tool):
             return generated_image_tool_result(artifacts)
         except (ArtifactError, ImageGenerationError, OSError) as exc:
             return f"Error: {exc}"
-
-
-def _is_relative_to(path: Path, root: Path) -> bool:
-    try:
-        path.relative_to(root)
-    except ValueError:
-        return False
-    return True

nanobot/agent/tools/long_task.py

@@ -16,6 +16,7 @@ There is **no** sub-agent orchestrator and **no** special WebSocket ``agent_ui``
 
 from __future__ import annotations
 
+from contextvars import ContextVar
 from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
@@ -45,15 +46,22 @@ class _GoalToolsMixin(ContextAware):
     def __init__(self, sessions: SessionManager, bus: Any | None = None) -> None:
         self._sessions = sessions
         self._bus = bus
-        self._request_ctx: RequestContext | None = None
+        # Each subclass gets its own ContextVar so concurrent tasks across
+        # different tool types (LongTaskTool vs CompleteGoalTool) do not
+        # interfere with each other.
+        self._request_ctx: ContextVar[RequestContext | None] = ContextVar(
+            f"{self.__class__.__name__}_request_ctx",
+            default=None,
+        )
 
     def set_context(self, ctx: RequestContext) -> None:
-        self._request_ctx = ctx
+        self._request_ctx.set(ctx)
 
     def _session(self):
-        if self._request_ctx is None:
+        request_ctx = self._request_ctx.get()
+        if request_ctx is None:
             return None
-        key = self._request_ctx.session_key
+        key = request_ctx.session_key
         if not key:
             return None
         return self._sessions.get_or_create(key)
@@ -61,7 +69,7 @@ class _GoalToolsMixin(ContextAware):
     async def _publish_goal_state_ws(self, metadata: dict[str, Any]) -> None:
         """Fan-out authoritative goal snapshot for this WebSocket chat only."""
         bus = self._bus
-        rc = self._request_ctx
+        rc = self._request_ctx.get()
         if bus is None or rc is None or rc.channel != "websocket":
             return
         cid = (rc.chat_id or "").strip()
@@ -224,4 +232,3 @@ class CompleteGoalTool(Tool, _GoalToolsMixin):
         if tail:
             return f"Goal marked complete ({ended}). Recap:\n{tail}"
         return f"Goal marked complete ({ended})."
-

nanobot/agent/tools/mcp.py

@@ -6,13 +6,20 @@ import re
 import shutil
 import urllib.parse
 from contextlib import AsyncExitStack, suppress
-from typing import Any
+from typing import Any, Mapping
+from weakref import WeakKeyDictionary
 
 import httpx
 from loguru import logger
 
 from nanobot.agent.tools.base import Tool
 from nanobot.agent.tools.registry import ToolRegistry
+from nanobot.bus.events import (
+    INBOUND_META_RUNTIME_CONTROL,
+    RUNTIME_CONTROL_ACK,
+    RUNTIME_CONTROL_MCP_RELOAD,
+    InboundMessage,
+)
 
 # Transient connection errors that warrant a single retry.
 # These typically happen when an MCP server restarts or a network
@@ -33,6 +40,7 @@ _WINDOWS_SHELL_LAUNCHERS: frozenset[str] = frozenset(("npx", "npm", "pnpm", "yar
 # Characters allowed in tool names by model providers (Anthropic, OpenAI, etc.).
 # Replace anything outside [a-zA-Z0-9_-] with underscore and collapse runs.
 _SANITIZE_RE = re.compile(r"_+")
+_RELOAD_LOCKS: WeakKeyDictionary[Any, asyncio.Lock] = WeakKeyDictionary()
 
 
 def _sanitize_name(name: str) -> str:
@@ -503,6 +511,7 @@ async def connect_mcp_servers(
                     command=command,
                     args=args,
                     env=env,
+                    cwd=cfg.cwd or None,
                 )
                 read, write = await server_stack.enter_async_context(stdio_client(params))
             elif transport_type == "sse":
@@ -662,3 +671,272 @@ async def connect_mcp_servers(
             server_stacks[result[0]] = result[1]
 
     return server_stacks
+
+
+def session_extra(metadata: Mapping[str, Any] | None) -> dict[str, Any]:
+    """Return persisted session kwargs for MCP preset attachments."""
+    mcp_presets = metadata.get("mcp_presets") if isinstance(metadata, Mapping) else None
+    return {"mcp_presets": mcp_presets} if isinstance(mcp_presets, list) and mcp_presets else {}
+
+
+def runtime_lines(
+    message: Any,
+    *,
+    available_server_names: set[str] | None = None,
+    configured_server_names: set[str] | None = None,
+    connected_server_names: set[str] | None = None,
+    skip: bool = False,
+) -> list[str]:
+    """Return model-visible MCP preset annotations for the current turn."""
+    if skip:
+        return []
+    if configured_server_names is None:
+        configured_server_names = available_server_names
+    if connected_server_names is None:
+        connected_server_names = available_server_names
+    metadata = message.metadata if isinstance(getattr(message, "metadata", None), Mapping) else None
+    structured = metadata.get("mcp_presets") if isinstance(metadata, Mapping) else None
+    if not isinstance(structured, list):
+        return []
+
+    lines: list[str] = []
+    for item in structured[:8]:
+        if not isinstance(item, Mapping):
+            continue
+        raw_name = str(item.get("name") or "").strip().lower()
+        if not raw_name:
+            continue
+        display = str(item.get("display_name") or raw_name).strip() or raw_name
+        transport = str(item.get("transport") or "mcp").strip() or "mcp"
+        prefix = f"mcp_{raw_name}_"
+        if configured_server_names is not None and raw_name not in configured_server_names:
+            lines.append(
+                "MCP Preset Attachment: "
+                f"@{raw_name} ({display}; transport={transport}) is configured in WebUI Settings, "
+                "but this gateway has not loaded the latest MCP settings yet. "
+                f"Tools with prefix `{prefix}` may not be available yet; if they are missing, "
+                "tell the user to restart nanobot."
+            )
+            continue
+        if connected_server_names is not None and raw_name not in connected_server_names:
+            lines.append(
+                "MCP Preset Attachment: "
+                f"@{raw_name} ({display}; transport={transport}) is configured, "
+                "but its MCP connection is not currently live. "
+                f"Tools with prefix `{prefix}` may be unavailable; tell the user to open Settings, "
+                "run the preset test, and restart nanobot only if hot reload is unavailable."
+            )
+            continue
+        lines.append(
+            "MCP Preset Attachment: "
+            f"@{raw_name} ({display}; transport={transport}; tool_prefix={prefix}). "
+            f"Prefer available tools whose names start with `{prefix}` for this request; "
+            "do not substitute shell commands for this MCP integration unless the user asks."
+        )
+    return lines
+
+
+async def connect_missing_servers(state: Any, registry: ToolRegistry) -> None:
+    """Connect configured MCP servers that are not currently live."""
+    missing_servers = {
+        name: cfg for name, cfg in state._mcp_servers.items() if name not in state._mcp_stacks
+    }
+    if state._mcp_connecting or not missing_servers:
+        return
+    state._mcp_connecting = True
+    try:
+        connected = await connect_mcp_servers(missing_servers, registry)
+        state._mcp_stacks.update(connected)
+        state._mcp_connected = bool(state._mcp_stacks)
+        if connected:
+            logger.info("MCP connected servers: {}", sorted(connected))
+        else:
+            logger.warning("No MCP servers connected successfully (will retry next message)")
+    except asyncio.CancelledError:
+        logger.warning("MCP connection cancelled (will retry next message)")
+        state._mcp_connected = bool(state._mcp_stacks)
+    except BaseException as e:
+        logger.warning("Failed to connect MCP servers (will retry next message): {}", e)
+        state._mcp_connected = bool(state._mcp_stacks)
+    finally:
+        state._mcp_connecting = False
+
+
+async def reload_servers(state: Any, registry: ToolRegistry) -> dict[str, Any]:
+    """Reconcile live MCP connections with the current config file."""
+    async with _reload_lock(state):
+        try:
+            from nanobot.config.loader import (load_config,
+                                               resolve_config_env_vars)
+
+            config = resolve_config_env_vars(load_config())
+            next_servers = dict(config.tools.mcp_servers)
+        except Exception as exc:
+            logger.warning("MCP hot reload could not read config: {}", exc)
+            return {
+                "ok": False,
+                "message": "Could not reload MCP config. Restart nanobot to pick up changes.",
+                "requires_restart": True,
+                "error": str(exc),
+            }
+
+        current_servers = dict(state._mcp_servers)
+        current_names = set(current_servers)
+        next_names = set(next_servers)
+        removed = sorted(current_names - next_names)
+        added = sorted(next_names - current_names)
+        changed = sorted(
+            name
+            for name in current_names & next_names
+            if _server_signature(current_servers[name]) != _server_signature(next_servers[name])
+        )
+
+        tools_removed = 0
+        for name in [*removed, *changed]:
+            tools_removed += _unregister_server_tools(state, registry, name)
+            await _close_server(state, name)
+
+        state._mcp_servers = next_servers
+        retry_missing = sorted(
+            name
+            for name in next_names
+            if name not in state._mcp_stacks and name not in set(added) | set(changed)
+        )
+        to_connect_names = sorted(set(added) | set(changed) | set(retry_missing))
+        to_connect = {name: next_servers[name] for name in to_connect_names}
+        connected: dict[str, AsyncExitStack] = {}
+        if to_connect:
+            connected = await connect_mcp_servers(to_connect, registry)
+            state._mcp_stacks.update(connected)
+
+        state._mcp_connected = bool(state._mcp_stacks)
+        failed = sorted(set(to_connect) - set(connected))
+        unchanged = not removed and not added and not changed and not retry_missing
+        ok = not failed
+        if failed:
+            message = "MCP config reloaded, but some servers did not connect: " + ", ".join(failed)
+        elif unchanged:
+            message = "MCP config is already live."
+        elif retry_missing and not added and not changed and not removed:
+            message = "MCP connections refreshed without restarting nanobot."
+        else:
+            message = "MCP config reloaded without restarting nanobot."
+
+        logger.info(
+            "MCP hot reload: added={} changed={} removed={} retried={} connected={} failed={} tools_removed={}",
+            added,
+            changed,
+            removed,
+            retry_missing,
+            sorted(connected),
+            failed,
+            tools_removed,
+        )
+        return {
+            "ok": ok,
+            "message": message,
+            "added": added,
+            "changed": changed,
+            "removed": removed,
+            "retried": retry_missing,
+            "connected": sorted(state._mcp_stacks),
+            "configured": sorted(state._mcp_servers),
+            "failed": failed,
+            "tools_removed": tools_removed,
+            "requires_restart": False,
+        }
+
+
+async def request_mcp_reload(bus: Any, *, timeout: float = 15.0) -> dict[str, Any]:
+    """Ask the running agent loop to reconcile live MCP connections."""
+    loop = asyncio.get_running_loop()
+    ack: asyncio.Future[dict[str, Any]] = loop.create_future()
+    await bus.publish_inbound(
+        InboundMessage(
+            channel="system",
+            sender_id="webui-settings",
+            chat_id="runtime",
+            content=RUNTIME_CONTROL_MCP_RELOAD,
+            metadata={
+                INBOUND_META_RUNTIME_CONTROL: RUNTIME_CONTROL_MCP_RELOAD,
+                RUNTIME_CONTROL_ACK: ack,
+            },
+        )
+    )
+    try:
+        result = await asyncio.wait_for(ack, timeout=timeout)
+    except asyncio.TimeoutError:
+        return {
+            "ok": False,
+            "message": "MCP hot reload timed out. Restart nanobot to pick up changes.",
+            "requires_restart": True,
+        }
+    return result if isinstance(result, dict) else {
+        "ok": False,
+        "message": "MCP hot reload returned an unexpected response.",
+        "requires_restart": True,
+    }
+
+
+async def handle_runtime_control(state: Any, msg: InboundMessage, registry: ToolRegistry) -> bool:
+    metadata = msg.metadata if isinstance(msg.metadata, dict) else {}
+    control = metadata.get(INBOUND_META_RUNTIME_CONTROL)
+    if control != RUNTIME_CONTROL_MCP_RELOAD:
+        return False
+
+    ack = metadata.get(RUNTIME_CONTROL_ACK)
+    try:
+        result = await reload_servers(state, registry)
+    except Exception as exc:
+        logger.exception("MCP hot reload failed")
+        result = {
+            "ok": False,
+            "message": "MCP hot reload failed. Restart nanobot to pick up changes.",
+            "requires_restart": True,
+            "error": str(exc),
+        }
+    if isinstance(ack, asyncio.Future) and not ack.done():
+        ack.set_result(result)
+    return True
+
+
+def _reload_lock(state: Any) -> asyncio.Lock:
+    try:
+        return _RELOAD_LOCKS[state]
+    except KeyError:
+        lock = asyncio.Lock()
+        _RELOAD_LOCKS[state] = lock
+        return lock
+
+
+def _server_signature(cfg: Any) -> Any:
+    if hasattr(cfg, "model_dump"):
+        return cfg.model_dump(mode="json")
+    return cfg
+
+
+def _tool_prefix(server_name: str) -> str:
+    safe_name = "".join(ch if ch.isalnum() or ch in {"_", "-"} else "_" for ch in server_name)
+    while "__" in safe_name:
+        safe_name = safe_name.replace("__", "_")
+    return f"mcp_{safe_name}_"
+
+
+def _unregister_server_tools(state: Any, registry: ToolRegistry, server_name: str) -> int:
+    prefix = _tool_prefix(server_name)
+    removed = 0
+    for tool_name in list(registry.tool_names):
+        if tool_name.startswith(prefix):
+            registry.unregister(tool_name)
+            removed += 1
+    return removed
+
+
+async def _close_server(state: Any, server_name: str) -> None:
+    stack = state._mcp_stacks.pop(server_name, None)
+    if stack is None:
+        return
+    try:
+        await stack.aclose()
+    except (RuntimeError, BaseExceptionGroup):
+        logger.debug("MCP server '{}' cleanup error (can be ignored)", server_name)

nanobot/agent/tools/message.py

@@ -4,10 +4,13 @@ from contextvars import ContextVar
 from pathlib import Path
 from typing import Any, Awaitable, Callable
 
+from loguru import logger
+
 from nanobot.agent.tools.base import Tool, tool_parameters
 from nanobot.agent.tools.context import ContextAware, RequestContext
 from nanobot.agent.tools.path_utils import resolve_workspace_path
 from nanobot.agent.tools.schema import ArraySchema, StringSchema, tool_parameters_schema
+from nanobot.security.workspace_access import current_tool_workspace
 from nanobot.bus.events import OutboundMessage
 from nanobot.config.paths import get_workspace_path
 
@@ -31,8 +34,8 @@ from nanobot.config.paths import get_workspace_path
         media=ArraySchema(
             StringSchema(""),
             description=(
-                "Optional list of existing file paths to attach for proactive or cross-channel delivery. "
+                "Optional list of existing file paths to attach. "
-                "Do not use this to resend generate_image outputs in the current chat."
+                "Use artifact paths returned by generate_image here when delivering generated images."
             ),
         ),
         buttons=ArraySchema(
@@ -82,6 +85,10 @@ class MessageTool(Tool, ContextAware):
             "message_record_channel_delivery",
             default=False,
         )
+        self._suppress_delivery_var: ContextVar[bool] = ContextVar(
+            "message_suppress_delivery",
+            default=False,
+        )
 
     @classmethod
     def create(cls, ctx: Any) -> Tool:
@@ -120,6 +127,14 @@ class MessageTool(Tool, ContextAware):
         """Restore previous proactive delivery recording state."""
         self._record_channel_delivery_var.reset(token)
 
+    def set_suppress_delivery(self, active: bool):
+        """Acknowledge but don't deliver tool sends (heartbeat internal check)."""
+        return self._suppress_delivery_var.set(active)
+
+    def reset_suppress_delivery(self, token) -> None:
+        """Restore previous delivery-suppression state."""
+        self._suppress_delivery_var.reset(token)
+
     @property
     def _sent_in_turn(self) -> bool:
         return self._sent_in_turn_var.get()
@@ -140,8 +155,8 @@ class MessageTool(Tool, ContextAware):
             "Do not use this for the normal reply in the current chat: answer naturally instead. "
             "If channel/chat_id would target the current runtime conversation, do not call this tool "
             "unless the user explicitly asked you to proactively send an existing file attachment. "
-            "When generate_image creates images in the current chat, the final assistant reply "
+            "When generate_image creates images in the current chat, use the message tool "
-            "automatically attaches them; do not call message just to announce or resend them. "
+            "with the artifact paths in the media parameter to deliver the images to the user. "
             "For proactive attachment delivery, use the 'media' parameter with file paths. "
             "Do NOT use read_file to send files — that only reads content for your own analysis."
         )
@@ -149,15 +164,19 @@ class MessageTool(Tool, ContextAware):
     def _resolve_media(self, media: list[str]) -> list[str]:
         """Resolve local media attachments and enforce workspace restriction when enabled."""
         resolved: list[str] = []
-        allowed_dir = self._workspace if self._restrict_to_workspace else None
+        access = current_tool_workspace(
+            self._workspace,
+            restrict_to_workspace=self._restrict_to_workspace,
+        )
+        workspace = access.project_path or self._workspace
         for p in media:
             if p.startswith(("http://", "https://")):
                 resolved.append(p)
-            elif not self._restrict_to_workspace:
+            elif not access.restrict_to_workspace:
                 path = Path(p).expanduser()
-                resolved.append(p if path.is_absolute() else str(self._workspace / path))
+                resolved.append(p if path.is_absolute() else str(workspace / path))
             else:
-                resolved.append(str(resolve_workspace_path(p, self._workspace, allowed_dir)))
+                resolved.append(str(resolve_workspace_path(p, workspace, access.allowed_root)))
         return resolved
 
     async def execute(
@@ -236,6 +255,10 @@ class MessageTool(Tool, ContextAware):
             metadata=metadata,
         )
 
+        if self._suppress_delivery_var.get():
+            logger.debug("MessageTool: delivery suppressed during internal check")
+            return f"Message acknowledged for {channel}:{chat_id} (not delivered)"
+
         try:
             await self._send_callback(msg)
             if channel == default_channel and chat_id == default_chat_id:

nanobot/agent/tools/notebook.py

@@ -1,162 +0,0 @@
-"""NotebookEditTool — edit Jupyter .ipynb notebooks."""
-
-from __future__ import annotations
-
-import json
-import uuid
-from typing import Any
-
-from nanobot.agent.tools.base import tool_parameters
-from nanobot.agent.tools.schema import IntegerSchema, StringSchema, tool_parameters_schema
-from nanobot.agent.tools.filesystem import _FsTool
-
-
-def _new_cell(source: str, cell_type: str = "code", generate_id: bool = False) -> dict:
-    cell: dict[str, Any] = {
-        "cell_type": cell_type,
-        "source": source,
-        "metadata": {},
-    }
-    if cell_type == "code":
-        cell["outputs"] = []
-        cell["execution_count"] = None
-    if generate_id:
-        cell["id"] = uuid.uuid4().hex[:8]
-    return cell
-
-
-def _make_empty_notebook() -> dict:
-    return {
-        "nbformat": 4,
-        "nbformat_minor": 5,
-        "metadata": {
-            "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
-            "language_info": {"name": "python"},
-        },
-        "cells": [],
-    }
-
-
-@tool_parameters(
-    tool_parameters_schema(
-        path=StringSchema("Path to the .ipynb notebook file"),
-        cell_index=IntegerSchema(0, description="0-based index of the cell to edit", minimum=0),
-        new_source=StringSchema("New source content for the cell"),
-        cell_type=StringSchema(
-            "Cell type: 'code' or 'markdown' (default: code)",
-            enum=["code", "markdown"],
-        ),
-        edit_mode=StringSchema(
-            "Mode: 'replace' (default), 'insert' (after target), or 'delete'",
-            enum=["replace", "insert", "delete"],
-        ),
-        required=["path", "cell_index"],
-    )
-)
-class NotebookEditTool(_FsTool):
-    """Edit Jupyter notebook cells: replace, insert, or delete."""
-    _scopes = {"core"}
-
-    _VALID_CELL_TYPES = frozenset({"code", "markdown"})
-    _VALID_EDIT_MODES = frozenset({"replace", "insert", "delete"})
-
-    @property
-    def name(self) -> str:
-        return "notebook_edit"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Edit a Jupyter notebook (.ipynb) cell. "
-            "Modes: replace (default) replaces cell content, "
-            "insert adds a new cell after the target index, "
-            "delete removes the cell at the index. "
-            "cell_index is 0-based."
-        )
-
-    async def execute(
-        self,
-        path: str | None = None,
-        cell_index: int = 0,
-        new_source: str = "",
-        cell_type: str = "code",
-        edit_mode: str = "replace",
-        **kwargs: Any,
-    ) -> str:
-        try:
-            if not path:
-                return "Error: path is required"
-
-            if not path.endswith(".ipynb"):
-                return "Error: notebook_edit only works on .ipynb files. Use edit_file for other files."
-
-            if edit_mode not in self._VALID_EDIT_MODES:
-                return (
-                    f"Error: Invalid edit_mode '{edit_mode}'. "
-                    "Use one of: replace, insert, delete."
-                )
-
-            if cell_type not in self._VALID_CELL_TYPES:
-                return (
-                    f"Error: Invalid cell_type '{cell_type}'. "
-                    "Use one of: code, markdown."
-                )
-
-            fp = self._resolve(path)
-
-            # Create new notebook if file doesn't exist and mode is insert
-            if not fp.exists():
-                if edit_mode != "insert":
-                    return f"Error: File not found: {path}"
-                nb = _make_empty_notebook()
-                cell = _new_cell(new_source, cell_type, generate_id=True)
-                nb["cells"].append(cell)
-                fp.parent.mkdir(parents=True, exist_ok=True)
-                fp.write_text(json.dumps(nb, indent=1, ensure_ascii=False), encoding="utf-8")
-                return f"Successfully created {fp} with 1 cell"
-
-            try:
-                nb = json.loads(fp.read_text(encoding="utf-8"))
-            except (json.JSONDecodeError, UnicodeDecodeError) as e:
-                return f"Error: Failed to parse notebook: {e}"
-
-            cells = nb.get("cells", [])
-            nbformat_minor = nb.get("nbformat_minor", 0)
-            generate_id = nb.get("nbformat", 0) >= 4 and nbformat_minor >= 5
-
-            if edit_mode == "delete":
-                if cell_index < 0 or cell_index >= len(cells):
-                    return f"Error: cell_index {cell_index} out of range (notebook has {len(cells)} cells)"
-                cells.pop(cell_index)
-                nb["cells"] = cells
-                fp.write_text(json.dumps(nb, indent=1, ensure_ascii=False), encoding="utf-8")
-                return f"Successfully deleted cell {cell_index} from {fp}"
-
-            if edit_mode == "insert":
-                insert_at = min(cell_index + 1, len(cells))
-                cell = _new_cell(new_source, cell_type, generate_id=generate_id)
-                cells.insert(insert_at, cell)
-                nb["cells"] = cells
-                fp.write_text(json.dumps(nb, indent=1, ensure_ascii=False), encoding="utf-8")
-                return f"Successfully inserted cell at index {insert_at} in {fp}"
-
-            # Default: replace
-            if cell_index < 0 or cell_index >= len(cells):
-                return f"Error: cell_index {cell_index} out of range (notebook has {len(cells)} cells)"
-            cells[cell_index]["source"] = new_source
-            if cell_type and cells[cell_index].get("cell_type") != cell_type:
-                cells[cell_index]["cell_type"] = cell_type
-                if cell_type == "code":
-                    cells[cell_index].setdefault("outputs", [])
-                    cells[cell_index].setdefault("execution_count", None)
-                elif "outputs" in cells[cell_index]:
-                    del cells[cell_index]["outputs"]
-                    cells[cell_index].pop("execution_count", None)
-            nb["cells"] = cells
-            fp.write_text(json.dumps(nb, indent=1, ensure_ascii=False), encoding="utf-8")
-            return f"Successfully edited cell {cell_index} in {fp}"
-
-        except PermissionError as e:
-            return f"Error: {e}"
-        except Exception as e:
-            return f"Error editing notebook: {e}"

nanobot/agent/tools/p2p.py

@@ -1,328 +0,0 @@
-"""P2P tools for inter-agent task dispatch and coordination."""
-
-from __future__ import annotations
-
-from typing import Any, Awaitable, Callable
-
-from nanobot.agent.tools.base import Tool
-from nanobot.bus.events import OutboundMessage
-
-
-class DispatchTaskTool(Tool):
-    """Asynchronously dispatch a task to another agent. Non-blocking."""
-
-    def __init__(self, shell: "P2PShell"):
-        self._shell = shell
-
-    @property
-    def name(self) -> str:
-        return "dispatch_task"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Dispatch a task to a specific target agent. Returns immediately with a receipt. "
-            "The target agent will process the task independently. Use poll_task_result later to check completion. "
-            "Do NOT block waiting for results."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "to": {"type": "string", "description": "Target agent ID"},
-                "task_description": {"type": "string", "description": "Clear description of the task"},
-                "parent_task_id": {"type": "string", "description": "Parent task ID for ancestry tracking"},
-                "deadline_seconds": {"type": "integer", "default": 300, "description": "Task deadline in seconds"},
-                "allow_redelegation": {"type": "boolean", "default": True, "description": "Whether the target may re-delegate"},
-            },
-            "required": ["to", "task_description"],
-        }
-
-    async def execute(
-        self,
-        to: str,
-        task_description: str,
-        parent_task_id: str | None = None,
-        deadline_seconds: int = 300,
-        allow_redelegation: bool = True,
-        **kwargs: Any,
-    ) -> str:
-        result = self._shell.dispatch(
-            to=to,
-            parent_task_id=parent_task_id,
-            description=task_description,
-            deadline_seconds=deadline_seconds,
-            allow_redelegation=allow_redelegation,
-        )
-        if result.get("status") == "rejected":
-            return f"Error: dispatch rejected — {result.get('reason', 'unknown')}"
-        if result.get("status") == "circuit_open":
-            failover = result.get("failover_to")
-            return f"Error: circuit open for {to}. Failover candidate: {failover or 'none'}"
-        return (
-            f"Dispatched to {to}. Task ID: {result.get('task_id')}. "
-            f"Depth: {result.get('depth', 0)}."
-        )
-
-
-class PollTaskResultTool(Tool):
-    """Poll the status of a previously dispatched task."""
-
-    def __init__(self, shell: "P2PShell"):
-        self._shell = shell
-
-    @property
-    def name(self) -> str:
-        return "poll_task_result"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Check the current status of a task you previously dispatched. "
-            "Returns completed, pending, timeout, failed, or not_found. "
-            "Call this proactively — do not wait for automatic notifications."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "task_id": {"type": "string", "description": "Task ID returned by dispatch_task"},
-            },
-            "required": ["task_id"],
-        }
-
-    async def execute(self, task_id: str, **kwargs: Any) -> str:
-        result = self._shell.poll(task_id)
-        status = result.get("status")
-        if status == "not_found":
-            return f"Task {task_id} not found."
-        if status == "pending":
-            return f"Task {task_id} is pending (elapsed {result.get('elapsed', '?')}s)."
-        if status == "timeout":
-            return f"Task {task_id} timed out after {result.get('elapsed', '?')}s."
-        if status in ("completed", "failed", "aborted"):
-            from_agent = result.get("from", "unknown")
-            content = result.get("result", "")
-            preview = content[:500] + "..." if len(content) > 500 else content
-            return f"Task {task_id} is {status} (from {from_agent}).\n\n{preview}"
-        return f"Task {task_id} status: {status}"
-
-
-class BroadcastTaskTool(Tool):
-    """Broadcast subtasks to discover capable agents."""
-
-    def __init__(self, shell: "P2PShell"):
-        self._shell = shell
-
-    @property
-    def name(self) -> str:
-        return "broadcast_task"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Announce subtasks to the agent network to collect BIDs. "
-            "Returns immediately. Use check_aggregation later to see which agents responded. "
-            "Each subtask should include a capability hint for matching."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "task_id": {"type": "string", "description": "Your task identifier"},
-                "subtasks": {
-                    "type": "array",
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "subtask_id": {"type": "string"},
-                            "description": {"type": "string"},
-                            "capability": {"type": "string", "description": "Required capability, e.g. 'web_search'"},
-                            "budget_seconds": {"type": "integer", "default": 300},
-                        },
-                        "required": ["subtask_id", "description", "capability"],
-                    },
-                },
-                "aggregation_timeout": {"type": "integer", "default": 30, "description": "Seconds to wait for BIDs"},
-            },
-            "required": ["task_id", "subtasks"],
-        }
-
-    async def execute(
-        self,
-        task_id: str,
-        subtasks: list[dict[str, Any]],
-        aggregation_timeout: int = 30,
-        **kwargs: Any,
-    ) -> str:
-        result = self._shell.broadcast(task_id, subtasks, aggregation_timeout)
-        invited = result.get("invited", 0)
-        return f"Broadcast opened for {task_id}. Invited {invited} agent(s). Use check_aggregation to collect BIDs."
-
-
-class CheckAggregationTool(Tool):
-    """Check the status of a broadcast aggregation window."""
-
-    def __init__(self, shell: "P2PShell"):
-        self._shell = shell
-
-    @property
-    def name(self) -> str:
-        return "check_aggregation"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Check whether a previously broadcast task has collected enough BIDs or timed out. "
-            "Returns the list of responding agents and their bids, or a pending status with counts."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "task_id": {"type": "string", "description": "Task ID used in broadcast_task"},
-            },
-            "required": ["task_id"],
-        }
-
-    async def execute(self, task_id: str, **kwargs: Any) -> str:
-        result = self._shell.check_aggregation(task_id)
-        status = result.get("status")
-        if status == "no_window":
-            return f"No broadcast window found for {task_id}."
-        if status == "pending":
-            received = result.get("received", 0)
-            expected = result.get("expected", "?")
-            remaining = result.get("seconds_remaining", 0)
-            return (
-                f"Aggregation pending for {task_id}: "
-                f"{received}/{expected} received, {remaining}s remaining."
-            )
-        if status == "closed":
-            entries = result.get("entries", [])
-            lines = [f"Aggregation closed for {task_id} ({result.get('reason', '')}):", ""]
-            for e in entries:
-                agent = e.get("from", "unknown")
-                sub = e.get("subtask_id", "")
-                lines.append(f"- {agent} bid for {sub}")
-            return "\n".join(lines)
-        return f"Unknown aggregation status for {task_id}: {status}"
-
-
-class ReportUserTool(Tool):
-    """Deliver a final answer to the user."""
-
-    def __init__(
-        self,
-        send_callback: Callable[[OutboundMessage], Awaitable[None]] | None = None,
-        default_channel: str = "",
-        default_chat_id: str = "",
-    ):
-        self._send_callback = send_callback
-        self._default_channel = default_channel
-        self._default_chat_id = default_chat_id
-
-    @property
-    def name(self) -> str:
-        return "report_user"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Report the final answer to the user. Use this when you have gathered enough results. "
-            "Status 'partial' means some subtasks are incomplete — list them in pending_items."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "final_answer": {"type": "string", "description": "Complete answer for the user"},
-                "status": {"type": "string", "enum": ["success", "partial", "failed"]},
-                "pending_items": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Incomplete items when status is partial",
-                },
-                "task_summary": {"type": "string", "description": "Optional brief summary"},
-            },
-            "required": ["final_answer", "status"],
-        }
-
-    async def execute(
-        self,
-        final_answer: str,
-        status: str,
-        pending_items: list[str] | None = None,
-        task_summary: str = "",
-        **kwargs: Any,
-    ) -> str:
-        if not self._send_callback:
-            return "Error: report_user not configured (no send callback)"
-
-        parts = [final_answer]
-        if pending_items:
-            parts.append(f"\n\nPending items:\n" + "\n".join(f"- {i}" for i in pending_items))
-        if task_summary:
-            parts.append(f"\n\nSummary: {task_summary}")
-
-        content = "\n".join(parts)
-        msg = OutboundMessage(
-            channel=self._default_channel,
-            chat_id=self._default_chat_id,
-            content=content,
-        )
-        await self._send_callback(msg)
-        return f"Reported to user (status={status})."
-
-
-class FinalizeTaskTool(Tool):
-    """Force-finalize a task and close its sessions."""
-
-    def __init__(self, shell: "P2PShell", session_manager: "SessionManager | None" = None):
-        self._shell = shell
-        self._session_manager = session_manager
-
-    @property
-    def name(self) -> str:
-        return "finalize_task"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Terminate a task and all its subtasks. Use when the user says 'stop', "
-            "or when a task is fundamentally blocked. outcome can be completed, failed, or aborted."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "task_id": {"type": "string"},
-                "outcome": {"type": "string", "enum": ["completed", "failed", "aborted"]},
-                "reason": {"type": "string", "description": "Why the task was finalized"},
-            },
-            "required": ["task_id", "outcome"],
-        }
-
-    async def execute(
-        self,
-        task_id: str,
-        outcome: str,
-        reason: str = "",
-        **kwargs: Any,
-    ) -> str:
-        self._shell.finalize(task_id, outcome, reason)
-        if self._session_manager:
-            self._session_manager.finalize_task_session(task_id)
-        return f"Task {task_id} finalized with outcome={outcome}."

nanobot/agent/tools/path_utils.py

@@ -3,21 +3,15 @@
 from pathlib import Path
 
 from nanobot.config.paths import get_media_dir
-
+from nanobot.security.workspace_policy import (
-WORKSPACE_BOUNDARY_NOTE = (
+    is_path_within,
-    " (this is a hard policy boundary, not a transient failure; "
+    resolve_allowed_path,
-    "do not retry with shell tricks or alternative tools, and ask "
-    "the user how to proceed if the resource is genuinely required)"
 )
 
 
 def is_under(path: Path, directory: Path) -> bool:
     """Return True when path resolves under directory."""
-    try:
+    return is_path_within(path, directory)
-        path.relative_to(directory.resolve())
-        return True
-    except ValueError:
-        return False
 
 
 def resolve_workspace_path(
@@ -27,16 +21,10 @@ def resolve_workspace_path(
     extra_allowed_dirs: list[Path] | None = None,
 ) -> Path:
     """Resolve path against workspace and enforce allowed directory containment."""
-    p = Path(path).expanduser()
+    extra_roots = [get_media_dir(), *(extra_allowed_dirs or [])] if allowed_dir else None
-    if not p.is_absolute() and workspace:
+    return resolve_allowed_path(
-        p = workspace / p
+        path,
-    resolved = p.resolve()
+        workspace=workspace,
-    if allowed_dir:
+        allowed_root=allowed_dir,
-        media_path = get_media_dir().resolve()
+        extra_allowed_roots=extra_roots,
-        all_dirs = [allowed_dir, media_path, *(extra_allowed_dirs or [])]
+    )
-        if not any(is_under(resolved, d) for d in all_dirs):
-            raise PermissionError(
-                f"Path {path} is outside allowed directory {allowed_dir}"
-                + WORKSPACE_BOUNDARY_NOTE
-            )
-    return resolved

nanobot/agent/tools/runtime_state.py

@@ -42,6 +42,9 @@ class RuntimeState(Protocol):
     @property
     def exec_config(self) -> Any: ...
 
+    @property
+    def workspace_sandbox(self) -> Any: ...
+
     @property
     def subagents(self) -> Any: ...
 

nanobot/agent/tools/search.py

@@ -1,4 +1,4 @@
-"""Search tools: grep."""
+"""Search tools: file discovery and grep."""
 
 from __future__ import annotations
 
@@ -12,6 +12,7 @@ from typing import Any, Iterable, TypeVar
 from nanobot.agent.tools.filesystem import ListDirTool, _FsTool
 
 _DEFAULT_HEAD_LIMIT = 250
+_DEFAULT_FILE_HEAD_LIMIT = 200
 T = TypeVar("T")
 _TYPE_GLOB_MAP = {
     "py": ("*.py", "*.pyi"),
@@ -88,13 +89,22 @@ def _matches_type(name: str, file_type: str | None) -> bool:
     return any(fnmatch.fnmatch(name.lower(), pattern.lower()) for pattern in patterns)
 
 
+def _matches_query(rel_path: str, query: str | None) -> bool:
+    if not query:
+        return True
+    haystack = rel_path.lower()
+    terms = [part for part in query.lower().split() if part]
+    return all(term in haystack for term in terms)
+
+
 class _SearchTool(_FsTool):
     _IGNORE_DIRS = set(ListDirTool._IGNORE_DIRS)
 
     def _display_path(self, target: Path, root: Path) -> str:
-        if self._workspace:
+        workspace = self._display_workspace()
+        if workspace:
             with suppress(ValueError):
-                return target.relative_to(self._workspace).as_posix()
+                return target.relative_to(workspace).as_posix()
         return target.relative_to(root).as_posix()
 
     def _iter_files(self, root: Path) -> Iterable[Path]:
@@ -109,6 +119,163 @@ class _SearchTool(_FsTool):
                 yield current / filename
 
 
+class FindFilesTool(_SearchTool):
+    """Find files by path fragment, glob, or type."""
+    _scopes = {"core", "subagent"}
+
+    @property
+    def name(self) -> str:
+        return "find_files"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Find files by path fragment, glob, or file type. "
+            "Use this before read_file when you need to locate files, and "
+            "prefer it over shell find/ls for ordinary workspace discovery. "
+            "Returns workspace-relative paths and skips common dependency/build "
+            "directories."
+        )
+
+    @property
+    def read_only(self) -> bool:
+        return True
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": "Directory or file to search in (default '.')",
+                },
+                "query": {
+                    "type": "string",
+                    "description": (
+                        "Optional case-insensitive path fragment search. "
+                        "Whitespace-separated terms must all be present."
+                    ),
+                },
+                "glob": {
+                    "type": "string",
+                    "description": "Optional file filter, e.g. '*.py' or 'tests/**/test_*.py'",
+                },
+                "type": {
+                    "type": "string",
+                    "description": "Optional file type shorthand, e.g. 'py', 'ts', 'md', 'json'",
+                },
+                "include_dirs": {
+                    "type": "boolean",
+                    "description": "Include matching directories as well as files (default false)",
+                },
+                "sort": {
+                    "type": "string",
+                    "enum": ["path", "modified"],
+                    "description": "Sort by path or most recently modified first (default path)",
+                },
+                "head_limit": {
+                    "type": "integer",
+                    "description": "Maximum number of paths to return (default 200, 0 for all, max 1000)",
+                    "minimum": 0,
+                    "maximum": 1000,
+                },
+                "offset": {
+                    "type": "integer",
+                    "description": "Skip the first N results before applying head_limit",
+                    "minimum": 0,
+                    "maximum": 100000,
+                },
+            },
+        }
+
+    def _iter_paths(self, root: Path, *, include_dirs: bool) -> Iterable[Path]:
+        if root.is_file():
+            yield root
+            return
+        if include_dirs:
+            yield root
+        for dirpath, dirnames, filenames in os.walk(root):
+            dirnames[:] = sorted(d for d in dirnames if d not in self._IGNORE_DIRS)
+            current = Path(dirpath)
+            if include_dirs and current != root:
+                yield current
+            for filename in sorted(filenames):
+                yield current / filename
+
+    async def execute(
+        self,
+        path: str = ".",
+        query: str | None = None,
+        glob: str | None = None,
+        type: str | None = None,
+        include_dirs: bool = False,
+        sort: str = "path",
+        head_limit: int | None = None,
+        offset: int = 0,
+        **kwargs: Any,
+    ) -> str:
+        try:
+            target = self._resolve(path or ".")
+            if not target.exists():
+                return f"Error: Path not found: {path}"
+            if not (target.is_dir() or target.is_file()):
+                return f"Error: Unsupported path: {path}"
+
+            if sort not in {"path", "modified"}:
+                return "Error: sort must be 'path' or 'modified'"
+
+            limit = (
+                _DEFAULT_FILE_HEAD_LIMIT
+                if head_limit is None
+                else None if head_limit == 0 else head_limit
+            )
+            root = target if target.is_dir() else target.parent
+            matches: list[tuple[str, float]] = []
+
+            for candidate in self._iter_paths(target, include_dirs=include_dirs):
+                if candidate.is_dir() and not include_dirs:
+                    continue
+                rel_path = candidate.relative_to(root).as_posix()
+                display_path = self._display_path(candidate, root)
+                name = candidate.name
+
+                if glob and not _match_glob(rel_path, name, glob):
+                    continue
+                if candidate.is_file() and not _matches_type(name, type):
+                    continue
+                if candidate.is_dir() and type:
+                    continue
+                if not _matches_query(display_path, query):
+                    continue
+                try:
+                    mtime = candidate.stat().st_mtime
+                except OSError:
+                    mtime = 0.0
+                suffix = "/" if candidate.is_dir() else ""
+                matches.append((display_path + suffix, mtime))
+
+            if sort == "modified":
+                matches.sort(key=lambda item: (-item[1], item[0]))
+            else:
+                matches.sort(key=lambda item: item[0])
+
+            paths = [item[0] for item in matches]
+            paged, truncated = _paginate(paths, limit, offset)
+            if not paged:
+                return "No files found"
+
+            result = "\n".join(paged)
+            note = _pagination_note(limit, offset, truncated)
+            if note:
+                result += "\n\n" + note
+            return result
+        except PermissionError as e:
+            return f"Error: {e}"
+        except Exception as e:
+            return f"Error finding files: {e}"
+
+
 class GrepTool(_SearchTool):
     """Search file contents using a regex-like pattern."""
     _scopes = {"core", "subagent"}
@@ -125,7 +292,8 @@ class GrepTool(_SearchTool):
         return (
             "Search file contents with a regex pattern. "
             "Default output_mode is files_with_matches (file paths only); "
-            "use content mode for matching lines with context. "
+            "use content mode for matching lines with context. Prefer this "
+            "over shell grep for ordinary workspace searches. "
             "Skips binary and files >2 MB. Supports glob/type filtering."
         )
 

nanobot/agent/tools/self.py

@@ -3,16 +3,18 @@
 from __future__ import annotations
 
 import time
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from loguru import logger
 
-from nanobot.agent.subagent import SubagentStatus
 from nanobot.agent.tools.base import Tool
 from nanobot.agent.tools.context import ContextAware, RequestContext
 from nanobot.agent.tools.runtime_state import RuntimeState
 from nanobot.config.schema import Base
 
+if TYPE_CHECKING:
+    from nanobot.agent.subagent import SubagentStatus
+
 
 class MyToolConfig(Base):
     """Self-inspection tool configuration."""
@@ -33,6 +35,12 @@ def _has_real_attr(obj: Any, key: str) -> bool:
     return False
 
 
+def _is_subagent_status(value: Any) -> bool:
+    from nanobot.agent.subagent import SubagentStatus
+
+    return isinstance(value, SubagentStatus)
+
+
 class MyTool(Tool, ContextAware):
     """Check and set the agent loop's runtime configuration."""
 
