maintainer edit: spell out that arbitrary named custom providers use the OpenAI-compatible request format only, and point Anthropic-compatible proxies to the built-in anthropic provider with apiBase.
maintainer edit: explain how to configure arbitrary OpenAI-compatible provider names, including multiple endpoints, model presets, and troubleshooting guidance.
- Add StepFunTranscriptionProvider class in nanobot/providers/transcription.py
- New _post_stepfun_asr_with_retry() function handling SSE stream parsing
(transcript.text.delta → transcript.text.done event sequence)
- Register 'stepfun' in transcription_registry.py with default model stepaudio-2.5-asr
- Reuse existing stepfun provider config (apiBase can point to Plan endpoint)
- Add 17 tests covering SSE parsing, retry contract, empty-text edge case, and registry integration
- Update docs/configuration.md with stepfun ASR documentation
StepFun ASR uses a dedicated SSE endpoint (/v1/audio/asr/sse) rather
than the chat-completions or Whisper multipart formats used by other
providers. Users on Step Plan can set apiBase to the Plan endpoint.
* docs: make onboarding friendlier for beginners
* docs: build clearer documentation paths
Maintainer edit: turn the onboarding follow-up into a layered docs structure for first-time setup, provider selection, troubleshooting, CLI reference, and source-level architecture. This keeps quick start focused while giving advanced users precise reference paths.
* docs: render architecture flow with mermaid
Maintainer edit: replace the ASCII architecture sketch with a GitHub-rendered Mermaid flowchart so the core runtime path is easier to scan in the PR and README docs.
* docs: recommend model presets for model config
Maintainer edit: make named modelPresets the primary model configuration path and expand fallback preset examples so string fallbacks are clearly preset names, not raw model IDs.
* docs: document api base urls and langfuse setup
Maintainer edit: explain when users need apiBase/base URL in quick start and provider docs, and add Langfuse tracing setup with troubleshooting links.
* docs: use python module pip consistently
Maintainer edit: keep install commands tied to the active Python interpreter by using python -m pip in the Azure optional dependency notes too.
* docs: add non-technical getting started path
Maintainer edit: add a wizard-first guide for users without terminal or JSON background, including a text TUI menu example and links from the main docs entrypoints.
* docs: avoid hard-wrapped prose in user docs
Maintainer edit: unwrap ordinary prose across user-facing documentation while preserving markdown structure, code blocks, tables, lists, and prompt/template files.
* docs: keep desktop list continuations nested
Maintainer edit: preserve list nesting after unwrapping prose in the desktop WebUI sync guide.
* docs: add one-command installer
Maintainer edit: add auditable macOS/Linux and Windows install scripts that install nanobot-ai and start the onboarding wizard, then document the commands in the main onboarding entrypoints.
* docs: add installer dry run mode
Maintainer edit: add --dry-run to the one-command installer scripts so users can preview Python detection, install source, pip command, and wizard behavior without changing their environment.
* docs: clean installer error output
Maintainer edit: make PowerShell installer failures print a concise Error: message instead of Write-Error call-site details.
* docs: add provider setup cookbook
Maintainer edit: add pasteable provider recipes for common hosted, local, fallback, runtime switching, and Langfuse setups, then link the cookbook from onboarding and troubleshooting entrypoints.
* docs: address review feedback
* docs: clarify reader paths
* docs: explain terminal basics for beginners
* docs: clarify wizard navigation
* docs: avoid duplicate onboarding steps
* docs: add setup status check
* docs: explain status output
* docs: remove provider recommendation wording
* docs: explain status diagnostics
* docs: reduce hard-wrapped guidance
* docs: migrate config examples to presets
* docs: clarify python command fallbacks
* docs: improve installer failure recovery
* docs: expand install troubleshooting
* docs: cover installer download failures
* docs: put stable install paths first
* docs: add bundled webui quick path
* docs: clarify provider-neutral setup
* docs: clarify gateway setup for chat surfaces
* docs: improve docs navigation paths
* docs: add configuration quick jump
* docs: clarify provider secret variables
* chore: request PR review acknowledgement
Empty commit: please read the PR review comments and reply on the PR to confirm that you have received them.
This commit intentionally changes no files; it exists only to notify the remote Codex run so it can end its active goal.