@@ -68,6 +76,7 @@ class MyTool(Tool, ContextAware):
         "_current_iteration",  # updated by runner only
         "exec_config",  # inspect allowed (e.g. check sandbox), modify blocked
         "web_config",  # inspect allowed (e.g. check enable), modify blocked
+        "workspace_sandbox",  # read-only view of workspace enforcement level
     })
 
     _DENIED_ATTRS = frozenset({
@@ -214,7 +223,7 @@ class MyTool(Tool, ContextAware):
     # ------------------------------------------------------------------
 
     @staticmethod
-    def _format_status(st: SubagentStatus, indent: str = "  ") -> str:
+    def _format_status(st: "SubagentStatus", indent: str = "  ") -> str:
         elapsed = time.monotonic() - st.started_at
         tool_summary = ", ".join(
             f"{e.get('name', '?')}({e.get('status', '?')})" for e in st.tool_events[-5:]
@@ -232,14 +241,14 @@ class MyTool(Tool, ContextAware):
 
     @staticmethod
     def _format_value(val: Any, key: str = "") -> str:
-        if isinstance(val, SubagentStatus):
+        if _is_subagent_status(val):
             header = f"Subagent [{val.task_id}] '{val.label}'"
             detail = MyTool._format_status(val, "  ")
             return f"{header}\n  task: {val.task_description}\n{detail}"
         # SubagentManager: delegate to its _task_statuses dict
         if hasattr(val, "_task_statuses") and isinstance(val._task_statuses, dict):
             return MyTool._format_value(val._task_statuses, key)
-        if isinstance(val, dict) and val and isinstance(next(iter(val.values())), SubagentStatus):
+        if isinstance(val, dict) and val and _is_subagent_status(next(iter(val.values()))):
             prefix = f"{key}: " if key else ""
             lines = [f"{prefix}{len(val)} subagent(s):"]
             for tid, st in val.items():
@@ -349,7 +358,7 @@ class MyTool(Tool, ContextAware):
             parts.append(self._format_value(getattr(state, k, None), k))
         parts.append(self._format_value(state.model_preset, "model_preset"))
         # Other useful top-level keys shown in description
-        for k in ("workspace", "provider_retry_mode", "max_tool_result_chars", "_current_iteration", "web_config", "exec_config", "subagents"):
+        for k in ("workspace", "provider_retry_mode", "max_tool_result_chars", "_current_iteration", "web_config", "exec_config", "workspace_sandbox", "subagents"):
             if _has_real_attr(state, k):
                 parts.append(self._format_value(getattr(state, k, None), k))
         # Token usage

nanobot/agent/tools/shell.py

@@ -8,6 +8,7 @@ import re
 import shutil
 import sys
 from contextlib import suppress
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 
@@ -15,10 +16,27 @@ from loguru import logger
 from pydantic import Field
 
 from nanobot.agent.tools.base import Tool, tool_parameters
+from nanobot.agent.tools.context import current_request_session_key
+from nanobot.agent.tools.exec_session import (
+    DEFAULT_EXEC_SESSION_MANAGER,
+    DEFAULT_MAX_OUTPUT_CHARS,
+    DEFAULT_YIELD_MS,
+    MAX_OUTPUT_CHARS,
+    MAX_YIELD_MS,
+    clamp_session_int,
+    format_session_poll,
+)
 from nanobot.agent.tools.sandbox import wrap_command
-from nanobot.agent.tools.schema import IntegerSchema, StringSchema, tool_parameters_schema
+from nanobot.agent.tools.schema import (
+    BooleanSchema,
+    IntegerSchema,
+    StringSchema,
+    tool_parameters_schema,
+)
 from nanobot.config.paths import get_media_dir
 from nanobot.config.schema import Base
+from nanobot.security.workspace_access import current_scope_allows_loopback, current_tool_workspace
+from nanobot.security.workspace_policy import is_path_within
 
 _IS_WINDOWS = sys.platform == "win32"
 
@@ -36,7 +54,7 @@ _WORKSPACE_BOUNDARY_NOTE = (
 class ExecToolConfig(Base):
     """Shell exec tool configuration."""
     enable: bool = True
-    timeout: int = 60
+    timeout: int = Field(default=60, ge=0)  # Hard timeout (s); 0 = no limit. Not capped by the per-call max.
     path_append: str = ""
     sandbox: str = ""
     allowed_env_keys: list[str] = Field(default_factory=list)
@@ -44,10 +62,22 @@ class ExecToolConfig(Base):
     deny_patterns: list[str] = Field(default_factory=list)
 
 
+@dataclass(slots=True)
+class _PreparedCommand:
+    command: str
+    cwd: str
+    env: dict[str, str]
+    timeout: int | None
+    shell_program: str | None
+    login: bool
+
+
 @tool_parameters(
     tool_parameters_schema(
         command=StringSchema("The shell command to execute"),
+        cmd=StringSchema("Compatibility alias for command"),
         working_dir=StringSchema("Optional working directory for the command"),
+        workdir=StringSchema("Compatibility alias for working_dir"),
         timeout=IntegerSchema(
             60,
             description=(
@@ -57,7 +87,44 @@ class ExecToolConfig(Base):
             minimum=1,
             maximum=600,
         ),
-        required=["command"],
+        shell=StringSchema(
+            "Optional shell binary to launch. On Unix, supports sh, bash, or zsh.",
+            nullable=True,
+        ),
+        login=BooleanSchema(
+            description="Whether to run bash/zsh with login shell semantics (default true).",
+            default=True,
+            nullable=True,
+        ),
+        yield_time_ms=IntegerSchema(
+            description=(
+                "Optional milliseconds to wait before returning output. "
+                "When set, a still-running command returns a session_id that "
+                "can be polled or written to with write_stdin. Omit this field "
+                "to keep one-shot exec behavior."
+            ),
+            minimum=0,
+            maximum=MAX_YIELD_MS,
+            nullable=True,
+        ),
+        max_output_chars=IntegerSchema(
+            description=(
+                "Maximum output characters to return when yield_time_ms is used "
+                "(default 10000, max 50000)."
+            ),
+            minimum=1000,
+            maximum=MAX_OUTPUT_CHARS,
+            nullable=True,
+        ),
+        max_output_tokens=IntegerSchema(
+            description=(
+                "Compatibility alias for max_output_chars. The current runtime "
+                "uses a character budget."
+            ),
+            minimum=1000,
+            maximum=MAX_OUTPUT_CHARS,
+            nullable=True,
+        ),
     )
 )
 class ExecTool(Tool):
@@ -81,6 +148,7 @@ class ExecTool(Tool):
             working_dir=ctx.workspace,
             timeout=cfg.timeout,
             restrict_to_workspace=ctx.config.restrict_to_workspace,
+            webui_allow_local_service_access=ctx.config.webui_allow_local_service_access,
             sandbox=cfg.sandbox,
             path_append=cfg.path_append,
             allowed_env_keys=cfg.allowed_env_keys,
@@ -95,9 +163,12 @@ class ExecTool(Tool):
         deny_patterns: list[str] | None = None,
         allow_patterns: list[str] | None = None,
         restrict_to_workspace: bool = False,
+        webui_allow_local_service_access: bool = True,
+        allow_local_preview_access: bool | None = None,
         sandbox: str = "",
         path_append: str = "",
         allowed_env_keys: list[str] | None = None,
+        session_manager: Any | None = None,
     ):
         self.timeout = timeout
         self.working_dir = working_dir
@@ -123,8 +194,12 @@ class ExecTool(Tool):
         ]
         self.allow_patterns = allow_patterns or []
         self.restrict_to_workspace = restrict_to_workspace
+        if allow_local_preview_access is not None:
+            webui_allow_local_service_access = allow_local_preview_access
+        self.webui_allow_local_service_access = webui_allow_local_service_access
         self.path_append = path_append
         self.allowed_env_keys = allowed_env_keys or []
+        self._session_manager = session_manager or DEFAULT_EXEC_SESSION_MANAGER
 
     @property
     def name(self) -> str:
@@ -150,10 +225,15 @@ class ExecTool(Tool):
     def description(self) -> str:
         return (
             "Execute a shell command and return its output. "
-            "Prefer read_file/write_file/edit_file over cat/echo/sed, "
+            "Use this for tests, builds, package commands, git commands, and "
-            "and grep/glob over shell find/grep. "
+            "other process execution. Prefer read_file/find_files/grep for "
+            "inspection and apply_patch/write_file/edit_file for file changes "
+            "instead of cat, shell find/grep, echo, or sed. "
             "Use -y or --yes flags to avoid interactive prompts. "
-            "Output is truncated at 10 000 chars; timeout defaults to 60s."
+            "For long-running or interactive commands, pass yield_time_ms; "
+            "if the command keeps running, exec returns a session_id that can "
+            "be polled or written to with write_stdin. Output is truncated at "
+            "10 000 chars; timeout defaults to 60s."
         )
 
     @property
@@ -161,67 +241,45 @@ class ExecTool(Tool):
         return True
 
     async def execute(
-        self, command: str, working_dir: str | None = None,
+        self, command: str | None = None, cmd: str | None = None,
-        timeout: int | None = None, **kwargs: Any,
+        working_dir: str | None = None, workdir: str | None = None,
+        timeout: int | None = None, shell: str | None = None,
+        login: bool | None = None, yield_time_ms: int | None = None,
+        max_output_chars: int | None = None,
+        max_output_tokens: int | None = None,
+        **kwargs: Any,
     ) -> str:
-        cwd = working_dir or self.working_dir or os.getcwd()
+        command = command or cmd
+        working_dir = working_dir or workdir
+        if not command:
+            return "Error: Missing command. Provide command or cmd."
+        if max_output_chars is None:
+            max_output_chars = max_output_tokens
 
-        # Prevent an LLM-supplied working_dir from escaping the configured
+        prepared = self._prepare_command(command, working_dir, timeout, shell, login)
-        # workspace when restrict_to_workspace is enabled (#2826). Without
+        if isinstance(prepared, str):
-        # this, a caller can pass working_dir="/etc" and then all absolute
+            return prepared
-        # paths under /etc would pass the _guard_command check that anchors
-        # on cwd.
-        if self.restrict_to_workspace and self.working_dir:
-            try:
-                requested = Path(cwd).expanduser().resolve()
-                workspace_root = Path(self.working_dir).expanduser().resolve()
-            except Exception:
-                return (
-                    "Error: working_dir could not be resolved"
-                    + _WORKSPACE_BOUNDARY_NOTE
-                )
-            if requested != workspace_root and workspace_root not in requested.parents:
-                return (
-                    "Error: working_dir is outside the configured workspace"
-                    + _WORKSPACE_BOUNDARY_NOTE
-                )
 
-        guard_error = self._guard_command(command, cwd)
+        if yield_time_ms is not None:
-        if guard_error:
+            return await self._execute_session(prepared, yield_time_ms, max_output_chars)
-            return guard_error
-
-        if self.sandbox:
-            if _IS_WINDOWS:
-                logger.warning(
-                    "Sandbox '{}' is not supported on Windows; running unsandboxed",
-                    self.sandbox,
-                )
-            else:
-                workspace = self.working_dir or cwd
-                command = wrap_command(self.sandbox, command, workspace, cwd)
-                cwd = str(Path(workspace).resolve())
-
-        effective_timeout = min(timeout or self.timeout, self._MAX_TIMEOUT)
-        env = self._build_env()
-
-        if self.path_append:
-            if _IS_WINDOWS:
-                env["PATH"] = env.get("PATH", "") + os.pathsep + self.path_append
-            else:
-                env["NANOBOT_PATH_APPEND"] = self.path_append
-                command = f'export PATH="$PATH{os.pathsep}$NANOBOT_PATH_APPEND"; {command}'
 
         try:
-            process = await self._spawn(command, cwd, env)
+            process = await self._spawn(
+                prepared.command,
+                prepared.cwd,
+                prepared.env,
+                prepared.shell_program,
+                prepared.login,
+            )
 
             try:
                 stdout, stderr = await asyncio.wait_for(
                     process.communicate(),
-                    timeout=effective_timeout,
+                    timeout=prepared.timeout,
                 )
             except asyncio.TimeoutError:
                 await self._kill_process(process)
-                return f"Error: Command timed out after {effective_timeout} seconds"
+                return f"Error: Command timed out after {prepared.timeout} seconds"
             except asyncio.CancelledError:
                 await self._kill_process(process)
                 raise
@@ -240,7 +298,7 @@ class ExecTool(Tool):
 
             result = "\n".join(output_parts) if output_parts else "(no output)"
 
-            max_len = self._MAX_OUTPUT
+            max_len = clamp_session_int(max_output_chars, self._MAX_OUTPUT, 1000, MAX_OUTPUT_CHARS)
             if len(result) > max_len:
                 half = max_len // 2
                 result = (
@@ -254,32 +312,192 @@ class ExecTool(Tool):
         except Exception as e:
             return f"Error executing command: {str(e)}"
 
+    async def _execute_session(
+        self,
+        prepared: _PreparedCommand,
+        yield_time_ms: int | None,
+        max_output_chars: int | None,
+    ) -> str:
+        try:
+            session_id, poll = await self._session_manager.start(
+                command=prepared.command,
+                cwd=prepared.cwd,
+                env=prepared.env,
+                timeout=prepared.timeout,
+                shell_program=prepared.shell_program,
+                login=prepared.login,
+                yield_time_ms=clamp_session_int(yield_time_ms, DEFAULT_YIELD_MS, 0, MAX_YIELD_MS),
+                owner_session_key=current_request_session_key(),
+                max_output_chars=clamp_session_int(
+                    max_output_chars,
+                    DEFAULT_MAX_OUTPUT_CHARS,
+                    1000,
+                    MAX_OUTPUT_CHARS,
+                ),
+            )
+            return format_session_poll(session_id, poll)
+        except Exception as exc:
+            return f"Error executing command: {exc}"
+
+    def _resolve_timeout(self, timeout: int | None) -> int | None:
+        """Resolve the effective hard timeout in seconds (None = no limit).
+
+        A per-call timeout supplied by the model stays capped at _MAX_TIMEOUT so
+        the LLM cannot request unbounded execution. The config-level default
+        (self.timeout) may exceed that cap, and 0 disables the limit entirely
+        for trusted long-running tasks (#3595).
+        """
+        if timeout:
+            return min(timeout, self._MAX_TIMEOUT)
+        if self.timeout and self.timeout > 0:
+            return self.timeout
+        return None
+
+    def _prepare_command(
+        self,
+        command: str,
+        working_dir: str | None = None,
+        timeout: int | None = None,
+        shell: str | None = None,
+        login: bool | None = None,
+    ) -> _PreparedCommand | str:
+        access = current_tool_workspace(
+            self.working_dir,
+            restrict_to_workspace=self.restrict_to_workspace,
+            sandbox_restricts_workspace=bool(self.sandbox),
+        )
+        workspace_root = str(access.project_path) if access.project_path is not None else self.working_dir
+        cwd = working_dir or workspace_root or os.getcwd()
+
+        # Prevent an LLM-supplied working_dir from escaping the configured
+        # workspace when restrict_to_workspace is enabled (#2826). Without
+        # this, a caller can pass working_dir="/etc" and then all absolute
+        # paths under /etc would pass the _guard_command check that anchors
+        # on cwd.
+        if access.restrict_to_workspace and workspace_root:
+            try:
+                requested = Path(cwd).expanduser().resolve()
+                resolved_root = Path(workspace_root).expanduser().resolve()
+            except Exception:
+                return (
+                    "Error: working_dir could not be resolved"
+                    + _WORKSPACE_BOUNDARY_NOTE
+                )
+            if not is_path_within(requested, resolved_root):
+                return (
+                    "Error: working_dir is outside the configured workspace"
+                    + _WORKSPACE_BOUNDARY_NOTE
+                )
+
+        guard_error = self._guard_command(
+            command,
+            cwd,
+            restrict_to_workspace=access.restrict_to_workspace,
+        )
+        if guard_error:
+            return guard_error
+
+        if self.sandbox:
+            if _IS_WINDOWS:
+                logger.warning(
+                    "Sandbox '{}' is not supported on Windows; running unsandboxed",
+                    self.sandbox,
+                )
+            else:
+                workspace = workspace_root or cwd
+                command = wrap_command(self.sandbox, command, workspace, cwd)
+                cwd = str(Path(workspace).resolve())
+
+        effective_timeout = self._resolve_timeout(timeout)
+        env = self._build_env()
+
+        if self.path_append:
+            if _IS_WINDOWS:
+                env["PATH"] = env.get("PATH", "") + os.pathsep + self.path_append
+            else:
+                env["NANOBOT_PATH_APPEND"] = self.path_append
+                command = f'export PATH="$PATH{os.pathsep}$NANOBOT_PATH_APPEND"; {command}'
+
+        shell_program, shell_error = self._resolve_shell(shell)
+        if shell_error:
+            return shell_error
+
+        return _PreparedCommand(
+            command=command,
+            cwd=cwd,
+            env=env,
+            timeout=effective_timeout,
+            shell_program=shell_program,
+            login=True if login is None else login,
+        )
+
     @staticmethod
     async def _spawn(
         command: str, cwd: str, env: dict[str, str],
+        shell_program: str | None = None,
+        login: bool = True,
+        *,
+        stdin: int = asyncio.subprocess.DEVNULL,
     ) -> asyncio.subprocess.Process:
         """Launch *command* in a platform-appropriate shell."""
         if _IS_WINDOWS:
-            # create_subprocess_exec re-quotes args via list2cmdline, which
+            if "\n" in command:
-            # breaks commands containing paths with spaces (e.g. "D:\Program
+                return await asyncio.create_subprocess_exec(
-            # Files\python.exe" "script.py"). create_subprocess_shell passes
+                    "powershell", "-NoProfile", "-Command", command,
-            # the raw command string to COMSPEC without re-quoting.
+                    stdin=stdin,
+                    stdout=asyncio.subprocess.PIPE,
+                    stderr=asyncio.subprocess.PIPE,
+                    cwd=cwd,
+                    env=env,
+                )
             return await asyncio.create_subprocess_shell(
                 command,
+                stdin=stdin,
                 stdout=asyncio.subprocess.PIPE,
                 stderr=asyncio.subprocess.PIPE,
                 cwd=cwd,
                 env=env,
             )
-        bash = shutil.which("bash") or "/bin/bash"
+        shell_program = shell_program or shutil.which("bash") or "/bin/bash"
+        args = [shell_program]
+        shell_name = Path(shell_program).name.lower()
+        if login and shell_name in {"bash", "bash.exe", "zsh", "zsh.exe"}:
+            args.append("-l")
+        args.extend(["-c", command])
         return await asyncio.create_subprocess_exec(
-            bash, "-l", "-c", command,
+            *args,
+            stdin=stdin,
             stdout=asyncio.subprocess.PIPE,
             stderr=asyncio.subprocess.PIPE,
             cwd=cwd,
             env=env,
         )
 
+    @staticmethod
+    def _resolve_shell(shell: str | None) -> tuple[str | None, str | None]:
+        if not shell:
+            return None, None
+        if _IS_WINDOWS:
+            return None, "Error: shell parameter is not supported on Windows"
+        if "\0" in shell or "\n" in shell or "\r" in shell:
+            return None, "Error: shell contains invalid characters"
+        allowed = {"sh", "bash", "zsh"}
+        path = Path(shell).expanduser()
+        if path.is_absolute():
+            if path.name not in allowed:
+                return None, f"Error: unsupported shell {shell!r}. Allowed: bash, sh, zsh"
+            if not path.is_file() or not os.access(path, os.X_OK):
+                return None, f"Error: shell is not executable: {shell}"
+            return str(path), None
+        if "/" in shell or "\\" in shell:
+            return None, "Error: shell must be a shell name or absolute path"
+        if shell not in allowed:
+            return None, f"Error: unsupported shell {shell!r}. Allowed: bash, sh, zsh"
+        resolved = shutil.which(shell)
+        if not resolved:
+            return None, f"Error: shell not found: {shell}"
+        return resolved, None
+
     @staticmethod
     async def _kill_process(process: asyncio.subprocess.Process) -> None:
         """Kill a subprocess and reap it to prevent zombies."""
@@ -342,7 +560,13 @@ class ExecTool(Tool):
                 env[key] = val
         return env
 
-    def _guard_command(self, command: str, cwd: str) -> str | None:
+    def _guard_command(
+        self,
+        command: str,
+        cwd: str,
+        *,
+        restrict_to_workspace: bool | None = None,
+    ) -> str | None:
         """Best-effort safety guard for potentially destructive commands."""
         cmd = command.strip()
         lower = cmd.lower()
@@ -362,11 +586,17 @@ class ExecTool(Tool):
                 return "Error: Command blocked by allowlist filter (not in allowlist)"
 
         from nanobot.security.network import contains_internal_url
-        if contains_internal_url(cmd):
+        if contains_internal_url(
+            cmd,
+            allow_loopback=current_scope_allows_loopback(
+                enabled=self.webui_allow_local_service_access,
+            ),
+        ):
             # The runner turns this marker into a non-retryable security hint.
             return "Error: Command blocked by safety guard (internal/private URL detected)"
 
-        if self.restrict_to_workspace:
+        should_restrict = self.restrict_to_workspace if restrict_to_workspace is None else restrict_to_workspace
+        if should_restrict:
             if "..\\" in cmd or "../" in cmd:
                 return (
                     "Error: Command blocked by safety guard (path traversal detected)"
@@ -391,11 +621,9 @@ class ExecTool(Tool):
                     continue
 
                 media_path = get_media_dir().resolve()
-                if (p.is_absolute()
+                if p.is_absolute() and not (
-                    and cwd_path not in p.parents
+                    is_path_within(p, cwd_path)
-                    and p != cwd_path
+                    or is_path_within(p, media_path)
-                    and media_path not in p.parents
-                    and p != media_path
                 ):
                     return (
                         "Error: Command blocked by safety guard (path outside working dir)"
@@ -416,7 +644,7 @@ class ExecTool(Tool):
         # Windows: match drive-root paths like `C:\` as well as `C:\path\to\file`, and UNC paths like `\\server\share`
         # NOTE: `*` is required so `C:\` (nothing after the slash) is still extracted.
         win_paths = re.findall(
-            r"(?:[A-Za-z]:[^\s\"'|><;]*|\\\\[^\s\"'|><;]+(?:\\[^\s\"'|><;]+)*)",
+            r"(?<![A-Za-z])(?:[A-Za-z]:[^\s\"'|><;]*|\\\\[^\s\"'|><;]+(?:\\[^\s\"'|><;]+)*)",
             command
         )
         posix_paths = re.findall(r"(?:^|[\s|>'\"])(/[^\s\"'>;|<]+)", command) # POSIX: /absolute only

nanobot/agent/tools/spawn.py

@@ -7,7 +7,8 @@ from typing import TYPE_CHECKING, Any
 
 from nanobot.agent.tools.base import Tool, tool_parameters
 from nanobot.agent.tools.context import ContextAware, RequestContext
-from nanobot.agent.tools.schema import StringSchema, tool_parameters_schema
+from nanobot.agent.tools.schema import NumberSchema, StringSchema, tool_parameters_schema
+from nanobot.security.workspace_access import current_workspace_scope
 
 if TYPE_CHECKING:
     from nanobot.agent.subagent import SubagentManager
@@ -17,6 +18,15 @@ if TYPE_CHECKING:
     tool_parameters_schema(
         task=StringSchema("The task for the subagent to complete"),
         label=StringSchema("Optional short label for the task (for display)"),
+        temperature=NumberSchema(
+            description=(
+                "Optional sampling temperature for the subagent "
+                "(0.0 = deterministic, higher = more creative). "
+                "Defaults to the provider's configured temperature."
+            ),
+            minimum=0.0,
+            maximum=2.0,
+        ),
         required=["task"],
     )
 )
@@ -58,7 +68,13 @@ class SpawnTool(Tool, ContextAware):
             "and use a dedicated subdirectory when helpful."
         )
 
-    async def execute(self, task: str, label: str | None = None, **kwargs: Any) -> str:
+    async def execute(
+        self,
+        task: str,
+        label: str | None = None,
+        temperature: float | None = None,
+        **kwargs: Any,
+    ) -> str:
         """Spawn a subagent to execute the given task."""
         running = self._manager.get_running_count()
         limit = self._manager.max_concurrent_subagents
@@ -75,4 +91,6 @@ class SpawnTool(Tool, ContextAware):
             origin_chat_id=self._origin_chat_id.get(),
             session_key=self._session_key.get(),
             origin_message_id=self._origin_message_id.get(),
+            temperature=temperature,
+            workspace_scope=current_workspace_scope(),
         )

nanobot/agent/tools/web.py

@@ -8,7 +8,7 @@ import json
 import os
 import re
 from typing import Any, Callable
-from urllib.parse import quote, urlparse
+from urllib.parse import quote, urljoin, urlparse
 
 import httpx
 from loguru import logger
@@ -78,9 +78,82 @@ def _validate_url(url: str) -> tuple[bool, str]:
 def _validate_url_safe(url: str) -> tuple[bool, str]:
     """Validate URL with SSRF protection: scheme, domain, and resolved IP check."""
     from nanobot.security.network import validate_url_target
+
     return validate_url_target(url)
 
 
+async def _get_with_safe_redirects(
+    client: httpx.AsyncClient,
+    url: str,
+    headers: dict[str, str] | None = None,
+) -> tuple[httpx.Response | None, str | None]:
+    """GET a URL while validating every redirect target before requesting it."""
+    current_url = url
+    for _ in range(MAX_REDIRECTS + 1):
+        is_valid, error_msg = _validate_url_safe(current_url)
+        if not is_valid:
+            return None, f"Redirect blocked: {error_msg}"
+
+        response = await client.get(current_url, headers=headers, follow_redirects=False)
+        is_redirect = 300 <= response.status_code < 400
+        if not is_redirect:
+            return response, None
+
+        location = response.headers.get("location")
+        if not location:
+            return response, None
+
+        next_url = urljoin(str(response.url), location)
+        is_valid, error_msg = _validate_url_safe(next_url)
+        if not is_valid:
+            await response.aclose()
+            return None, f"Redirect blocked: {error_msg}"
+
+        await response.aclose()
+        current_url = next_url
+
+    return None, f"Too many redirects: exceeded limit of {MAX_REDIRECTS}"
+
+
+async def _stream_with_safe_redirects(
+    client: httpx.AsyncClient,
+    url: str,
+    headers: dict[str, str] | None = None,
+) -> tuple[httpx.Response | None, Any | None, str | None]:
+    """Open a streamed response while validating every redirect target first."""
+    current_url = url
+    for _ in range(MAX_REDIRECTS + 1):
+        is_valid, error_msg = _validate_url_safe(current_url)
+        if not is_valid:
+            return None, None, f"Redirect blocked: {error_msg}"
+
+        stream = client.stream(
+            "GET",
+            current_url,
+            headers=headers,
+            follow_redirects=False,
+        )
+        response = await stream.__aenter__()
+        is_redirect = 300 <= response.status_code < 400
+        if not is_redirect:
+            return response, stream, None
+
+        location = response.headers.get("location")
+        if not location:
+            return response, stream, None
+
+        next_url = urljoin(str(response.url), location)
+        is_valid, error_msg = _validate_url_safe(next_url)
+        if not is_valid:
+            await stream.__aexit__(None, None, None)
+            return None, None, f"Redirect blocked: {error_msg}"
+
+        await stream.__aexit__(None, None, None)
+        current_url = next_url
+
+    return None, None, f"Too many redirects: exceeded limit of {MAX_REDIRECTS}"
+
+
 def _format_results(query: str, items: list[dict[str, Any]], n: int) -> str:
     """Format provider results into shared plaintext output."""
     if not items:
@@ -382,17 +455,16 @@ class WebSearchTool(Tool):
             return await self._search_duckduckgo(query, n)
         try:
             async with httpx.AsyncClient(proxy=self.proxy) as client:
-                r = await client.get(
+                r = await client.post(
-                    "https://kagi.com/api/v0/search",
+                    "https://kagi.com/api/v1/search",
-                    params={"q": query, "limit": n},
+                    json={"query": query, "limit": n},
-                    headers={"Authorization": f"Bot {api_key}", "User-Agent": self.user_agent},
+                    headers={"Authorization": f"Bearer {api_key}", "User-Agent": self.user_agent},
                     timeout=10.0,
                 )
                 r.raise_for_status()
-            # t=0 items are search results; other values are related searches, etc.
             items = [
                 {"title": d.get("title", ""), "url": d.get("url", ""), "content": d.get("snippet", "")}
-                for d in r.json().get("data", []) if d.get("t") == 0
+                for d in r.json().get("data", {}).get("search", [])
             ]
             return _format_results(query, items, n)
         except Exception as e:
@@ -488,19 +560,26 @@ class WebFetchTool(Tool):
 
         # Detect and fetch images directly to avoid Jina's textual image captioning
         try:
-            async with httpx.AsyncClient(proxy=self.proxy, follow_redirects=True, max_redirects=MAX_REDIRECTS, timeout=15.0) as client:
+            async with httpx.AsyncClient(proxy=self.proxy, timeout=15.0) as client:
-                async with client.stream("GET", url, headers={"User-Agent": self.user_agent}) as r:
+                r, stream, redirect_error = await _stream_with_safe_redirects(
-                    from nanobot.security.network import validate_resolved_url
+                    client,
-
+                    url,
-                    redir_ok, redir_err = validate_resolved_url(str(r.url))
+                    headers={"User-Agent": self.user_agent},
-                    if not redir_ok:
+                )
-                        return json.dumps({"error": f"Redirect blocked: {redir_err}", "url": url}, ensure_ascii=False)
+                if redirect_error:
+                    return json.dumps({"error": redirect_error, "url": url}, ensure_ascii=False)
+                if r is None:
+                    return json.dumps({"error": "Fetch failed", "url": url}, ensure_ascii=False)
 
+                try:
                     ctype = r.headers.get("content-type", "")
                     if ctype.startswith("image/"):
                         r.raise_for_status()
                         raw = await r.aread()
                         return build_image_content_blocks(raw, ctype, url, f"(Image fetched from: {url})")
+                finally:
+                    if stream is not None:
+                        await stream.__aexit__(None, None, None)
         except Exception as e:
             logger.debug("Pre-fetch image detection failed for {}: {}", url, e)
 
@@ -549,23 +628,22 @@ class WebFetchTool(Tool):
 
     async def _fetch_readability(self, url: str, extract_mode: str, max_chars: int) -> Any:
         """Local fallback using readability-lxml."""
-        from readability import Document
-
         try:
             async with httpx.AsyncClient(
-                follow_redirects=True,
-                max_redirects=MAX_REDIRECTS,
                 timeout=30.0,
                 proxy=self.proxy,
             ) as client:
-                r = await client.get(url, headers={"User-Agent": self.user_agent})
+                r, redirect_error = await _get_with_safe_redirects(
+                    client,
+                    url,
+                    headers={"User-Agent": self.user_agent},
+                )
+                if redirect_error:
+                    return json.dumps({"error": redirect_error, "url": url}, ensure_ascii=False)
+                if r is None:
+                    return json.dumps({"error": "Fetch failed", "url": url}, ensure_ascii=False)
                 r.raise_for_status()
 
-            from nanobot.security.network import validate_resolved_url
-            redir_ok, redir_err = validate_resolved_url(str(r.url))
-            if not redir_ok:
-                return json.dumps({"error": f"Redirect blocked: {redir_err}", "url": url}, ensure_ascii=False)
-
             ctype = r.headers.get("content-type", "")
             if ctype.startswith("image/"):
                 return build_image_content_blocks(r.content, ctype, url, f"(Image fetched from: {url})")
@@ -573,6 +651,8 @@ class WebFetchTool(Tool):
             if "application/json" in ctype:
                 text, extractor = json.dumps(r.json(), indent=2, ensure_ascii=False), "json"
             elif "text/html" in ctype or r.text[:256].lower().startswith(("<!doctype", "<html")):
+                from readability import Document
+
                 doc = Document(r.text)
                 content = self._to_markdown(doc.summary()) if extract_mode == "markdown" else _strip_tags(doc.summary())
                 text = f"# {doc.title()}\n\n{content}" if doc.title() else content

nanobot/apps/__init__.py

@@ -0,0 +1,5 @@
+"""Shared app protocol helpers."""
+
+from nanobot.apps.protocol import APP_PROTOCOL_SCHEMA, app_manifest
+
+__all__ = ["APP_PROTOCOL_SCHEMA", "app_manifest"]

nanobot/apps/cli/__init__.py

@@ -0,0 +1,13 @@
+"""CLI app adapter for the unified Apps domain."""
+
+from nanobot.apps.cli.service import (
+    CliAppError,
+    CliAppManager,
+    CliAppsRuntimeConfig,
+)
+
+__all__ = [
+    "CliAppError",
+    "CliAppManager",
+    "CliAppsRuntimeConfig",
+]

nanobot/apps/cli/utils.py

@@ -0,0 +1,62 @@
+"""CLI Apps helpers shared by the agent loop and settings surfaces."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Mapping
+
+
+def session_extra(metadata: Mapping[str, Any] | None) -> dict[str, Any]:
+    """Return persisted session kwargs for CLI app attachments."""
+    cli_apps = metadata.get("cli_apps") if isinstance(metadata, Mapping) else None
+    return {"cli_apps": cli_apps} if isinstance(cli_apps, list) and cli_apps else {}
+
+
+def runtime_lines(message: Any, workspace: Path, *, skip: bool = False) -> list[str]:
+    """Return model-visible CLI app annotations for the current turn."""
+    if skip:
+        return []
+    text = message.content if isinstance(getattr(message, "content", None), str) else ""
+    metadata = message.metadata if isinstance(getattr(message, "metadata", None), Mapping) else None
+    return _cli_app_runtime_lines(text, metadata, workspace)
+
+
+def _cli_app_runtime_lines(
+    text: str,
+    metadata: Mapping[str, Any] | None,
+    workspace: Path,
+) -> list[str]:
+    structured = metadata.get("cli_apps") if isinstance(metadata, Mapping) else None
+    if isinstance(structured, list):
+        mentions = [
+            item for item in structured
+            if isinstance(item, Mapping) and isinstance(item.get("name"), str)
+        ]
+        if mentions:
+            return [
+                "CLI App Attachment: "
+                f"@{str(item['name']).strip().lower()} "
+                f"(installed; tool=run_cli_app; "
+                f"entry_point={str(item.get('entry_point') or 'unknown')}; "
+                f"skill=skills/cli-app-{str(item['name']).strip().lower()}/SKILL.md). "
+                "Read the skill when useful, then run this app with `run_cli_app`; do not bypass it with shell."
+                for item in mentions
+                if str(item.get("name") or "").strip()
+            ]
+    if "@" not in text:
+        return []
+    try:
+        from nanobot.apps.cli import CliAppManager
+
+        mentions = CliAppManager(workspace=workspace).mentioned_installed_apps(text)
+    except Exception:
+        return []
+    return [
+        "CLI App Mention: "
+        f"@{item['name']} "
+        f"(installed; tool={item['tool']}; "
+        f"entry_point={item['entry_point'] or 'unknown'}; "
+        f"skill={item['skill']}). "
+        "Read the skill when useful, then run this app with `run_cli_app`; do not bypass it with shell."
+        for item in mentions
+    ]

nanobot/apps/protocol.py

@@ -0,0 +1,56 @@
+"""Neutral manifest shape for settings-managed agent apps.
+
+The manifest is intentionally descriptive. Installers still live in their
+own adapters, while this protocol gives the WebUI and future registries one
+small vocabulary for capabilities, trust, and verified install/remove plans.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+APP_PROTOCOL_SCHEMA = "agent-app.v1"
+
+
+def compact_dict(values: dict[str, Any]) -> dict[str, Any]:
+    """Drop empty optional values while preserving explicit booleans and zeros."""
+    return {
+        key: value
+        for key, value in values.items()
+        if value is not None and value != "" and value != [] and value != {}
+    }
+
+
+def app_manifest(
+    *,
+    app_id: str,
+    display_name: str,
+    description: str,
+    category: str,
+    source: str,
+    capabilities: list[dict[str, Any]],
+    install: dict[str, Any],
+    remove: dict[str, Any],
+    trust: dict[str, Any],
+    version: str | None = None,
+    logo_url: str | None = None,
+    brand_color: str | None = None,
+    docs_url: str | None = None,
+) -> dict[str, Any]:
+    """Build a stable app manifest dictionary."""
+    return compact_dict({
+        "schema": APP_PROTOCOL_SCHEMA,
+        "id": app_id,
+        "display_name": display_name,
+        "version": version,
+        "description": description,
+        "category": category,
+        "source": source,
+        "logo_url": logo_url,
+        "brand_color": brand_color,
+        "docs_url": docs_url,
+        "capabilities": capabilities,
+        "install": install,
+        "remove": remove,
+        "trust": trust,
+    })

nanobot/bus/events.py

@@ -9,6 +9,12 @@ from typing import Any
 # render it and other channels may ignore unknown keys.
 OUTBOUND_META_AGENT_UI = "_agent_ui"
 
+# Internal-only inbound metadata used by in-process channels to ask the agent
+# loop to update runtime state without going through a user session.
+INBOUND_META_RUNTIME_CONTROL = "_runtime_control"
+RUNTIME_CONTROL_ACK = "_ack"
+RUNTIME_CONTROL_MCP_RELOAD = "mcp_reload"
+
 
 @dataclass
 class InboundMessage:
@@ -45,4 +51,3 @@ class OutboundMessage:
     media: list[str] = field(default_factory=list)
     metadata: dict[str, Any] = field(default_factory=dict)
     buttons: list[list[str]] = field(default_factory=list)
-

nanobot/channels/discord.py

@@ -207,6 +207,16 @@ if DISCORD_AVAILABLE:
                 ) -> None:
                     await self._forward_slash_command(interaction, _command_text)
 
+            @self.tree.command(name="model", description="Show or switch runtime model preset")
+            @app_commands.describe(preset="Optional model preset name, such as default")
+            async def model_command(
+                interaction: discord.Interaction,
+                preset: str | None = None,
+            ) -> None:
+                preset = (preset or "").strip()
+                command_text = f"/model {preset}" if preset else "/model"
+                await self._forward_slash_command(interaction, command_text)
+
             @self.tree.command(name="help", description="Show available commands")
             async def help_command(interaction: discord.Interaction) -> None:
                 sender_id = str(interaction.user.id)

nanobot/channels/manager.py

@@ -57,11 +57,17 @@ class ChannelManager:
         *,
         session_manager: "SessionManager | None" = None,
         webui_runtime_model_name: Callable[[], str | None] | None = None,
+        webui_static_dist: bool = True,
+        webui_runtime_surface: str = "browser",
+        webui_runtime_capabilities: dict[str, Any] | None = None,
     ):
         self.config = config
         self.bus = bus
         self._session_manager = session_manager
         self._webui_runtime_model_name = webui_runtime_model_name
+        self._webui_static_dist = webui_static_dist
+        self._webui_runtime_surface = webui_runtime_surface
+        self._webui_runtime_capabilities = dict(webui_runtime_capabilities or {})
         self.channels: dict[str, BaseChannel] = {}
         self._dispatch_task: asyncio.Task | None = None
         self._origin_reply_fingerprints: dict[tuple[str, str, str], str] = {}
@@ -70,36 +76,52 @@ class ChannelManager:
 
     def _init_channels(self) -> None:
         """Initialize channels discovered via pkgutil scan + entry_points plugins."""
-        from nanobot.channels.registry import discover_all
+        from nanobot.channels.registry import discover_channel_names, discover_enabled
 
         transcription_provider = self.config.channels.transcription_provider
         transcription_key = self._resolve_transcription_key(transcription_provider)
         transcription_base = self._resolve_transcription_base(transcription_provider)
         transcription_language = self.config.channels.transcription_language
 