* docs: add README start here guide
* docs: avoid provider recommendation wording
* docs: guide next steps after first reply
* docs: explain merging JSON snippets
* docs: add CLI command chooser
* docs: add configuration task map
* docs: add deployment readiness guide
* docs: simplify WebUI entry paths
* docs: add provider recipe chooser
* docs: fix provider factual references
Update OpenRouter and LongCat model examples, align Bedrock guidance, and make fallback snippets schema-valid.
Also correct group policy wording and image-generation provider lists to match the current code.
* fix: keep PowerShell installer from closing caller shell
* docs: mention self-guided configuration
Add AssemblyAI as a third transcription provider option alongside
OpenAI and Groq. AssemblyAI offers better accuracy for certain
audio types (distant voices, noisy environments) and serves as a
reliable fallback when other providers struggle.
Changes:
- Add AssemblyAITranscriptionProvider class in providers/transcription.py
- Add 'assemblyai' option in base channel's transcribe_audio()
- Per-channel configuration via transcriptionProvider in config
Usage:
Set transcriptionProvider: 'assemblyai' and provide an AssemblyAI
API key via transcriptionApiKey in the channel config.
Add support for Xiaomi MiMo ASR as a third transcription backend alongside
Groq and OpenAI Whisper. Xiaomi ASR uses the /v1/chat/completions endpoint
with base64-encoded audio input, rather than the standard Whisper multipart
upload format.
Co-Authored-By:连 <lian@tangping.homes>
Add a `transcriptionModel` channel setting and an OpenRouter transcription
backend so voice messages can be transcribed through OpenRouter's
speech-to-text endpoint (e.g. nvidia/parakeet-tdt-0.6b-v3, openai/whisper-1),
alongside the existing Groq/OpenAI Whisper providers.
- schema: add channels.transcriptionModel (None = provider default)
- providers/transcription: extract a shared POST/retry skeleton; add a
JSON+base64 OpenRouterTranscriptionProvider; make the STT model a
constructor param on all providers instead of hardcoding it
- channels: route transcriptionProvider="openrouter" and thread the model
through the manager to each channel
- docs + tests
Only dedicated STT models work on OpenRouter's transcription endpoint;
chat LLMs (e.g. google/gemini-3.5-flash) are rejected there.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Maintainer edit: explain that HTTP/SSE MCP now uses the shared SSRF guard before connecting and before following redirects, so local or private HTTP MCP endpoints require an explicit tools.ssrfWhitelist entry.
Remove standalone nanobot/heartbeat/ service and replace it with an
auto-registered system cron job on gateway startup. Key behaviors preserved:
- HeartbeatConfig (enabled, interval_s, keep_recent_messages) remains in
GatewayConfig for backward compatibility.
- On startup, if enabled, a system cron job "heartbeat" is registered with
schedule derived from interval_s.
- HEARTBEAT.md is checked on each tick; empty/template-identical files skip
to avoid wasting LLM calls.
- Post-run evaluate_response and session history truncation
(keep_recent_messages) are retained.
- Delivery target selection, deliverable filtering, and preamble guidance
are preserved.
Files removed:
- nanobot/heartbeat/__init__.py
- nanobot/heartbeat/service.py
- tests/heartbeat/*
- tests/agent/test_heartbeat_service.py
Templates and docs updated to reflect cron-based usage.
Document how to use StepFun's Step Plan subscription endpoint with the
existing `stepfun` provider by overriding `apiBase`, following the same
pattern as the `zhipu` provider's coding plan documentation.
- **Base URL**: `https://api.stepfun.com/step_plan/v1` (dedicated endpoint)
- **API Key**: same `STEPFUN_API_KEY` as the regular `stepfun` provider
- **Models**: `step-3.5-flash`, `step-3.5-flash-2603`, `step-router-v1`
Changes:
- `docs/configuration.md` — provider tip, and config example showing
`apiBase` override on the existing `stepfun` provider
Test: 488/488 provider tests passed.
Replace standalone 'Token Plan' section with general Xiaomi MiMo
section using the built-in xiaomi_mimo provider. Token plan becomes
a note within the section, since it's just an apiBase override.
Key changes:
- Use xiaomi_mimo provider (auto-matches via 'mimo' keyword in model name)
- Drop redundant provider field (auto-detected)
- Add token plan tip to provider tips block
- Restructure as general Xiaomi MiMo section with token plan as note
Add APIFree as a built-in OpenAI-compatible provider. APIFree offers
agent-optimised models such as skywork-ai/skyclaw-v1 through an
OpenAI-compatible API at https://api.apifree.ai/agent/v1.
Changes:
- Register apifree provider in the provider registry
- Add config schema field
- Add documentation with configuration example
- Add provider tests, websocket channel tests, and webui tests
- Add provider icon in settings UI
- Note that any string field supports ${VAR_NAME} and resolved values are
never written back to disk.
- Document the failure mode for unset variables.
- Add MCP (stdio env + HTTP headers) and web-search examples.
- Add Docker, direnv, and secret-manager (1Password / pass / Bitwarden)
delivery patterns alongside the existing systemd example.
- Replace plaintext apiKey values in tools.web.search examples (Brave,
Tavily, Jina, Kagi, Olostep) with ${PROVIDER_API_KEY} placeholders so
the docs stop modelling the anti-pattern.
- Cross-link from the Security section.
Refs: HKUDS/nanobot#2172
Resolve fallbackModels as preset references or explicit inline provider configs so failover uses complete model settings without exposing fallback logic to the agent loop.
Co-authored-by: Cursor <cursoragent@cursor.com>
Bind fallback model chains to the active model configuration so defaults and presets do not inherit or merge fallback behavior implicitly. Require explicit fallback providers while preserving per-fallback generation overrides and context-window safety.
Co-authored-by: Cursor <cursoragent@cursor.com>
Reasoning now flows as its own stream — symmetric to the answer's
``delta`` / ``stream_end`` pair — instead of being shipped as one
oversized progress message. This lets WebUI render a live "Thinking…"
bubble that updates in place, then auto-collapses when the stream
closes. Other channels remain plugin no-ops by default.
## Protocol
New metadata: ``_reasoning_delta`` (chunk) and ``_reasoning_end``
(close marker). ChannelManager routes both to the dedicated plugin
hooks below; the legacy one-shot ``_reasoning`` is kept for back-compat
and BaseChannel expands it into a single delta + end pair so plugins
only ever implement the streaming primitives.
WebSocket emits two new events:
- ``reasoning_delta`` (event, chat_id, text, optional stream_id)
- ``reasoning_end`` (event, chat_id, optional stream_id)
## BaseChannel surface
- ``send_reasoning_delta(chat_id, delta, metadata)`` — no-op default
- ``send_reasoning_end(chat_id, metadata)`` — no-op default
- ``send_reasoning(msg)`` — back-compat wrapper, base impl forwards
to the streaming primitives
A channel adds reasoning support by overriding the two streaming
primitives. Telegram / Slack / Discord / Feishu / WeChat / Matrix keep
the base no-ops until their bubble UIs are adapted; reasoning silently
drops at dispatch, never as a stray text message.
## AgentHook
Adds ``emit_reasoning_end`` to the hook lifecycle. ``_LoopHook`` tracks
whether a reasoning segment is open and closes it on:
- the first answer delta arriving (so the UI locks the bubble before
the answer renders below),
- ``on_stream_end``,
- one-shot ``reasoning_content`` / ``thinking_blocks`` after a single
non-streaming response.
## WebUI
- ``UIMessage.reasoning`` is now a single accumulated string with a
companion ``reasoningStreaming`` flag.
- ``useNanobotStream`` consumes ``reasoning_delta`` / ``reasoning_end``;
legacy ``kind: "reasoning"`` is auto-translated to a delta + end.
- New ``ReasoningBubble``: shimmer header + auto-expanded while
streaming, collapses to a clickable "Thinking" pill once closed,
respects ``prefers-reduced-motion``.
- Answer deltas adopt the reasoning placeholder so the bubble and the
answer share one assistant row.
## Tests
- ``tests/channels/test_channel_manager_reasoning.py`` — manager routes
delta + end, drops on channel opt-out, expands one-shot back-compat.
- ``tests/channels/test_websocket_channel.py`` — new ``reasoning_delta``
/ ``reasoning_end`` frames, empty-chunk safety, no-subscriber safety,
back-compat expansion.
- ``tests/agent/test_runner_reasoning.py`` — runner closes the segment
on streaming answer start and after one-shot reasoning.
- WebUI ``useNanobotStream`` + ``message-bubble`` cover the new
protocol and the shimmer styling.
## Docs
``docs/configuration.md`` and ``docs/websocket.md`` document the new
events and the plugin contract.