-        for name, cls in discover_all().items():
+        # Collect enabled module names first, then only import those.
+        # Channel configs live in ChannelsConfig's extra fields (via
+        # extra="allow"), so we enumerate candidates from pkgutil scan
+        # (cheap, no imports) and any plugin keys in __pydantic_extra__.
+        names = discover_channel_names()
+        candidate_names = set(names)
+        extra = getattr(self.config.channels, "__pydantic_extra__", None) or {}
+        candidate_names.update(extra.keys())
+
+        enabled_names: set[str] = set()
+        for name in candidate_names:
             section = getattr(self.config.channels, name, None)
             if section is None:
                 continue
-            enabled = (
+            if (
                 section.get("enabled", False)
                 if isinstance(section, dict)
                 else getattr(section, "enabled", False)
-            )
+            ):
-            if not enabled:
+                enabled_names.add(name)
+
+        for name, cls in discover_enabled(enabled_names, _names=names).items():
+            section = getattr(self.config.channels, name, None)
+            if section is None:
                 continue
             try:
                 kwargs: dict[str, Any] = {}
-                # Only the WebSocket channel currently hosts the embedded webui
-                # surface; other channels stay oblivious to these knobs.
                 if cls.name == "websocket":
                     if self._session_manager is not None:
                         kwargs["session_manager"] = self._session_manager
-                        static_path = _default_webui_dist()
+                        static_path = _default_webui_dist() if self._webui_static_dist else None
                         if static_path is not None:
                             kwargs["static_dist_path"] = static_path
+                    kwargs["workspace_path"] = self.config.workspace_path
+                    kwargs["restrict_to_workspace"] = self.config.tools.restrict_to_workspace
                     if self._webui_runtime_model_name is not None:
                         kwargs["runtime_model_name"] = self._webui_runtime_model_name
+                    kwargs["runtime_surface"] = self._webui_runtime_surface
+                    kwargs["runtime_capabilities_overrides"] = self._webui_runtime_capabilities
                 channel = cls(section, self.bus, **kwargs)
                 channel.transcription_provider = transcription_provider
                 channel.transcription_api_key = transcription_key

nanobot/channels/matrix.py

@@ -8,21 +8,28 @@ from contextlib import suppress
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Literal, TypeAlias
+from urllib.parse import quote, urlparse
 
 from pydantic import Field
 
+from nanobot.security.workspace_policy import is_path_within
+
 try:
+    import aiohttp
     import nh3
     from mistune import create_markdown
     from nio import (
         AsyncClient,
         AsyncClientConfig,
-        DownloadError,
         InviteEvent,
         JoinError,
+        KeyVerificationCancel,
+        KeyVerificationEvent,
+        KeyVerificationKey,
+        KeyVerificationMac,
+        KeyVerificationStart,
         LoginResponse,
         MatrixRoom,
-        MemoryDownloadResponse,
         RoomEncryptedMedia,
         RoomMessage,
         RoomMessageMedia,
@@ -31,6 +38,7 @@ try:
         RoomSendResponse,
         RoomTypingError,
         SyncError,
+        ToDeviceError,
         UploadError,
     )
     from nio.crypto.attachments import decrypt_attachment
@@ -62,6 +70,10 @@ _MSGTYPE_MAP = {"m.image": "image", "m.audio": "audio", "m.video": "video", "m.f
 MATRIX_MEDIA_EVENT_FILTER = (RoomMessageMedia, RoomEncryptedMedia)
 MatrixMediaEvent: TypeAlias = RoomMessageMedia | RoomEncryptedMedia
 
+
+class _MediaTooLargeError(Exception):
+    """Raised when an inbound Matrix media download exceeds the configured cap."""
+
 MATRIX_MARKDOWN = create_markdown(
     escape=True,
     plugins=["table", "strikethrough", "url", "superscript", "subscript"],
@@ -188,8 +200,10 @@ class MatrixConfig(Base):
     access_token: str = ""
     device_id: str = ""
     e2ee_enabled: bool = Field(default=True, alias="e2eeEnabled")
+    sas_verification: bool = Field(default=False, alias="sasVerification")
     sync_stop_grace_seconds: int = 2
     max_media_bytes: int = 20 * 1024 * 1024
+    max_concurrent_media_downloads: int = 2
     allow_from: list[str] = Field(default_factory=list)
     group_policy: Literal["open", "mention", "allowlist"] = "open"
     group_allow_from: list[str] = Field(default_factory=list)
@@ -231,6 +245,9 @@ class MatrixChannel(BaseChannel):
         self._server_upload_limit_checked = False
         self._stream_bufs: dict[str, _StreamBuf] = {}
         self._started_at_ms: int = 0
+        self._media_download_semaphore = asyncio.Semaphore(
+            max(1, int(self.config.max_concurrent_media_downloads))
+        )
 
 
     async def start(self) -> None:
@@ -258,6 +275,7 @@ class MatrixChannel(BaseChannel):
         )
 
         self._register_event_callbacks()
+        self._register_to_device_callbacks()
         self._register_response_callbacks()
 
         if not self.config.e2ee_enabled:
@@ -344,11 +362,7 @@ class MatrixChannel(BaseChannel):
         """Check path is inside workspace (when restriction enabled)."""
         if not self._restrict_to_workspace or not self._workspace:
             return True
-        try:
+        return is_path_within(path, self._workspace)
-            path.resolve(strict=False).relative_to(self._workspace)
-            return True
-        except ValueError:
-            return False
 
     def _collect_outbound_media_candidates(self, media: list[str]) -> list[Path]:
         """Deduplicate and resolve outbound attachment paths."""
@@ -566,11 +580,77 @@ class MatrixChannel(BaseChannel):
         self.client.add_event_callback(self._on_media_message, MATRIX_MEDIA_EVENT_FILTER)
         self.client.add_event_callback(self._on_room_invite, InviteEvent)
 
+    def _register_to_device_callbacks(self) -> None:
+        if self.config.e2ee_enabled and self.config.sas_verification:
+            self.client.add_to_device_callback(
+                self._on_key_verification_event,
+                (KeyVerificationEvent,),
+            )
+
     def _register_response_callbacks(self) -> None:
         self.client.add_response_callback(self._on_sync_error, SyncError)
         self.client.add_response_callback(self._on_join_error, JoinError)
         self.client.add_response_callback(self._on_send_error, RoomSendError)
 
+    def _is_sas_sender_allowed(self, sender: str) -> bool:
+        return bool(sender and self.is_allowed(sender))
+
+    async def _on_key_verification_event(self, event: KeyVerificationEvent) -> None:
+        try:
+            await self._handle_key_verification_event(event)
+        except asyncio.CancelledError:
+            raise
+        except Exception:
+            self.logger.exception("Matrix SAS verification handling failed")
+
+    async def _handle_key_verification_event(self, event: KeyVerificationEvent) -> None:
+        if not (self.config.e2ee_enabled and self.config.sas_verification):
+            return
+        if not self.client:
+            return
+
+        sender = str(getattr(event, "sender", "") or "")
+        transaction_id = str(getattr(event, "transaction_id", "") or "")
+        if not transaction_id or not self._is_sas_sender_allowed(sender):
+            return
+
+        if isinstance(event, KeyVerificationStart):
+            if "emoji" not in (getattr(event, "short_authentication_string", None) or []):
+                self.logger.info(
+                    "Ignoring Matrix SAS verification from {} without emoji support",
+                    sender,
+                )
+                return
+
+            response = await self.client.accept_key_verification(transaction_id)
+            if isinstance(response, ToDeviceError):
+                self.logger.warning("Matrix SAS accept failed for {}: {}", sender, response)
+            return
+
+        if isinstance(event, KeyVerificationKey):
+            responses = await self.client.send_to_device_messages()
+            if any(isinstance(response, ToDeviceError) for response in responses):
+                self.logger.warning("Matrix SAS key share failed for {}", sender)
+                return
+
+            response = await self.client.confirm_short_auth_string(transaction_id)
+            if isinstance(response, ToDeviceError):
+                self.logger.warning("Matrix SAS confirm failed for {}: {}", sender, response)
+            return
+
+        if isinstance(event, KeyVerificationMac):
+            sas = getattr(self.client, "key_verifications", {}).get(transaction_id)
+            if sas is not None and getattr(sas, "verified", False):
+                self.logger.info("Matrix SAS verification completed for {}", sender)
+            return
+
+        if isinstance(event, KeyVerificationCancel):
+            self.logger.info(
+                "Matrix SAS verification cancelled by {}: {}",
+                sender,
+                getattr(event, "reason", ""),
+            )
+
     def _is_fatal_auth_response(self, response: Any) -> bool:
         code = getattr(response, "status_code", None)
         is_auth = code in {"M_UNKNOWN_TOKEN", "M_FORBIDDEN", "M_UNAUTHORIZED"}
@@ -743,7 +823,7 @@ class MatrixChannel(BaseChannel):
     def _event_declared_size_bytes(self, event: MatrixMediaEvent) -> int | None:
         info = self._event_source_content(event).get("info")
         size = info.get("size") if isinstance(info, dict) else None
-        return size if isinstance(size, int) and size >= 0 else None
+        return size if type(size) is int and size >= 0 else None
 
     def _event_mime(self, event: MatrixMediaEvent) -> str | None:
         info = self._event_source_content(event).get("info")
@@ -772,26 +852,48 @@ class MatrixChannel(BaseChannel):
         event_prefix = (event_id[:24] or "evt").strip("_")
         return self._media_dir() / f"{event_prefix}_{stem}{suffix}"
 
-    async def _download_media_bytes(self, mxc_url: str) -> bytes | None:
+    async def _download_media_bytes(self, mxc_url: str, limit_bytes: int) -> bytes | None:
-        if not self.client:
+        if not self.client or limit_bytes <= 0:
+            raise _MediaTooLargeError
+
+        parsed = urlparse(mxc_url)
+        if parsed.scheme != "mxc" or not parsed.netloc or not parsed.path.strip("/"):
             return None
-        response = await self.client.download(mxc=mxc_url)
+
-        if isinstance(response, DownloadError):
+        homeserver = str(getattr(self.client, "homeserver", "") or self.config.homeserver).rstrip("/")
-            self.logger.warning("download failed for {}: {}", mxc_url, response)
+        media_url = (
+            f"{homeserver}/_matrix/client/v1/media/download/"
+            f"{quote(parsed.netloc, safe='')}/{quote(parsed.path.strip('/'), safe='')}"
+        )
+        token = getattr(self.client, "access_token", None) or self.config.access_token
+        headers = {"Authorization": f"Bearer {token}"} if token else None
+        timeout = aiohttp.ClientTimeout(total=None)
+
+        try:
+            async with aiohttp.ClientSession(timeout=timeout, headers=headers) as session:
+                async with session.get(media_url, params={"allow_remote": "true"}) as response:
+                    if response.status >= 400:
+                        self.logger.warning("download failed for {}: HTTP {}", mxc_url, response.status)
+                        return None
+                    content_length = response.headers.get("Content-Length")
+                    if content_length is not None:
+                        try:
+                            if int(content_length) > limit_bytes:
+                                raise _MediaTooLargeError
+                        except ValueError:
+                            pass
+
+                    chunks = bytearray()
+                    async for chunk in response.content.iter_chunked(64 * 1024):
+                        chunks.extend(chunk)
+                        if len(chunks) > limit_bytes:
+                            raise _MediaTooLargeError
+                    return bytes(chunks)
+        except _MediaTooLargeError:
+            raise
+        except (aiohttp.ClientError, asyncio.TimeoutError, OSError):
+            self.logger.warning("download failed for {}", mxc_url, exc_info=True)
             return None
-        body = getattr(response, "body", None)
-        if isinstance(body, (bytes, bytearray)):
-            return bytes(body)
-        if isinstance(response, MemoryDownloadResponse):
-            return bytes(response.body)
-        if isinstance(body, (str, Path)):
-            path = Path(body)
-            if path.is_file():
-                try:
-                    return path.read_bytes()
-                except OSError:
-                    return None
-        return None
 
     def _decrypt_media_bytes(self, event: MatrixMediaEvent, ciphertext: bytes) -> bytes | None:
         key_obj, hashes, iv = getattr(event, "key", None), getattr(event, "hashes", None), getattr(event, "iv", None)
@@ -820,10 +922,14 @@ class MatrixChannel(BaseChannel):
 
         limit_bytes = await self._effective_media_limit_bytes()
         declared = self._event_declared_size_bytes(event)
-        if declared is not None and declared > limit_bytes:
+        if declared is None or declared > limit_bytes:
             return None, _ATTACH_TOO_LARGE.format(filename)
 
-        downloaded = await self._download_media_bytes(mxc_url)
+        try:
+            async with self._media_download_semaphore:
+                downloaded = await self._download_media_bytes(mxc_url, limit_bytes)
+        except _MediaTooLargeError:
+            return None, _ATTACH_TOO_LARGE.format(filename)
         if downloaded is None:
             return None, fail
 

nanobot/channels/msteams.py

@@ -53,6 +53,13 @@ if MSTEAMS_AVAILABLE:
 
 MSTEAMS_REF_TTL_DAYS = 30
 MSTEAMS_WEBCHAT_HOST = "webchat.botframework.com"
+MSTEAMS_DEFAULT_TRUSTED_SERVICE_URL_HOSTS = [
+    "smba.trafficmanager.net",
+    "smba.infra.gcc.teams.microsoft.com",
+    "smba.infra.gov.teams.microsoft.us",
+    "smba.infra.dod.teams.microsoft.us",
+    "*.botframework.com",
+]
 MSTEAMS_REF_META_FILENAME = "msteams_conversations_meta.json"
 MSTEAMS_REF_LOCK_FILENAME = "msteams_conversations.lock"
 MSTEAMS_REF_TOUCH_INTERVAL_S = 300
@@ -76,6 +83,9 @@ class MSTeamsConfig(Base):
     prune_web_chat_refs: bool = True
     prune_non_personal_refs: bool = True
     ref_touch_interval_s: int = Field(default=MSTEAMS_REF_TOUCH_INTERVAL_S, ge=0)
+    trusted_service_url_hosts: list[str] = Field(
+        default_factory=lambda: MSTEAMS_DEFAULT_TRUSTED_SERVICE_URL_HOSTS.copy()
+    )
 
 
 @dataclass
@@ -242,6 +252,11 @@ class MSTeamsChannel(BaseChannel):
         if not ref:
             raise RuntimeError(f"MSTeams conversation ref not found for chat_id={msg.chat_id}")
 
+        if not self._is_trusted_service_url(ref.service_url):
+            raise RuntimeError(
+                f"MSTeams conversation ref has untrusted service_url for chat_id={msg.chat_id}"
+            )
+
         token = await self._get_access_token()
         base_url = f"{ref.service_url.rstrip('/')}/v3/conversations/{ref.conversation_id}/activities"
         use_thread_reply = self.config.reply_in_thread and bool(ref.activity_id)
@@ -284,6 +299,13 @@ class MSTeamsChannel(BaseChannel):
         if not sender_id or not conversation_id or not service_url:
             return
 
+        if not self._is_trusted_service_url(service_url):
+            self.logger.warning(
+                "Ignoring MSTeams activity with untrusted serviceUrl host: {}",
+                service_url,
+            )
+            return
+
         if recipient.get("id") and from_user.get("id") == recipient.get("id"):
             return
 
@@ -626,6 +648,29 @@ class MSTeamsChannel(BaseChannel):
             return host == MSTEAMS_WEBCHAT_HOST or host.endswith(f".{MSTEAMS_WEBCHAT_HOST}")
         return MSTEAMS_WEBCHAT_HOST in normalized.lower()
 
+    def _is_trusted_service_url(self, service_url: str) -> bool:
+        """Return True for HTTPS Bot Framework service URLs trusted for bearer replies."""
+        parsed = urlparse(service_url.strip())
+        if parsed.scheme.lower() != "https":
+            return False
+
+        host = (parsed.hostname or "").strip().lower().rstrip(".")
+        if not host:
+            return False
+
+        for pattern in self.config.trusted_service_url_hosts:
+            trusted_host = str(pattern or "").strip().lower().rstrip(".")
+            if not trusted_host:
+                continue
+            if trusted_host.startswith("*."):
+                suffix = trusted_host[1:]
+                if host.endswith(suffix) and host != suffix.lstrip("."):
+                    return True
+                continue
+            if host == trusted_host:
+                return True
+        return False
+
     def _prune_conversation_refs(self, *, now: float | None = None) -> bool:
         """Remove stale and unsupported conversation refs from memory."""
         if not self._conversation_refs:
@@ -637,6 +682,10 @@ class MSTeamsChannel(BaseChannel):
         keys_to_drop: list[str] = []
 
         for key, ref in self._conversation_refs.items():
+            if not self._is_trusted_service_url(ref.service_url):
+                keys_to_drop.append(key)
+                continue
+
             if self.config.prune_web_chat_refs and self._is_webchat_service_url(ref.service_url):
                 keys_to_drop.append(key)
                 continue

nanobot/channels/registry.py

@@ -1,5 +1,4 @@
 """Auto-discovery for built-in channel modules and external plugins."""
-
 from __future__ import annotations
 
 import importlib
@@ -37,12 +36,14 @@ def load_channel_class(module_name: str) -> type[BaseChannel]:
     raise ImportError(f"No BaseChannel subclass in nanobot.channels.{module_name}")
 
 
-def discover_plugins() -> dict[str, type[BaseChannel]]:
+def discover_plugins(enabled_names: set[str] | None = None) -> dict[str, type[BaseChannel]]:
     """Discover external channel plugins registered via entry_points."""
     from importlib.metadata import entry_points
 
     plugins: dict[str, type[BaseChannel]] = {}
     for ep in entry_points(group="nanobot.channels"):
+        if enabled_names is not None and ep.name not in enabled_names:
+            continue
         try:
             cls = ep.load()
             plugins[ep.name] = cls
@@ -51,21 +52,44 @@ def discover_plugins() -> dict[str, type[BaseChannel]]:
     return plugins
 
 
+def discover_enabled(
+    enabled_names: set[str],
+    *,
+    _names: list[str] | None = None,
+    _include_all_external: bool = False,
+) -> dict[str, type[BaseChannel]]:
+    """Return channels whose module names are in *enabled_names*.
+
+    Uses cheap ``pkgutil.iter_modules`` to list names, then imports only
+    those that match — skipping the heavy third-party SDK imports of
+    unneeded channels.
+    """
+    names = _names if _names is not None else discover_channel_names()
+    result: dict[str, type[BaseChannel]] = {}
+    for modname in names:
+        if modname not in enabled_names:
+            continue
+        try:
+            result[modname] = load_channel_class(modname)
+        except ImportError as e:
+            logger.debug("Skipping built-in channel '{}': {}", modname, e)
+
+    external = discover_plugins(None if _include_all_external else enabled_names)
+    shadowed = set(external) & set(result)
+    if shadowed:
+        logger.warning("Plugin(s) shadowed by built-in channels (ignored): {}", shadowed)
+    if _include_all_external:
+        result.update({k: v for k, v in external.items() if k not in shadowed})
+    else:
+        result.update({k: v for k, v in external.items() if k not in shadowed and k in enabled_names})
+
+    return result
+
+
 def discover_all() -> dict[str, type[BaseChannel]]:
     """Return all channels: built-in (pkgutil) merged with external (entry_points).
 
     Built-in channels take priority — an external plugin cannot shadow a built-in name.
     """
-    builtin: dict[str, type[BaseChannel]] = {}
+    names = discover_channel_names()
-    for modname in discover_channel_names():
+    return discover_enabled(set(names), _names=names, _include_all_external=True)
-        try:
-            builtin[modname] = load_channel_class(modname)
-        except ImportError as e:
-            logger.debug("Skipping built-in channel '{}': {}", modname, e)
-
-    external = discover_plugins()
-    shadowed = set(external) & set(builtin)
-    if shadowed:
-        logger.warning("Plugin(s) shadowed by built-in channels (ignored): {}", shadowed)
-
-    return {**external, **builtin}

nanobot/channels/telegram.py

@@ -10,8 +10,9 @@ from contextlib import suppress
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Literal
+from urllib.parse import urlparse
 
-from pydantic import Field
+from pydantic import Field, field_validator, model_validator
 from telegram import (
     BotCommand,
     InlineKeyboardButton,
@@ -225,11 +226,22 @@ class _StreamBuf:
     stream_id: str | None = None
 
 
+@dataclass
+class _QueuedTelegramUpdate:
+    """Telegram update staged for per-session ordered processing."""
+
+    kind: Literal["command", "message"]
+    update: Update
+    context: Any
+    sort_key: tuple[int, int]
+
+
 class TelegramConfig(Base):
     """Telegram channel configuration."""
 
     enabled: bool = False
     token: str = ""
+    mode: Literal["polling", "webhook"] = "polling"
     allow_from: list[str] = Field(default_factory=list)
     proxy: str | None = None
     reply_to_message: bool = False
@@ -241,13 +253,48 @@ class TelegramConfig(Base):
     # Enable inline keyboard buttons in Telegram messages.
     inline_keyboards: bool = False
     stream_edit_interval: float = Field(default=_STREAM_EDIT_INTERVAL_DEFAULT, ge=0.1)
+    webhook_url: str = ""
+    webhook_listen_host: str = "127.0.0.1"
+    webhook_listen_port: int = Field(default=8081, ge=1, le=65535)
+    webhook_path: str = "/telegram"
+    webhook_secret_token: str = ""
+    webhook_max_connections: int = Field(default=4, ge=1, le=100)
+
+    @field_validator("webhook_path")
+    @classmethod
+    def webhook_path_must_start_with_slash(cls, value: str) -> str:
+        value = value.strip() or "/telegram"
+        if not value.startswith("/"):
+            raise ValueError('webhook_path must start with "/"')
+        return value
+
+    @model_validator(mode="after")
+    def validate_webhook_config(self) -> "TelegramConfig":
+        if self.mode != "webhook":
+            return self
+
+        url = self.webhook_url.strip()
+        if not url:
+            raise ValueError("webhook_url is required when Telegram mode is webhook")
+        parsed = urlparse(url)
+        if parsed.scheme != "https" or not parsed.netloc:
+            raise ValueError("webhook_url must be a public HTTPS URL")
+        secret = self.webhook_secret_token.strip()
+        if not secret:
+            raise ValueError("webhook_secret_token is required when Telegram mode is webhook")
+        if len(secret) > 256 or re.match(r"^[A-Za-z0-9_-]+$", secret) is None:
+            raise ValueError(
+                "webhook_secret_token must be 1-256 characters using only A-Z, a-z, 0-9, _ and -"
+            )
+        return self
 
 
 class TelegramChannel(BaseChannel):
     """
-    Telegram channel using long polling.
+    Telegram channel using long polling or webhook mode.
 
-    Simple and reliable - no webhook/public IP needed.
+    Long polling is the default. Webhook mode requires a public HTTPS URL and a
+    Telegram secret token.
     """
 
     name = "telegram"
@@ -294,6 +341,8 @@ class TelegramChannel(BaseChannel):
         self._bot_user_id: int | None = None
         self._bot_username: str | None = None
         self._stream_bufs: dict[str, _StreamBuf] = {}  # chat_id -> streaming state
+        self._inbound_buffers: dict[str, list[_QueuedTelegramUpdate]] = {}
+        self._inbound_workers: dict[str, asyncio.Task] = {}
 
     def is_allowed(self, sender_id: str) -> bool:
         """Preserve Telegram's legacy id|username allowlist matching."""
@@ -326,7 +375,7 @@ class TelegramChannel(BaseChannel):
         return content
 
     async def start(self) -> None:
-        """Start the Telegram bot with long polling."""
+        """Start the Telegram bot."""
         if not self.config.token:
             self.logger.error("bot token not configured")
             return
@@ -394,9 +443,12 @@ class TelegramChannel(BaseChannel):
         else:
             allowed_updates = ["message"]
 
-        self.logger.info("Starting bot (polling mode)...")
+        if self.config.mode == "webhook":
+            self.logger.info("Starting bot (webhook mode)...")
+        else:
+            self.logger.info("Starting bot (polling mode)...")
 
-        # Initialize and start polling
+        # Initialize and start receiving updates
         await self._app.initialize()
         await self._app.start()
 
@@ -412,12 +464,26 @@ class TelegramChannel(BaseChannel):
         except Exception as e:
             self.logger.warning("Failed to register bot commands: {}", e)
 
-        # Start polling (this runs until stopped)
+        if self.config.mode == "webhook":
-        await self._app.updater.start_polling(
+            # ``url_path`` is the local HTTP route. ``webhook_url`` is the
-            allowed_updates=allowed_updates,
+            # public HTTPS URL Telegram calls; reverse proxies may rewrite it.
-            drop_pending_updates=False,  # Process pending messages on startup
+            await self._app.updater.start_webhook(
-            error_callback=self._on_polling_error,
+                listen=self.config.webhook_listen_host,
-        )
+                port=self.config.webhook_listen_port,
+                url_path=self.config.webhook_path.lstrip("/"),
+                webhook_url=self.config.webhook_url.strip(),
+                allowed_updates=allowed_updates,
+                drop_pending_updates=False,
+                secret_token=self.config.webhook_secret_token.strip(),
+                max_connections=self.config.webhook_max_connections,
+            )
+        else:
+            # Start polling (this runs until stopped)
+            await self._app.updater.start_polling(
+                allowed_updates=allowed_updates,
+                drop_pending_updates=False,  # Process pending messages on startup
+                error_callback=self._on_polling_error,
+            )
 
         # Keep running until stopped
         while self._running:
@@ -436,6 +502,11 @@ class TelegramChannel(BaseChannel):
         self._media_group_tasks.clear()
         self._media_group_buffers.clear()
 
+        for task in self._inbound_workers.values():
+            task.cancel()
+        self._inbound_workers.clear()
+        self._inbound_buffers.clear()
+
         if self._app:
             self.logger.info("Stopping bot...")
             await self._app.updater.stop()
@@ -995,10 +1066,85 @@ class TelegramChannel(BaseChannel):
         if len(self._message_threads) > 1000:
             self._message_threads.pop(next(iter(self._message_threads)))
 
+    @staticmethod
+    def _queue_key_for_message(message) -> str:
+        """Return the final nanobot session key used for ordered Telegram ingress."""
+        return TelegramChannel._derive_topic_session_key(message) or f"telegram:{message.chat_id}"
+
+    @staticmethod
+    def _sort_key_for_update(update: Update) -> tuple[int, int]:
+        """Sort by chat message id first, then Telegram update id."""
+        message = getattr(update, "message", None)
+        message_id = int(getattr(message, "message_id", 0) or 0)
+        update_id = int(getattr(update, "update_id", 0) or 0)
+        return (message_id, update_id)
+
+    def _enqueue_ordered_update(
+        self,
+        *,
+        kind: Literal["command", "message"],
+        update: Update,
+        context: ContextTypes.DEFAULT_TYPE,
+    ) -> None:
+        """Stage a Telegram update behind a short per-session reorder window."""
+        message = update.message
+        key = self._queue_key_for_message(message)
+        self._inbound_buffers.setdefault(key, []).append(
+            _QueuedTelegramUpdate(
+                kind=kind,
+                update=update,
+                context=context,
+                sort_key=self._sort_key_for_update(update),
+            )
+        )
+        if key not in self._inbound_workers:
+            self._inbound_workers[key] = asyncio.create_task(
+                self._drain_ordered_updates(key)
+            )
+
+    async def _drain_ordered_updates(self, key: str) -> None:
+        """Drain one Telegram session buffer in stable message order."""
+        try:
+            while self._running:
+                await asyncio.sleep(0.2)
+                batch = self._inbound_buffers.get(key, [])
+                if not batch:
+                    break
+                self._inbound_buffers[key] = []
+                batch.sort(key=lambda item: item.sort_key)
+                for item in batch:
+                    try:
+                        if item.kind == "command":
+                            await self._process_forward_command(item.update, item.context)
+                        else:
+                            await self._process_message_update(item.update, item.context)
+                    except Exception as e:
+                        self.logger.warning(
+                            "Telegram queued update handling failed for {}: {}",
+                            key,
+                            e,
+                        )
+            if not self._inbound_buffers.get(key):
+                self._inbound_buffers.pop(key, None)
+        except asyncio.CancelledError:
+            raise
+        except Exception as e:
+            self.logger.warning("Telegram ordered update worker failed for {}: {}", key, e)
+        finally:
+            if not self._inbound_buffers.get(key):
+                self._inbound_workers.pop(key, None)
+
     async def _forward_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
         """Forward slash commands to the bus for unified handling in AgentLoop."""
         if not update.message or not update.effective_user:
             return
+        if not self._running:
+            await self._process_forward_command(update, context)
+            return
+        self._enqueue_ordered_update(kind="command", update=update, context=context)
+
+    async def _process_forward_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+        """Process a queued slash command."""
         message = update.message
         user = update.effective_user
         sender_id = self._sender_id(user)
@@ -1027,6 +1173,13 @@ class TelegramChannel(BaseChannel):
         """Handle incoming messages (text, photos, voice, documents)."""
         if not update.message or not update.effective_user:
             return
+        if not self._running:
+            await self._process_message_update(update, context)
+            return
+        self._enqueue_ordered_update(kind="message", update=update, context=context)
+
+    async def _process_message_update(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+        """Process a queued Telegram message update."""
 
         message = update.message
         user = update.effective_user

nanobot/channels/weixin.py

@@ -79,6 +79,12 @@ BASE_INFO: dict[str, str] = {"channel_version": WEIXIN_CHANNEL_VERSION}
 ERRCODE_SESSION_EXPIRED = -14
 SESSION_PAUSE_DURATION_S = 60 * 60
 
+# iLink context_token is observed to expire server-side after ~90-160s of
+# agent inactivity (openclaw/openclaw#61174). Proactively refresh before
+# sending if the cached token is older than this threshold.
+CONTEXT_TOKEN_MAX_AGE_S = 60
+
+
 # Retry constants (matching the reference plugin's monitor.ts)
 MAX_CONSECUTIVE_FAILURES = 3
 BACKOFF_DELAY_S = 30
@@ -159,6 +165,8 @@ class WeixinChannel(BaseChannel):
         self._session_pause_until: float = 0.0
         self._typing_tasks: dict[str, asyncio.Task] = {}
         self._typing_tickets: dict[str, dict[str, Any]] = {}
+        self._context_token_at: dict[str, float] = {}
+        self._pending_tool_hints: dict[str, list[str]] = {}
 
     # ------------------------------------------------------------------
     # State persistence
@@ -486,6 +494,7 @@ class WeixinChannel(BaseChannel):
             except Exception:
                 if not self._running:
                     break
+                self.logger.exception("WeChat poll loop error")
                 consecutive_failures += 1
                 if consecutive_failures >= MAX_CONSECUTIVE_FAILURES:
                     consecutive_failures = 0
@@ -495,6 +504,7 @@ class WeixinChannel(BaseChannel):
 
     async def stop(self) -> None:
         self._running = False
+        self._pending_tool_hints.clear()
         if self._poll_task and not self._poll_task.done():
             self._poll_task.cancel()
         for chat_id in list(self._typing_tasks):
@@ -545,6 +555,7 @@ class WeixinChannel(BaseChannel):
         # Check for API-level errors (monitor.ts checks both ret and errcode)
         ret = data.get("ret", 0)
         errcode = data.get("errcode", 0)
+
         is_error = (ret is not None and ret != 0) or (errcode is not None and errcode != 0)
 
         if is_error:
@@ -575,8 +586,10 @@ class WeixinChannel(BaseChannel):
         # Process messages (WeixinMessage[] from types.ts)
         msgs: list[dict] = data.get("msgs", []) or []
         for msg in msgs:
-            with suppress(Exception):
+            try:
                 await self._process_message(msg)
+            except Exception:
+                self.logger.exception("Failed to process WeChat message")
 
     # ------------------------------------------------------------------
     # Inbound message processing  (matches inbound.ts + process-message.ts)
@@ -610,6 +623,7 @@ class WeixinChannel(BaseChannel):
         ctx_token = msg.get("context_token", "")
         if ctx_token:
             self._context_tokens[from_user_id] = ctx_token
+            self._context_token_at[from_user_id] = time.time()
             self._save_state()
 
         # Parse item_list (WeixinMessage.item_list — types.ts:161)
@@ -915,6 +929,99 @@ class WeixinChannel(BaseChannel):
         }
         return ""
 
+    async def _refresh_context_token_if_stale(
+        self, chat_id: str, context_token: str
+    ) -> str:
+        """Return a fresh context_token if the cached one is too old.
+
+        iLink context_token expires server-side after a short idle period
+        (empirically ~90s). Proactively refreshing before sending prevents
+        silent message loss on long agent turns or cron pushes.
+        """
+        if not context_token:
+            return context_token
+
+        now = time.time()
+        cached_at = self._context_token_at.get(chat_id, 0)
+        age = now - cached_at
+
+        if age < CONTEXT_TOKEN_MAX_AGE_S:
+            return context_token
+
+        self.logger.debug(
+            "WeChat context_token for {} is {:.0f}s old; refreshing via getconfig",
+            chat_id,
+            age,
+        )
+
+        body: dict[str, Any] = {
+            "ilink_user_id": chat_id,
+            "context_token": context_token,
+            "base_info": BASE_INFO,
+        }
+        try:
+            data = await self._api_post("ilink/bot/getconfig", body)
+        except Exception as e:
+            self.logger.warning("WeChat getconfig failed for {}: {}", chat_id, e)
+            return context_token
+
+        if data.get("ret", 0) != 0:
+            self.logger.warning(
+                "WeChat getconfig returned ret={} for {}: {}",
+                data.get("ret"),
+                chat_id,
+                data.get("errmsg", ""),
+            )
+            return context_token
+
+        new_token = str(data.get("context_token", "") or "")
+        if new_token and new_token != context_token:
+            self.logger.info(
+                "WeChat context_token refreshed for {} (age {:.0f}s -> fresh)",
+                chat_id,
+                age,
+            )
+            self._context_tokens[chat_id] = new_token
+            self._context_token_at[chat_id] = now
+            self._save_state()
+            return new_token
+
+        return context_token
+
+    async def _flush_tool_hints(self, chat_id: str) -> None:
+        """Send any buffered tool hints for *chat_id* as a single message.
+
+        Tool hints are coalesced to reduce message count and avoid hitting the
+        WeChat iLink rate limit (~7 msgs / 5 min).  Failures are logged but
+        not raised so that the main message send is never blocked.
+        """
+        hints = self._pending_tool_hints.pop(chat_id, None)
+        if not hints:
+            return
+
+        self.logger.info(
+            "Flushing {} buffered tool hint(s) for {}",
+            len(hints),
+            chat_id,
+        )
+
+        ctx_token = self._context_tokens.get(chat_id, "")
+        ctx_token = await self._refresh_context_token_if_stale(chat_id, ctx_token)
+        if not ctx_token:
+            self.logger.warning(
+                "Dropped {} buffered tool hint(s) for {}: no context_token",
+                len(hints),
+                chat_id,
+            )
+            return
+
+        try:
+            await self._send_text(chat_id, "\n\n".join(hints), ctx_token)
+        except Exception:
+            self.logger.exception(
+                "Failed to flush buffered tool hints for {}", chat_id
+            )
+
     async def _send_typing(self, user_id: str, typing_ticket: str, status: int) -> None:
         """Best-effort sendtyping wrapper."""
         if not typing_ticket:
@@ -944,11 +1051,47 @@ class WeixinChannel(BaseChannel):
         self._assert_session_active()
 
         is_progress = bool((msg.metadata or {}).get("_progress", False))
+
+        # Buffer tool hints to coalesce consecutive ones and avoid burning
+        # WeChat iLink rate-limit quota (~7 msgs / 5 min).
+        if is_progress and (msg.metadata or {}).get("_tool_hint"):
+            if not self.send_tool_hints:
+                return
+            self._pending_tool_hints.setdefault(msg.chat_id, []).append(msg.content)
+            self.logger.debug(
+                "Buffered tool hint for {} (count={})",
+                msg.chat_id,
+                len(self._pending_tool_hints[msg.chat_id]),
+            )
+            return
+
+        # Reasoning deltas are invisible in WeChat (there is no reasoning
+        # UI).  Skip them entirely — do not send and do not flush buffer.
+        if is_progress and (msg.metadata or {}).get("_reasoning_delta"):
+            self.logger.debug(
+                "Dropped invisible reasoning delta for {}", msg.chat_id
+            )
+            return
+
+        content = msg.content.strip()
+
+        # Empty progress messages (e.g. after_iteration tool_events) must
+        # NOT act as separators — they have no visible content.
+        if is_progress and not content and not (msg.media or []):
+            self.logger.debug(
+                "Skipped empty progress message for {} (no visible content)",
+                msg.chat_id,
+            )
+            return
+
+        # Flush buffered hints before sending any visible message.
+        await self._flush_tool_hints(msg.chat_id)
+
         if not is_progress:
             await self._stop_typing(msg.chat_id, clear_remote=True)
 
-        content = msg.content.strip()
         ctx_token = self._context_tokens.get(msg.chat_id, "")
+        ctx_token = await self._refresh_context_token_if_stale(msg.chat_id, ctx_token)
         if not ctx_token:
             raise RuntimeError(
                 f"WeChat context_token missing for chat_id={msg.chat_id}, cannot send"
@@ -1037,6 +1180,18 @@ class WeixinChannel(BaseChannel):
                 with suppress(Exception):
                     await self._send_typing(msg.chat_id, typing_ticket, TYPING_STATUS_CANCEL)
 
+    async def send_delta(
+        self, chat_id: str, delta: str, metadata: dict[str, Any] | None = None
+    ) -> None:
+        """Weixin iLink does not support native streaming deltas.
+
+        We only hook ``_stream_end`` so buffered tool hints are flushed even
+        when the final answer carries the ``_streamed`` flag and bypasses
+        :meth:`send`.
+        """
+        if metadata and metadata.get("_stream_end"):
+            await self._flush_tool_hints(chat_id)
+
     async def _start_typing(self, chat_id: str, context_token: str = "") -> None:
         """Start typing indicator immediately when a message is received."""
         if not self._client or not self._token or not chat_id:
@@ -1120,10 +1275,11 @@ class WeixinChannel(BaseChannel):
         }
 
         data = await self._api_post("ilink/bot/sendmessage", body)
+        ret = data.get("ret", 0)
         errcode = data.get("errcode", 0)
-        if errcode and errcode != 0:
+        if (ret is not None and ret != 0) or (errcode is not None and errcode != 0):
             raise RuntimeError(
-                f"WeChat send text error (code {errcode}): {data.get('errmsg', '')}"
+                f"WeChat send text error (ret={ret}, errcode={errcode}): {data.get('errmsg', '')}"
             )
 
     async def _send_media_file(
@@ -1270,10 +1426,11 @@ class WeixinChannel(BaseChannel):
         }
 
         data = await self._api_post("ilink/bot/sendmessage", body)
+        ret = data.get("ret", 0)
         errcode = data.get("errcode", 0)
-        if errcode and errcode != 0:
+        if (ret is not None and ret != 0) or (errcode is not None and errcode != 0):
             raise RuntimeError(
-                f"WeChat send media error (code {errcode}): {data.get('errmsg', '')}"
+                f"WeChat send media error (ret={ret}, errcode={errcode}): {data.get('errmsg', '')}"
             )
 
 

nanobot/cli/commands.py

@@ -75,7 +75,7 @@ class SafeFileHistory(FileHistory):
 from nanobot.cli.stream import StreamRenderer, ThinkingSpinner
 from nanobot.config.paths import get_workspace_path, is_default_workspace
 from nanobot.config.schema import Config
-from nanobot.p2p.shell import P2PShell
+from nanobot.utils.evaluator import evaluate_response
 from nanobot.utils.helpers import sync_workspace_templates
 from nanobot.utils.restart import (
     consume_restart_notice_from_env,
@@ -92,17 +92,41 @@ app = typer.Typer(
 
 console = Console()
 EXIT_COMMANDS = {"exit", "quit", "/exit", "/quit", ":q"}
+_REASONING_SENTENCE_ENDINGS = (".", "!", "?", "。", "！", "？")
+_REASONING_FLUSH_CHARS = 60
+
+_HEARTBEAT_PREAMBLE = (
+    "[Your response will be delivered directly to the user's messaging app. "
+    "Output ONLY the final user-facing message. Never reference internal "
+    "files (HEARTBEAT.md, AWARENESS.md, etc.), your instructions, or your "
+    "decision process. If nothing needs reporting, respond with just "
+    "'All clear.' and nothing else.]\n\n"
+)
 
 
-def _resolve_p2p(config: Config) -> P2PShell | None:
+def _heartbeat_has_active_tasks(content: str) -> bool:
-    """Resolve P2P config and create the stateless P2P shell."""
+    """True if HEARTBEAT.md has task lines, ignoring headers, blanks and comments."""
-    mb_cfg = config.mailbox
+    in_comment = False
-    if not mb_cfg.enabled:
+    in_active_section: bool = False
-        return None
+    for line in content.splitlines():
-    return P2PShell(
+        stripped = line.strip()
-        agent_id=mb_cfg.agent_id,
+        if in_comment:
-        mailboxes_root=mb_cfg.mailboxes_root,
+            if "-->" in stripped:
-    )
+                in_comment = False
+            continue
+        if not stripped or stripped.startswith("#"):
+            if stripped.startswith("##") and not stripped.startswith("###"):
+                heading = stripped.lstrip("#").strip().lower()
+                in_active_section = heading.startswith("active tasks")
+            continue
+        if stripped.startswith("<!--"):
+            if "-->" not in stripped[4:]:
+                in_comment = True
+            continue
+        if in_active_section is False:
+            continue
+        return True
+    return False
 
 # ---------------------------------------------------------------------------
 # CLI input: prompt_toolkit for editing, paste, history, and display
@@ -254,6 +278,35 @@ def _print_cli_progress_line(text: str, thinking: ThinkingSpinner | None, render
         target.print(f"  [dim]↳ {text}[/dim]")
 
 
+class _ReasoningBuffer:
+    def __init__(self) -> None:
+        self._text = ""
+
+    def add(self, text: str) -> str | None:
+        if not text:
+            return None
+        self._text += text
+        if self._should_flush(text):
+            return self.flush()
+        return None
+
+    def flush(self) -> str | None:
+        text = self._text.strip()
+        self._text = ""
+        return text or None
+
+    def clear(self) -> None:
+        self._text = ""
+
+    def _should_flush(self, text: str) -> bool:
+        stripped = text.rstrip()
+        return (
+            "\n" in text
+            or stripped.endswith(_REASONING_SENTENCE_ENDINGS)
+            or len(self._text) >= _REASONING_FLUSH_CHARS
+        )
+
+
 def _print_cli_reasoning(text: str, thinking: ThinkingSpinner | None, renderer: StreamRenderer | None = None) -> None:
     """Print reasoning/thinking content in a distinct style."""
     if not text.strip():
@@ -266,6 +319,16 @@ def _print_cli_reasoning(text: str, thinking: ThinkingSpinner | None, renderer:
         target.print(f"[dim italic]✻ {text}[/dim italic]")
 
 
+def _flush_cli_reasoning(
+    reasoning_buffer: _ReasoningBuffer,
+    thinking: ThinkingSpinner | None,
+    renderer: StreamRenderer | None = None,
+) -> None:
+    text = reasoning_buffer.flush()
+    if text:
+        _print_cli_reasoning(text, thinking, renderer)
+
+
 async def _print_interactive_progress_line(text: str, thinking: ThinkingSpinner | None, renderer: StreamRenderer | None = None) -> None:
     """Print an interactive progress line, pausing the spinner if needed."""
     if not text.strip():
@@ -284,6 +347,7 @@ async def _maybe_print_interactive_progress(
     thinking: ThinkingSpinner | None,
     channels_config: Any,
     renderer: StreamRenderer | None = None,
+    reasoning_buffer: _ReasoningBuffer | None = None,
 ) -> bool:
     metadata = msg.metadata or {}
     if metadata.get("_retry_wait"):
@@ -293,12 +357,24 @@ async def _maybe_print_interactive_progress(
     if not metadata.get("_progress"):
         return False
 
+    reasoning_buffer = reasoning_buffer or _ReasoningBuffer()
+
+    if metadata.get("_reasoning_end"):
+        if channels_config and not channels_config.show_reasoning:
+            reasoning_buffer.clear()
+        else:
+            _flush_cli_reasoning(reasoning_buffer, thinking, renderer)
+        return True
+
     is_tool_hint = metadata.get("_tool_hint", False)
     is_reasoning = metadata.get("_reasoning", False) or metadata.get("_reasoning_delta", False)
     if is_reasoning:
         if channels_config and not channels_config.show_reasoning:
+            reasoning_buffer.clear()
             return True
-        _print_cli_reasoning(msg.content, thinking, renderer)
+        text = reasoning_buffer.add(msg.content)
+        if text:
+            _print_cli_reasoning(text, thinking, renderer)
         return True
     if channels_config and is_tool_hint and not channels_config.send_tool_hints:
         return True
@@ -578,6 +654,7 @@ def serve(
 
     from nanobot.api.server import create_app
     from nanobot.bus.queue import MessageBus
+    from nanobot.providers.image_generation import image_gen_provider_configs
     from nanobot.session.manager import SessionManager
 
     if verbose:
@@ -593,17 +670,11 @@ def serve(
     sync_workspace_templates(runtime_config.workspace_path)
     bus = MessageBus()
     session_manager = SessionManager(runtime_config.workspace_path)
-    p2p_shell = _resolve_p2p(runtime_config)
-
     try:
         agent_loop = AgentLoop.from_config(
             runtime_config, bus,
             session_manager=session_manager,
-            p2p_shell=p2p_shell,
+            image_generation_provider_configs=image_gen_provider_configs(runtime_config),
-            image_generation_provider_configs={
-                "openrouter": runtime_config.providers.openrouter,
-                "aihubmix": runtime_config.providers.aihubmix,
-            },
         )
     except ValueError as exc:
         console.print(f"[red]Error: {exc}[/red]")
@@ -667,11 +738,144 @@ def gateway(
     _run_gateway(cfg, port=port)
 
 
+def _load_or_create_desktop_config(config: str | None, workspace: str | None) -> Config:
+    """Load the desktop-owned config, creating it on first launch."""
+    from nanobot.config.loader import (
+        get_config_path,
+        load_config,
+        resolve_config_env_vars,
+        save_config,
+        set_config_path,
+    )
+    from nanobot.config.schema import Config as NanobotConfig
+
+    config_path = Path(config).expanduser().resolve() if config else get_config_path()
+    set_config_path(config_path)
+    created = False
+    if config_path.exists():
+        try:
+            loaded = resolve_config_env_vars(load_config(config_path))
+        except ValueError as e:
+            console.print(f"[red]Error: {e}[/red]")
+            raise typer.Exit(1)
+    else:
+        loaded = NanobotConfig()
+        created = True
+
+    if workspace:
+        workspace_path = Path(workspace).expanduser()
+        loaded.agents.defaults.workspace = str(workspace_path)
+        created = True
+
+    if created:
+        save_config(loaded, config_path)
+    return loaded
+
+
+def _configure_desktop_gateway(
+    config: Config,
+    *,
+    webui_port: int,
+    webui_socket: str | None,
+    token_issue_secret: str,
+) -> None:
+    """Force a local WebSocket-only gateway for the desktop app process."""
+    config.gateway.host = "127.0.0.1"
+    config.gateway.port = webui_port
+    config.gateway.heartbeat.enabled = False
+
+    extras = dict(getattr(config.channels, "__pydantic_extra__", None) or {})
+    for name, section in list(extras.items()):
+        if name == "websocket":
+            continue
+        if isinstance(section, dict):
+            extras[name] = {**section, "enabled": False}
+        else:
+            with suppress(Exception):
+                setattr(section, "enabled", False)
+            extras[name] = section
+
+    websocket_cfg = extras.get("websocket")
+    if not isinstance(websocket_cfg, dict):
+        websocket_cfg = {}
+    websocket_cfg.update(
+        {
+            "enabled": True,
+            "host": "127.0.0.1",
+            "port": webui_port,
+            "unix_socket_path": webui_socket or "",
+            "path": "/",
+            "token_issue_secret": token_issue_secret,
+            "websocket_requires_token": True,
+            "allow_from": ["*"],
+            "streaming": True,
+        }
+    )
+    extras["websocket"] = websocket_cfg
+    config.channels.__pydantic_extra__ = extras
+
+
+@app.command("desktop-gateway", hidden=True)
+def desktop_gateway(
+    webui_port: int = typer.Option(0, "--webui-port", min=0, max=65535),
+    webui_socket: str | None = typer.Option(None, "--webui-socket", help="Unix socket path for desktop IPC"),
+    token_issue_secret: str = typer.Option(..., "--token-issue-secret"),
+    workspace: str | None = typer.Option(None, "--workspace", "-w", help="Desktop workspace directory"),
+    config: str | None = typer.Option(None, "--config", "-c", help="Desktop config file"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Verbose output"),
+):
+    """Start the private local gateway used by nanobot Desktop."""
+    if not token_issue_secret.strip():
+        console.print("[red]Error: --token-issue-secret is required[/red]")
+        raise typer.Exit(1)
+    if webui_port <= 0 and not (webui_socket or "").strip():
+        console.print("[red]Error: --webui-port or --webui-socket is required[/red]")
+        raise typer.Exit(1)
+    if verbose:
+        logger.remove(_log_handler_id)
+        logger.add(
+            sys.stderr,
+            format=(
+                "<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
+                "<level>{level: <5}</level> | "
+                "<cyan>{extra[channel]}</cyan> | "
+                "<level>{message}</level>"
+            ),
+            level="DEBUG",
+            colorize=None,
+            filter=lambda record: record["extra"].setdefault("channel", "-") or True,
+        )
+    cfg = _load_or_create_desktop_config(config, workspace)
+    _configure_desktop_gateway(
+        cfg,
+        webui_port=webui_port,
+        webui_socket=webui_socket,
+        token_issue_secret=token_issue_secret,
+    )
+    _run_gateway(
+        cfg,
+        port=webui_port,
+        webui_static_dist=False,
+        webui_runtime_surface="native",
+        webui_runtime_capabilities={
+            "can_restart_engine": True,
+            "can_pick_folder": True,
+            "can_open_logs": True,
+            "can_export_diagnostics": True,
+        },
+        health_server_enabled=False,
+    )
+
+
 def _run_gateway(
     config: Config,
     *,
     port: int | None = None,
     open_browser_url: str | None = None,
+    webui_static_dist: bool = True,
+    webui_runtime_surface: str = "browser",
+    webui_runtime_capabilities: dict[str, Any] | None = None,
+    health_server_enabled: bool = True,
 ) -> None:
     """Shared gateway runtime; ``open_browser_url`` opens a tab once channels are up."""
     from nanobot.agent.tools.cron import CronTool
@@ -681,8 +885,8 @@ def _run_gateway(
     from nanobot.channels.websocket import publish_runtime_model_update
     from nanobot.cron.service import CronService
     from nanobot.cron.types import CronJob
-    from nanobot.heartbeat.service import HeartbeatService
     from nanobot.providers.factory import build_provider_snapshot, load_provider_snapshot
+    from nanobot.providers.image_generation import image_gen_provider_configs
     from nanobot.session.manager import SessionManager
 
     port = port if port is not None else config.gateway.port
@@ -705,8 +909,6 @@ def _run_gateway(
     cron_store_path = config.workspace_path / "cron" / "jobs.json"
     cron = CronService(cron_store_path)
 
-    p2p_shell = _resolve_p2p(config)
-
     # Create agent with cron service
     agent = AgentLoop.from_config(
         config, bus,
@@ -715,10 +917,7 @@ def _run_gateway(
         context_window_tokens=provider_snapshot.context_window_tokens,
         cron_service=cron,
         session_manager=session_manager,
-        image_generation_provider_configs={
+        image_generation_provider_configs=image_gen_provider_configs(config),
-            "openrouter": config.providers.openrouter,
-            "aihubmix": config.providers.aihubmix,
-        },
         provider_snapshot_loader=load_provider_snapshot,
         runtime_model_publisher=lambda model, preset: publish_runtime_model_update(
             bus,
@@ -726,7 +925,6 @@ def _run_gateway(
             preset,
         ),
         provider_signature=provider_snapshot.signature,
-        p2p_shell=p2p_shell,
     )
 
     from nanobot.agent.loop import UNIFIED_SESSION_KEY
@@ -778,6 +976,9 @@ def _run_gateway(
     # Set cron callback (needs agent)
     async def on_cron_job(job: CronJob) -> str | None:
         """Execute a cron job through the agent."""
+        async def _silent(*_args, **_kwargs):
+            pass
+
         # Dream is an internal job — run directly, not through the agent loop.
         if job.name == "dream":
             try:
@@ -787,7 +988,67 @@ def _run_gateway(
                 logger.exception("Dream cron job failed")
             return None
 
-        from nanobot.utils.evaluator import evaluate_response
+        # Heartbeat is a system job that checks HEARTBEAT.md for active tasks.
+        if job.name == "heartbeat":
+            heartbeat_file = config.workspace_path / "HEARTBEAT.md"
+            try:
+                content = heartbeat_file.read_text(encoding="utf-8")
+            except OSError:
+                logger.debug("Heartbeat: HEARTBEAT.md missing")
+                return None
+            if not _heartbeat_has_active_tasks(content):
+                logger.debug("Heartbeat: HEARTBEAT.md has no active tasks")
+                return None
+
+            channel, chat_id = _pick_heartbeat_target()
+            if channel == "cli":
+                return None
+
+            prompt = (
+                _HEARTBEAT_PREAMBLE
+                + f"Review the following HEARTBEAT.md and report any active tasks:\n\n{content}"
+            )
+
+            # Internal check: funnel all output through the post-run gate so the
+            # turn can't deliver directly via the message tool and skip it.
+            suppress_token = None
+            if isinstance(message_tool, MessageTool):
+                suppress_token = message_tool.set_suppress_delivery(True)
+            try:
+                resp = await agent.process_direct(
+                    prompt,
+                    session_key="heartbeat",
+                    channel=channel,
+                    chat_id=chat_id,
+                    on_progress=_silent,
+                )
+            finally:
+                if isinstance(message_tool, MessageTool) and suppress_token is not None:
+                    message_tool.reset_suppress_delivery(suppress_token)
+            response = resp.content if resp else ""
+
+            # Keep a small tail of heartbeat history so the loop stays bounded.
+            session = agent.sessions.get_or_create("heartbeat")
+            session.retain_recent_legal_suffix(hb_cfg.keep_recent_messages)
+            agent.sessions.save(session)
+
+            if not response:
+                return None
+
+            # Fail closed: stay silent on evaluator failure instead of notifying.
+            should_notify = await evaluate_response(
+                response, prompt, agent.provider, agent.model,
+                default_notify=False,
+            )
+            if should_notify:
+                logger.info("Heartbeat: completed, delivering response")
+                await _deliver_to_channel(
+                    OutboundMessage(channel=channel, chat_id=chat_id, content=response),
+                    record=True,
+                )
+            else:
+                logger.info("Heartbeat: silenced by post-run evaluation")
+            return response
 
         reminder_note = (
             "The scheduled time has arrived. Deliver this reminder to the user now, "
@@ -802,9 +1063,6 @@ def _run_gateway(
         if isinstance(cron_tool, CronTool):
             cron_token = cron_tool.set_cron_context(True)
 
-        async def _silent(*_args, **_kwargs):
-            pass
-
         message_record_token = None
         if isinstance(message_tool, MessageTool):
             message_record_token = message_tool.set_record_channel_delivery(True)
@@ -861,12 +1119,14 @@ def _run_gateway(
         bus,
         session_manager=session_manager,
         webui_runtime_model_name=_webui_runtime_model_name,
+        webui_static_dist=webui_static_dist,
+        webui_runtime_surface=webui_runtime_surface,
+        webui_runtime_capabilities=webui_runtime_capabilities,
     )
 
     def _pick_heartbeat_target() -> tuple[str, str]:
         """Pick a routable channel/chat target for heartbeat-triggered messages."""
         enabled = set(channels.enabled_channels)
-        # Prefer the most recently updated non-internal session on an enabled channel.
         for item in session_manager.list_sessions():
             key = item.get("key") or ""
             if ":" not in key:
@@ -876,73 +1136,8 @@ def _run_gateway(
                 continue
             if channel in enabled and chat_id:
                 return channel, chat_id
-        # Fallback keeps prior behavior but remains explicit.
         return "cli", "direct"
 
-    # Create heartbeat service
-    heartbeat_preamble = (
-        "[Your response will be delivered directly to the user's messaging app. "
-        "Output ONLY the final user-facing message. Never reference internal "
-        "files (HEARTBEAT.md, AWARENESS.md, etc.), your instructions, or your "
-        "decision process. If nothing needs reporting, respond with just "
-        "'All clear.' and nothing else.]\n\n"
-    )
-
-    async def on_heartbeat_execute(tasks: str) -> str:
-        """Phase 2: execute heartbeat tasks through the full agent loop."""
-        channel, chat_id = _pick_heartbeat_target()
-
-        async def _silent(*_args, **_kwargs):
-            pass
-
-        resp = await agent.process_direct(
-            heartbeat_preamble + tasks,
-            session_key="heartbeat",
-            channel=channel,
-            chat_id=chat_id,
-            on_progress=_silent,
-        )
-
-        # Keep a small tail of heartbeat history so the loop stays bounded
-        # without losing all short-term context between runs.
-        session = agent.sessions.get_or_create("heartbeat")
-        session.retain_recent_legal_suffix(hb_cfg.keep_recent_messages)
-        agent.sessions.save(session)
-
-        return resp.content if resp else ""
-
-    async def on_heartbeat_notify(response: str) -> None:
-        """Deliver a heartbeat response to the user's channel.
-
-        In addition to publishing the outbound message, this injects the
-        delivered text as an assistant turn into the *target channel's*
-        session.  Without this, a user reply on the channel (e.g. "Sure")
-        lands in a session that has no context about the heartbeat message
-        and the agent cannot follow through.
-        """
-        channel, chat_id = _pick_heartbeat_target()
-        if channel == "cli":
-            return  # No external channel available to deliver to
-
-        await _deliver_to_channel(
-            OutboundMessage(channel=channel, chat_id=chat_id, content=response),
-            record=True,
-        )
-
-    hb_cfg = config.gateway.heartbeat
-    heartbeat = HeartbeatService(
-        workspace=config.workspace_path,
-        provider=agent.provider,
-        model=agent.model,
-        on_execute=on_heartbeat_execute,
-        on_notify=on_heartbeat_notify,
-        interval_s=hb_cfg.interval_s,
-        enabled=hb_cfg.enabled,
-        timezone=config.agents.defaults.timezone,
-        p2p_shell=p2p_shell,
-        bus=bus,
-    )
-
     if channels.enabled_channels:
         console.print(f"[green]✓[/green] Channels enabled: {', '.join(channels.enabled_channels)}")
     else:
@@ -952,7 +1147,11 @@ def _run_gateway(
     if cron_status["jobs"] > 0:
         console.print(f"[green]✓[/green] Cron: {cron_status['jobs']} scheduled jobs")
 
-    console.print(f"[green]✓[/green] Heartbeat: every {hb_cfg.interval_s}s")
+    hb_cfg = config.gateway.heartbeat
+    if hb_cfg.enabled:
+        console.print(f"[green]✓[/green] Heartbeat: every {hb_cfg.interval_s}s")
+    else:
+        console.print("[yellow]✗[/yellow] Heartbeat: disabled")
 
     async def _health_server(host: str, health_port: int):
         """Lightweight HTTP health endpoint on the gateway port."""
@@ -996,21 +1195,37 @@ def _run_gateway(
         console.print(f"[green]✓[/green] Health endpoint: http://{host}:{health_port}/health")
         async with server:
             await server.serve_forever()
-    # Register Dream system job (always-on, idempotent on restart)
+    # Register Dream system job (idempotent on restart)
     dream_cfg = config.agents.defaults.dream
     if dream_cfg.model_override:
         agent.dream.model = dream_cfg.model_override
     agent.dream.max_batch_size = dream_cfg.max_batch_size
     agent.dream.max_iterations = dream_cfg.max_iterations
     agent.dream.annotate_line_ages = dream_cfg.annotate_line_ages
-    from nanobot.cron.types import CronJob, CronPayload
+    from nanobot.cron.types import CronJob, CronPayload, CronSchedule
-    cron.register_system_job(CronJob(
+    if dream_cfg.enabled:
-        id="dream",
+        cron.register_system_job(CronJob(
-        name="dream",
+            id="dream",
-        schedule=dream_cfg.build_schedule(config.agents.defaults.timezone),
+            name="dream",
-        payload=CronPayload(kind="system_event"),
+            schedule=dream_cfg.build_schedule(config.agents.defaults.timezone),
-    ))
+            payload=CronPayload(kind="system_event"),
-    console.print(f"[green]✓[/green] Dream: {dream_cfg.describe_schedule()}")
+        ))
+        console.print(f"[green]✓[/green] Dream: {dream_cfg.describe_schedule()}")
+    else:
+        console.print("[yellow]○[/yellow] Dream: disabled")
+
+    # Register Heartbeat system job (idempotent on restart)
+    if hb_cfg.enabled:
+        cron.register_system_job(CronJob(
+            id="heartbeat",
+            name="heartbeat",
+            schedule=CronSchedule(
+                kind="every",
+                every_ms=hb_cfg.interval_s * 1000,
+                tz=config.agents.defaults.timezone,
+            ),
+            payload=CronPayload(kind="system_event"),
+        ))
 
     async def _open_browser_when_ready() -> None:
         """Wait for the gateway to bind, then point the user's browser at the webui."""
@@ -1038,12 +1253,12 @@ def _run_gateway(
     async def run():
         try:
             await cron.start()
-            await heartbeat.start()
             tasks = [
                 agent.run(),
                 channels.start_all(),
-                _health_server(config.gateway.host, port),
             ]
+            if health_server_enabled:
+                tasks.append(_health_server(config.gateway.host, port))
             if open_browser_url:
                 tasks.append(_open_browser_when_ready())
             await asyncio.gather(*tasks)
@@ -1056,7 +1271,6 @@ def _run_gateway(
             console.print(traceback.format_exc())
         finally:
             await agent.close_mcp()
-            heartbeat.stop()
             cron.stop()
             agent.stop()
             await channels.stop_all()
@@ -1089,6 +1303,7 @@ def agent(
 
     from nanobot.bus.queue import MessageBus
     from nanobot.cron.service import CronService
+    from nanobot.providers.image_generation import image_gen_provider_configs
 
     config = _load_runtime_config(config, workspace)
     sync_workspace_templates(config.workspace_path)
@@ -1103,8 +1318,6 @@ def agent(
     cron_store_path = config.workspace_path / "cron" / "jobs.json"
     cron = CronService(cron_store_path)
 
-    p2p_shell = _resolve_p2p(config)
-
     if logs:
         logger.enable("nanobot")
     else:
@@ -1114,7 +1327,7 @@ def agent(
         agent_loop = AgentLoop.from_config(
             config, bus,
             cron_service=cron,
-            p2p_shell=p2p_shell,
+            image_generation_provider_configs=image_gen_provider_configs(config),
         )
     except ValueError as exc:
         console.print(f"[red]Error: {exc}[/red]")
@@ -1130,12 +1343,25 @@ def agent(
     _thinking: ThinkingSpinner | None = None
 
     def _make_progress(renderer: StreamRenderer | None = None):
+        reasoning_buffer = _ReasoningBuffer()
+
         async def _cli_progress(content: str, *, tool_hint: bool = False, reasoning: bool = False, **_kwargs: Any) -> None:
             ch = agent_loop.channels_config
+
+            if _kwargs.get("reasoning_end"):
+                if ch and not ch.show_reasoning:
+                    reasoning_buffer.clear()
+                else:
+                    _flush_cli_reasoning(reasoning_buffer, _thinking, renderer)
+                return
+
             if reasoning:
                 if ch and not ch.show_reasoning:
+                    reasoning_buffer.clear()
                     return
-                _print_cli_reasoning(content, _thinking, renderer)
+                text = reasoning_buffer.add(content)
+                if text:
+                    _print_cli_reasoning(text, _thinking, renderer)
                 return
             if ch and tool_hint and not ch.send_tool_hints:
                 return
@@ -1206,6 +1432,7 @@ def agent(
             turn_done.set()
             turn_response: list[tuple[str, dict]] = []
             renderer: StreamRenderer | None = None
+            reasoning_buffer = _ReasoningBuffer()
 
             async def _consume_outbound():
                 while True:
@@ -1231,6 +1458,7 @@ def agent(
                             renderer,
                             agent_loop.channels_config,
                             renderer,
+                            reasoning_buffer,
                         ):
                             continue
 
@@ -1271,6 +1499,7 @@ def agent(
 
                         turn_done.clear()
                         turn_response.clear()
+                        reasoning_buffer.clear()
                         renderer = StreamRenderer(
                             render_markdown=markdown,
                             bot_name=config.agents.defaults.bot_name,
@@ -1312,7 +1541,6 @@ def agent(
                         console.print("\nGoodbye!")
                         break
             finally:
-                pass
                 agent_loop.stop()
                 outbound_task.cancel()
                 await asyncio.gather(bus_task, outbound_task, return_exceptions=True)

nanobot/cli/onboard.py

@@ -22,7 +22,7 @@ from nanobot.cli.models import (
     get_model_suggestions,
 )
 from nanobot.config.loader import get_config_path, load_config
-from nanobot.config.schema import Config
+from nanobot.config.schema import Config, ModelPresetConfig
 
 console = Console()
 
@@ -49,6 +49,10 @@ _SELECT_FIELD_HINTS: dict[str, tuple[list[str], str]] = {
 
 _BACK_PRESSED = object()  # Sentinel value for back navigation
 
+# Cache of model-preset names populated at runtime so that field handlers can
+# offer existing presets as choices (e.g. AgentDefaults.model_preset).
+_MODEL_PRESET_CACHE: set[str] = set()
+
 
 def _get_questionary():
     """Return questionary or raise a clear error when wizard deps are unavailable."""
@@ -588,9 +592,102 @@ def _handle_context_window_field(
         setattr(working_model, field_name, new_value)
 
 
+def _handle_model_preset_field(
+    working_model: BaseModel, field_name: str, field_display: str, current_value: Any
+) -> None:
+    """Handle the 'model_preset' field with a list of existing presets."""
+    preset_names = sorted(_MODEL_PRESET_CACHE)
+    choices = ["(clear/unset)"] + preset_names
+    default_choice = str(current_value) if current_value else "(clear/unset)"
+    new_value = _select_with_back(field_display, choices, default=default_choice)
+    if new_value is _BACK_PRESSED:
+        return
+    if new_value == "(clear/unset)":
+        setattr(working_model, field_name, None)
+    elif new_value is not None:
+        setattr(working_model, field_name, new_value)
+
+
+def _handle_provider_field(
+    working_model: BaseModel, field_name: str, field_display: str, current_value: Any
+) -> None:
+    """Handle the 'provider' field with a list of registered providers."""
+    provider_names = sorted(_get_provider_names().keys())
+    choices = ["auto"] + provider_names
+    default_choice = str(current_value) if current_value else "auto"
+    new_value = _select_with_back(field_display, choices, default=default_choice)
+    if new_value is _BACK_PRESSED:
+        return
+    if new_value is not None:
+        setattr(working_model, field_name, new_value)
+
+
+def _handle_fallback_models_field(
+    working_model: BaseModel, field_name: str, field_display: str, current_value: Any
+) -> None:
+    """Handle the 'fallback_models' field with preset-aware list management."""
+    from nanobot.config.schema import InlineFallbackConfig
+
+    items: list[Any] = list(current_value) if isinstance(current_value, list) else []
+    preset_names = sorted(_MODEL_PRESET_CACHE)
+
+    while True:
+        console.clear()
+        console.print(f"[bold]{field_display}[/bold]")
+        if items:
+            for idx, item in enumerate(items, 1):
+                if isinstance(item, InlineFallbackConfig):
+                    console.print(f"  {idx}. {item.model} ({item.provider}) [inline]")
+                else:
+                    console.print(f"  {idx}. {item}")
+        else:
+            console.print("  [dim](empty)[/dim]")
+        console.print()
+
+        choices = ["[+] Add preset"]
+        if items:
+            choices.append("[-] Remove last")
+            choices.append("[X] Clear all")
+        choices.append("[Done]")
+        choices.append("<- Back")
+
+        answer = _get_questionary().select(
+            "Manage fallback models:",
+            choices=choices,
+            qmark=">",
+        ).ask()
+
+        if answer is None or answer == "<- Back":
+            return
+        if answer == "[Done]":
+            setattr(working_model, field_name, items)
+            return
+        if answer == "[+] Add preset":
+            if not preset_names:
+                console.print("[yellow]! No presets defined yet.[/yellow]")
+                _get_questionary().press_any_key_to_continue().ask()
+                continue
+            add_choices = [p for p in preset_names if p not in items]
+            if not add_choices:
+                console.print("[yellow]! All presets already added.[/yellow]")
+                _get_questionary().press_any_key_to_continue().ask()
+                continue
+            picked = _select_with_back("Select preset:", add_choices)
+            if picked is _BACK_PRESSED or picked is None:
+                continue
+            items.append(picked)
+        elif answer == "[-] Remove last" and items:
+            items.pop()
+        elif answer == "[X] Clear all" and items:
+            items.clear()
+
+
 _FIELD_HANDLERS: dict[str, Any] = {
     "model": _handle_model_field,
     "context_window_tokens": _handle_context_window_field,
+    "model_preset": _handle_model_preset_field,
+    "provider": _handle_provider_field,
+    "fallback_models": _handle_fallback_models_field,
 }
 
 
@@ -757,6 +854,116 @@ def _try_auto_fill_context_window(model: BaseModel, new_model_name: str) -> None
         console.print("[dim](i) Could not auto-fill context window (model not in database)[/dim]")
 
 
+# --- Model Preset Configuration ---
+
+
+def _sync_preset_cache(config: Config) -> None:
+    """Synchronise the module-level preset name cache from config."""
+    _MODEL_PRESET_CACHE.clear()
+    _MODEL_PRESET_CACHE.update(config.model_presets.keys())
+
+
+def _configure_model_presets(config: Config) -> None:
+    """Configure model presets (CRUD)."""
+    _sync_preset_cache(config)
+
+    def get_preset_choices() -> list[str]:
+        choices: list[str] = []
+        for name, preset in config.model_presets.items():
+            choices.append(f"{name} ({preset.model})")
+        choices.append("[+] Add new preset")
+        choices.append("<- Back")
+        return choices
+
+    last_preset_name: str | None = None
+    while True:
+        try:
+            console.clear()
+            _show_section_header(
+                "Model Presets",
+                "Create, edit or delete named model presets for quick switching",
+            )
+            choices = get_preset_choices()
+            default_choice = None
+            if last_preset_name:
+                for c in choices:
+                    if c.startswith(last_preset_name + " ("):
+                        default_choice = c
+                        break
+            answer = _select_with_back(
+                "Select preset:", choices, default=default_choice
+            )
+
+            if answer is _BACK_PRESSED or answer is None or answer == "<- Back":
+                break
+
+            assert isinstance(answer, str)
+
+            if answer == "[+] Add new preset":
+                name_input = _get_questionary().text(
+                    "Preset name:",
+                    validate=lambda t: True if t and t.strip() else "Name cannot be empty",
+                ).ask()
+                if not name_input:
+                    continue
+                name = name_input.strip()
+                if name in config.model_presets:
+                    console.print(f"[yellow]! Preset '{name}' already exists[/yellow]")
+                    _pause()
+                    continue
+                if name == "default":
+                    console.print("[yellow]! 'default' is reserved (auto-generated from Agent Settings)[/yellow]")
+                    _pause()
+                    continue
+                new_preset = ModelPresetConfig(model="")
+                updated = _configure_pydantic_model(new_preset, f"New Preset: {name}")
+                if updated is not None:
+                    config.model_presets[name] = updated
+                    _sync_preset_cache(config)
+                    last_preset_name = name
+                continue
+
+            # Editing / deleting an existing preset
+            preset_name = answer.split(" (", 1)[0]
+            preset = config.model_presets.get(preset_name)
+            if preset is None:
+                continue
+
+            last_preset_name = preset_name
+
+            choices = ["Edit", "Cancel"]
+            if preset_name != "default":
+                choices.insert(1, "Delete")
+            action = _select_with_back(
+                f"Preset: {preset_name}",
+                choices,
+                default="Edit",
+            )
+            if action is _BACK_PRESSED or action == "Cancel" or action is None:
+                continue
+
+            if action == "Delete":
+                confirm = _get_questionary().confirm(
+                    f"Delete preset '{preset_name}'?",
+                    default=False,
+                ).ask()
+                if confirm:
+                    del config.model_presets[preset_name]
+                    _sync_preset_cache(config)
+                    last_preset_name = None
+                continue
+
+            if action == "Edit":
+                updated = _configure_pydantic_model(preset, f"Edit Preset: {preset_name}")
+                if updated is not None:
+                    config.model_presets[preset_name] = updated
+                    _sync_preset_cache(config)
+
+        except KeyboardInterrupt:
+            console.print("\n[dim]Returning to main menu...[/dim]")
+            break
+
+
 # --- Provider Configuration ---
 
 
@@ -948,7 +1155,7 @@ _SETTINGS_SECTIONS: dict[str, tuple[str, str, set[str] | None]] = {
     "Agent Settings": ("Agent Defaults", "Configure default model, temperature, and behavior", None),
     "Channel Common": ("Channel Common", "Configure cross-channel behavior: progress, tool hints, retries", None),
     "API Server": ("API Server", "Configure OpenAI-compatible API endpoint", None),
-    "Gateway": ("Gateway Settings", "Configure server host, port, and heartbeat", None),
+    "Gateway": ("Gateway Settings", "Configure server host, port", None),
     "Tools": ("Tools Settings", "Configure web search, shell exec, and other tools", {"mcp_servers"}),
 }
 
@@ -1043,6 +1250,12 @@ def _show_summary(config: Config) -> None:
         channel_rows.append((display, status))
     _print_summary_panel(channel_rows, "Chat Channels")
 
+    # Model Presets
+    preset_rows = []
+    for name, preset in config.model_presets.items():
+        preset_rows.append((name, f"{preset.model} (ctx={preset.context_window_tokens})"))
+    _print_summary_panel(preset_rows, "Model Presets")
+
     # Settings sections
     for title, model in [
         ("Agent Settings", config.agents.defaults),
@@ -1112,6 +1325,7 @@ def run_onboard(initial_config: Config | None = None) -> OnboardResult:
 
     original_config = base_config.model_copy(deep=True)
     config = base_config.model_copy(deep=True)
+    _sync_preset_cache(config)
 
     last_main_choice: str | None = None
     while True:
@@ -1123,6 +1337,7 @@ def run_onboard(initial_config: Config | None = None) -> OnboardResult:
                 "What would you like to configure?",
                 choices=[
                     "[P] LLM Provider",
+                    "[M] Model Presets",
                     "[C] Chat Channel",
                     "[H] Channel Common",
                     "[A] Agent Settings",
@@ -1149,6 +1364,7 @@ def run_onboard(initial_config: Config | None = None) -> OnboardResult:
 
         _menu_dispatch = {
             "[P] LLM Provider": lambda: _configure_providers(config),
+            "[M] Model Presets": lambda: _configure_model_presets(config),
             "[C] Chat Channel": lambda: _configure_channels(config),
             "[H] Channel Common": lambda: _configure_general_settings(config, "Channel Common"),
             "[A] Agent Settings": lambda: _configure_general_settings(config, "Agent Settings"),

nanobot/command/builtin.py

@@ -123,7 +123,7 @@ async def cmd_stop(ctx: CommandContext) -> OutboundMessage:
     """Cancel all active tasks and subagents for the session."""
     loop = ctx.loop
     msg = ctx.msg
-    total = await loop._cancel_active_tasks(msg.session_key)
+    total = await loop._cancel_active_tasks(ctx.key)
     content = f"Stopped {total} task(s)." if total else "No active task to stop."
     return OutboundMessage(
         channel=msg.channel, chat_id=msg.chat_id, content=content,

nanobot/config/loader.py

@@ -10,10 +10,11 @@ import pydantic
 from loguru import logger
 from pydantic import BaseModel
 
-from nanobot.config.schema import Config
+from nanobot.config.schema import Config, _resolve_tool_config_refs
 
 # Global variable to store current config path (for multi-instance support)
 _current_config_path: Path | None = None
+_schema_refs_ready = False
 
 
 def set_config_path(path: Path) -> None:
@@ -39,6 +40,11 @@ def load_config(config_path: Path | None = None) -> Config:
     Returns:
         Loaded configuration object.
     """
+    global _schema_refs_ready
+    if not _schema_refs_ready:
+        _resolve_tool_config_refs()
+        _schema_refs_ready = True
+
     path = config_path or get_config_path()
 
     config = Config()

nanobot/config/schema.py

@@ -11,6 +11,7 @@ from pydantic_settings import BaseSettings
 from nanobot.cron.types import CronSchedule
 
 if TYPE_CHECKING:
+    from nanobot.agent.tools.cli_apps import CliAppsToolConfig
     from nanobot.agent.tools.image_generation import ImageGenerationToolConfig
     from nanobot.agent.tools.self import MyToolConfig
     from nanobot.agent.tools.shell import ExecToolConfig
@@ -36,6 +37,7 @@ class ChannelsConfig(Base):
     send_progress: bool = True  # stream agent's text progress to the channel
     send_tool_hints: bool = False  # stream tool-call hints (e.g. read_file("…"))
     show_reasoning: bool = True  # surface model reasoning when channel implements it
+    extract_document_text: bool = True  # extract text from document attachments before sending to the model
     send_max_retries: int = Field(default=3, ge=0, le=10)  # Max delivery attempts (initial send included)
     transcription_provider: str = "groq"  # Voice transcription backend: "groq" or "openai"
     transcription_language: str | None = Field(default=None, pattern=r"^[a-z]{2,3}$")  # Optional ISO-639-1 hint for audio transcription
@@ -46,6 +48,7 @@ class DreamConfig(Base):
 
     _HOUR_MS = 3_600_000
 
+    enabled: bool = True  # Register the periodic Dream consolidation job on startup
     interval_h: int = Field(default=2, ge=1)  # Every 2 hours by default
     cron: str | None = Field(default=None, exclude=True)  # Legacy compatibility override
     model_override: str | None = Field(
@@ -91,6 +94,7 @@ FallbackCandidate = str | InlineFallbackConfig
 class ModelPresetConfig(Base):
     """A named set of model + generation parameters for quick switching."""
 
+    label: str | None = None
     model: str
     provider: str = "auto"
     max_tokens: int = 8192
@@ -169,8 +173,9 @@ class ProviderConfig(Base):
 
     api_key: str | None = None
     api_base: str | None = None
+    api_type: Literal["auto", "chat_completions", "responses"] = "auto"  # Request API surface
     extra_headers: dict[str, str] | None = None  # Custom headers (e.g. APP-Code for AiHubMix)
-    extra_body: dict[str, Any] | None = None  # Extra fields merged into every request body
+    extra_body: dict[str, Any] | None = None  # Extra provider request fields; shape depends on provider/API surface
 
 
 class BedrockProviderConfig(ProviderConfig):
@@ -190,6 +195,7 @@ class ProvidersConfig(Base):
     openai: ProviderConfig = Field(default_factory=ProviderConfig)
     openrouter: ProviderConfig = Field(default_factory=ProviderConfig)
     huggingface: ProviderConfig = Field(default_factory=ProviderConfig)
+    skywork: ProviderConfig = Field(default_factory=ProviderConfig)  # Skywork / APIFree API gateway
     deepseek: ProviderConfig = Field(default_factory=ProviderConfig)
     groq: ProviderConfig = Field(default_factory=ProviderConfig)
     zhipu: ProviderConfig = Field(default_factory=ProviderConfig)
@@ -207,8 +213,10 @@ class ProvidersConfig(Base):
     stepfun: ProviderConfig = Field(default_factory=ProviderConfig)  # Step Fun (阶跃星辰)
     xiaomi_mimo: ProviderConfig = Field(default_factory=ProviderConfig)  # Xiaomi MIMO (小米)
     longcat: ProviderConfig = Field(default_factory=ProviderConfig)  # LongCat
+    ant_ling: ProviderConfig = Field(default_factory=ProviderConfig)  # Ant Ling
     aihubmix: ProviderConfig = Field(default_factory=ProviderConfig)  # AiHubMix API gateway
     siliconflow: ProviderConfig = Field(default_factory=ProviderConfig)  # SiliconFlow (硅基流动)
+    novita: ProviderConfig = Field(default_factory=ProviderConfig)  # Novita AI
     volcengine: ProviderConfig = Field(default_factory=ProviderConfig)  # VolcEngine (火山引擎)
     volcengine_coding_plan: ProviderConfig = Field(default_factory=ProviderConfig)  # VolcEngine Coding Plan
     byteplus: ProviderConfig = Field(default_factory=ProviderConfig)  # BytePlus (VolcEngine international)
@@ -218,9 +226,19 @@ class ProvidersConfig(Base):
     qianfan: ProviderConfig = Field(default_factory=ProviderConfig)  # Qianfan (百度千帆)
     nvidia: ProviderConfig = Field(default_factory=ProviderConfig)  # NVIDIA NIM (nvapi- keys)
 
+    @model_validator(mode="after")
+    def _validate_api_type_scope(self) -> "ProvidersConfig":
+        for name in self.__class__.model_fields:
+            if name == "openai":
+                continue
+            provider = getattr(self, name, None)
+            if isinstance(provider, ProviderConfig) and provider.api_type != "auto":
+                raise ValueError("providers.<name>.api_type is only supported for providers.openai")
+        return self
+
 
 class HeartbeatConfig(Base):
-    """Heartbeat service configuration."""
+    """Heartbeat service configuration (now backed by cron)."""
 
     enabled: bool = True
     interval_s: int = 30 * 60  # 30 minutes
@@ -250,6 +268,7 @@ class MCPServerConfig(Base):
     command: str = ""  # Stdio: command to run (e.g. "npx")
     args: list[str] = Field(default_factory=list)  # Stdio: command arguments
     env: dict[str, str] = Field(default_factory=dict)  # Stdio: extra env vars
+    cwd: str = ""  # Stdio: working directory for MCP server runtime artifacts
     url: str = ""  # HTTP/SSE: endpoint URL
     headers: dict[str, str] = Field(default_factory=dict)  # HTTP/SSE: custom headers
     tool_timeout: int = 30  # seconds before a tool call is cancelled
@@ -273,28 +292,25 @@ class ToolsConfig(Base):
 
     web: WebToolsConfig = Field(default_factory=lambda: _lazy_default("nanobot.agent.tools.web", "WebToolsConfig"))
     exec: ExecToolConfig = Field(default_factory=lambda: _lazy_default("nanobot.agent.tools.shell", "ExecToolConfig"))
+    cli_apps: CliAppsToolConfig = Field(default_factory=lambda: _lazy_default("nanobot.agent.tools.cli_apps", "CliAppsToolConfig"))
     my: MyToolConfig = Field(default_factory=lambda: _lazy_default("nanobot.agent.tools.self", "MyToolConfig"))
     image_generation: ImageGenerationToolConfig = Field(
         default_factory=lambda: _lazy_default("nanobot.agent.tools.image_generation", "ImageGenerationToolConfig"),
     )
-    restrict_to_workspace: bool = False  # restrict all tool access to workspace directory
+    restrict_to_workspace: bool = False  # policy intent: keep tool access inside workspace when possible
+    webui_allow_local_service_access: bool = Field(
+        default=True,
+        validation_alias=AliasChoices(
+            "webuiAllowLocalServiceAccess",
+            "webui_allow_local_service_access",
+            "allowLocalPreviewAccess",
+            "allow_local_preview_access",
+        ),
+    )  # allow WebUI Full Access shell checks against localhost services; legacy allowLocalPreviewAccess still reads
     mcp_servers: dict[str, MCPServerConfig] = Field(default_factory=dict)
     ssrf_whitelist: list[str] = Field(default_factory=list)  # CIDR ranges to exempt from SSRF blocking (e.g. ["100.64.0.0/10"] for Tailscale)
 
 
-class P2PConfig(Base):
-    """P2P collaboration network configuration."""
-
-    enabled: bool = False
-    agent_id: str = ""
-    description: str = ""
-    capabilities: list[str] = Field(default_factory=list)
-    allow_from: list[str] = Field(default_factory=lambda: ["*"])
-    max_concurrent_tasks: int = 3
-    poll_interval: float = 5.0
-    mailboxes_root: str = "~/.nanobot/mailboxes"
-
-
 class Config(BaseSettings):
     """Root configuration for nanobot."""
 