Co-authored-by: Cursor <cursoragent@cursor.com>
Reasoning was being shipped to every channel as a generic progress
message with a `_reasoning: true` flag. Two problems with that:
1. Channels without a low-emphasis UI primitive (Telegram, Slack,
Discord, Feishu...) would dump raw model thoughts as ordinary
replies, polluting the conversation.
2. The agent loop double-gated by inspecting `channels_config`, which
coupled the loop to display policy.
Treat reasoning as its own plugin action — `BaseChannel.send_reasoning`
defaults to a documented no-op; channels that have a fitting affordance
override. ChannelManager routes `_reasoning` outbounds to that method
only when the channel opts in via `show_reasoning` (camelCase alias
`showReasoning` mirrors `sendProgress`). Plugins that don't override
silently drop reasoning — "no fit, no leak" is the contract.
Reference implementation lands for WebSocket / WebUI: a new
`kind: "reasoning"` frame, parked on the active assistant bubble as a
collapsible `Thinking` group above the answer. CLI keeps its existing
direct path (it doesn't go through the bus). `ChannelsConfig.show_reasoning`
flips to `true` by default — only adapted channels surface anything,
others stay quiet.
Loop net diff is -3 lines: the `channels_config.show_reasoning` check
moves out, leaving emit_reasoning a one-liner that publishes and trusts
the channel to decide.
Co-authored-by: Cursor <cursoragent@cursor.com>
Resolves conflicts after main landed the state-machine turn refactor
and the test_runner.py 9-file split:
- nanobot/agent/loop.py: take main's `_state_build`/`_persist_user_message_early`
flow; restore the `reasoning: bool` parameter on `_build_bus_progress_callback`
so the loop hook can mark progress as reasoning-channel without coupling to
the answer stream.
- nanobot/cli/stream.py: keep main's configurable `bot_name`/`bot_icon` header
while preserving the PR's `transient=True` Live + `self._console` routing
+ `_renderable()` final-render path that fixed TUI duplication.
- tests/agent/test_runner.py was deleted on main and split into 9 focused
files; relocated all 6 reasoning tests into a new `test_runner_reasoning.py`
matching the new layout, deduplicated the per-test `ReasoningHook` boilerplate
through a shared `_RecordingHook` helper.
Co-authored-by: Cursor <cursoragent@cursor.com>
Reasoning surfacing was split across three branches in runner.py plus
two separate streaming buffers (loop hook and runner progress stream),
with three independent display-side gates in the CLI. This collapsed
the policy into one source of truth and fixed two real bugs:
- Structured `reasoning_content` was suppressed whenever the answer was
streamed, because the runner gated emission on `streamed_content`.
Providers don't stream `reasoning_content`; it only arrives on the
final response, so the answer stream and the reasoning channel are
independent. Added `streamed_reasoning` to `AgentHookContext` to track
the right bit.
- `channels.showReasoning` was subordinated to `sendProgress`. They are
orthogonal — turning off progress streaming shouldn't silence
reasoning. Reworked the CLI gates accordingly.
Single-helper consolidation:
- `extract_reasoning(reasoning_content, thinking_blocks, content)`
returns `(reasoning_text, cleaned_content)` with a defined fallback
order: dedicated field → Anthropic thinking_blocks → inline
`<think>`/`<thought>` tags. Models that expose none of these
short-circuit to `(None, content)` — zero overhead.
- `IncrementalThinkExtractor` replaces the ad-hoc `emit_incremental_think`
function and its hand-rolled "emitted cursor" state in both the loop
hook and the runner progress stream.
Also documented the new `showReasoning` channel option in
docs/configuration.md and noted its independence from sendProgress.
Co-authored-by: Cursor <cursoragent@cursor.com>
The config field was added but never passed from config to AgentLoop.
The value was always falling back to the default (40) regardless of
what was set in config.json.
Now passes tool_hint_max_length through all AgentLoop() call sites:
- nanobot/nanobot.py (main bot)
- nanobot/cli/commands.py (CLI agent, dev, webui commands)
Also adds documentation in docs/configuration.md.
Replace the asyncio.Semaphore queueing approach with a simple count
check in SpawnTool.execute(). When the concurrency limit is reached,
the tool returns an error string so the agent can perceive the reason
and adjust its behavior instead of silently queueing.
- Remove max_concurrent_subagents parameter threading through
AgentLoop, commands.py, and nanobot.py
- SubagentManager reads the limit directly from AgentDefaults
- SpawnTool checks get_running_count() before calling spawn()
- Simplify tests to verify rejection behavior