@@ -308,7 +324,11 @@ class Config(BaseSettings):
         default_factory=dict,
         validation_alias=AliasChoices("modelPresets", "model_presets"),
     )
-    mailbox: P2PConfig = Field(default_factory=P2PConfig)
+
+    def __init__(self, **values: Any) -> None:
+        if not type(self).__pydantic_complete__:
+            _resolve_tool_config_refs()
+        super().__init__(**values)
 
     @model_validator(mode="after")
     def _validate_model_preset(self) -> "Config":
@@ -473,6 +493,7 @@ def _resolve_tool_config_refs() -> None:
     """
     import sys
 
+    from nanobot.agent.tools.cli_apps import CliAppsToolConfig
     from nanobot.agent.tools.image_generation import ImageGenerationToolConfig
     from nanobot.agent.tools.self import MyToolConfig
     from nanobot.agent.tools.shell import ExecToolConfig
@@ -481,6 +502,7 @@ def _resolve_tool_config_refs() -> None:
     # Re-export into this module's namespace
     mod = sys.modules[__name__]
     mod.ExecToolConfig = ExecToolConfig  # type: ignore[attr-defined]
+    mod.CliAppsToolConfig = CliAppsToolConfig  # type: ignore[attr-defined]
     mod.WebToolsConfig = WebToolsConfig  # type: ignore[attr-defined]
     mod.WebSearchConfig = WebSearchConfig  # type: ignore[attr-defined]
     mod.WebFetchConfig = WebFetchConfig  # type: ignore[attr-defined]

nanobot/cron/__init__.py

@@ -1,6 +1,18 @@
 """Cron service for scheduled agent tasks."""
 
-from nanobot.cron.service import CronService
 from nanobot.cron.types import CronJob, CronSchedule
 
 __all__ = ["CronService", "CronJob", "CronSchedule"]
+
+_LAZY = {"CronService": ".service"}
+
+
+def __getattr__(name: str):
+    module_path = _LAZY.get(name)
+    if module_path is None:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    from importlib import import_module
+    mod = import_module(module_path, __name__)
+    val = getattr(mod, name)
+    globals()[name] = val
+    return val

nanobot/heartbeat/__init__.py

@@ -1,5 +0,0 @@
-"""Heartbeat service for periodic agent wake-ups."""
-
-from nanobot.heartbeat.service import HeartbeatService
-
-__all__ = ["HeartbeatService"]

nanobot/heartbeat/service.py

@@ -1,267 +0,0 @@
-"""Heartbeat service - periodic agent wake-up to check for tasks."""
-
-from __future__ import annotations
-
-import asyncio
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Coroutine
-
-from loguru import logger
-
-if TYPE_CHECKING:
-    from nanobot.providers.base import LLMProvider
-
-_HEARTBEAT_TOOL = [
-    {
-        "type": "function",
-        "function": {
-            "name": "heartbeat",
-            "description": "Report heartbeat decision after reviewing tasks.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "action": {
-                        "type": "string",
-                        "enum": ["skip", "run"],
-                        "description": "skip = nothing to do, run = has active tasks",
-                    },
-                    "tasks": {
-                        "type": "string",
-                        "description": "Natural-language summary of active tasks (required for run)",
-                    },
-                },
-                "required": ["action"],
-            },
-        },
-    }
-]
-
-
-class HeartbeatService:
-    """
-    Periodic heartbeat service that wakes the agent to check for tasks.
-
-    Phase 1 (decision): reads HEARTBEAT.md and asks the LLM — via a virtual
-    tool call — whether there are active tasks.  This avoids free-text parsing
-    and the unreliable HEARTBEAT_OK token.
-
-    Phase 2 (execution): only triggered when Phase 1 returns ``run``.  The
-    ``on_execute`` callback runs the task through the full agent loop and
-    returns the result to deliver.
-    """
-
-    def __init__(
-        self,
-        workspace: Path,
-        provider: LLMProvider,
-        model: str,
-        on_execute: Callable[[str], Coroutine[Any, Any, str]] | None = None,
-        on_notify: Callable[[str], Coroutine[Any, Any, None]] | None = None,
-        interval_s: int = 30 * 60,
-        enabled: bool = True,
-        timezone: str | None = None,
-        p2p_shell: Any | None = None,
-        bus: Any | None = None,
-    ):
-        self.workspace = workspace
-        self.provider = provider
-        self.model = model
-        self.on_execute = on_execute
-        self.on_notify = on_notify
-        self.interval_s = interval_s
-        self.enabled = enabled
-        self.timezone = timezone
-        self.p2p_shell = p2p_shell
-        self.bus = bus
-        self._running = False
-        self._task: asyncio.Task | None = None
-        self._last_inbox_scan: float = 0.0
-
-    @property
-    def heartbeat_file(self) -> Path:
-        return self.workspace / "HEARTBEAT.md"
-
-    def _read_heartbeat_file(self) -> str | None:
-        if self.heartbeat_file.exists():
-            try:
-                return self.heartbeat_file.read_text(encoding="utf-8")
-            except Exception:
-                return None
-        return None
-
-    async def _decide(self, content: str) -> tuple[str, str]:
-        """Phase 1: ask LLM to decide skip/run via virtual tool call.
-
-        Returns (action, tasks) where action is 'skip' or 'run'.
-        """
-        from nanobot.utils.helpers import current_time_str
-
-        response = await self.provider.chat_with_retry(
-            messages=[
-                {"role": "system", "content": "You are a heartbeat agent. Call the heartbeat tool to report your decision."},
-                {"role": "user", "content": (
-                    f"Current Time: {current_time_str(self.timezone)}\n\n"
-                    "Review the following HEARTBEAT.md and decide whether there are active tasks.\n\n"
-                    f"{content}"
-                )},
-            ],
-            tools=_HEARTBEAT_TOOL,
-            model=self.model,
-        )
-
-        if not response.should_execute_tools:
-            if response.has_tool_calls:
-                logger.warning(
-                    "Ignoring heartbeat tool calls under finish_reason='{}'",
-                    response.finish_reason,
-                )
-            return "skip", ""
-
-        args = response.tool_calls[0].arguments
-        return args.get("action", "skip"), args.get("tasks", "")
-
-    async def start(self) -> None:
-        """Start the heartbeat service."""
-        if not self.enabled:
-            logger.info("Heartbeat disabled")
-            return
-        if self._running:
-            logger.warning("Heartbeat already running")
-            return
-
-        self._running = True
-        self._task = asyncio.create_task(self._run_loop())
-        logger.info("Heartbeat started (every {}s)", self.interval_s)
-
-    def stop(self) -> None:
-        """Stop the heartbeat service."""
-        self._running = False
-        if self._task:
-            self._task.cancel()
-            self._task = None
-
-    async def _run_loop(self) -> None:
-        """Main heartbeat loop."""
-        while self._running:
-            try:
-                await asyncio.sleep(self.interval_s)
-                if self._running:
-                    await self._tick()
-            except asyncio.CancelledError:
-                break
-            except Exception:
-                logger.exception("Heartbeat error")
-
-    @staticmethod
-    def _is_deliverable(response: str) -> bool:
-        """Check if a heartbeat response is suitable for user delivery.
-
-        Filters out two classes of bad output before the evaluator runs:
-
-        1. **Finalization fallback** — the runner hit empty-response retries
-           and produced a canned error message.  For heartbeat, empty output
-           is a valid "nothing to report" outcome, not a failure.
-        2. **Leaked reasoning** — the model reflected internal file names,
-           decision logic, or meta-commentary instead of a user-facing report.
-        """
-        text = response.lower()
-
-        # Runner finalization fallback
-        if "couldn't produce a final answer" in text:
-            return False
-
-        # Leaked internal reasoning patterns
-        leaked_patterns = [
-            "heartbeat.md",
-            "awareness.md",
-            "judgment call:",
-            "decision logic",
-            "valid options are",
-            "my instructions",
-            "i am supposed to",
-            "strict heartbeat interpretation",
-        ]
-        if any(pattern in text for pattern in leaked_patterns):
-            return False
-
-        return True
-
-    async def _tick(self) -> None:
-        """Execute a single heartbeat tick."""
-        from nanobot.utils.evaluator import evaluate_response
-
-        # --- P2P inbox scan ---
-        if self.p2p_shell and self.bus:
-            try:
-                new_msgs = self.p2p_shell.scan_new_inbox(since=self._last_inbox_scan)
-                if new_msgs:
-                    self._last_inbox_scan = time.time()
-                    from nanobot.bus.events import InboundMessage
-                    for msg in new_msgs:
-                        await self.bus.publish_inbound(
-                            InboundMessage(
-                                channel="p2p",
-                                sender_id=msg.get("from", "unknown"),
-                                chat_id=msg.get("task_id", ""),
-                                content=msg.get("payload", {}).get("description", ""),
-                                metadata={"p2p_msg": msg},
-                            )
-                        )
-                        logger.info(
-                            "Heartbeat: injected P2P task {} from {}",
-                            msg.get("task_id", ""),
-                            msg.get("from", "unknown"),
-                        )
-            except Exception:
-                logger.exception("Heartbeat P2P scan failed")
-
-        # --- Legacy heartbeat file check ---
-        content = self._read_heartbeat_file()
-        if not content:
-            logger.debug("Heartbeat: HEARTBEAT.md missing or empty")
-            return
-
-        logger.info("Heartbeat: checking for tasks...")
-
-        try:
-            action, tasks = await self._decide(content)
-
-            if action != "run":
-                logger.info("Heartbeat: OK (nothing to report)")
-                return
-
-            logger.info("Heartbeat: tasks found, executing...")
-            if self.on_execute:
-                response = await self.on_execute(tasks)
-
-                if not response:
-                    logger.info("Heartbeat: no response from execution")
-                    return
-
-                if not self._is_deliverable(response):
-                    logger.info(
-                        "Heartbeat: suppressed non-deliverable response ({})",
-                        response[:80],
-                    )
-                    return
-
-                should_notify = await evaluate_response(
-                    response, tasks, self.provider, self.model,
-                )
-                if should_notify and self.on_notify:
-                    logger.info("Heartbeat: completed, delivering response")
-                    await self.on_notify(response)
-                else:
-                    logger.info("Heartbeat: silenced by post-run evaluation")
-        except Exception:
-            logger.exception("Heartbeat execution failed")
-
-    async def trigger_now(self) -> str | None:
-        """Manually trigger a heartbeat."""
-        content = self._read_heartbeat_file()
-        if not content:
-            return None
-        action, tasks = await self._decide(content)
-        if action != "run" or not self.on_execute:
-            return None
-        return await self.on_execute(tasks)

nanobot/nanobot.py

@@ -8,6 +8,7 @@ from typing import Any
 
 from nanobot.agent.hook import AgentHook, SDKCaptureHook
 from nanobot.agent.loop import AgentLoop
+from nanobot.providers.image_generation import image_gen_provider_configs
 
 
 @dataclass(slots=True)
@@ -63,10 +64,7 @@ class Nanobot:
 
         loop = AgentLoop.from_config(
             config,
-            image_generation_provider_configs={
+            image_generation_provider_configs=image_gen_provider_configs(config),
-                "openrouter": config.providers.openrouter,
-                "aihubmix": config.providers.aihubmix,
-            },
         )
         return cls(loop)
 

nanobot/p2p/__init__.py

@@ -1,5 +0,0 @@
-"""P2P inter-agent coordination layer."""
-
-from nanobot.p2p.shell import P2PShell
-
-__all__ = ["P2PShell"]

nanobot/p2p/shell.py

@@ -1,426 +0,0 @@
-"""P2P shell: filesystem-backed inter-agent coordination.
-
-All state is stored in the mailbox filesystem; this class is stateless.
-Restarting the gateway restores all task state by scanning files.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-import time
-from pathlib import Path
-from typing import Any, Literal
-
-from loguru import logger
-
-
-class P2PShell:
-    """Stateless P2P coordination shell backed by the mailbox filesystem."""
-
-    def __init__(self, agent_id: str, mailboxes_root: str):
-        self.agent_id = agent_id
-        self.root = Path(mailboxes_root).expanduser()
-        self.inbox = self.root / agent_id / "inbox"
-        self.processed = self.root / agent_id / "processed"
-        self.links_dir = self.root / "_links"
-        self.windows_dir = self.root / "_windows"
-
-        for d in (self.inbox, self.processed, self.links_dir, self.windows_dir):
-            d.mkdir(parents=True, exist_ok=True)
-
-    # ------------------------------------------------------------------
-    # Discovery
-    # ------------------------------------------------------------------
-
-    def discover(self, capability: str, top_k: int = 3) -> list[dict[str, Any]]:
-        """Read _registry.json and return candidates matching capability."""
-        registry = self._load_json(self.root / "_registry.json", default={})
-        candidates: list[dict[str, Any]] = []
-        for aid, info in registry.items():
-            if aid == self.agent_id:
-                continue
-            caps = info.get("capabilities", [])
-            if capability.lower() in " ".join(caps).lower():
-                candidates.append({"agent_id": aid, **info})
-        # Sort: idle first, then by current task load
-        candidates.sort(key=lambda x: (x.get("status") != "idle", x.get("current_tasks", 0)))
-        return candidates[:top_k]
-
-    def heartbeat(self, description: str, capabilities: list[str]) -> None:
-        """Write self state into the shared _registry.json."""
-        registry = self._load_json(self.root / "_registry.json", default={})
-        registry[self.agent_id] = {
-            "description": description,
-            "capabilities": capabilities,
-            "status": "idle",
-            "last_heartbeat": int(time.time()),
-            "endpoint": "",
-        }
-        self._atomic_write(self.root / "_registry.json", registry)
-
-    # ------------------------------------------------------------------
-    # Task dispatch
-    # ------------------------------------------------------------------
-
-    def dispatch(
-        self,
-        to: str,
-        parent_task_id: str | None,
-        description: str,
-        deadline_seconds: int = 300,
-        allow_redelegation: bool = True,
-    ) -> dict[str, Any]:
-        """Write a task into the target agent's inbox and return a receipt."""
-        task_id = (
-            f"{parent_task_id}.{int(time.time())}"
-            if parent_task_id
-            else f"root_{int(time.time())}"
-        )
-
-        depth = self._get_depth(parent_task_id) if parent_task_id else 0
-        if depth >= 3:
-            return {"status": "rejected", "reason": "max_depth_exceeded"}
-
-        if parent_task_id and self._is_ancestor(to, parent_task_id):
-            return {"status": "rejected", "reason": "ancestry_loop"}
-
-        if not self._circuit_allow(to):
-            failover = self._find_failover(to)
-            return {"status": "circuit_open", "failover_to": failover}
-
-        target_inbox = self.root / to / "inbox"
-        target_inbox.mkdir(parents=True, exist_ok=True)
-        if list(target_inbox.glob(f"task_{task_id}_from_{self.agent_id}_*.json")):
-            return {"status": "dispatched", "task_id": task_id, "note": "cached"}
-
-        ancestry = (
-            (self._get_ancestry(parent_task_id) + [self.agent_id])
-            if parent_task_id
-            else [self.agent_id]
-        )
-
-        msg: dict[str, Any] = {
-            "version": "p2p/v1",
-            "type": "task_dispatch",
-            "from": self.agent_id,
-            "to": to,
-            "task_id": task_id,
-            "ancestry": ancestry,
-            "depth": depth + 1,
-            "payload": {
-                "description": description,
-                "allow_redelegation": allow_redelegation,
-            },
-            "deadline": int(time.time()) + deadline_seconds,
-            "timestamp": int(time.time()),
-        }
-
-        path = target_inbox / f"task_{task_id}_from_{self.agent_id}_{os.urandom(4).hex()}.json"
-        self._atomic_write(path, msg)
-        logger.info("P2P dispatch: {} -> {} (task_id={})", self.agent_id, to, task_id)
-        return {"status": "dispatched", "task_id": task_id, "depth": depth + 1}
-
-    def poll(self, task_id: str) -> dict[str, Any]:
-        """Scan inbox/processed and return task status."""
-        # Check processed results first
-        results = list(self.processed.glob(f"result_{task_id}_from_*.json"))
-        if results:
-            data = self._load_json(results[0])
-            payload = data.get("payload", {})
-            return {
-                "status": payload.get("outcome", "completed"),
-                "result": payload.get("content", ""),
-                "from": data["from"],
-            }
-
-        # Check inbox for results (not yet moved to processed)
-        inbox_results = list(self.inbox.glob(f"result_{task_id}_from_*.json"))
-        if inbox_results:
-            data = self._load_json(inbox_results[0])
-            payload = data.get("payload", {})
-            return {
-                "status": payload.get("outcome", "completed"),
-                "result": payload.get("content", ""),
-                "from": data["from"],
-            }
-
-        # Check inbox for pending task dispatches
-        pending = list(self.inbox.glob(f"task_{task_id}_from_*.json"))
-        if pending:
-            data = self._load_json(pending[0])
-            deadline = data.get("deadline", 0)
-            elapsed = int(time.time() - data["timestamp"])
-            if time.time() > deadline:
-                return {"status": "timeout", "elapsed": elapsed}
-            return {"status": "pending", "elapsed": elapsed}
-
-        return {"status": "not_found"}
-
-    # ------------------------------------------------------------------
-    # Aggregation (broadcast + check)
-    # ------------------------------------------------------------------
-
-    def broadcast(
-        self,
-        task_id: str,
-        subtasks: list[dict[str, Any]],
-        aggregation_timeout: int = 30,
-    ) -> dict[str, Any]:
-        """Write bid requests to candidate agents and create a window descriptor."""
-        targets: list[tuple[str, str]] = []  # (subtask_id, agent_id)
-        for sub in subtasks:
-            caps = sub.get("capability", "")
-            found = self.discover(caps, top_k=3)
-            targets.extend([(sub["subtask_id"], a["agent_id"]) for a in found])
-
-        for subtask_id, target in targets:
-            msg: dict[str, Any] = {
-                "version": "p2p/v1",
-                "type": "bid_request",
-                "from": self.agent_id,
-                "to": target,
-                "task_id": task_id,
-                "subtask_id": subtask_id,
-                "payload": sub,
-                "deadline": int(time.time()) + aggregation_timeout,
-                "timestamp": int(time.time()),
-            }
-            target_inbox = self.root / target / "inbox"
-            target_inbox.mkdir(parents=True, exist_ok=True)
-            path = target_inbox / f"bid_{task_id}_{subtask_id}_from_{self.agent_id}.json"
-            self._atomic_write(path, msg)
-
-        window: dict[str, Any] = {
-            "task_id": task_id,
-            "mode": "bid",
-            "expected": len(targets),
-            "deadline": int(time.time()) + aggregation_timeout,
-            "created_at": int(time.time()),
-        }
-        self._atomic_write(self.windows_dir / f"{task_id}.json", window)
-        logger.info(
-            "P2P broadcast: {} invited {} agents for task_id={}",
-            self.agent_id,
-            len(targets),
-            task_id,
-        )
-        return {"status": "bidding_opened", "task_id": task_id, "invited": len(targets)}
-
-    def check_aggregation(self, task_id: str) -> dict[str, Any]:
-        """Lazily check aggregation status by scanning files."""
-        window_path = self.windows_dir / f"{task_id}.json"
-        if not window_path.exists():
-            return {"status": "no_window"}
-
-        window = self._load_json(window_path)
-        mode = window.get("mode", "bid")
-        deadline = window.get("deadline", 0)
-
-        pattern = f"{mode}_{task_id}_*_from_*.json"
-        entries: list[dict[str, Any]] = []
-        for f in self.inbox.glob(pattern):
-            data = self._load_json(f)
-            entries.append(
-                {
-                    "from": data.get("from", ""),
-                    "subtask_id": data.get("subtask_id", ""),
-                    "payload": data.get("payload", {}),
-                }
-            )
-
-        is_timeout = time.time() > deadline
-        is_full = window.get("expected") and len(entries) >= window["expected"]
-
-        if is_timeout or is_full:
-            self._atomic_write(
-                self.processed / f"window_{task_id}.json",
-                {**window, "closed_at": int(time.time()), "received": len(entries)},
-            )
-            window_path.unlink(missing_ok=True)
-            return {
-                "status": "closed",
-                "mode": mode,
-                "entries": entries,
-                "reason": "timeout" if is_timeout else "full",
-            }
-
-        return {
-            "status": "pending",
-            "received": len(entries),
-            "expected": window.get("expected"),
-            "seconds_remaining": max(0, deadline - int(time.time())),
-        }
-
-    # ------------------------------------------------------------------
-    # Result reporting
-    # ------------------------------------------------------------------
-
-    def report_result(
-        self,
-        to: str,
-        task_id: str,
-        outcome: Literal["completed", "failed", "aborted"],
-        content: str,
-        callback: dict[str, Any] | None = None,
-    ) -> None:
-        """Worker calls this to write a result into the manager's inbox."""
-        msg: dict[str, Any] = {
-            "version": "p2p/v1",
-            "type": "result",
-            "from": self.agent_id,
-            "to": to,
-            "task_id": task_id,
-            "payload": {"outcome": outcome, "content": content},
-            "timestamp": int(time.time()),
-        }
-        if callback:
-            msg["callback"] = callback
-        target_inbox = self.root / to / "inbox"
-        target_inbox.mkdir(parents=True, exist_ok=True)
-        path = target_inbox / f"result_{task_id}_from_{self.agent_id}_{os.urandom(4).hex()}.json"
-        self._atomic_write(path, msg)
-        logger.info("P2P result: {} -> {} (task_id={}, outcome={})", self.agent_id, to, task_id, outcome)
-
-    # ------------------------------------------------------------------
-    # Finalization
-    # ------------------------------------------------------------------
-
-    def finalize(self, task_id: str, outcome: str, reason: str = "") -> None:
-        """Move all task files from inbox to processed and mark outcome."""
-        for src in list(self.inbox.glob(f"*{task_id}*")):
-            data = self._load_json(src)
-            data.setdefault("payload", {})
-            data["payload"]["outcome"] = outcome
-            data["payload"]["reason"] = reason
-            dst = self.processed / src.name
-            self._atomic_write(dst, data)
-            src.unlink(missing_ok=True)
-        logger.info("P2P finalize: task_id={} outcome={}", task_id, outcome)
-
-    # ------------------------------------------------------------------
-    # Circuit breaker
-    # ------------------------------------------------------------------
-
-    def _circuit_allow(self, to: str) -> bool:
-        link = self._load_json(
-            self.links_dir / f"{to}.json",
-            default={"failures": 0, "last_failure": 0, "open": False},
-        )
-        if not link.get("open"):
-            return True
-        backoff = 300 * (2 ** max(0, link.get("failures", 0) - 3))
-        if time.time() - link.get("last_failure", 0) > backoff:
-            link["open"] = False
-            self._atomic_write(self.links_dir / f"{to}.json", link)
-            return True
-        return False
-
-    def record_failure(self, to: str) -> None:
-        link = self._load_json(
-            self.links_dir / f"{to}.json",
-            default={"failures": 0, "last_failure": 0, "open": False},
-        )
-        link["failures"] = link.get("failures", 0) + 1
-        link["last_failure"] = int(time.time())
-        if link["failures"] >= 3:
-            link["open"] = True
-        self._atomic_write(self.links_dir / f"{to}.json", link)
-
-    def record_success(self, to: str) -> None:
-        link = self._load_json(
-            self.links_dir / f"{to}.json",
-            default={"failures": 0, "last_failure": 0, "open": False},
-        )
-        link["failures"] = 0
-        link["open"] = False
-        self._atomic_write(self.links_dir / f"{to}.json", link)
-
-    # ------------------------------------------------------------------
-    # Inbox scanning (for HeartbeatService)
-    # ------------------------------------------------------------------
-
-    def scan_inbox(self) -> list[dict[str, Any]]:
-        """Return all task_dispatch messages currently in inbox."""
-        messages: list[dict[str, Any]] = []
-        for f in sorted(self.inbox.glob("task_*_from_*.json"), key=lambda p: p.stat().st_mtime):
-            data = self._load_json(f)
-            # Skip expired tasks
-            if time.time() > data.get("deadline", 0):
-                continue
-            data["_filename"] = f.name
-            messages.append(data)
-        return messages
-
-    def scan_new_inbox(self, since: float | None = None) -> list[dict[str, Any]]:
-        """Return inbox messages newer than the given timestamp."""
-        messages: list[dict[str, Any]] = []
-        for f in self.inbox.glob("task_*_from_*.json"):
-            mtime = f.stat().st_mtime
-            if since is not None and mtime <= since:
-                continue
-            data = self._load_json(f)
-            if time.time() > data.get("deadline", 0):
-                continue
-            data["_filename"] = f.name
-            data["_mtime"] = mtime
-            messages.append(data)
-        return sorted(messages, key=lambda x: x.get("_mtime", 0))
-
-    def mark_processed(self, filename: str) -> None:
-        """Move a single inbox file to processed."""
-        src = self.inbox / filename
-        if not src.exists():
-            return
-        dst = self.processed / filename
-        try:
-            import shutil
-            shutil.move(str(src), str(dst))
-        except Exception:
-            logger.warning("Failed to mark processed: {}", filename)
-
-    # ------------------------------------------------------------------
-    # Helpers
-    # ------------------------------------------------------------------
-
-    def _load_json(self, path: Path, default: Any | None = None) -> Any:
-        if not path.exists():
-            return default if default is not None else {}
-        with open(path, "r", encoding="utf-8") as f:
-            return json.load(f)
-
-    def _atomic_write(self, path: Path, data: dict[str, Any]) -> None:
-        tmp = path.with_suffix(".tmp")
-        with open(tmp, "w", encoding="utf-8") as f:
-            json.dump(data, f, ensure_ascii=False, indent=2)
-        tmp.rename(path)
-
-    def _get_depth(self, task_id: str) -> int:
-        return task_id.count(".")
-
-    def _is_ancestor(self, agent_id: str, parent_task_id: str) -> bool:
-        for f in list(self.processed.glob(f"*{parent_task_id}*")) + list(
-            self.inbox.glob(f"*{parent_task_id}*")
-        ):
-            data = self._load_json(f)
-            if agent_id in data.get("ancestry", []):
-                return True
-        return False
-
-    def _get_ancestry(self, task_id: str) -> list[str]:
-        for f in list(self.processed.glob(f"*{task_id}*")) + list(
-            self.inbox.glob(f"*{task_id}*")
-        ):
-            data = self._load_json(f)
-            return data.get("ancestry", [])
-        return []
-
-    def _find_failover(self, to: str) -> str | None:
-        registry = self._load_json(self.root / "_registry.json", default={})
-        target_caps = registry.get(to, {}).get("capabilities", [])
-        for aid, info in registry.items():
-            if aid == to:
-                continue
-            if any(c in info.get("capabilities", []) for c in target_caps):
-                return aid
-        return None

nanobot/providers/anthropic_provider.py

@@ -45,13 +45,21 @@ class AnthropicProvider(LLMProvider):
         if api_key:
             client_kw["api_key"] = api_key
         if api_base:
-            client_kw["base_url"] = api_base
+            client_kw["base_url"] = self._normalize_base_url(api_base)
         if extra_headers:
             client_kw["default_headers"] = extra_headers
         # Keep retries centralized in LLMProvider._run_with_retry to avoid retry amplification.
         client_kw["max_retries"] = 0
         self._client = AsyncAnthropic(**client_kw)
 
+    @staticmethod
+    def _normalize_base_url(api_base: str) -> str:
+        """Anthropic SDK appends /v1 to request paths internally."""
+        normalized = api_base.rstrip("/")
+        if normalized.endswith("/v1"):
+            return normalized[: -len("/v1")]
+        return normalized
+
     @classmethod
     def _handle_error(cls, e: Exception) -> LLMResponse:
         response = getattr(e, "response", None)
@@ -228,6 +236,13 @@ class AnthropicProvider(LLMProvider):
                 if converted:
                     result.append(converted)
                 continue
+            if not item.get("type"):
+                # Anthropic requires every content block to declare a "type".
+                # A tool that returned a bare dict (or a list of dicts) lands
+                # here; coerce it to a text block instead of emitting a block
+                # the API rejects with "content.0.type: Field required".
+                result.append({"type": "text", "text": str(item)})
+                continue
             result.append(item)
         return result or "(empty)"
 
@@ -590,6 +605,7 @@ class AnthropicProvider(LLMProvider):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         kwargs = self._build_kwargs(
             messages, tools, model, max_tokens, temperature,
@@ -598,11 +614,12 @@ class AnthropicProvider(LLMProvider):
         idle_timeout_s = int(os.environ.get("NANOBOT_STREAM_IDLE_TIMEOUT_S", "90"))
         try:
             async with self._client.messages.stream(**kwargs) as stream:
-                if on_content_delta or on_thinking_delta:
+                if on_content_delta or on_thinking_delta or on_tool_call_delta:
                     # Idle timeout must track *any* SSE chunk (thinking_delta,
                     # tool JSON deltas, etc.), not only text_stream tokens.
                     # Otherwise extended thinking can stall text_stream for minutes
                     # while the connection is healthy (e.g. MiniMax Anthropic).
+                    tool_blocks: dict[int, dict[str, str]] = {}
                     while True:
                         try:
                             chunk = await asyncio.wait_for(
@@ -611,7 +628,22 @@ class AnthropicProvider(LLMProvider):
                             )
                         except StopAsyncIteration:
                             break
-                        if (
+                        if chunk.type == "content_block_start":
+                            block = getattr(chunk, "content_block", None)
+                            if getattr(block, "type", None) == "tool_use":
+                                index = int(getattr(chunk, "index", 0) or 0)
+                                state = {
+                                    "call_id": str(getattr(block, "id", "") or ""),
+                                    "name": str(getattr(block, "name", "") or ""),
+                                }
+                                tool_blocks[index] = state
+                                if on_tool_call_delta:
+                                    await on_tool_call_delta({
+                                        "index": index,
+                                        **state,
+                                        "arguments_delta": "",
+                                    })
+                        elif (
                             chunk.type == "content_block_delta"
                             and getattr(chunk.delta, "type", None) == "thinking_delta"
                         ):
@@ -625,6 +657,20 @@ class AnthropicProvider(LLMProvider):
                             text = getattr(chunk.delta, "text", None) or ""
                             if text and on_content_delta:
                                 await on_content_delta(text)
+                        elif (
+                            chunk.type == "content_block_delta"
+                            and getattr(chunk.delta, "type", None) == "input_json_delta"
+                        ):
+                            partial = getattr(chunk.delta, "partial_json", None) or ""
+                            if partial and on_tool_call_delta:
+                                index = int(getattr(chunk, "index", 0) or 0)
+                                state = tool_blocks.get(index, {})
+                                await on_tool_call_delta({
+                                    "index": index,
+                                    "call_id": state.get("call_id", ""),
+                                    "name": state.get("name", ""),
+                                    "arguments_delta": partial,
+                                })
                 response = await asyncio.wait_for(
                     stream.get_final_message(),
                     timeout=idle_timeout_s,

nanobot/providers/azure_openai_provider.py

@@ -158,6 +158,7 @@ class AzureOpenAIProvider(LLMProvider):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         _ = on_thinking_delta
         body = self._build_body(
@@ -169,7 +170,7 @@ class AzureOpenAIProvider(LLMProvider):
         try:
             stream = await self._client.responses.create(**body)
             content, tool_calls, finish_reason, usage, reasoning_content = (
-                await consume_sdk_stream(stream, on_content_delta)
+                await consume_sdk_stream(stream, on_content_delta, on_tool_call_delta)
             )
             return LLMResponse(
                 content=content or None,

nanobot/providers/base.py

@@ -70,11 +70,11 @@ class LLMResponse:
 
     @property
     def should_execute_tools(self) -> bool:
-        """Tools execute only when has_tool_calls AND finish_reason is ``tool_calls`` / ``stop``.
+        """Tools execute only when has_tool_calls AND finish_reason is a tool-capable stop.
         Blocks gateway-injected calls under ``refusal`` / ``content_filter`` / ``error`` (#3220)."""
         if not self.has_tool_calls:
             return False
-        return self.finish_reason in ("tool_calls", "stop")
+        return self.finish_reason in ("tool_calls", "function_call", "stop")
 
 
 @dataclass(frozen=True)
@@ -112,6 +112,7 @@ class LLMProvider(ABC):
         "server error",
         "temporarily unavailable",
         "速率限制",
+        "访问量过大",
     )
     _RETRYABLE_STATUS_CODES = frozenset({408, 409, 429})
     _TRANSIENT_ERROR_KINDS = frozenset({"timeout", "connection"})
@@ -314,6 +315,29 @@ class LLMProvider(ABC):
 
         return cls._is_transient_error(response.content)
 
+    @classmethod
+    def is_arrearage_response(cls, response: LLMResponse) -> bool:
+        """Detect API-key arrearage / quota / billing errors that won't clear on retry.
+
+        These surface as HTTP 402 or as billing semantic tokens (e.g.
+        ``insufficient_quota``, ``payment_required``); reuses the same token and
+        text markers the 429 retry policy treats as non-retryable.
+        """
+        if response.error_status_code is not None and int(response.error_status_code) == 402:
+            return True
+
+        type_token = cls._normalize_error_token(response.error_type)
+        code_token = cls._normalize_error_token(response.error_code)
+        if any(
+            token in cls._NON_RETRYABLE_429_ERROR_TOKENS
+            for token in (type_token, code_token)
+            if token is not None
+        ):
+            return True
+
+        content = (response.content or "").lower()
+        return any(marker in content for marker in cls._NON_RETRYABLE_429_TEXT_MARKERS)
+
     @staticmethod
     def _normalize_error_token(value: Any) -> str | None:
         if value is None:
@@ -500,6 +524,7 @@ class LLMProvider(ABC):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         """Stream a chat completion, calling *on_content_delta* for each text chunk.
 
@@ -513,7 +538,7 @@ class LLMProvider(ABC):
         full content as a single delta.  Providers that support native
         streaming should override this method.
         """
-        _ = on_thinking_delta
+        _ = on_thinking_delta, on_tool_call_delta
         response = await self.chat(
             messages=messages, tools=tools, model=model,
             max_tokens=max_tokens, temperature=temperature,
@@ -543,6 +568,7 @@ class LLMProvider(ABC):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
         retry_mode: str = "standard",
         on_retry_wait: Callable[[str], Awaitable[None]] | None = None,
     ) -> LLMResponse:
@@ -554,12 +580,22 @@ class LLMProvider(ABC):
         if reasoning_effort is self._SENTINEL:
             reasoning_effort = self.generation.reasoning_effort
 
+        has_streamed_content = False
+
+        async def _tracking_delta(text: str) -> None:
+            nonlocal has_streamed_content
+            if text:
+                has_streamed_content = True
+            if on_content_delta:
+                await on_content_delta(text)
+
         kw: dict[str, Any] = dict(
             messages=messages, tools=tools, model=model,
             max_tokens=max_tokens, temperature=temperature,
             reasoning_effort=reasoning_effort, tool_choice=tool_choice,
-            on_content_delta=on_content_delta,
+            on_content_delta=_tracking_delta if on_content_delta is not None else None,
             on_thinking_delta=on_thinking_delta,
+            on_tool_call_delta=on_tool_call_delta,
         )
         return await self._run_with_retry(
             self._safe_chat_stream,
@@ -567,6 +603,7 @@ class LLMProvider(ABC):
             messages,
             retry_mode=retry_mode,
             on_retry_wait=on_retry_wait,
+            should_retry_guard=lambda: not has_streamed_content,
         )
 
     async def chat_with_retry(
@@ -713,6 +750,7 @@ class LLMProvider(ABC):
         *,
         retry_mode: str,
         on_retry_wait: Callable[[str], Awaitable[None]] | None,
+        should_retry_guard: Callable[[], bool] | None = None,
     ) -> LLMResponse:
         attempt = 0
         delays = list(self._CHAT_RETRY_DELAYS)
@@ -726,6 +764,11 @@ class LLMProvider(ABC):
             if response.finish_reason != "error":
                 return response
             last_response = response
+            if should_retry_guard is not None and not should_retry_guard():
+                logger.warning(
+                    "LLM stream failed after content was emitted; skipping retry"
+                )
+                return response
             error_key = ((response.content or "").strip().lower() or None)
             if error_key and error_key == last_error_key:
                 identical_error_count += 1

nanobot/providers/bedrock_provider.py

@@ -704,8 +704,9 @@ class BedrockProvider(LLMProvider):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
-        _ = on_thinking_delta
+        _ = on_thinking_delta, on_tool_call_delta
         idle_timeout_s = int(os.environ.get("NANOBOT_STREAM_IDLE_TIMEOUT_S", "90"))
         content_parts: list[str] = []
         reasoning_parts: list[str] = []

nanobot/providers/factory.py

@@ -98,6 +98,7 @@ def _make_provider_core(
             extra_headers=p.extra_headers if p else None,
             spec=spec,
             extra_body=p.extra_body if p else None,
+            api_type=p.api_type if p and provider_name == "openai" else "auto",
         )
 
     provider.generation = resolved.to_generation_settings()
@@ -183,6 +184,7 @@ def provider_signature(
             config.get_api_base(fallback.model, preset=fallback),
             fp.extra_headers if fp else None,
             fp.extra_body if fp else None,
+            fp.api_type if fp else "auto",
             getattr(fp, "region", None) if fp else None,
             getattr(fp, "profile", None) if fp else None,
             fallback.max_tokens,
@@ -199,6 +201,7 @@ def provider_signature(
         config.get_api_base(resolved.model, preset=resolved),
         p.extra_headers if p else None,
         p.extra_body if p else None,
+        p.api_type if p else "auto",
         getattr(p, "region", None) if p else None,
         getattr(p, "profile", None) if p else None,
         resolved.max_tokens,

nanobot/providers/github_copilot_provider.py

@@ -207,8 +207,9 @@ class GitHubCopilotProvider(OpenAICompatProvider):
 
     async def _refresh_client_api_key(self) -> str:
         token = await self._get_copilot_access_token()
+        client = await self._ensure_client()
         self.api_key = token
-        self._client.api_key = token
+        client.api_key = token
         return token
 
     async def chat(
@@ -243,6 +244,7 @@ class GitHubCopilotProvider(OpenAICompatProvider):
         tool_choice: str | dict[str, object] | None = None,
         on_content_delta: Callable[[str], None] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, object]], Awaitable[None]] | None = None,
     ):
         await self._refresh_client_api_key()
         return await super().chat_stream(
@@ -255,4 +257,5 @@ class GitHubCopilotProvider(OpenAICompatProvider):
             tool_choice=tool_choice,
             on_content_delta=on_content_delta,
             on_thinking_delta=on_thinking_delta,
+            on_tool_call_delta=on_tool_call_delta,
         )

nanobot/providers/openai_codex_provider.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import asyncio
 import hashlib
 import json
+import os
 from collections.abc import Awaitable, Callable
 from typing import Any
 
@@ -14,7 +15,7 @@ from oauth_cli_kit import get_token as get_codex_token
 
 from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
 from nanobot.providers.openai_responses import (
-    consume_sse,
+    consume_sse_with_reasoning,
     convert_messages,
     convert_tools,
 )
@@ -40,6 +41,8 @@ class OpenAICodexProvider(LLMProvider):
         reasoning_effort: str | None,
         tool_choice: str | dict[str, Any] | None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         """Shared request logic for both chat() and chat_stream()."""
         model = model or self.default_model
@@ -60,30 +63,52 @@ class OpenAICodexProvider(LLMProvider):
             "tool_choice": tool_choice or "auto",
             "parallel_tool_calls": True,
         }
-        if reasoning_effort and reasoning_effort.lower() != "none":
+        reasoning_options = _build_reasoning_options(reasoning_effort)
-            body["reasoning"] = {"effort": reasoning_effort}
+        if reasoning_options:
+            body["reasoning"] = reasoning_options
         if tools:
             body["tools"] = convert_tools(tools)
 
         try:
             try:
-                content, tool_calls, finish_reason = await _request_codex(
+                content, tool_calls, finish_reason, reasoning_content = await _request_codex(
                     DEFAULT_CODEX_URL, headers, body, verify=True,
                     on_content_delta=on_content_delta,
+                    on_thinking_delta=on_thinking_delta,
+                    on_tool_call_delta=on_tool_call_delta,
                 )
             except Exception as e:
                 if "CERTIFICATE_VERIFY_FAILED" not in str(e):
                     raise
                 logger.warning("SSL verification failed for Codex API; retrying with verify=False")
-                content, tool_calls, finish_reason = await _request_codex(
+                content, tool_calls, finish_reason, reasoning_content = await _request_codex(
                     DEFAULT_CODEX_URL, headers, body, verify=False,
                     on_content_delta=on_content_delta,
+                    on_thinking_delta=on_thinking_delta,
+                    on_tool_call_delta=on_tool_call_delta,
                 )
-            return LLMResponse(content=content, tool_calls=tool_calls, finish_reason=finish_reason)
+            return LLMResponse(
+                content=content,
+                tool_calls=tool_calls,
+                finish_reason=finish_reason,
+                reasoning_content=reasoning_content,
+            )
         except Exception as e:
-            msg = f"Error calling Codex: {e}"
+            response = _codex_error_response(e)
-            retry_after = getattr(e, "retry_after", None) or self._extract_retry_after(msg)
+            exc_type = "CodexHTTPError" if isinstance(e, _CodexHTTPError) else type(e).__name__
-            return LLMResponse(content=msg, finish_reason="error", retry_after=retry_after)
+            logger.warning(
+                "Codex API request failed: type={} kind={} retryable={} status={} "
+                "error_type={} error_code={} retry_after={} summary={}",
+                exc_type,
+                response.error_kind,
+                response.error_should_retry,
+                response.error_status_code,
+                response.error_type,
+                response.error_code,
+                response.retry_after,
+                _codex_log_summary(exc_type, response),
+            )
+            return response
 
     async def chat(
         self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
@@ -100,9 +125,18 @@ class OpenAICodexProvider(LLMProvider):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
-        _ = on_thinking_delta
+        return await self._call_codex(
-        return await self._call_codex(messages, tools, model, reasoning_effort, tool_choice, on_content_delta)
+            messages,
+            tools,
+            model,
+            reasoning_effort,
+            tool_choice,
+            on_content_delta,
+            on_thinking_delta,
+            on_tool_call_delta,
+        )
 
     def get_default_model(self) -> str:
         return self.default_model
@@ -114,6 +148,16 @@ def _strip_model_prefix(model: str) -> str:
     return model
 
 
+def _build_reasoning_options(reasoning_effort: str | None) -> dict[str, str] | None:
+    """Opt in to visible summaries without changing provider-default effort."""
+    if reasoning_effort and reasoning_effort.lower() == "none":
+        return {"effort": "none"}
+    options = {"summary": "auto"}
+    if reasoning_effort:
+        options["effort"] = reasoning_effort
+    return options
+
+
 def _build_headers(account_id: str, token: str) -> dict[str, str]:
     return {
         "Authorization": f"Bearer {token}",
@@ -127,9 +171,22 @@ def _build_headers(account_id: str, token: str) -> dict[str, str]:
 
 
 class _CodexHTTPError(RuntimeError):
-    def __init__(self, message: str, retry_after: float | None = None):
+    def __init__(
+        self,
+        message: str,
+        *,
+        status_code: int | None = None,
+        retry_after: float | None = None,
+        error_type: str | None = None,
+        error_code: str | None = None,
+        should_retry: bool | None = None,
+    ):
         super().__init__(message)
+        self.status_code = status_code
         self.retry_after = retry_after
+        self.error_type = error_type
+        self.error_code = error_code
+        self.should_retry = should_retry
 
 
 async def _request_codex(
@@ -138,17 +195,31 @@ async def _request_codex(
     body: dict[str, Any],
     verify: bool,
     on_content_delta: Callable[[str], Awaitable[None]] | None = None,
-) -> tuple[str, list[ToolCallRequest], str]:
+    on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
-    async with httpx.AsyncClient(timeout=60.0, verify=verify) as client:
+    on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
+) -> tuple[str, list[ToolCallRequest], str, str | None]:
+    idle_timeout_s = int(os.environ.get("NANOBOT_STREAM_IDLE_TIMEOUT_S", "90"))
+    async with httpx.AsyncClient(timeout=idle_timeout_s, verify=verify) as client:
         async with client.stream("POST", url, headers=headers, json=body) as response:
             if response.status_code != 200:
                 text = await response.aread()
+                raw = text.decode("utf-8", "ignore")
                 retry_after = LLMProvider._extract_retry_after_from_headers(response.headers)
+                error_type, error_code = LLMProvider._extract_error_type_code(raw)
                 raise _CodexHTTPError(
-                    _friendly_error(response.status_code, text.decode("utf-8", "ignore")),
+                    _friendly_error(response.status_code, raw),
+                    status_code=response.status_code,
                     retry_after=retry_after,
+                    error_type=error_type,
+                    error_code=error_code,
+                    should_retry=_should_retry_status(response.status_code, error_type, error_code, raw),
                 )
-            return await consume_sse(response, on_content_delta)
+            return await consume_sse_with_reasoning(
+                response,
+                on_content_delta=on_content_delta,
+                on_tool_call_delta=on_tool_call_delta,
+                on_reasoning_delta=on_thinking_delta,
+            )
 
 
 def _prompt_cache_key(messages: list[dict[str, Any]]) -> str:
@@ -157,6 +228,94 @@ def _prompt_cache_key(messages: list[dict[str, Any]]) -> str:
 
 
 def _friendly_error(status_code: int, raw: str) -> str:
+    _ = raw
     if status_code == 429:
         return "ChatGPT usage quota exceeded or rate limit triggered. Please try again later."
-    return f"HTTP {status_code}: {raw}"
+    return f"HTTP {status_code}: Codex API request failed"
+
+
+def _codex_error_response(exc: Exception) -> LLMResponse:
+    """Convert Codex transport/API failures into actionable, retryable metadata."""
+    exc_type = "CodexHTTPError" if isinstance(exc, _CodexHTTPError) else type(exc).__name__
+    detail = str(exc).strip()
+
+    status_code = getattr(exc, "status_code", None)
+    error_kind: str | None = None
+    default_detail: str | None = None
+    should_retry: bool | None = getattr(exc, "should_retry", None)
+
+    if isinstance(exc, (httpx.TimeoutException, asyncio.TimeoutError)):
+        error_kind = "timeout"
+        default_detail = "timed out waiting for response"
+        should_retry = True if should_retry is None else should_retry
+    elif isinstance(exc, httpx.RemoteProtocolError):
+        error_kind = "connection"
+        default_detail = "network protocol error while reading response"
+        should_retry = True if should_retry is None else should_retry
+    elif isinstance(exc, (httpx.NetworkError, httpx.TransportError)):
+        error_kind = "connection"
+        default_detail = "network connection failed"
+        should_retry = True if should_retry is None else should_retry
+    elif isinstance(exc, _CodexHTTPError):
+        error_kind = "http"
+        default_detail = "HTTP request failed"
+
+    if status_code is not None and should_retry is None:
+        retry_content = None if int(status_code) == 429 and isinstance(exc, _CodexHTTPError) else detail
+        should_retry = _should_retry_status(
+            int(status_code),
+            getattr(exc, "error_type", None),
+            getattr(exc, "error_code", None),
+            retry_content,
+        )
+
+    detail = detail or default_detail or "unexpected error"
+    message = f"Error calling Codex ({exc_type}): {detail}"
+    retry_after = getattr(exc, "retry_after", None) or LLMProvider._extract_retry_after(message)
+    return LLMResponse(
+        content=message,
+        finish_reason="error",
+        retry_after=retry_after,
+        error_status_code=int(status_code) if status_code is not None else None,
+        error_kind=error_kind,
+        error_type=getattr(exc, "error_type", None),
+        error_code=getattr(exc, "error_code", None),
+        error_retry_after_s=retry_after,
+        error_should_retry=should_retry,
+    )
+
+
+def _codex_log_summary(exc_type: str, response: LLMResponse) -> str:
+    """Return a bounded diagnostic summary without request body or raw upstream payload."""
+    if response.error_status_code is not None:
+        parts = [f"HTTP {response.error_status_code}"]
+        if response.error_type:
+            parts.append(f"type={response.error_type}")
+        if response.error_code:
+            parts.append(f"code={response.error_code}")
+        return " ".join(parts)
+
+    kind = (response.error_kind or "").strip()
+    if kind:
+        return f"{exc_type} {kind}"
+
+    return exc_type
+
+
+def _should_retry_status(
+    status_code: int,
+    error_type: str | None,
+    error_code: str | None,
+    content: str | None,
+) -> bool:
+    if status_code == 429:
+        return LLMProvider._is_retryable_429_response(
+            LLMResponse(
+                content=content or "",
+                finish_reason="error",
+                error_status_code=status_code,
+                error_type=error_type,
+                error_code=error_code,
+            )
+        )
+    return status_code in LLMProvider._RETRYABLE_STATUS_CODES or status_code >= 500

nanobot/providers/openai_compat_provider.py

@@ -11,25 +11,15 @@ import secrets
 import string
 import time
 import uuid
+from collections import deque
 from collections.abc import Awaitable, Callable
 from ipaddress import ip_address
 from typing import TYPE_CHECKING, Any
 from urllib.parse import urlparse
 
-import httpx
 import json_repair
 from loguru import logger
 
-if os.environ.get("LANGFUSE_SECRET_KEY") and importlib.util.find_spec("langfuse"):
-    from langfuse.openai import AsyncOpenAI
-else:
-    if os.environ.get("LANGFUSE_SECRET_KEY"):
-        logger.warning(
-            "LANGFUSE_SECRET_KEY is set but langfuse is not installed; "
-            "install with `pip install langfuse` to enable tracing"
-        )
-    from openai import AsyncOpenAI
-
 from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
 from nanobot.providers.openai_responses import (
     consume_sdk_stream,
@@ -39,8 +29,15 @@ from nanobot.providers.openai_responses import (
 )
 
 if TYPE_CHECKING:
+    from openai import AsyncOpenAI as AsyncOpenAIType
+
     from nanobot.providers.registry import ProviderSpec
 
+# Module-level placeholder — set lazily by _ensure_client on first real
+# use, or replaced by tests via ``patch(...)``.  Kept as a plain name so
+# that ``unittest.mock.patch`` can find and replace it.
+AsyncOpenAI: Any = None
+
 _ALLOWED_MSG_KEYS = frozenset({
     "role", "content", "tool_calls", "tool_call_id", "name",
     "reasoning_content", "extra_content",
@@ -78,41 +75,43 @@ _THINKING_STYLE_MAP: dict[str, Any] = {
     "enable_thinking": lambda on: {"enable_thinking": on},
     "reasoning_split": lambda on: {"reasoning_split": on},
 }
+_GATEWAY_REASONING_STYLE_MAP: dict[str, Any] = {
+    "reasoning_effort": lambda effort: {"reasoning": {"effort": effort}},
+}
+_MODEL_THINKING_STYLES: dict[str, str] = {
+    **dict.fromkeys(_KIMI_THINKING_MODELS, "thinking_type"),
+    **dict.fromkeys(_MIMO_THINKING_MODELS, "thinking_type"),
+}
 
 
-def _is_kimi_thinking_model(model_name: str) -> bool:
+def _model_slug(model_name: str) -> str:
-    """Return True if model_name refers to a Kimi thinking-capable model.
+    return model_name.lower().rsplit("/", 1)[-1]
-
-    Supports two forms:
-    - Exact match: e.g. kimi-k2.5 / kimi-k2.6 in _KIMI_THINKING_MODELS
-    - Slug match:  moonshotai/kimi-k2.5 -> the part after the last "/"
-                   is checked against _KIMI_THINKING_MODELS
-
-    This covers both the native Moonshot provider (bare slug) and
-    OpenRouter-style names (``"publisher/slug"``).
-    """
-    name = model_name.lower()
-    if name in _KIMI_THINKING_MODELS:
-        return True
-    if "/" in name and name.rsplit("/", 1)[1] in _KIMI_THINKING_MODELS:
-        return True
-    return False
 
 
-def _is_mimo_thinking_model(model_name: str) -> bool:
+def _model_thinking_style(model_name: str) -> str:
-    """Return True if model_name refers to a MiMo thinking-capable model.
+    return _MODEL_THINKING_STYLES.get(_model_slug(model_name), "")
 
-    Mirrors _is_kimi_thinking_model: gateway providers (e.g. OpenRouter
+
-    routing ``xiaomi/mimo-v2.5-pro``) have no ``thinking_style`` on their
+def _thinking_styles_for(spec: ProviderSpec | None, model_name: str) -> list[str]:
-    spec, so the spec-driven branch in _build_kwargs misses them. The
+    styles: list[str] = []
-    model-name path catches those cases.
+    if spec and spec.thinking_style:
-    """
+        styles.append(spec.thinking_style)
-    name = model_name.lower()
+    model_style = _model_thinking_style(model_name)
-    if name in _MIMO_THINKING_MODELS:
+    if model_style and model_style not in styles:
-        return True
+        styles.append(model_style)
-    if "/" in name and name.rsplit("/", 1)[1] in _MIMO_THINKING_MODELS:
+    return styles
-        return True
+
-    return False
+
+def _thinking_extra_body(style: str, thinking_enabled: bool) -> dict[str, Any] | None:
+    builder = _THINKING_STYLE_MAP.get(style)
+    return builder(thinking_enabled) if builder else None
+
+
+def _gateway_reasoning_extra_body(style: str, effort: str | None) -> dict[str, Any] | None:
+    if not effort:
+        return None
+    builder = _GATEWAY_REASONING_STYLE_MAP.get(style)
+    return builder(effort) if builder else None
 
 
 def _openai_compat_timeout_s() -> float:
@@ -275,6 +274,47 @@ def _deep_merge(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any
     return merged
 
 
+def _merge_unique_list(base: Any, override: Any) -> Any:
+    """Append list values while preserving order and removing duplicates."""
+    if not isinstance(base, list) or not isinstance(override, list):
+        return override
+    result: list[Any] = []
+    seen: set[str] = set()
+    for value in [*base, *override]:
+        try:
+            key = json.dumps(value, sort_keys=True, ensure_ascii=False)
+        except Exception:
+            key = repr(value)
+        if key in seen:
+            continue
+        seen.add(key)
+        result.append(value)
+    return result
+
+
+def _merge_responses_extra_body(
+    body: dict[str, Any],
+    extra_body: dict[str, Any],
+) -> dict[str, Any]:
+    """Merge configured Responses API body fields without clobbering tools."""
+    reserved = {"include", "tools"}
+    regular_extra = {key: value for key, value in extra_body.items() if key not in reserved}
+    merged = _deep_merge(body, regular_extra)
+
+    if "include" in extra_body:
+        merged["include"] = _merge_unique_list(body.get("include"), extra_body["include"])
+
+    if "tools" in extra_body:
+        current_tools = body.get("tools")
+        configured_tools = extra_body["tools"]
+        if isinstance(current_tools, list) and isinstance(configured_tools, list):
+            merged["tools"] = [*current_tools, *configured_tools]
+        else:
+            merged["tools"] = configured_tools
+
+    return merged
+
+
 class OpenAICompatProvider(LLMProvider):
     """Unified provider for all OpenAI-compatible APIs.
 
@@ -290,55 +330,90 @@ class OpenAICompatProvider(LLMProvider):
         extra_headers: dict[str, str] | None = None,
         spec: ProviderSpec | None = None,
         extra_body: dict[str, Any] | None = None,
+        api_type: str = "auto",
     ):
         super().__init__(api_key, api_base)
         self.default_model = default_model
         self.extra_headers = extra_headers or {}
         self._spec = spec
         self._extra_body = extra_body or {}
+        self._api_type = api_type if spec and spec.name == "openai" else "auto"
 
         if api_key and spec and spec.env_key:
             self._setup_env(api_key, api_base)
 
         effective_base = api_base or (spec.default_api_base if spec else None) or None
         self._effective_base = effective_base
-        default_headers = {"x-session-affinity": uuid.uuid4().hex}
+        self._default_headers = {"x-session-affinity": uuid.uuid4().hex}
         if _uses_openrouter_attribution(spec, effective_base):
-            default_headers.update(_DEFAULT_OPENROUTER_HEADERS)
+            self._default_headers.update(_DEFAULT_OPENROUTER_HEADERS)
         if extra_headers:
-            default_headers.update(extra_headers)
+            self._default_headers.update(extra_headers)
+        self._api_key_for_client = api_key or "no-key"
+        self._is_local = _is_local_endpoint(spec, effective_base)
 
-        # Local model servers (Ollama, llama.cpp, vLLM) often close idle
+        # Lazy-init: the OpenAI client and its httpx transport are expensive
-        # HTTP connections before the client-side keepalive expires.  When
+        # to create (~700 ms on Windows). Defer until first use.
-        # two LLM calls happen seconds apart (e.g. heartbeat _decide then
+        self._client: AsyncOpenAIType | None = None
-        # process_direct), the second call may grab a now-dead pooled
+        self._client_lock = asyncio.Lock()
-        # connection, causing a transient APIConnectionError on every first
-        # attempt.  Disabling keepalive for local endpoints avoids this by
-        # opening a fresh connection for each request, which is cheap on a
-        # LAN.  Cloud providers benefit from keepalive, so we leave the
-        # default pool settings for them.
-        timeout_s = _openai_compat_timeout_s()
-        http_client: httpx.AsyncClient | None = None
-        if _is_local_endpoint(spec, effective_base):
-            http_client = httpx.AsyncClient(
-                limits=httpx.Limits(keepalive_expiry=0),
-                timeout=timeout_s,
-            )
-
-        self._client = AsyncOpenAI(
-            api_key=api_key or "no-key",
-            base_url=effective_base,
-            default_headers=default_headers,
-            max_retries=0,
-            timeout=timeout_s,
-            http_client=http_client,
-        )
 
         # Responses API circuit breaker: skip after repeated failures,
         # probe again after _RESPONSES_PROBE_INTERVAL_S seconds.
         self._responses_failures: dict[str, int] = {}
         self._responses_tripped_at: dict[str, float] = {}
 
+    def _build_client(self) -> None:
+        """Create the OpenAI client using the current module-level AsyncOpenAI."""
+        import httpx
+
+        timeout_s = _openai_compat_timeout_s()
+        http_client: httpx.AsyncClient | None = None
+        if self._is_local:
+            # Local model servers (Ollama, llama.cpp, vLLM) often close idle
+            # HTTP connections before the client-side keepalive expires. When
+            # two LLM calls happen seconds apart (e.g. heartbeat _decide then
+            # process_direct), the second call may grab a now-dead pooled
+            # connection, causing a transient APIConnectionError on every first
+            # attempt. Disabling keepalive for local endpoints avoids this by
+            # opening a fresh connection for each request, which is cheap on a
+            # LAN. Cloud providers benefit from keepalive, so we leave the
+            # default pool settings for them.
+            http_client = httpx.AsyncClient(
+                limits=httpx.Limits(keepalive_expiry=0),
+                timeout=timeout_s,
+            )
+        self._client = AsyncOpenAI(
+            api_key=self._api_key_for_client,
+            base_url=self._effective_base,
+            default_headers=self._default_headers,
+            max_retries=0,
+            timeout=timeout_s,
+            http_client=http_client,
+        )
+
+    async def _ensure_client(self):
+        """Return the shared OpenAI client, creating it on first call."""
+        if self._client is not None:
+            return self._client
+        async with self._client_lock:
+            if self._client is not None:
+                return self._client
+            global AsyncOpenAI
+            if AsyncOpenAI is None:
+                if os.environ.get("LANGFUSE_SECRET_KEY") and importlib.util.find_spec("langfuse"):
+                    from langfuse.openai import AsyncOpenAI as _AsyncOpenAI
+                else:
+                    if os.environ.get("LANGFUSE_SECRET_KEY"):
+                        logger.warning(
+                            "LANGFUSE_SECRET_KEY is set but langfuse is not installed; "
+                            "install with `pip install langfuse` to enable tracing"
+                        )
+                    from openai import AsyncOpenAI as _AsyncOpenAI
+                AsyncOpenAI = _AsyncOpenAI
+
+            self._build_client()
+            return self._client
+
     def _setup_env(self, api_key: str, api_base: str | None) -> None:
         """Set environment variables based on provider spec."""
         spec = self._spec
@@ -396,6 +471,10 @@ class OpenAICompatProvider(LLMProvider):
             return tool_call_id
         return hashlib.sha1(tool_call_id.encode()).hexdigest()[:9]
 
+    def _should_normalize_tool_call_ids(self) -> bool:
+        """Return True for providers that reject normal OpenAI tool call IDs."""
+        return bool(self._spec and self._spec.name == "mistral")
+
     @staticmethod
     def _normalize_tool_call_arguments(arguments: Any) -> str:
         """Force function.arguments into a valid JSON object string."""
@@ -432,22 +511,60 @@ class OpenAICompatProvider(LLMProvider):
         """Strip non-standard keys, normalize tool_call IDs."""
         sanitized = LLMProvider._sanitize_request_messages(messages, _ALLOWED_MSG_KEYS)
         id_map: dict[str, str] = {}
+        pending_tool_ids: dict[str, deque[str]] = {}
         force_string_content = bool(self._spec and self._spec.name == "deepseek")
+        normalize_tool_ids = self._should_normalize_tool_call_ids()
 
         def map_id(value: Any) -> Any:
             if not isinstance(value, str):
                 return value
+            if not normalize_tool_ids:
+                return value
             return id_map.setdefault(value, self._normalize_tool_call_id(value))
 
+        def unique_tool_id(value: Any, used_ids: set[str], idx: int) -> str:
+            if isinstance(value, str) and value:
+                base = map_id(value)
+            else:
+                base = _short_tool_id()
+            if not isinstance(base, str) or not base:
+                base = _short_tool_id()
+            if base not in used_ids:
+                return base
+            seed = value if isinstance(value, str) and value else base
+            salt = 1
+            while True:
+                candidate = self._normalize_tool_call_id(f"{seed}:{idx}:{salt}")
+                if isinstance(candidate, str) and candidate not in used_ids:
+                    return candidate
+                salt += 1
+
+        def map_tool_result_id(value: Any) -> Any:
+            if not isinstance(value, str):
+                return value
+            queue = pending_tool_ids.get(value)
+            if queue:
+                mapped = queue.popleft()
+                if not queue:
+                    pending_tool_ids.pop(value, None)
+                return mapped
+            return map_id(value)
+
         for clean in sanitized:
             if isinstance(clean.get("tool_calls"), list):
                 normalized = []
-                for tc in clean["tool_calls"]:
+                used_ids: set[str] = set()
+                for idx, tc in enumerate(clean["tool_calls"]):
                     if not isinstance(tc, dict):
                         normalized.append(tc)
                         continue
                     tc_clean = dict(tc)
-                    tc_clean["id"] = map_id(tc_clean.get("id"))
+                    raw_id = tc_clean.get("id")
+                    mapped_id = unique_tool_id(raw_id, used_ids, idx)
+                    tc_clean["id"] = mapped_id
+                    used_ids.add(mapped_id)
+                    if isinstance(raw_id, str) and raw_id:
+                        pending_tool_ids.setdefault(raw_id, deque()).append(mapped_id)
                     function = tc_clean.get("function")
                     if isinstance(function, dict):
                         function_clean = dict(function)
@@ -465,7 +582,7 @@ class OpenAICompatProvider(LLMProvider):
                     # that mix non-empty content with tool_calls.
                     clean["content"] = None
             if "tool_call_id" in clean and clean["tool_call_id"]:
-                clean["tool_call_id"] = map_id(clean["tool_call_id"])
+                clean["tool_call_id"] = map_tool_result_id(clean["tool_call_id"])
             if (
                 force_string_content
                 and not (clean.get("role") == "assistant" and clean.get("tool_calls"))
@@ -552,39 +669,27 @@ class OpenAICompatProvider(LLMProvider):
         if wire_effort and semantic_effort != "none":
             kwargs["reasoning_effort"] = wire_effort
 
-        # Provider-specific thinking parameters.
+        # Only send thinking controls when reasoning_effort is explicit so
-        # Only sent when reasoning_effort is explicitly configured so that
+        # omitting the config preserves each provider's default.
-        # the provider default is preserved otherwise.
+        if reasoning_effort is not None:
-        # The mapping is driven by ProviderSpec.thinking_style so that adding
-        # a new provider never requires touching this function.
-        if spec and spec.thinking_style and reasoning_effort is not None:
             thinking_enabled = semantic_effort not in ("none", "minimal")
-            extra = _THINKING_STYLE_MAP.get(spec.thinking_style, lambda _: None)(thinking_enabled)
+            for thinking_style in _thinking_styles_for(spec, model_name):
-            if extra:
+                extra = _thinking_extra_body(thinking_style, thinking_enabled)
-                kwargs.setdefault("extra_body", {}).update(extra)
+                if extra:
+                    kwargs.setdefault("extra_body", {}).update(extra)
+            gateway_style = getattr(spec, "gateway_reasoning_style", "") if spec else ""
+            if gateway_style and _model_thinking_style(model_name):
+                extra = _gateway_reasoning_extra_body(gateway_style, semantic_effort)
+                if extra:
+                    kwargs.setdefault("extra_body", {}).update(extra)
 
-        # Model-level thinking injection for Kimi thinking-capable models.
+            # Moonshot rejects requests that carry both 'reasoning_effort'
-        # Strip any provider prefix (e.g. "moonshotai/") before the set lookup
+            # and the native 'thinking' param.  We already expressed the
-        # so that OpenRouter-style names like "moonshotai/kimi-k2.5" are handled
+            # user's intent via the provider-native shape, so drop the
-        # identically to bare names like "kimi-k2.5".
+            # redundant wire-level kwarg.  Only kimi models need this —
-        if reasoning_effort is not None and _is_kimi_thinking_model(model_name):
+            # Xiaomi's API accepts both params.
-            thinking_enabled = semantic_effort not in ("none", "minimal")
+            if _model_slug(model_name) in _KIMI_THINKING_MODELS:
-            kwargs.setdefault("extra_body", {}).update(
+                kwargs.pop("reasoning_effort", None)
-                {"thinking": {"type": "enabled" if thinking_enabled else "disabled"}}
-            )
-
-        # Model-level thinking injection for MiMo thinking-capable models.
-        # Same shape as Kimi: gateway providers (OpenRouter, etc.) lack the
-        # xiaomi_mimo spec's thinking_style, so the spec-driven branch above
-        # misses them — match by model name to catch "xiaomi/mimo-v2.5-pro"
-        # and friends. (Direct xiaomi_mimo requests are also covered here;
-        # both branches write the same payload, so the dict update is a
-        # safe no-op for already-handled cases.)
-        if reasoning_effort is not None and _is_mimo_thinking_model(model_name):
-            thinking_enabled = semantic_effort not in ("none", "minimal")
-            kwargs.setdefault("extra_body", {}).update(
-                {"thinking": {"type": "enabled" if thinking_enabled else "disabled"}}
-            )
 
         if tools:
             kwargs["tools"] = tools
@@ -599,8 +704,7 @@ class OpenAICompatProvider(LLMProvider):
             and semantic_effort not in ("none", "minimal")
             and (
                 (spec and spec.thinking_style)
-                or _is_kimi_thinking_model(model_name)
+                or _model_thinking_style(model_name)
-                or _is_mimo_thinking_model(model_name)
             )
         )
         implicit_deepseek_thinking = (
@@ -631,8 +735,14 @@ class OpenAICompatProvider(LLMProvider):
         reasoning_effort: str | None,
     ) -> bool:
         """Use Responses API only for direct OpenAI requests that benefit from it."""
+        if self._api_type == "chat_completions":
+            return False
         if self._spec and self._spec.name not in ("openai", "github_copilot"):
             return False
+        if self._api_type == "responses":
+            # Explicit configuration means Responses is mandatory; do not
+            # consult the circuit breaker or fall back to Chat Completions.
+            return True
         if self._spec is None or self._spec.name != "github_copilot":
             if not _is_direct_openai_base(self._effective_base):
                 return False
@@ -646,7 +756,14 @@ class OpenAICompatProvider(LLMProvider):
         if not wants:
             return False
 
-        # Circuit breaker: skip after repeated failures, probe periodically.
+        return self._responses_circuit_allows_probe(model, reasoning_effort)
+
+    def _responses_circuit_allows_probe(
+        self,
+        model: str | None,
+        reasoning_effort: str | None,
+    ) -> bool:
+        """Return False when the Responses API circuit breaker is open."""
         key = _responses_circuit_key(model, self.default_model, reasoning_effort)
         failures = self._responses_failures.get(key, 0)
         if failures >= _RESPONSES_FAILURE_THRESHOLD:
@@ -738,6 +855,10 @@ class OpenAICompatProvider(LLMProvider):
             body["tools"] = convert_tools(tools)
             body["tool_choice"] = tool_choice or "auto"
 
+        extra_body = getattr(self, "_extra_body", {})
+        if extra_body:
+            body = _merge_responses_extra_body(body, extra_body)
+
         return body
 
     # ------------------------------------------------------------------
@@ -902,7 +1023,7 @@ class OpenAICompatProvider(LLMProvider):
                     args = json_repair.loads(args)
                 ec, prov, fn_prov = _extract_tc_extras(tc)
                 parsed_tool_calls.append(ToolCallRequest(
-                    id=_short_tool_id(),
+                    id=str(tc_map.get("id") or _short_tool_id()),
                     name=str(fn.get("name") or ""),
                     arguments=args if isinstance(args, dict) else {},
                     extra_content=ec,
@@ -945,7 +1066,7 @@ class OpenAICompatProvider(LLMProvider):
                 args = json_repair.loads(args)
             ec, prov, fn_prov = _extract_tc_extras(tc)
             tool_calls.append(ToolCallRequest(
-                id=_short_tool_id(),
+                id=str(getattr(tc, "id", None) or _short_tool_id()),
                 name=tc.function.name,
                 arguments=args,
                 extra_content=ec,
@@ -999,6 +1120,21 @@ class OpenAICompatProvider(LLMProvider):
             if fn_prov:
                 buf["fn_prov"] = fn_prov
 
+        def _accum_legacy_function_call(function_call: Any) -> None:
+            """Accumulate legacy ``delta.function_call`` streaming chunks."""
+            if not function_call:
+                return
+            buf = tc_bufs.setdefault(0, {
+                "id": "", "name": "", "arguments": "",
+                "extra_content": None, "prov": None, "fn_prov": None,
+            })
+            fn_name = _get(function_call, "name")
+            if fn_name:
+                buf["name"] = str(fn_name)
+            fn_args = _get(function_call, "arguments")
+            if fn_args:
+                buf["arguments"] += str(fn_args)
+
         for chunk in chunks:
             if isinstance(chunk, str):
                 content_parts.append(chunk)
@@ -1029,6 +1165,7 @@ class OpenAICompatProvider(LLMProvider):
                     reasoning_parts.append(text)
                 for idx, tc in enumerate(delta.get("tool_calls") or []):
                     _accum_tc(tc, idx)
+                _accum_legacy_function_call(delta.get("function_call"))
                 usage = cls._extract_usage(chunk_map) or usage
                 continue
 
@@ -1047,8 +1184,19 @@ class OpenAICompatProvider(LLMProvider):
                     reasoning = getattr(delta, "reasoning", None)
                 if reasoning:
                     reasoning_parts.append(reasoning)
-            for tc in (delta.tool_calls or []) if delta else []:
+            for tc in (getattr(delta, "tool_calls", None) or []) if delta else []:
                 _accum_tc(tc, getattr(tc, "index", 0))
+            if delta:
+                _accum_legacy_function_call(getattr(delta, "function_call", None))
+
+        # Some providers (e.g. Zhipu/GLM) reuse the same tool_call id for
+        # parallel tool calls in streaming mode. Deduplicate before building
+        # the response so downstream tool messages don't collide.
+        _seen_tc_ids: set[str] = set()
+        for b in tc_bufs.values():
+            if not b["id"] or b["id"] in _seen_tc_ids:
+                b["id"] = _short_tool_id()
+            _seen_tc_ids.add(b["id"])
 
         return LLMResponse(
             content="".join(content_parts) or None,
@@ -1164,6 +1312,7 @@ class OpenAICompatProvider(LLMProvider):
         reasoning_effort: str | None = None,
         tool_choice: str | dict[str, Any] | None = None,
     ) -> LLMResponse:
+        await self._ensure_client()
         try:
             if self._should_use_responses_api(model, reasoning_effort):
                 try:
@@ -1180,6 +1329,8 @@ class OpenAICompatProvider(LLMProvider):
                         # falling back to /chat/completions cannot succeed and would
                         # hide the real error.
                         raise
+                    if self._api_type == "responses":
+                        raise
                     if not self._should_fallback_from_responses_error(responses_error):
                         raise
                     self._record_responses_failure(model, reasoning_effort)
@@ -1203,7 +1354,9 @@ class OpenAICompatProvider(LLMProvider):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
+        on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
     ) -> LLMResponse:
+        await self._ensure_client()
         idle_timeout_s = int(os.environ.get("NANOBOT_STREAM_IDLE_TIMEOUT_S", "90"))
         try:
             if self._should_use_responses_api(model, reasoning_effort):
@@ -1226,9 +1379,16 @@ class OpenAICompatProvider(LLMProvider):
                             except StopAsyncIteration:
                                 break
 
-                    content, tool_calls, finish_reason, usage, reasoning_content = await consume_sdk_stream(
+                    (
+                        content,
+                        tool_calls,
+                        finish_reason,
+                        usage,
+                        reasoning_content,
+                    ) = await consume_sdk_stream(
                         _timed_stream(),
                         on_content_delta,
+                        on_tool_call_delta=on_tool_call_delta,
                     )
                     self._record_responses_success(model, reasoning_effort)
                     return LLMResponse(
@@ -1244,6 +1404,8 @@ class OpenAICompatProvider(LLMProvider):
                         # falling back to /chat/completions cannot succeed and would
                         # hide the real error.
                         raise
+                    if self._api_type == "responses":
+                        raise
                     if not self._should_fallback_from_responses_error(responses_error):
                         raise
                     self._record_responses_failure(model, reasoning_effort)
@@ -1252,6 +1414,12 @@ class OpenAICompatProvider(LLMProvider):
                 messages, tools, model, max_tokens, temperature,
                 reasoning_effort, tool_choice,
             )
+            if self._spec and self._spec.name == "zhipu" and tools and on_tool_call_delta:
+                # Z.AI/GLM keeps streaming tool-call arguments behind an
+                # explicit provider flag.  Pass it through the OpenAI SDK's
+                # extra_body escape hatch so the usual delta.tool_calls path
+                # can surface live file-edit progress.
+                kwargs.setdefault("extra_body", {})["tool_stream"] = True
             kwargs["stream"] = True
             kwargs["stream_options"] = {"include_usage": True}
             stream = await self._client.chat.completions.create(**kwargs)
@@ -1279,6 +1447,28 @@ class OpenAICompatProvider(LLMProvider):
                         r_text = self._extract_text_content(reasoning)
                         if r_text:
                             await on_thinking_delta(r_text)
+                    if on_tool_call_delta:
+                        for idx, tool_delta in enumerate(
+                            getattr(delta_obj, "tool_calls", None) or []
+                        ):
+                            fn = _get(tool_delta, "function")
+                            tool_index = _get(tool_delta, "index")
+                            await on_tool_call_delta({
+                                "index": tool_index if tool_index is not None else idx,
+                                "call_id": str(_get(tool_delta, "id") or ""),
+                                "name": str(_get(fn, "name") or "") if fn is not None else "",
+                                "arguments_delta": (
+                                    str(_get(fn, "arguments") or "") if fn is not None else ""
+                                ),
+                            })
+                        function_call = getattr(delta_obj, "function_call", None)
+                        if function_call:
+                            await on_tool_call_delta({
+                                "index": 0,
+                                "call_id": "",
+                                "name": str(_get(function_call, "name") or ""),
+                                "arguments_delta": str(_get(function_call, "arguments") or ""),
+                            })
             return self._parse_chunks(chunks)
         except asyncio.TimeoutError:
             return LLMResponse(

nanobot/providers/openai_responses/__init__.py

@@ -10,6 +10,7 @@ from nanobot.providers.openai_responses.parsing import (
     FINISH_REASON_MAP,
     consume_sdk_stream,
     consume_sse,
+    consume_sse_with_reasoning,
     iter_sse,
     map_finish_reason,
     parse_response_output,
@@ -22,6 +23,7 @@ __all__ = [
     "split_tool_call_id",
     "iter_sse",
     "consume_sse",
+    "consume_sse_with_reasoning",
     "consume_sdk_stream",
     "map_finish_reason",
     "parse_response_output",

nanobot/providers/openai_responses/converters.py

@@ -15,6 +15,7 @@ def convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str
     """
     system_prompt = ""
     input_items: list[dict[str, Any]] = []
+    used_item_ids: set[str] = set()
 
     for idx, msg in enumerate(messages):
         role = msg.get("role")
@@ -30,17 +31,19 @@ def convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str
 
         if role == "assistant":
             if isinstance(content, str) and content:
+                message_id = _unique_item_id(f"msg_{idx}", used_item_ids)
                 input_items.append({
                     "type": "message", "role": "assistant",
                     "content": [{"type": "output_text", "text": content}],
-                    "status": "completed", "id": f"msg_{idx}",
+                    "status": "completed", "id": message_id,
                 })
             for tool_call in msg.get("tool_calls", []) or []:
                 fn = tool_call.get("function") or {}
                 call_id, item_id = split_tool_call_id(tool_call.get("id"))
+                response_item_id = _unique_item_id(item_id or f"fc_{idx}", used_item_ids)
                 input_items.append({
                     "type": "function_call",
-                    "id": item_id or f"fc_{idx}",
+                    "id": response_item_id,
                     "call_id": call_id or f"call_{idx}",
                     "name": fn.get("name"),
                     "arguments": fn.get("arguments") or "{}",
@@ -97,6 +100,20 @@ def convert_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
     return converted
 
 
+def _unique_item_id(item_id: str, used: set[str]) -> str:
+    """Return a Responses input item id that is unique within one request."""
+    if item_id not in used:
+        used.add(item_id)
+        return item_id
+
+    suffix = 2
+    while f"{item_id}_{suffix}" in used:
+        suffix += 1
+    unique = f"{item_id}_{suffix}"
+    used.add(unique)
+    return unique
+
+
 def split_tool_call_id(tool_call_id: Any) -> tuple[str, str | None]:
     """Split a compound ``call_id|item_id`` string.
 

nanobot/providers/openai_responses/parsing.py

@@ -62,12 +62,31 @@ async def iter_sse(response: httpx.Response) -> AsyncGenerator[dict[str, Any], N
 async def consume_sse(
     response: httpx.Response,
     on_content_delta: Callable[[str], Awaitable[None]] | None = None,
+    on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
 ) -> tuple[str, list[ToolCallRequest], str]:
     """Consume a Responses API SSE stream into ``(content, tool_calls, finish_reason)``."""
+    content, tool_calls, finish_reason, _ = await consume_sse_with_reasoning(
+        response,
+        on_content_delta=on_content_delta,
+        on_tool_call_delta=on_tool_call_delta,
+    )
+    return content, tool_calls, finish_reason
+
+
+async def consume_sse_with_reasoning(
+    response: httpx.Response,
+    on_content_delta: Callable[[str], Awaitable[None]] | None = None,
+    on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
+    on_reasoning_delta: Callable[[str], Awaitable[None]] | None = None,
+) -> tuple[str, list[ToolCallRequest], str, str | None]:
+    """Consume a Responses API SSE stream, including visible reasoning summaries."""
     content = ""
     tool_calls: list[ToolCallRequest] = []
     tool_call_buffers: dict[str, dict[str, Any]] = {}
+    tool_call_args_emitted: set[str] = set()
     finish_reason = "stop"
+    reasoning_content: str | None = None
+    streamed_reasoning = False
 
     async for event in iter_sse(response):
         event_type = event.get("type")
@@ -82,19 +101,60 @@ async def consume_sse(
                     "name": item.get("name"),
                     "arguments": item.get("arguments") or "",
                 }
+                if on_tool_call_delta:
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(item.get("name") or ""),
+                        "arguments_delta": "",
+                    })
         elif event_type == "response.output_text.delta":
             delta_text = event.get("delta") or ""
             content += delta_text
             if on_content_delta and delta_text:
                 await on_content_delta(delta_text)
+        elif event_type == "response.reasoning_summary_text.delta":
+            delta_text = event.get("delta") or ""
+            if delta_text:
+                reasoning_content = (reasoning_content or "") + delta_text
+                streamed_reasoning = True
+                if on_reasoning_delta:
+                    await on_reasoning_delta(delta_text)
+        elif event_type == "response.reasoning_summary_text.done":
+            text = event.get("text") or ""
+            if text and not streamed_reasoning and not reasoning_content:
+                reasoning_content = text
+                if on_reasoning_delta:
+                    await on_reasoning_delta(text)
+        elif event_type == "response.reasoning_summary_part.done":
+            part = event.get("part") or {}
+            text = part.get("text") if part.get("type") == "summary_text" else None
+            if text and not streamed_reasoning and not reasoning_content:
+                reasoning_content = text
+                if on_reasoning_delta:
+                    await on_reasoning_delta(text)
         elif event_type == "response.function_call_arguments.delta":
             call_id = event.get("call_id")
             if call_id and call_id in tool_call_buffers:
-                tool_call_buffers[call_id]["arguments"] += event.get("delta") or ""
+                delta = event.get("delta") or ""
+                tool_call_buffers[call_id]["arguments"] += delta
+                if on_tool_call_delta and delta:
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(tool_call_buffers[call_id].get("name") or ""),
+                        "arguments_delta": str(delta),
+                    })
         elif event_type == "response.function_call_arguments.done":
             call_id = event.get("call_id")
             if call_id and call_id in tool_call_buffers:
-                tool_call_buffers[call_id]["arguments"] = event.get("arguments") or ""
+                arguments = event.get("arguments") or ""
+                tool_call_buffers[call_id]["arguments"] = arguments
+                if on_tool_call_delta:
+                    tool_call_args_emitted.add(str(call_id))
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(tool_call_buffers[call_id].get("name") or ""),
+                        "arguments": str(arguments),
+                    })
         elif event_type == "response.output_item.done":
             item = event.get("item") or {}
             if item.get("type") == "function_call":
@@ -103,6 +163,13 @@ async def consume_sse(
                     continue
                 buf = tool_call_buffers.get(call_id) or {}
                 args_raw = buf.get("arguments") or item.get("arguments") or "{}"
+                if on_tool_call_delta and str(call_id) not in tool_call_args_emitted:
+                    tool_call_args_emitted.add(str(call_id))
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(buf.get("name") or item.get("name") or ""),
+                        "arguments": str(args_raw),
+                    })
                 try:
                     args = json.loads(args_raw)
                 except Exception:
@@ -121,14 +188,44 @@ async def consume_sse(
                         arguments=args,
                     )
                 )
+            elif item.get("type") == "reasoning" and not reasoning_content:
+                summary = _extract_reasoning_summary_from_output([item])
+                if summary:
+                    reasoning_content = summary
+                    if on_reasoning_delta:
+                        await on_reasoning_delta(summary)
         elif event_type == "response.completed":
-            status = (event.get("response") or {}).get("status")
+            response_obj = event.get("response") or {}
+            status = response_obj.get("status")
             finish_reason = map_finish_reason(status)
+            if not reasoning_content:
+                summary = _extract_reasoning_summary_from_output(response_obj.get("output") or [])
+                if summary:
+                    reasoning_content = summary
+                    if on_reasoning_delta:
+                        await on_reasoning_delta(summary)
         elif event_type in {"error", "response.failed"}:
             detail = event.get("error") or event.get("message") or event
             raise RuntimeError(f"Response failed: {str(detail)[:500]}")
 
-    return content, tool_calls, finish_reason
+    return content, tool_calls, finish_reason, reasoning_content
+
+
+def _extract_reasoning_summary_from_output(output: Any) -> str | None:
+    parts: list[str] = []
+    for item in output or []:
+        if not isinstance(item, dict):
+            dump = getattr(item, "model_dump", None)
+            item = dump() if callable(dump) else vars(item)
+        if item.get("type") != "reasoning":
+            continue
+        for summary in item.get("summary") or []:
+            if not isinstance(summary, dict):
+                dump = getattr(summary, "model_dump", None)
+                summary = dump() if callable(dump) else vars(summary)
+            if summary.get("type") == "summary_text" and summary.get("text"):
+                parts.append(summary["text"])
+    return "".join(parts) or None
 
 
 def parse_response_output(response: Any) -> LLMResponse:
@@ -210,11 +307,13 @@ def parse_response_output(response: Any) -> LLMResponse:
 async def consume_sdk_stream(
     stream: Any,
     on_content_delta: Callable[[str], Awaitable[None]] | None = None,
+    on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
 ) -> tuple[str, list[ToolCallRequest], str, dict[str, int], str | None]:
     """Consume an SDK async stream from ``client.responses.create(stream=True)``."""
     content = ""
     tool_calls: list[ToolCallRequest] = []
     tool_call_buffers: dict[str, dict[str, Any]] = {}
+    tool_call_args_emitted: set[str] = set()
     finish_reason = "stop"
     usage: dict[str, int] = {}
     reasoning_content: str | None = None
@@ -232,6 +331,12 @@ async def consume_sdk_stream(
                     "name": getattr(item, "name", None),
                     "arguments": getattr(item, "arguments", None) or "",
                 }
+                if on_tool_call_delta:
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(getattr(item, "name", None) or ""),
+                        "arguments_delta": "",
+                    })
         elif event_type == "response.output_text.delta":
             delta_text = getattr(event, "delta", "") or ""
             content += delta_text
@@ -240,11 +345,26 @@ async def consume_sdk_stream(
         elif event_type == "response.function_call_arguments.delta":
             call_id = getattr(event, "call_id", None)
             if call_id and call_id in tool_call_buffers:
-                tool_call_buffers[call_id]["arguments"] += getattr(event, "delta", "") or ""
+                delta = getattr(event, "delta", "") or ""
+                tool_call_buffers[call_id]["arguments"] += delta
+                if on_tool_call_delta and delta:
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(tool_call_buffers[call_id].get("name") or ""),
+                        "arguments_delta": str(delta),
+                    })
         elif event_type == "response.function_call_arguments.done":
             call_id = getattr(event, "call_id", None)
             if call_id and call_id in tool_call_buffers:
-                tool_call_buffers[call_id]["arguments"] = getattr(event, "arguments", "") or ""
+                arguments = getattr(event, "arguments", "") or ""
+                tool_call_buffers[call_id]["arguments"] = arguments
+                if on_tool_call_delta:
+                    tool_call_args_emitted.add(str(call_id))
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(tool_call_buffers[call_id].get("name") or ""),
+                        "arguments": str(arguments),
+                    })
         elif event_type == "response.output_item.done":
             item = getattr(event, "item", None)
             if item and getattr(item, "type", None) == "function_call":
@@ -253,6 +373,13 @@ async def consume_sdk_stream(
                     continue
                 buf = tool_call_buffers.get(call_id) or {}
                 args_raw = buf.get("arguments") or getattr(item, "arguments", None) or "{}"
+                if on_tool_call_delta and str(call_id) not in tool_call_args_emitted:
+                    tool_call_args_emitted.add(str(call_id))
+                    await on_tool_call_delta({
+                        "call_id": str(call_id),
+                        "name": str(buf.get("name") or getattr(item, "name", None) or ""),
+                        "arguments": str(args_raw),
+                    })
                 try:
                     args = json.loads(args_raw)
                 except Exception:

nanobot/providers/registry.py

@@ -71,6 +71,11 @@ class ProviderSpec:
     # "reasoning_split" — {"reasoning_split": true/false}  (MiniMax)
     thinking_style: str = ""
 
+    # Gateway-native reasoning control to pair with model-level thinking styles.
+    # "reasoning_effort" — {"reasoning": {"effort": <none|minimal|...>}}
+    #                      (OpenRouter)
+    gateway_reasoning_style: str = ""
+
     # When True, treat the "reasoning" response field as formal content
     # when "content" is empty.  Only set this for providers (e.g. StepFun)
     # whose API returns the actual answer in "reasoning" instead of "content".
@@ -142,6 +147,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         detect_by_base_keyword="openrouter",
         default_api_base="https://openrouter.ai/api/v1",
         supports_prompt_caching=True,
+        gateway_reasoning_style="reasoning_effort",
     ),
     # Hugging Face Inference Providers: OpenAI-compatible router for chat models.
     ProviderSpec(
@@ -155,6 +161,18 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         detect_by_base_keyword="huggingface",
         default_api_base="https://router.huggingface.co/v1",
     ),
+    # Skywork API platform (APIFree): OpenAI-compatible MaaS gateway.
+    ProviderSpec(
+        name="skywork",
+        keywords=("skywork", "skyclaw", "apifree"),
+        env_key="SKYWORK_API_KEY",
+        display_name="Skywork",
+        backend="openai_compat",
+        env_extras=(("APIFREE_API_KEY", "{api_key}"),),
+        is_gateway=True,
+        detect_by_base_keyword="apifree.ai",
+        default_api_base="https://api.apifree.ai/agent/v1",
+    ),
     # AiHubMix: global gateway, OpenAI-compatible interface.
     # strip_model_prefix=True: doesn't understand "anthropic/claude-3",
     # strips to bare "claude-3".
@@ -181,6 +199,18 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         default_api_base="https://api.siliconflow.cn/v1",
     ),
 
+    # Novita AI: OpenAI-compatible gateway for hosted model APIs.
+    ProviderSpec(
+        name="novita",
+        keywords=("novita",),
+        env_key="NOVITA_API_KEY",
+        display_name="Novita AI",
+        backend="openai_compat",
+        is_gateway=True,
+        detect_by_base_keyword="novita",
+        default_api_base="https://api.novita.ai/openai",
+    ),
+
     # VolcEngine (火山引擎): OpenAI-compatible gateway, pay-per-use models
     ProviderSpec(
         name="volcengine",
@@ -390,13 +420,23 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         backend="openai_compat",
         default_api_base="https://api.longcat.chat/openai/v1",
     ),
+    # Ant Ling: OpenAI-compatible API for Ling/Ring model families.
+    ProviderSpec(
+        name="ant_ling",
+        keywords=("ant_ling", "ant-ling", "ling-", "ring-"),
+        env_key="ANT_LING_API_KEY",
+        display_name="Ant Ling",
+        backend="openai_compat",
+        detect_by_base_keyword="ant-ling.com",
+        default_api_base="https://api.ant-ling.com/v1",
+    ),
     # === Local deployment (matched by config key, NOT by api_base) =========
     # vLLM / any OpenAI-compatible local server
     ProviderSpec(
         name="vllm",
         keywords=("vllm",),
         env_key="HOSTED_VLLM_API_KEY",
-        display_name="vLLM/Local",
+        display_name="vLLM",
         backend="openai_compat",
         is_local=True,
     ),

nanobot/providers/transcription.py

@@ -7,6 +7,25 @@ from pathlib import Path
 import httpx
 from loguru import logger
 
+_TRANSCRIPTIONS_PATH = "audio/transcriptions"
+
+
+def _resolve_transcription_url(api_base: str | None, default_url: str) -> str:
+    """Resolve the full transcription endpoint URL.
+
+    Accepts either a chat-style base (e.g. ``https://api.groq.com/openai/v1``)
+    or a complete URL already ending in ``/audio/transcriptions``. A chat-style
+    base — the form users naturally copy from their LLM provider config — gets
+    the path appended instead of being POSTed verbatim and 404ing (#3637).
+    """
+    if not api_base:
+        return default_url
+    base = api_base.rstrip("/")
+    if base.endswith(_TRANSCRIPTIONS_PATH):
+        return base
+    return f"{base}/{_TRANSCRIPTIONS_PATH}"
+
+
 # Up to 3 retries (4 attempts total) with exponential backoff on transient
 # failures. Whisper endpoints occasionally return 502/503 under load, and
 # mobile-network transcription callers hit sporadic connect/read errors.
@@ -127,12 +146,12 @@ class OpenAITranscriptionProvider:
         language: str | None = None,
     ):
         self.api_key = api_key or os.environ.get("OPENAI_API_KEY")
-        self.api_url = (
+        self.api_url = _resolve_transcription_url(
-            api_base
+            api_base or os.environ.get("OPENAI_TRANSCRIPTION_BASE_URL"),
-            or os.environ.get("OPENAI_TRANSCRIPTION_BASE_URL")
+            "https://api.openai.com/v1/audio/transcriptions",
-            or "https://api.openai.com/v1/audio/transcriptions"
         )
         self.language = language or None
+        logger.debug("OpenAI transcription endpoint: {}", self.api_url)
 
     async def transcribe(self, file_path: str | Path) -> str:
         if not self.api_key:
@@ -166,12 +185,12 @@ class GroqTranscriptionProvider:
         language: str | None = None,
     ):
         self.api_key = api_key or os.environ.get("GROQ_API_KEY")
-        self.api_url = (
+        self.api_url = _resolve_transcription_url(
-            api_base
+            api_base or os.environ.get("GROQ_BASE_URL"),
-            or os.environ.get("GROQ_BASE_URL")
+            "https://api.groq.com/openai/v1/audio/transcriptions",
-            or "https://api.groq.com/openai/v1/audio/transcriptions"
         )
         self.language = language or None
+        logger.debug("Groq transcription endpoint: {}", self.api_url)
 
     async def transcribe(self, file_path: str | Path) -> str:
         """

nanobot/security/network.py

@@ -36,15 +36,36 @@ def configure_ssrf_whitelist(cidrs: list[str]) -> None:
     _allowed_networks = nets
 
 
+def _normalize_addr(
+    addr: ipaddress.IPv4Address | ipaddress.IPv6Address,
+) -> ipaddress.IPv4Address | ipaddress.IPv6Address:
+    """Normalize IPv6-mapped IPv4 addresses to their IPv4 form.
+
+    ``::ffff:127.0.0.1`` is semantically identical to ``127.0.0.1`` but
+    Python's ipaddress treats it as an IPv6Address that matches neither
+    ``127.0.0.0/8`` nor ``::1/128``.  Converting it to IPv4 ensures
+    blocklist/allowlist checks work correctly.
+    """
+    if isinstance(addr, ipaddress.IPv6Address) and addr.ipv4_mapped is not None:
+        return addr.ipv4_mapped
+    return addr
+
+
 def _is_private(addr: ipaddress.IPv4Address | ipaddress.IPv6Address) -> bool:
-    if _allowed_networks and any(addr in net for net in _allowed_networks):
+    normalized = _normalize_addr(addr)
+    if _allowed_networks and any(normalized in net for net in _allowed_networks):
         return False
-    return any(addr in net for net in _BLOCKED_NETWORKS)
+    return any(normalized in net for net in _BLOCKED_NETWORKS)
 
 
-def validate_url_target(url: str) -> tuple[bool, str]:
+def validate_url_target(url: str, *, allow_loopback: bool = False) -> tuple[bool, str]:
     """Validate a URL is safe to fetch: scheme, hostname, and resolved IPs.
 
+    ``allow_loopback`` is intentionally narrow: it only permits literal
+    loopback hosts (localhost, 127.0.0.0/8, ::1) when every resolved address is
+    loopback. It does not allow RFC1918, link-local, metadata, or public DNS
+    names that happen to resolve to loopback.
+
     Returns (ok, error_message).  When ok is True, error_message is empty.
     """
     try:
@@ -66,11 +87,16 @@ def validate_url_target(url: str) -> tuple[bool, str]:
     except socket.gaierror:
         return False, f"Cannot resolve hostname: {hostname}"
 
+    addrs: list[ipaddress.IPv4Address | ipaddress.IPv6Address] = []
     for info in infos:
         try:
             addr = ipaddress.ip_address(info[4][0])
         except ValueError:
             continue
+        addrs.append(addr)
+    if allow_loopback and _is_allowed_loopback_target(hostname, addrs):
+        return True, ""
+    for addr in addrs:
         if _is_private(addr):
             return False, f"Blocked: {hostname} resolves to private/internal address {addr}"
 
@@ -109,11 +135,25 @@ def validate_resolved_url(url: str) -> tuple[bool, str]:
     return True, ""
 
 
-def contains_internal_url(command: str) -> bool:
+def contains_internal_url(command: str, *, allow_loopback: bool = False) -> bool:
     """Return True if the command string contains a URL targeting an internal/private address."""
     for m in _URL_RE.finditer(command):
         url = m.group(0)
-        ok, _ = validate_url_target(url)
+        ok, _ = validate_url_target(url, allow_loopback=allow_loopback)
         if not ok:
             return True
     return False
+
+
+def _is_allowed_loopback_target(
+    hostname: str,
+    addrs: list[ipaddress.IPv4Address | ipaddress.IPv6Address],
+) -> bool:
+    if not addrs or not all(_normalize_addr(addr).is_loopback for addr in addrs):
+        return False
+    normalized = hostname.rstrip(".").lower()
+    if normalized == "localhost":
+        return True
+    with suppress(ValueError):
+        return ipaddress.ip_address(hostname).is_loopback
+    return False

nanobot/security/workspace_access.py

@@ -0,0 +1,430 @@
+"""Workspace access scope and sandbox capability helpers."""
+
+from __future__ import annotations
+
+import os
+from contextvars import ContextVar, Token
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Literal
+
+WorkspaceAccessMode = Literal["restricted", "full"]
+WORKSPACE_SCOPE_METADATA_KEY = "workspace_scope"
+_ACCESS_MODES = {"restricted", "full"}
+
+_TRUE_VALUES = {"1", "true", "yes", "on", "enabled"}
+_FALSE_VALUES = {"0", "false", "no", "off", "disabled", ""}
+_PROVIDER_LABELS = {
+    "none": "None",
+    "unknown": "Unknown system sandbox",
+    "macos_app_sandbox": "macOS App Sandbox",
+    "bwrap": "Bubblewrap",
+}
+
+_CURRENT_WORKSPACE_SCOPE: ContextVar["WorkspaceScope | None"] = ContextVar(
+    "nanobot_workspace_scope",
+    default=None,
+)
+
+
+class WorkspaceScopeError(ValueError):
+    """Raised when a requested WebUI workspace scope is invalid."""
+
+    status = 400
+
+    def __init__(self, message: str, *, status: int = 400) -> None:
+        super().__init__(message)
+        self.message = message
+        self.status = status
+
+
+@dataclass(frozen=True)
+class WorkspaceSandboxStatus:
+    """Resolved workspace sandbox state for runtime display and tooling."""
+
+    restrict_to_workspace: bool
+    workspace_root: str
+    level: str
+    enforced: bool
+    provider: str
+    provider_label: str
+    summary: str
+
+    def as_dict(self) -> dict[str, object]:
+        return {
+            "restrict_to_workspace": self.restrict_to_workspace,
+            "workspace_root": self.workspace_root,
+            "level": self.level,
+            "enforced": self.enforced,
+            "provider": self.provider,
+            "provider_label": self.provider_label,
+            "summary": self.summary,
+        }
+
+
+@dataclass(frozen=True)
+class WorkspaceScope:
+    """Effective project root and access mode for one agent turn."""
+
+    project_path: Path
+    access_mode: WorkspaceAccessMode
+    restrict_to_workspace: bool
+    sandbox_status: WorkspaceSandboxStatus
+    source_channel: str | None = None
+
+    @property
+    def project_name(self) -> str:
+        return self.project_path.name or str(self.project_path)
+
+    def metadata(self) -> dict[str, str]:
+        return {
+            "project_path": str(self.project_path),
+            "access_mode": self.access_mode,
+        }
+
+    def payload(self) -> dict[str, Any]:
+        return {
+            **self.metadata(),
+            "project_name": self.project_name,
+            "restrict_to_workspace": self.restrict_to_workspace,
+            "sandbox_status": self.sandbox_status.as_dict(),
+        }
+
+
+@dataclass(frozen=True)
+class ToolWorkspace:
+    """Workspace policy resolved for a tool call."""
+
+    project_path: Path | None
+    restrict_to_workspace: bool
+    scope: WorkspaceScope | None = None
+
+    @property
+    def allowed_root(self) -> Path | None:
+        if self.restrict_to_workspace and self.project_path is not None:
+            return self.project_path
+        return None
+
+
+@dataclass(frozen=True)
+class WorkspaceScopeResolver:
+    """Resolve the effective workspace scope at an agent turn boundary."""
+
+    default_workspace: str | Path
+    default_restrict_to_workspace: bool
+    scoped_channel: str = "websocket"
+
+    @property
+    def sandbox_status(self) -> WorkspaceSandboxStatus:
+        return self.default().sandbox_status
+
+    def default(self) -> WorkspaceScope:
+        return default_workspace_scope(
+            self.default_workspace,
+            self.default_restrict_to_workspace,
+        )
+
+    def for_message(
+        self,
+        msg: Any,
+        session_metadata: Any,
+    ) -> WorkspaceScope:
+        return self.for_turn(
+            channel=getattr(msg, "channel", None),
+            message_metadata=getattr(msg, "metadata", None),
+            session_metadata=session_metadata,
+        )
+
+    def for_turn(
+        self,
+        *,
+        channel: str | None,
+        message_metadata: Any,
+        session_metadata: Any,
+    ) -> WorkspaceScope:
+        if channel != self.scoped_channel:
+            return self.default()
+        return resolve_effective_workspace_scope(
+            message_metadata=message_metadata,
+            session_metadata=session_metadata,
+            default_workspace=self.default_workspace,
+            default_restrict_to_workspace=self.default_restrict_to_workspace,
+            source_channel=channel,
+        )
+
+    def persist_message_scope(self, session: Any, msg: Any) -> None:
+        if getattr(msg, "channel", None) != self.scoped_channel:
+            return
+        metadata = getattr(msg, "metadata", None)
+        if not isinstance(metadata, dict):
+            return
+        raw = metadata.get(WORKSPACE_SCOPE_METADATA_KEY)
+        if isinstance(raw, dict):
+            session.metadata[WORKSPACE_SCOPE_METADATA_KEY] = dict(raw)
+
+
+def workspace_sandbox_status(
+    *,
+    restrict_to_workspace: bool,
+    workspace: str | Path,
+    environ: dict[str, str] | None = None,
+) -> WorkspaceSandboxStatus:
+    """Return how workspace restriction is enforced in the current host."""
+
+    workspace_root = str(Path(workspace).expanduser().resolve(strict=False))
+    provider = _env_system_provider(environ)
+    if not restrict_to_workspace:
+        return WorkspaceSandboxStatus(
+            restrict_to_workspace=False,
+            workspace_root=workspace_root,
+            level="off",
+            enforced=False,
+            provider="none",
+            provider_label=_provider_label("none"),
+            summary="Workspace restriction is disabled.",
+        )
+
+    if provider:
+        label = _provider_label(provider)
+        return WorkspaceSandboxStatus(
+            restrict_to_workspace=True,
+            workspace_root=workspace_root,
+            level="system",
+            enforced=True,
+            provider=provider,
+            provider_label=label,
+            summary=f"Workspace restriction is system-enforced by {label}.",
+        )
+
+    return WorkspaceSandboxStatus(
+        restrict_to_workspace=True,
+        workspace_root=workspace_root,
+        level="application",
+        enforced=False,
+        provider="none",
+        provider_label=_provider_label("none"),
+        summary="Workspace restriction uses nanobot application-level guards.",
+    )
+
+
+def default_access_mode(restrict_to_workspace: bool) -> WorkspaceAccessMode:
+    return "restricted" if restrict_to_workspace else "full"
+
+
+def build_workspace_scope(
+    project_path: str | Path,
+    access_mode: str,
+    *,
+    source_channel: str | None = None,
+) -> WorkspaceScope:
+    mode = _normalize_access_mode(access_mode)
+    root = Path(project_path).expanduser().resolve(strict=False)
+    restrict = mode == "restricted"
+    return WorkspaceScope(
+        project_path=root,
+        access_mode=mode,
+        restrict_to_workspace=restrict,
+        sandbox_status=workspace_sandbox_status(
+            restrict_to_workspace=restrict,
+            workspace=root,
+        ),
+        source_channel=source_channel,
+    )
+
+
+def default_workspace_scope(
+    workspace: str | Path,
+    restrict_to_workspace: bool,
+    *,
+    source_channel: str | None = None,
+) -> WorkspaceScope:
+    return build_workspace_scope(
+        workspace,
+        default_access_mode(restrict_to_workspace),
+        source_channel=source_channel,
+    )
+
+
+def validate_workspace_scope_payload(
+    raw: Any,
+    *,
+    default_workspace: str | Path,
+    default_restrict_to_workspace: bool,
+    source_channel: str | None = None,
+) -> WorkspaceScope:
+    """Validate a client-requested workspace scope."""
+    if raw is None:
+        return default_workspace_scope(
+            default_workspace,
+            default_restrict_to_workspace,
+            source_channel=source_channel,
+        )
+    if not isinstance(raw, dict):
+        raise WorkspaceScopeError("workspace_scope must be an object")
+
+    raw_path = raw.get("project_path") or raw.get("path")
+    if raw_path is None or raw_path == "":
+        raw_path = str(Path(default_workspace).expanduser().resolve(strict=False))
+    if not isinstance(raw_path, str):
+        raise WorkspaceScopeError("project_path must be a string")
+    if "\0" in raw_path:
+        raise WorkspaceScopeError("project_path contains invalid characters")
+
+    project = Path(raw_path).expanduser()
+    if not project.is_absolute():
+        raise WorkspaceScopeError("project_path must be absolute")
+    project = project.resolve(strict=False)
+    if not project.is_dir():
+        raise WorkspaceScopeError("project_path must be an existing directory")
+
+    raw_mode = raw.get("access_mode")
+    if raw_mode is None:
+        raw_mode = default_access_mode(default_restrict_to_workspace)
+    if not isinstance(raw_mode, str):
+        raise WorkspaceScopeError("access_mode must be a string")
+    return build_workspace_scope(project, raw_mode, source_channel=source_channel)
+
+
+def workspace_scope_from_metadata(
+    metadata: Any,
+    *,
+    default_workspace: str | Path,
+    default_restrict_to_workspace: bool,
+    source_channel: str | None = None,
+) -> WorkspaceScope:
+    """Resolve persisted metadata, falling back safely for old or stale sessions."""
+    if not isinstance(metadata, dict):
+        return default_workspace_scope(
+            default_workspace,
+            default_restrict_to_workspace,
+            source_channel=source_channel,
+        )
+    try:
+        return validate_workspace_scope_payload(
+            metadata.get(WORKSPACE_SCOPE_METADATA_KEY),
+            default_workspace=default_workspace,
+            default_restrict_to_workspace=default_restrict_to_workspace,
+            source_channel=source_channel,
+        )
+    except WorkspaceScopeError:
+        return default_workspace_scope(
+            default_workspace,
+            default_restrict_to_workspace,
+            source_channel=source_channel,
+        )
+
+
+def resolve_effective_workspace_scope(
+    *,
+    message_metadata: Any,
+    session_metadata: Any,
+    default_workspace: str | Path,
+    default_restrict_to_workspace: bool,
+    source_channel: str | None = None,
+) -> WorkspaceScope:
+    if isinstance(message_metadata, dict) and WORKSPACE_SCOPE_METADATA_KEY in message_metadata:
+        return workspace_scope_from_metadata(
+            message_metadata,
+            default_workspace=default_workspace,
+            default_restrict_to_workspace=default_restrict_to_workspace,
+            source_channel=source_channel,
+        )
+    return workspace_scope_from_metadata(
+        session_metadata,
+        default_workspace=default_workspace,
+        default_restrict_to_workspace=default_restrict_to_workspace,
+        source_channel=source_channel,
+    )
+
+
+def bind_workspace_scope(scope: WorkspaceScope) -> Token[WorkspaceScope | None]:
+    return _CURRENT_WORKSPACE_SCOPE.set(scope)
+
+
+def reset_workspace_scope(token: Token[WorkspaceScope | None]) -> None:
+    _CURRENT_WORKSPACE_SCOPE.reset(token)
+
+
+def current_workspace_scope() -> WorkspaceScope | None:
+    return _CURRENT_WORKSPACE_SCOPE.get()
+
+
+def current_tool_workspace(
+    default_workspace: str | Path | None,
+    *,
+    restrict_to_workspace: bool = False,
+    sandbox_restricts_workspace: bool = False,
+) -> ToolWorkspace:
+    """Return the workspace/access policy for the current tool call."""
+
+    scope = current_workspace_scope()
+    project_path = (
+        scope.project_path
+        if scope is not None
+        else Path(default_workspace).expanduser() if default_workspace is not None else None
+    )
+    restrict = (
+        scope.restrict_to_workspace
+        if scope is not None
+        else bool(restrict_to_workspace)
+    ) or sandbox_restricts_workspace
+    return ToolWorkspace(
+        project_path=project_path,
+        restrict_to_workspace=restrict,
+        scope=scope,
+    )
+
+
+def current_scope_allows_loopback(*, enabled: bool) -> bool:
+    """Return True when the current WebUI Full Access turn may touch loopback URLs."""
+
+    scope = current_workspace_scope()
+    return bool(
+        enabled
+        and scope is not None
+        and scope.source_channel == "websocket"
+        and scope.access_mode == "full"
+        and not scope.restrict_to_workspace
+    )
+
+
+def _env_system_provider(environ: dict[str, str] | None = None) -> str | None:
+    env = environ if environ is not None else os.environ
+    explicit_provider = env.get("NANOBOT_WORKSPACE_SANDBOX_PROVIDER")
+    enforced = env.get("NANOBOT_WORKSPACE_SANDBOX_ENFORCED")
+    compatibility = env.get("NANOBOT_SANDBOX_ENFORCED")
+
+    marker = enforced if enforced is not None else compatibility
+    if marker is None:
+        return None
+
+    normalized_marker = marker.strip().lower()
+    if normalized_marker in _FALSE_VALUES:
+        return None
+    if normalized_marker in _TRUE_VALUES:
+        return _normalize_provider(explicit_provider)
+    return _normalize_provider(marker)
+
+
+def _normalize_provider(value: str | None) -> str:
+    if not value:
+        return "unknown"
+    normalized = value.strip().lower().replace("-", "_").replace(" ", "_")
+    return normalized or "unknown"
+
+
+def _provider_label(provider: str) -> str:
+    if provider in _PROVIDER_LABELS:
+        return _PROVIDER_LABELS[provider]
+    return provider.replace("_", " ").title()
+
+
+def _normalize_access_mode(value: str) -> WorkspaceAccessMode:
+    mode = value.strip().lower().replace("_", "-")
+    if mode == "restrict":
+        mode = "restricted"
+    if mode == "full-access":
+        mode = "full"
+    if mode not in _ACCESS_MODES:
+        raise WorkspaceScopeError("access_mode must be restricted or full")
+    return mode  # type: ignore[return-value]

nanobot/security/workspace_policy.py

@@ -0,0 +1,85 @@
+"""Workspace path boundary helpers.
+
+These helpers are application-level guards.  They make path decisions
+consistent across tools, but they are not a replacement for an OS sandbox.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Iterable
+
+WORKSPACE_BOUNDARY_NOTE = (
+    " (this is a hard policy boundary, not a transient failure; "
+    "do not retry with shell tricks or alternative tools, and ask "
+    "the user how to proceed if the resource is genuinely required)"
+)
+
+
+class WorkspaceBoundaryError(PermissionError):
+    """Raised when a requested path escapes an allowed workspace boundary."""
+
+
+def resolve_path(path: str | Path, workspace: str | Path | None = None, *, strict: bool = False) -> Path:
+    """Resolve *path*, interpreting relative paths against *workspace* when set."""
+    candidate = Path(path).expanduser()
+    if not candidate.is_absolute() and workspace is not None:
+        candidate = Path(workspace).expanduser() / candidate
+    return candidate.resolve(strict=strict)
+
+
+def is_path_within(path: str | Path, root: str | Path) -> bool:
+    """Return True when *path* resolves to *root* or a descendant of *root*."""
+    try:
+        resolved_path = Path(path).expanduser().resolve(strict=False)
+        resolved_root = Path(root).expanduser().resolve(strict=False)
+        resolved_path.relative_to(resolved_root)
+        return True
+    except (OSError, RuntimeError, TypeError, ValueError):
+        return False
+
+
+def is_path_allowed(path: str | Path, roots: Iterable[str | Path]) -> bool:
+    """Return True when *path* is inside any allowed root."""
+    return any(is_path_within(path, root) for root in roots)
+
+
+def require_path_within(
+    path: str | Path,
+    root: str | Path,
+    *,
+    message: str | None = None,
+) -> Path:
+    """Resolve *path* and require it to be inside *root*."""
+    resolved = Path(path).expanduser().resolve(strict=False)
+    if not is_path_within(resolved, root):
+        raise WorkspaceBoundaryError(
+            message
+            or f"Path {path} is outside allowed directory {Path(root).expanduser()}"
+            + WORKSPACE_BOUNDARY_NOTE
+        )
+    return resolved
+
+
+def resolve_allowed_path(
+    path: str | Path,
+    *,
+    workspace: str | Path | None = None,
+    allowed_root: str | Path | None = None,
+    extra_allowed_roots: Iterable[str | Path] | None = None,
+    strict: bool = False,
+) -> Path:
+    """Resolve a path and enforce containment in allowed roots when configured."""
+    resolved = resolve_path(path, workspace, strict=False)
+    if allowed_root is None:
+        return resolve_path(path, workspace, strict=strict) if strict else resolved
+
+    roots = [allowed_root, *(extra_allowed_roots or [])]
+    if not is_path_allowed(resolved, roots):
+        raise WorkspaceBoundaryError(
+            f"Path {path} is outside allowed directory {Path(allowed_root).expanduser()}"
+            + WORKSPACE_BOUNDARY_NOTE
+        )
+    if strict:
+        return resolve_path(path, workspace, strict=True)
+    return resolved

nanobot/session/goal_state.py

@@ -43,6 +43,19 @@ def sustained_goal_active(metadata: Mapping[str, Any] | None) -> bool:
     return isinstance(goal, dict) and goal.get("status") == "active"
 
 
+def sustained_goal_turn(
+    metadata: Mapping[str, Any] | None,
+    *,
+    message_metadata: Mapping[str, Any] | None = None,
+) -> bool:
+    """True when this turn should use sustained-goal runtime limits."""
+    if sustained_goal_active(metadata):
+        return True
+    if not message_metadata:
+        return False
+    return str(message_metadata.get("original_command") or "").strip() == "/goal"
+
+
 def parse_goal_state(blob: Any) -> dict[str, Any] | None:
     if blob is None:
         return None
@@ -98,14 +111,16 @@ def runner_wall_llm_timeout_s(
     session_key: str | None,
     *,
     metadata: Mapping[str, Any] | None = None,
+    message_metadata: Mapping[str, Any] | None = None,
 ) -> float | None:
     """Wall-clock cap for :class:`~nanobot.agent.runner.AgentRunner` when streaming an LLM.
 
-    Returns ``0.0`` to disable ``asyncio.wait_for`` around the request when a sustained goal is
+    Returns ``0.0`` to disable ``asyncio.wait_for`` around the request when this is a
-    active; ``None`` means use ``NANOBOT_LLM_TIMEOUT_S``. Pass in-memory ``metadata`` when the
+    sustained-goal turn; ``None`` means use ``NANOBOT_LLM_TIMEOUT_S``. Pass in-memory
-    caller already holds :attr:`~nanobot.session.manager.Session.metadata` for this turn.
+    ``metadata`` when the caller already holds :attr:`~nanobot.session.manager.Session.metadata`
+    for this turn.
     """
     meta: Mapping[str, Any] | None = metadata
     if meta is None and session_key:
         meta = sessions.get_or_create(session_key).metadata
-    return 0.0 if sustained_goal_active(meta) else None
+    return 0.0 if sustained_goal_turn(meta, message_metadata=message_metadata) else None

nanobot/session/manager.py

@@ -8,7 +8,7 @@ from contextlib import suppress
 from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Literal
+from typing import Any
 
 from loguru import logger
 
@@ -19,6 +19,7 @@ from nanobot.utils.helpers import (
     find_legal_message_start,
     image_placeholder_text,
     safe_filename,
+    strip_think,
 )
 from nanobot.utils.subagent_channel_display import scrub_subagent_announce_body
 
@@ -27,6 +28,8 @@ _MESSAGE_TIME_PREFIX_RE = re.compile(r"^\[Message Time: [^\]]+\]\n?")
 _LOCAL_IMAGE_BREADCRUMB_RE = re.compile(r"^\[image: (?:/|~)[^\]]+\]\s*$")
 _TOOL_CALL_ECHO_RE = re.compile(r'^\s*(?:generate_image|message)\([^)]*\)\s*$')
 _SESSION_PREVIEW_MAX_CHARS = 120
+_SESSION_LIST_PREVIEW_MAX_RECORDS = 200
+_SESSION_LIST_PREVIEW_MAX_CHARS = 1_000_000
 
 
 def _sanitize_assistant_replay_text(content: str) -> str:
@@ -74,6 +77,17 @@ def _message_preview_text(message: dict[str, Any]) -> str:
     return _text_preview(content)
 
 
+def _metadata_title(metadata: Any) -> str:
+    if not isinstance(metadata, dict):
+        return ""
+    title = metadata.get("title")
+    if not isinstance(title, str):
+        return ""
+    if metadata.get("title_user_edited") is True:
+        return title
+    return strip_think(title)
+
+
 @dataclass
 class Session:
     """A conversation session."""
@@ -165,6 +179,45 @@ class Session:
                     image_placeholder_text(p) for p in media if isinstance(p, str) and p
                 )
                 content = f"{content}\n{breadcrumbs}" if content else breadcrumbs
+            cli_apps = message.get("cli_apps")
+            if role == "user" and isinstance(cli_apps, list) and cli_apps and isinstance(content, str):
+                cli_lines: list[str] = []
+                for item in cli_apps[:8]:
+                    if not isinstance(item, dict):
+                        continue
+                    name = str(item.get("name") or "").strip().lower()
+                    if not name:
+                        continue
+                    entry = str(item.get("entry_point") or "unknown").strip() or "unknown"
+                    cli_lines.append(
+                        f"[CLI App Attachment: @{name}; tool=run_cli_app; entry_point={entry}; "
+                        f"skill=skills/cli-app-{name}/SKILL.md]"
+                    )
+                if cli_lines:
+                    breadcrumbs = "\n".join(cli_lines)
+                    content = f"{content}\n{breadcrumbs}" if content else breadcrumbs
+            mcp_presets = message.get("mcp_presets")
+            if (
+                role == "user"
+                and isinstance(mcp_presets, list)
+                and mcp_presets
+                and isinstance(content, str)
+            ):
+                mcp_lines: list[str] = []
+                for item in mcp_presets[:8]:
+                    if not isinstance(item, dict):
+                        continue
+                    name = str(item.get("name") or "").strip().lower()
+                    if not name:
+                        continue
+                    transport = str(item.get("transport") or "mcp").strip() or "mcp"
+                    mcp_lines.append(
+                        f"[MCP Preset Attachment: @{name}; tool_prefix=mcp_{name}_; "
+                        f"transport={transport}]"
+                    )
+                if mcp_lines:
+                    breadcrumbs = "\n".join(mcp_lines)
+                    content = f"{content}\n{breadcrumbs}" if content else breadcrumbs
             if include_timestamps:
                 content = self._annotate_message_time(message, content)
             if role == "assistant" and isinstance(content, str) and not content.strip():
@@ -216,13 +269,25 @@ class Session:
         self.updated_at = datetime.now()
         self.metadata.pop("_last_summary", None)
 
-    def retain_recent_legal_suffix(self, max_messages: int) -> None:
+    def retain_recent_legal_suffix(self, max_messages: int) -> tuple[list[dict], int]:
-        """Keep a legal recent suffix constrained by a hard message cap."""
+        """Keep a legal recent suffix constrained by a hard message cap.
+
+        Returns ``(dropped, already_consolidated_count)`` where *dropped* is
+        the list of removed messages (in original order) and
+        *already_consolidated_count* is how many of those were inside the
+        pre-existing ``last_consolidated`` prefix and therefore do not need
+        raw archiving.
+        """
         if max_messages <= 0:
+            dropped = list(self.messages)
+            lc = self.last_consolidated
             self.clear()
-            return
+            return dropped, min(lc, len(dropped))
         if len(self.messages) <= max_messages:
-            return
+            return [], 0
+
+        original = list(self.messages)
+        before_lc = self.last_consolidated
 
         retained = list(self.messages[-max_messages:])
 
@@ -253,10 +318,32 @@ class Session:
             if start:
                 retained = retained[start:]
 
-        dropped = len(self.messages) - len(retained)
+        # Compute actually-dropped messages using identity comparison so that
+        # even when retained is a non-contiguous slice of original (the else
+        # branch above), we never duplicate or lose messages.
+        retained_ids = set(id(m) for m in retained)
+        dropped = [m for m in original if id(m) not in retained_ids]
+
+        # Count how many dropped messages were in the already-consolidated
+        # prefix of the original list.  This cannot be a simple min() because
+        # dropped may include messages from *after* the consolidated prefix
+        # (e.g. in the else branch).
+        already_consolidated = sum(
+            1 for i, m in enumerate(original)
+            if i < before_lc and id(m) not in retained_ids
+        )
+
+        # New last_consolidated = count of retained messages that were inside
+        # the old consolidated prefix.
+        new_lc = sum(
+            1 for i, m in enumerate(original)
+            if i < before_lc and id(m) in retained_ids
+        )
+
         self.messages = retained
-        self.last_consolidated = max(0, self.last_consolidated - dropped)
+        self.last_consolidated = new_lc
         self.updated_at = datetime.now()
+        return dropped, already_consolidated
 
     def enforce_file_cap(
         self,
@@ -267,23 +354,17 @@ class Session:
         if limit <= 0 or len(self.messages) <= limit:
             return
 
-        before = list(self.messages)
+        dropped, already_consolidated = self.retain_recent_legal_suffix(limit)
-        before_last_consolidated = self.last_consolidated
+        if not dropped:
-        before_count = len(before)
-        self.retain_recent_legal_suffix(limit)
-        dropped_count = before_count - len(self.messages)
-        if dropped_count <= 0:
             return
 
-        dropped = before[:dropped_count]
-        already_consolidated = min(before_last_consolidated, dropped_count)
         archive_chunk = dropped[already_consolidated:]
         if archive_chunk and on_archive:
             on_archive(archive_chunk)
         logger.info(
             "Session file cap hit for {}: dropped {}, raw-archived {}, kept {}",
             self.key,
-            dropped_count,
+            len(dropped),
             len(archive_chunk),
             len(self.messages),
         )
@@ -581,36 +662,6 @@ class SessionManager:
                 return self._session_payload(repaired)
             return None
 
-    def get_or_create_task_session(
-        self,
-        base_key: str,
-        task_id: str,
-        role: Literal["manager", "worker"] = "worker",
-    ) -> Session:
-        """Get or create an isolated session for a specific task.
-
-        Key format: task:{base_key}:{task_id}:{role}
-        Example: task:slack:C123:root_qml:manager
-        """
-        task_key = f"task:{base_key}:{task_id}:{role}"
-        return self.get_or_create(task_key)
-
-    def list_task_sessions(self, base_key: str) -> list[Session]:
-        """List all task-scoped sessions for a given base key."""
-        prefix = f"task:{base_key}:"
-        return [
-            session for key, session in self._cache.items()
-            if key.startswith(prefix)
-        ]
-
-    def finalize_task_session(self, task_id: str) -> None:
-        """Mark a task session as finalized (read-only) by setting metadata."""
-        prefix = f"task:"
-        for key, session in list(self._cache.items()):
-            if f":{task_id}:" in key and key.startswith(prefix):
-                session.metadata["finalized"] = True
-                self.save(session)
-
     def list_sessions(self) -> list[dict[str, Any]]:
         """
         List all sessions.
@@ -631,12 +682,21 @@ class SessionManager:
                         if data.get("_type") == "metadata":
                             key = data.get("key") or path.stem.replace("_", ":", 1)
                             metadata = data.get("metadata", {})
-                            title = metadata.get("title") if isinstance(metadata, dict) else None
+                            title = _metadata_title(metadata)
                             preview = ""
                             fallback_preview = ""
+                            scanned_records = 0
+                            scanned_chars = 0
                             for line in f:
                                 if not line.strip():
                                     continue
+                                scanned_records += 1
+                                scanned_chars += len(line)
+                                if (
+                                    scanned_records > _SESSION_LIST_PREVIEW_MAX_RECORDS
+                                    or scanned_chars > _SESSION_LIST_PREVIEW_MAX_CHARS
+                                ):
+                                    break
                                 item = json.loads(line)
                                 if item.get("_type") == "metadata":
                                     continue
@@ -653,7 +713,7 @@ class SessionManager:
                                 "key": key,
                                 "created_at": data.get("created_at"),
                                 "updated_at": data.get("updated_at"),
-                                "title": title if isinstance(title, str) else "",
+                                "title": title,
                                 "preview": preview,
                                 "path": str(path)
                             })
@@ -664,11 +724,7 @@ class SessionManager:
                         "key": repaired.key,
                         "created_at": repaired.created_at.isoformat(),
                         "updated_at": repaired.updated_at.isoformat(),
-                        "title": (
+                        "title": _metadata_title(repaired.metadata),
-                            repaired.metadata.get("title")
-                            if isinstance(repaired.metadata.get("title"), str)
-                            else ""
-                        ),
                         "preview": next(
                             (
                                 text

nanobot/session/turn_continuation.py

@@ -0,0 +1,240 @@
+"""Internal turn continuation helpers.
+
+This module keeps budget-boundary continuation policy out of ``AgentLoop``.
+The loop calls a small set of helpers; those helpers decide whether an internal
+continuation is allowed and, when it is, queue the next turn directly.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Mapping, MutableMapping
+
+from loguru import logger
+
+from nanobot.session.goal_state import (
+    goal_state_runtime_lines,
+    sustained_goal_active,
+    sustained_goal_turn,
+)
+
+INTERNAL_CONTINUATION_META = "_internal_continuation"
+INTERNAL_CONTINUATION_KIND_META = "_internal_continuation_kind"
+INTERNAL_CONTINUATION_PENDING_META = "_internal_continuation_pending"
+INTERNAL_CONTINUATION_RUN_STARTED_AT_META = "_internal_continuation_run_started_at"
+
+_GOAL_CONTINUATION_KIND = "sustained_goal"
+_GOAL_CONTINUATION_SENDER = "system:continuation"
+_GOAL_CONTINUATION_ROUNDS_KEY = "_sustained_goal_continuation_rounds"
+_MAX_GOAL_CONTINUATION_ROUNDS = 12
+_STRIPPED_INBOUND_META_KEYS = {
+    "_stream_id",
+    "_stream_delta",
+    "_stream_end",
+    "_resuming",
+    INTERNAL_CONTINUATION_PENDING_META,
+}
+
+
+def internal_continuation_inbound(metadata: Mapping[str, Any] | None) -> bool:
+    """True for an inbound message created by an internal continuation policy."""
+    return bool(metadata and metadata.get(INTERNAL_CONTINUATION_META) is True)
+
+
+def internal_continuation_pending(metadata: Mapping[str, Any] | None) -> bool:
+    """True when the current turn scheduled an invisible continuation slice."""
+    return bool(metadata and metadata.get(INTERNAL_CONTINUATION_PENDING_META) is True)
+
+
+def internal_continuation_run_started_at(metadata: Mapping[str, Any] | None) -> float | None:
+    """Return the user-visible run start propagated across continuation slices."""
+    if not metadata:
+        return None
+    value = metadata.get(INTERNAL_CONTINUATION_RUN_STARTED_AT_META)
+    if not isinstance(value, int | float):
+        return None
+    started_at = float(value)
+    return started_at if started_at > 0 else None
+
+
+def should_persist_user_message(metadata: Mapping[str, Any] | None) -> bool:
+    """Return whether this inbound message should be persisted as user input."""
+    return not internal_continuation_inbound(metadata)
+
+
+def should_stream_budget_response(
+    *,
+    stop_reason: str,
+    pending_queue_available: bool,
+    session_metadata: Mapping[str, Any] | None,
+    message_metadata: Mapping[str, Any] | None = None,
+) -> bool:
+    """Return whether the budget-boundary response should be sent to the user."""
+    return not _continuation_available(
+        stop_reason=stop_reason,
+        pending_queue_available=pending_queue_available,
+        session_metadata=session_metadata,
+        message_metadata=message_metadata,
+    )
+
+
+async def maybe_continue_turn(ctx: Any) -> bool:
+    """Queue an internal continuation for *ctx* when policy allows it."""
+    if ctx.session is None or ctx.pending_queue is None:
+        return False
+    if not _continuation_available(
+        stop_reason=ctx.stop_reason,
+        pending_queue_available=True,
+        session_metadata=ctx.session.metadata,
+        message_metadata=ctx.msg.metadata,
+    ):
+        return False
+
+    metadata = _internal_continuation_metadata(
+        ctx.msg.metadata,
+        run_started_at=getattr(ctx, "visible_run_started_at", None),
+    )
+    content = _goal_continuation_prompt(ctx.session.metadata)
+    messages = _strip_terminal_assistant(ctx.all_messages, ctx.final_content)
+    _increment_goal_continuation_round(ctx.session.metadata)
+
+    logger.info("Turn budget reached; scheduling internal continuation")
+    ctx.msg.metadata[INTERNAL_CONTINUATION_PENDING_META] = True
+    ctx.final_content = ""
+    ctx.all_messages = messages
+    ctx.suppress_response = True
+    await ctx.pending_queue.put(
+        dataclasses.replace(
+            ctx.msg,
+            sender_id=_GOAL_CONTINUATION_SENDER,
+            content=content,
+            media=[],
+            metadata=metadata,
+            session_key_override=ctx.session_key,
+        )
+    )
+    return True
+
+
+def prepare_save_boundary(ctx: Any) -> None:
+    """Prepare continuation bookkeeping and the history append boundary."""
+    if ctx.session is not None:
+        clear_internal_continuation_state(ctx.session.metadata)
+
+    ctx.save_skip = _save_skip_for_turn(
+        message_metadata=ctx.msg.metadata,
+        initial_message_count=len(ctx.initial_messages),
+        history_count=len(ctx.history),
+        user_persisted_early=ctx.user_persisted_early,
+    )
+
+
+def _continuation_available(
+    *,
+    stop_reason: str,
+    pending_queue_available: bool,
+    session_metadata: Mapping[str, Any] | None,
+    message_metadata: Mapping[str, Any] | None = None,
+) -> bool:
+    if stop_reason != "max_iterations" or not pending_queue_available:
+        return False
+    return _goal_continuation_available(
+        session_metadata,
+        message_metadata=message_metadata,
+    )
+
+
+def clear_internal_continuation_state(metadata: MutableMapping[str, Any]) -> None:
+    """Reset policy bookkeeping once its owning runtime mode is inactive."""
+    if not sustained_goal_active(metadata):
+        metadata.pop(_GOAL_CONTINUATION_ROUNDS_KEY, None)
+
+
+def _save_skip_for_turn(
+    *,
+    message_metadata: Mapping[str, Any] | None,
+    initial_message_count: int,
+    history_count: int,
+    user_persisted_early: bool,
+) -> int:
+    """Return the persisted-message append boundary for this turn."""
+    if internal_continuation_inbound(message_metadata):
+        return initial_message_count
+    return 1 + history_count + (1 if user_persisted_early else 0)
+
+
+def _goal_continuation_available(
+    session_metadata: Mapping[str, Any] | None,
+    *,
+    message_metadata: Mapping[str, Any] | None = None,
+    max_rounds: int = _MAX_GOAL_CONTINUATION_ROUNDS,
+) -> bool:
+    if not sustained_goal_turn(session_metadata, message_metadata=message_metadata):
+        return False
+    if not sustained_goal_active(session_metadata):
+        return False
+    try:
+        rounds = int((session_metadata or {}).get(_GOAL_CONTINUATION_ROUNDS_KEY) or 0)
+    except (TypeError, ValueError):
+        rounds = 0
+    return rounds < max(0, max_rounds)
+
+
+def _increment_goal_continuation_round(session_metadata: MutableMapping[str, Any]) -> None:
+    try:
+        rounds = int(session_metadata.get(_GOAL_CONTINUATION_ROUNDS_KEY) or 0)
+    except (TypeError, ValueError):
+        rounds = 0
+    session_metadata[_GOAL_CONTINUATION_ROUNDS_KEY] = rounds + 1
+
+
+def _internal_continuation_metadata(
+    message_metadata: Mapping[str, Any] | None,
+    *,
+    run_started_at: float | None = None,
+) -> dict[str, Any]:
+    metadata = dict(message_metadata or {})
+    metadata[INTERNAL_CONTINUATION_META] = True
+    metadata[INTERNAL_CONTINUATION_KIND_META] = _GOAL_CONTINUATION_KIND
+    if run_started_at is not None:
+        metadata[INTERNAL_CONTINUATION_RUN_STARTED_AT_META] = float(run_started_at)
+    for key in _STRIPPED_INBOUND_META_KEYS:
+        metadata.pop(key, None)
+    return metadata
+
+
+def _goal_continuation_prompt(metadata: Mapping[str, Any] | None) -> str:
+    lines = goal_state_runtime_lines(metadata)
+    if lines:
+        goal = "\n".join(lines)
+        return (
+            "Continue the active sustained goal after the previous turn reached "
+            "its tool-call budget.\n\n"
+            f"{goal}\n\n"
+            "Continue from the saved context. Do not mention the continuation "
+            "boundary to the user. Use tools as needed, and call complete_goal "
+            "when the objective is truly finished."
+        )
+    return (
+        "Continue the active sustained goal after the previous turn reached "
+        "its tool-call budget. Continue from the saved context. Do not mention "
+        "the continuation boundary to the user. Use tools as needed, and call "
+        "complete_goal when the objective is truly finished."
+    )
+
+
+def _strip_terminal_assistant(
+    messages: list[dict[str, Any]],
+    final_content: str | None,
+) -> list[dict[str, Any]]:
+    """Drop the synthetic max-iteration assistant message before saving history."""
+    if not messages:
+        return messages
+    last = messages[-1]
+    if last.get("role") != "assistant":
+        return messages
+    if final_content is None or last.get("content") != final_content:
+        return messages
+    if last.get("tool_calls"):
+        return messages
+    return messages[:-1]

nanobot/session/webui_turns.py

@@ -0,0 +1,372 @@
+"""Session turn helpers for WebUI-capable WebSocket sessions.
+
+AgentLoop uses these without importing a concrete channel plugin; only
+``channel == "websocket"`` messages are affected.
+"""
+
+from __future__ import annotations
+
+import re
+import time
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+from loguru import logger
+
+from nanobot.bus.events import InboundMessage, OutboundMessage
+from nanobot.bus.queue import MessageBus
+from nanobot.providers.base import LLMProvider
+from nanobot.session.goal_state import goal_state_ws_blob
+from nanobot.session.manager import Session, SessionManager
+from nanobot.utils.helpers import strip_think, truncate_text
+from nanobot.utils.llm_runtime import LLMRuntime
+
+WEBUI_SESSION_METADATA_KEY = "webui"
+WEBUI_TITLE_METADATA_KEY = "title"
+WEBUI_TITLE_USER_EDITED_METADATA_KEY = "title_user_edited"
+TITLE_MAX_CHARS = 60
+TITLE_GENERATION_MAX_TOKENS = 96
+TITLE_GENERATION_REASONING_EFFORT = "none"
+
+# Wall-clock turn start per ``chat_id`` (websocket only). Survives browser refresh while the
+# gateway process stays up; cleared on idle/stop and implicitly dropped on restart.
+_WEBSOCKET_TURN_WALL_STARTED_AT: dict[str, float] = {}
+
+
+def mark_webui_session(session: Session, metadata: dict[str, Any]) -> bool:
+    """Persist a WebUI marker only when the inbound websocket frame opted in."""
+    if metadata.get(WEBUI_SESSION_METADATA_KEY) is not True:
+        return False
+    session.metadata[WEBUI_SESSION_METADATA_KEY] = True
+    return True
+
+
+def clean_generated_title(raw: str | None) -> str:
+    text = (raw or "").strip()
+    if not text:
+        return ""
+    text = re.sub(r"^\s*(title|标题)\s*[:：]\s*", "", text, flags=re.IGNORECASE)
+    text = text.strip().strip("\"'`“”‘’")
+    text = strip_think(text)
+    text = re.sub(r"\s+", " ", text).strip()
+    text = text.rstrip("。.!！?？,，;；:")
+    if len(text) > TITLE_MAX_CHARS:
+        text = text[: TITLE_MAX_CHARS - 1].rstrip() + "…"
+    return text
+
+
+def _title_inputs(session: Session) -> tuple[str, str]:
+    user_text = ""
+    assistant_text = ""
+    for message in session.messages:
+        if message.get("_command") is True:
+            continue
+        role = message.get("role")
+        content = message.get("content")
+        if not isinstance(content, str) or not content.strip():
+            continue
+        content = strip_think(content)
+        if not content:
+            continue
+        if role == "user" and not user_text:
+            user_text = content.strip()
+        elif role == "assistant" and not assistant_text:
+            assistant_text = content.strip()
+        if user_text and assistant_text:
+            break
+    return user_text, assistant_text
+
+
+async def maybe_generate_webui_title(
+    *,
+    sessions: SessionManager,
+    session_key: str,
+    provider: LLMProvider,
+    model: str,
+) -> bool:
+    """Generate and persist a short title for WebUI-owned sessions only."""
+    session = sessions.get_or_create(session_key)
+    if session.metadata.get(WEBUI_SESSION_METADATA_KEY) is not True:
+        return False
+    if session.metadata.get(WEBUI_TITLE_USER_EDITED_METADATA_KEY) is True:
+        return False
+    current_title = session.metadata.get(WEBUI_TITLE_METADATA_KEY)
+    if isinstance(current_title, str) and current_title.strip():
+        cleaned_current_title = clean_generated_title(current_title)
+        if cleaned_current_title:
+            if cleaned_current_title != current_title:
+                session.metadata[WEBUI_TITLE_METADATA_KEY] = cleaned_current_title
+                sessions.save(session)
+            return False
+        session.metadata.pop(WEBUI_TITLE_METADATA_KEY, None)
+
+    user_text, assistant_text = _title_inputs(session)
+    if not user_text:
+        return False
+
+    prompt = (
+        "Generate a concise title for this chat.\n"
+        "Rules:\n"
+        "- Use the same language as the user when practical.\n"
+        "- 3 to 8 words.\n"
+        "- No quotes.\n"
+        "- No punctuation at the end.\n"
+        "- Return only the title.\n\n"
+        f"User: {truncate_text(user_text, 1_000)}"
+    )
+    if assistant_text:
+        prompt += f"\nAssistant: {truncate_text(assistant_text, 1_000)}"
+
+    try:
+        response = await provider.chat_with_retry(
+            [
+                {
+                    "role": "system",
+                    "content": (
+                        "You write short, neutral chat titles. "
+                        "Return only the title text."
+                    ),
+                },
+                {"role": "user", "content": prompt},
+            ],
+            tools=None,
+            model=model,
+            max_tokens=TITLE_GENERATION_MAX_TOKENS,
+            temperature=0.2,
+            reasoning_effort=TITLE_GENERATION_REASONING_EFFORT,
+            retry_mode="standard",
+        )
+    except Exception:
+        logger.debug("Failed to generate webui session title for {}", session_key, exc_info=True)
+        return False
+
+    title = clean_generated_title(response.content)
+    if not title or title.lower().startswith("error"):
+        logger.debug(
+            "WebUI title generation returned no usable title for {} (finish_reason={})",
+            session_key,
+            response.finish_reason,
+        )
+        return False
+    session.metadata[WEBUI_TITLE_METADATA_KEY] = title
+    sessions.save(session)
+    return True
+
+
+async def maybe_generate_webui_title_after_turn(
+    *,
+    channel: str,
+    metadata: dict[str, Any],
+    sessions: SessionManager,
+    session_key: str,
+    provider: LLMProvider,
+    model: str,
+) -> bool:
+    if channel != "websocket" or metadata.get(WEBUI_SESSION_METADATA_KEY) is not True:
+        return False
+    return await maybe_generate_webui_title(
+        sessions=sessions,
+        session_key=session_key,
+        provider=provider,
+        model=model,
+    )
+
+
+def websocket_turn_wall_started_at(chat_id: str) -> float | None:
+    """Return ``time.time()`` when the active user turn began, if still running."""
+    return _WEBSOCKET_TURN_WALL_STARTED_AT.get(chat_id)
+
+
+async def publish_turn_run_status(
+    bus: MessageBus,
+    msg: InboundMessage,
+    status: str,
+    *,
+    started_at: float | None = None,
+) -> None:
+    """Notify WebSocket clients while a user turn is executing (timing strip)."""
+    if msg.channel != "websocket":
+        return
+    cid = str(msg.chat_id)
+    meta: dict[str, Any] = {
+        **dict(msg.metadata or {}),
+        "_goal_status": True,
+        "goal_status": status,
+    }
+    if status == "running":
+        if isinstance(started_at, int | float) and started_at > 0:
+            t0 = float(started_at)
+        else:
+            t0 = time.time()
+        meta["started_at"] = t0
+        _WEBSOCKET_TURN_WALL_STARTED_AT[cid] = t0
+    else:
+        _WEBSOCKET_TURN_WALL_STARTED_AT.pop(cid, None)
+    await bus.publish_outbound(
+        OutboundMessage(
+            channel=msg.channel,
+            chat_id=cid,
+            content="",
+            metadata=meta,
+        ),
+    )
+
+
+def build_bus_progress_callback(
+    bus: MessageBus,
+    msg: InboundMessage,
+) -> Callable[..., Awaitable[None]]:
+    """Return the bus progress callback for agent runtime events."""
+
+    async def _publish_progress(
+        content: str,
+        *,
+        tool_hint: bool = False,
+        tool_events: list[dict[str, Any]] | None = None,
+        file_edit_events: list[dict[str, Any]] | None = None,
+        reasoning: bool = False,
+        reasoning_end: bool = False,
+    ) -> None:
+        meta = dict(msg.metadata or {})
+        meta["_progress"] = True
+        meta["_tool_hint"] = tool_hint
+        if reasoning:
+            meta["_reasoning_delta"] = True
+        if reasoning_end:
+            meta["_reasoning_end"] = True
+        if tool_events:
+            meta["_tool_events"] = tool_events
+        if file_edit_events:
+            meta["_file_edit_events"] = file_edit_events
+        await bus.publish_outbound(
+            OutboundMessage(
+                channel=msg.channel,
+                chat_id=msg.chat_id,
+                content=content,
+                metadata=meta,
+            )
+        )
+
+    if msg.channel == "websocket":
+        async def _websocket_progress(
+            content: str,
+            *,
+            tool_hint: bool = False,
+            tool_events: list[dict[str, Any]] | None = None,
+            file_edit_events: list[dict[str, Any]] | None = None,
+            reasoning: bool = False,
+            reasoning_end: bool = False,
+        ) -> None:
+            await _publish_progress(
+                content,
+                tool_hint=tool_hint,
+                tool_events=tool_events,
+                file_edit_events=file_edit_events,
+                reasoning=reasoning,
+                reasoning_end=reasoning_end,
+            )
+
+        return _websocket_progress
+
+    async def _bus_progress(
+        content: str,
+        *,
+        tool_hint: bool = False,
+        tool_events: list[dict[str, Any]] | None = None,
+        reasoning: bool = False,
+        reasoning_end: bool = False,
+    ) -> None:
+        await _publish_progress(
+            content,
+            tool_hint=tool_hint,
+            tool_events=tool_events,
+            reasoning=reasoning,
+            reasoning_end=reasoning_end,
+        )
+
+    return _bus_progress
+
+
+@dataclass
+class WebuiTurnCoordinator:
+    """Own the WebUI/WebSocket wire details that hang off AgentLoop turns."""
+
+    bus: MessageBus
+    sessions: SessionManager
+    schedule_background: Callable[[Awaitable[None]], None]
+    _title_contexts: dict[str, LLMRuntime] = field(default_factory=dict)
+
+    def capture_title_context(
+        self,
+        session_key: str,
+        msg: InboundMessage,
+        llm: LLMRuntime,
+    ) -> None:
+        if msg.channel == "websocket" and msg.metadata.get("webui") is True:
+            self._title_contexts[session_key] = llm
+
+    def discard(self, session_key: str) -> None:
+        self._title_contexts.pop(session_key, None)
+
+    async def publish_run_status(
+        self,
+        msg: InboundMessage,
+        status: str,
+        *,
+        started_at: float | None = None,
+    ) -> None:
+        await publish_turn_run_status(self.bus, msg, status, started_at=started_at)
+
+    async def handle_turn_end(
+        self,
+        msg: InboundMessage,
+        *,
+        session_key: str,
+        latency_ms: int | None,
+    ) -> None:
+        if msg.channel != "websocket":
+            return
+
+        turn_metadata: dict[str, Any] = {**msg.metadata, "_turn_end": True}
+        if latency_ms is not None:
+            turn_metadata["latency_ms"] = int(latency_ms)
+        session = self.sessions.get_or_create(session_key)
+        turn_metadata["goal_state"] = goal_state_ws_blob(session.metadata)
+        await self.bus.publish_outbound(OutboundMessage(
+            channel=msg.channel,
+            chat_id=msg.chat_id,
+            content="",
+            metadata=turn_metadata,
+        ))
+        self._schedule_title_update(msg, session_key=session_key)
+
+    def _schedule_title_update(self, msg: InboundMessage, *, session_key: str) -> None:
+        title_context = self._title_contexts.pop(session_key, None)
+        if msg.metadata.get("webui") is not True or title_context is None:
+            return
+
+        async def _generate_title_and_notify(
+            title_llm: LLMRuntime = title_context,
+        ) -> None:
+            generated = await maybe_generate_webui_title_after_turn(
+                channel=msg.channel,
+                metadata=msg.metadata,
+                sessions=self.sessions,
+                session_key=session_key,
+                provider=title_llm.provider,
+                model=title_llm.model,
+            )
+            if generated:
+                await self.bus.publish_outbound(OutboundMessage(
+                    channel=msg.channel,
+                    chat_id=msg.chat_id,
+                    content="",
+                    metadata={
+                        **msg.metadata,
+                        "_session_updated": True,
+                        "_session_update_scope": "metadata",
+                    },
+                ))
+
+        self.schedule_background(_generate_title_and_notify())

nanobot/skills/create-instance/SKILL.md

@@ -1,64 +0,0 @@
----
-name: create-instance
-description: "Create a new nanobot instance with separate config and workspace. Use when the user wants to set up a new bot, create a new instance for a different channel, persona, or purpose. Triggers on: create instance, new bot, set up bot, add bot, create telegram/discord/feishu/slack/wechat/wecom/dingtalk/qq/email/matrix/msteams/whatsapp bot, multi-instance setup, inter-agent communication."
----
-
-# Create Instance
-
-Set up a new nanobot instance with its own config and workspace.
-
-## Steps
-
-1. **Collect information** (ask one at a time if not already provided):
-   - **Instance name** (required): short identifier, e.g. `telegram-bot`, `work-slack`
-   - **Channel type** (required): see table below
-   - **Model** (optional): LLM model, defaults to current instance
-
-2. **Do NOT collect secrets** in the chat (API keys, bot tokens). API keys are automatically inherited from the current instance via `--inherit-config`. Channel-specific tokens must be filled in manually after creation.
-
-3. **Run the creation script**:
-
-```bash
-python <skill-dir>/scripts/create_instance.py --name <name> --channel <channel> --inherit-config <current-config>
-```
-
-- `<skill-dir>` — the directory containing this SKILL.md
-- `<current-config>` — current instance's config path, typically `~/.nanobot/config.json`
-- Optional: `--model <model>`, `--config-dir <path>`
-
-**Exec tool constraints:**
-- Use forward-slash paths (works on all platforms)
-- Do not wrap paths in quotes
-- Do not use `cd`; pass the full script path directly
-
-4. **Report results** to the user:
-   - Config and workspace paths (script outputs them)
-   - Required fields to fill in (script lists them)
-   - Start command: `nanobot gateway --config <config-path>`
-
-## Available Channels
-
-| Channel | Key | Required Fields |
-|---------|-----|-----------------|
-| Telegram | `telegram` | token |
-| Discord | `discord` | token |
-| Feishu / Lark | `feishu` | app_id, app_secret |
-| DingTalk | `dingtalk` | client_id, client_secret |
-| Slack | `slack` | bot_token, app_token |
-| WeCom | `wecom` | bot_id, secret |
-| WeChat OA | `weixin` | token |
-| WhatsApp | `whatsapp` | bridge_token |
-| QQ | `qq` | app_id, secret |
-| Email | `email` | imap_host, imap_username, imap_password, smtp_host, smtp_username, smtp_password, from_address |
-| Matrix | `matrix` | user_id, password or access_token |
-| MS Teams | `msteams` | app_id, app_password, tenant_id |
-| MoChat | `mochat` | claw_token |
-| WebSocket | `websocket` | token |
-
-For detailed channel configuration including optional fields, see `references/channels.md`.
-
-## Troubleshooting
-
-- **"Unknown channel"**: Channel name must match the Key column exactly. Run the script without arguments to see usage.
-- **"Config already exists"**: Use a different `--name` or `--config-dir` to create in a new location.
-- **Port conflicts**: The script auto-assigns free ports for gateway and API if defaults are in use.

nanobot/skills/create-instance/references/channels.md

@@ -1,195 +0,0 @@
-# Channel Configuration Reference
-
-Detailed configuration for each supported channel.
-
-## Field Types
-
-- **Required**: defaults to empty string `""`, must be filled in before the instance can start
-- **Optional**: has a sensible default, can be customized
-
----
-
-## telegram
-
-**Required:**
-- `token` — Bot token from @BotFather
-
-**Notable optional:**
-- `proxy` — HTTP proxy URL
-- `group_policy` — `"open"` (all messages) or `"mention"` (default, only when @mentioned)
-- `streaming` — Enable streaming responses (default: true)
-- `reply_to_message` — Reply to the triggering message (default: false)
-- `react_emoji` — Emoji for "thinking" reaction (default: `"eyes"`)
-- `inline_keyboards` — Enable inline keyboard buttons (default: false)
-
-## discord
-
-**Required:**
-- `token` — Bot token from Discord Developer Portal
-
-**Notable optional:**
-- `allow_channels` — Restrict to specific channel IDs
-- `group_policy` — `"mention"` (default) or `"open"`
-- `streaming` — Enable streaming (default: true)
-- `proxy` — HTTP proxy URL
-- `intents` — Discord gateway intents (default: 37377)
-- `read_receipt_emoji` — Emoji for read receipt
-- `working_emoji` — Emoji for "working" indicator
-
-## feishu
-
-**Required:**
-- `app_id` — Feishu app ID
-- `app_secret` — Feishu app secret
-
-**Notable optional:**
-- `encrypt_key` — Event encryption key
-- `verification_token` — Event verification token
-- `domain` — `"feishu"` (default) or `"lark"`
-- `group_policy` — `"mention"` (default) or `"open"`
-- `streaming` — Enable streaming (default: true)
-
-## dingtalk
-
-**Required:**
-- `client_id` — DingTalk app client ID
-- `client_secret` — DingTalk app client secret
-
-**Notable optional:**
-- `allow_from` — Allowed user IDs
-
-## slack
-
-**Required:**
-- `bot_token` — Bot OAuth token (`xoxb-...`)
-- `app_token` — App-level token (`xapp-...`)
-
-**Notable optional:**
-- `mode` — `"socket"` (default, Socket Mode) or `"webhook"`
-- `reply_in_thread` — Reply in thread (default: true)
-- `react_emoji` — "thinking" emoji (default: `"eyes"`)
-- `done_emoji` — "done" emoji (default: `"white_check_mark"`)
-- `group_policy` — `"mention"` (default) or `"open"`
-- `dm.enabled` — Enable DM support
-- `dm.policy` — DM policy
-- `dm.allow_from` — Allowed DM users
-
-## wecom
-
-**Required:**
-- `bot_id` — WeCom bot ID
-- `secret` — WeCom bot secret
-
-**Notable optional:**
-- `allow_from` — Allowed users
-- `welcome_message` — Welcome message for new chats
-
-## weixin
-
-**Required:**
-- `token` — WeChat Official Account token
-
-**Notable optional:**
-- `base_url` — API base URL
-- `cdn_base_url` — CDN base URL
-- `state_dir` — State persistence directory
-- `poll_timeout` — Long polling timeout
-
-## whatsapp
-
-**Required:**
-- `bridge_token` — WhatsApp bridge token (auto-generated if absent)
-
-**Notable optional:**
-- `bridge_url` — Bridge WebSocket URL (default: `"ws://localhost:3001"`)
-- `group_policy` — `"open"` (default) or `"mention"`
-
-## qq
-
-**Required:**
-- `app_id` — QQ bot app ID
-- `secret` — QQ bot secret
-
-**Notable optional:**
-- `msg_format` — `"plain"` or `"markdown"`
-- `ack_message` — Acknowledgment message text
-- `media_dir` — Media file directory
-
-## email
-
-**Required:**
-- `imap_host` — IMAP server hostname
-- `imap_username` — IMAP login username
-- `imap_password` — IMAP login password
-- `smtp_host` — SMTP server hostname
-- `smtp_username` — SMTP login username
-- `smtp_password` — SMTP login password
-- `from_address` — Sender email address
-
-**Notable optional:**
-- `imap_port` — IMAP port (default: 993)
-- `smtp_port` — SMTP port (default: 587)
-- `imap_use_ssl` — Use SSL for IMAP (default: true)
-- `smtp_use_tls` — Use TLS for SMTP (default: true)
-- `poll_interval_seconds` — Polling interval (default: 30)
-- `mark_seen` — Mark emails as read (default: true)
-- `max_body_chars` — Max email body length (default: 12000)
-- `subject_prefix` — Reply subject prefix (default: `"Re: "`)
-- `verify_dkim` — Verify DKIM signatures (default: true)
-- `verify_spf` — Verify SPF records (default: true)
-- `allowed_attachment_types` — Allowed file extensions
-- `max_attachment_size` — Max attachment size in bytes
-- `consent_granted` — Must be set to `true` for the channel to start (default: false)
-- `auto_reply_enabled` — Enable auto-reply (default: true)
-
-## matrix
-
-**Required:**
-- `user_id` — Matrix user ID (e.g. `@bot:matrix.org`)
-- `password` or `access_token` — Login password OR access token
-
-**Notable optional:**
-- `homeserver` — Homeserver URL (default: `"https://matrix.org"`)
-- `device_id` — Device ID
-- `e2eeEnabled` — Enable end-to-end encryption (default: true)
-- `group_policy` — `"open"`, `"mention"`, or `"allowlist"`
-- `streaming` — Enable streaming (default: false)
-- `max_media_bytes` — Max media file size (default: 20MB)
-
-## msteams
-
-**Required:**
-- `app_id` — Azure AD app ID
-- `app_password` — Azure AD app password/secret
-- `tenant_id` — Azure AD tenant ID
-
-**Notable optional:**
-- `host` — Listen host (default: `"0.0.0.0"`)
-- `port` — Listen port (default: 3978)
-- `reply_in_thread` — Reply in thread (default: true)
-- `validate_inbound_auth` — Validate incoming auth (default: true)
-
-## mochat
-
-**Required:**
-- `claw_token` — MoChat Claw token
-
-**Notable optional:**
-- `base_url` — API base URL
-- `socket_url` — WebSocket URL
-- `refresh_interval_ms` — Refresh interval in ms
-- `watch_timeout_ms` — Watch timeout in ms
-
-## websocket
-
-Built-in WebSocket channel for programmatic access.
-
-**Required:**
-- `token` — Authentication token (enabled by default; set `websocket_requires_token: false` to disable)
-
-**Notable optional:**
-- `host` — Listen host (default: `"127.0.0.1"`)
-- `port` — Listen port (default: 8765)
-- `allow_from` — Allowed origins (default: `["*"]`)
-- `streaming` — Enable streaming (default: true)
-

nanobot/skills/create-instance/scripts/create_instance.py

@@ -1,252 +0,0 @@
-#!/usr/bin/env python3
-"""Create a new nanobot instance with a dedicated config and workspace.
-
-Usage:
-    create_instance.py --name <name> --channel <channel> [--model <model>] [--config-dir <dir>]
-
-Examples:
-    create_instance.py --name telegram-bot --channel telegram
-    create_instance.py --name discord-bot --channel discord --model deepseek/deepseek-chat
-    create_instance.py --name my-bot --channel telegram --config-dir ~/.nanobot-custom
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import socket
-import sys
-from pathlib import Path
-
-
-def _validate_name(name: str) -> str:
-    """Normalize and validate instance name."""
-    name = name.strip().lower()
-    name = re.sub(r"[^a-z0-9-]", "-", name)
-    name = re.sub(r"-{2,}", "-", name)
-    name = name.strip("-")
-    if not name:
-        print("[ERROR] Instance name must contain at least one letter or digit.", file=sys.stderr)
-        sys.exit(1)
-    if len(name) > 64:
-        print(f"[ERROR] Instance name too long ({len(name)} chars, max 64).", file=sys.stderr)
-        sys.exit(1)
-    return name
-
-
-def _get_available_channels() -> list[str]:
-    """Get list of available channel names without importing channel classes."""
-    from nanobot.channels.registry import discover_channel_names
-
-    return discover_channel_names()
-
-
-def _run_onboard(config_path: Path, workspace: Path) -> None:
-    """Create skeleton config + workspace using nanobot's programmatic API."""
-    from nanobot.cli.commands import _onboard_plugins
-    from nanobot.config.loader import save_config, set_config_path
-    from nanobot.config.paths import get_workspace_path
-    from nanobot.config.schema import Config
-    from nanobot.utils.helpers import sync_workspace_templates
-
-    config = Config()
-    config.agents.defaults.workspace = str(workspace)
-    set_config_path(config_path)
-    save_config(config, config_path)
-    _onboard_plugins(config_path)
-
-    workspace_path = get_workspace_path(config.workspace_path)
-    if not workspace_path.exists():
-        workspace_path.mkdir(parents=True, exist_ok=True)
-    sync_workspace_templates(workspace_path)
-
-
-def _patch_config(
-    config_path: Path,
-    *,
-    channel: str,
-    workspace: Path,
-    model: str | None,
-    name: str | None = None,
-    inherit_config_path: Path | None = None,
-) -> dict:
-    """Patch the generated config: enable channel, set workspace, optionally set model."""
-    data = json.loads(config_path.read_text(encoding="utf-8"))
-
-    # Inherit providers and model from current instance
-    if inherit_config_path and inherit_config_path.exists():
-        try:
-            src = json.loads(inherit_config_path.read_text(encoding="utf-8"))
-
-            # Inherit providers (API keys, api_base, etc.)
-            src_providers = src.get("providers", {})
-            if src_providers:
-                data.setdefault("providers", {})
-                for key, val in src_providers.items():
-                    if isinstance(val, dict) and val.get("apiKey"):
-                        data["providers"][key] = val
-
-            # Inherit model if not explicitly overridden
-            if not model:
-                parent_model = src.get("agents", {}).get("defaults", {}).get("model")
-                if parent_model:
-                    model = parent_model
-
-        except Exception as exc:
-            print(f"[WARN] Could not inherit from {inherit_config_path}: {exc}", file=sys.stderr)
-
-    # Set workspace and model
-    data.setdefault("agents", {}).setdefault("defaults", {})
-    data["agents"]["defaults"]["workspace"] = str(workspace)
-    if model:
-        data["agents"]["defaults"]["model"] = model
-
-    # Enable the target channel
-    channels = data.setdefault("channels", {})
-    if channel in channels and isinstance(channels[channel], dict):
-        channels[channel]["enabled"] = True
-    else:
-        channels[channel] = {"enabled": True}
-
-    # Auto-assign ports if defaults are already in use
-    _assign_free_ports(data)
-
-    # Validate with Pydantic, then save
-    from nanobot.config.schema import Config
-
-    Config.model_validate(data)
-    config_path.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8")
-    return data
-
-
-def _is_port_in_use(port: int, host: str = "127.0.0.1") -> bool:
-    """Check if a port is already in use."""
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        try:
-            s.bind((host, port))
-            return False
-        except OSError:
-            return True
-
-
-def _find_free_port(start: int, host: str = "127.0.0.1", max_tries: int = 100) -> int:
-    """Find the first free port starting from `start`."""
-    for port in range(start, start + max_tries):
-        if not _is_port_in_use(port, host):
-            return port
-    # OS-level fallback: ask the kernel for an ephemeral port
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        s.bind((host, 0))
-        return s.getsockname()[1]
-
-
-def _assign_free_ports(data: dict) -> None:
-    """If default gateway or API ports are in use, assign free ones."""
-    from nanobot.config.schema import ApiConfig, GatewayConfig
-
-    defaults = [
-        ("gateway", GatewayConfig()),
-        ("api", ApiConfig()),
-    ]
-    for key, default_cfg in defaults:
-        section = data.setdefault(key, {})
-        port = section.get("port", default_cfg.port)
-        host = section.get("host", default_cfg.host)
-        if _is_port_in_use(port, host):
-            section["port"] = _find_free_port(port + 1, host)
-
-
-def _get_channel_required_fields(channel: str) -> list[str]:
-    """Inspect a channel's default config and list fields that are empty strings."""
-    try:
-        from nanobot.channels.registry import load_channel_class
-
-        cls = load_channel_class(channel)
-        default = cls.default_config()
-        return sorted(k for k, v in default.items() if isinstance(v, str) and v == "" and k != "enabled")
-    except Exception as exc:
-        print(f"[WARN] Could not inspect channel '{channel}' defaults: {exc}", file=sys.stderr)
-        return []
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Create a new nanobot instance.",
-    )
-    parser.add_argument("--name", required=True, help="Instance name (e.g. telegram-bot)")
-    parser.add_argument("--channel", required=True, help="Channel type (e.g. telegram, discord)")
-    parser.add_argument("--model", default=None, help="LLM model (default: same as current instance)")
-    parser.add_argument(
-        "--config-dir",
-        default=None,
-        help="Config directory (default: ~/.nanobot-{name})",
-    )
-    parser.add_argument(
-        "--inherit-config",
-        default=None,
-        help="Path to current instance's config.json to copy API keys from",
-    )
-    args = parser.parse_args()
-
-    # Validate name
-    name = _validate_name(args.name)
-
-    # Validate channel
-    available = _get_available_channels()
-    if args.channel not in available:
-        print(f"[ERROR] Unknown channel: {args.channel}", file=sys.stderr)
-        print(f"Available channels: {', '.join(sorted(available))}", file=sys.stderr)
-        sys.exit(1)
-
-    # Resolve paths
-    home = Path.home()
-    config_dir = Path(args.config_dir).expanduser().resolve() if args.config_dir else home / f".nanobot-{name}"
-    config_path = config_dir / "config.json"
-    workspace = config_dir / "workspace"
-
-    # Check for duplicate
-    if config_path.exists():
-        print(f"[ERROR] Config already exists at {config_path}", file=sys.stderr)
-        print("Delete it first or use a different --config-dir.", file=sys.stderr)
-        sys.exit(1)
-
-    print(f"Creating instance '{name}'...")
-    print(f"  Config dir: {config_dir}")
-    print(f"  Workspace:  {workspace}")
-    print(f"  Channel:    {args.channel}")
-    if args.model:
-        print(f"  Model:      {args.model}")
-
-    # Run onboard
-    _run_onboard(config_path, workspace)
-
-    # Patch config
-    inherit_path = Path(args.inherit_config).expanduser().resolve() if args.inherit_config else None
-    _patch_config(
-        config_path,
-        channel=args.channel,
-        workspace=workspace,
-        model=args.model,
-        name=name,
-        inherit_config_path=inherit_path,
-    )
-
-    # Report
-    print(f"\n[OK] Instance '{name}' created successfully.")
-    print(f"  Config: {config_path}")
-    print(f"  Workspace: {workspace}")
-
-    # List fields the user needs to fill in
-    required_fields = _get_channel_required_fields(args.channel)
-    if required_fields:
-        print(f"\n[IMPORTANT] Edit {config_path} and fill in these fields:")
-        for field in required_fields:
-            print(f"  - channels.{args.channel}.{field}")
-
-    print(f"\nTo start the instance:")
-    print(f"  nanobot gateway --config {config_path}")
-
-
-if __name__ == "__main__":
-    main()

nanobot/skills/image-generation/SKILL.md

@@ -15,7 +15,7 @@ If the `generate_image` tool is not available in the current tool list, tell the
 - Image editing: pass the saved artifact path or user image path in `reference_images`.
 - Iterative edits in the same conversation: prefer the most recent generated image artifact if the user says things like "make it brighter", "change the background", or "try another version".
 - Ambiguous edits: ask a short clarifying question if multiple recent images could be the target.
-- In the current chat, do not call `message` just to announce or resend generated images. The runtime attaches images from `generate_image` to the final assistant reply automatically.
+- After generating images, call the `message` tool with the artifact paths in the `media` parameter to deliver them to the user.
 
 ## Prompt Rules
 
@@ -42,52 +42,6 @@ For follow-up edits, pass the prior artifact `path` to `reference_images`. If th
 
 Do not include internal replay markers such as `[Message Time: ...]`, `[image: /local/path]`, `generate_image(...)`, or `message(...)` in user-facing replies.
 
-## Provider Notes
-
-Do not ask users to paste API keys into chat. If configuration is needed, describe the fields; LLM provider and BYOK changes are hot-reloaded for new turns.
-
-For OpenRouter, the image tool expects:
-
-```json
-{
-  "providers": {
-    "openrouter": {
-      "apiKey": "sk-or-..."
-    }
-  },
-  "tools": {
-    "imageGeneration": {
-      "enabled": true,
-      "provider": "openrouter",
-      "model": "openai/gpt-5.4-image-2"
-    }
-  }
-}
-```
-
-For AIHubMix, the image tool expects:
-
-```json
-{
-  "providers": {
-    "aihubmix": {
-      "apiKey": "sk-..."
-    }
-  },
-  "tools": {
-    "imageGeneration": {
-      "enabled": true,
-      "provider": "aihubmix",
-      "model": "gpt-image-2-free"
-    }
-  }
-}
-```
-
-AIHubMix `gpt-image-2-free` uses AIHubMix's unified predictions endpoint internally (`/v1/models/openai/gpt-image-2-free/predictions`), not the OpenAI Images `/v1/images/generations` endpoint. If it fails with "Incorrect model ID", do not assume the key lacks permission until the provider config, model name, and gateway restart have been checked.
-
-`providers.aihubmix.extraBody` can be used for provider-specific options. For example, `"extraBody": {"quality": "low"}` is optional but can make `gpt-image-2-free` faster and less likely to time out.
-
 ## Examples
 
 Generate a new image:

nanobot/templates/AGENTS.md

@@ -1,5 +1,9 @@
 # Agent Instructions
 
+## Workspace Guidance
+
+Use this file for project-specific preferences, recurring workflow conventions, and instructions you want the agent to remember for this workspace. Keep durable facts about the user in `USER.md`, personality/style guidance in `SOUL.md`, and long-term memory in `memory/MEMORY.md`.
+
 ## Scheduled Reminders
 
 Before scheduling reminders, check available skills and follow skill guidance first.
@@ -10,10 +14,10 @@ Get USER_ID and CHANNEL from the current session (e.g., `8281248569` and `telegr
 
 ## Heartbeat Tasks
 
-`HEARTBEAT.md` is checked on the configured heartbeat interval. Use file tools to manage periodic tasks:
+`HEARTBEAT.md` is checked periodically when registered as a cron job. Use the built-in `cron` tool to schedule it (e.g. `cron add --name heartbeat --schedule "every 30m" --message "Check HEARTBEAT.md"`).
 
-- **Add**: `edit_file` to append new tasks
+- Use `apply_patch` for normal task-list updates, especially when adding, removing, or changing multiple lines.
-- **Remove**: `edit_file` to delete completed tasks
+- Use `edit_file` only for small exact replacements copied from the current `HEARTBEAT.md`.
-- **Rewrite**: `write_file` to replace all tasks
+- Use `write_file` for first creation or intentional full-file rewrites.
 
-When the user asks for a recurring/periodic task, update `HEARTBEAT.md` instead of creating a one-time cron reminder.
+When the user asks for a recurring/periodic task, update `HEARTBEAT.md` and register it via `cron` instead of creating a one-time reminder.