Merge remote-tracking branch 'origin/main' into pr-2646

.gitignore

@@ -2,6 +2,7 @@
 .assets
 .docs
 .env
+.web
 *.pyc
 dist/
 build/

README.md

@@ -20,13 +20,20 @@
 
 ## 📢 News
 
-> [!IMPORTANT]
-> **Security note:** Due to `litellm` supply chain poisoning, **please check your Python environment ASAP** and refer to this [advisory](https://github.com/HKUDS/nanobot/discussions/2445) for details. We have fully removed the `litellm` since **v0.1.4.post6**.
-
+- **2026-04-02** 🧱 **Long-running tasks** run more reliably — core runtime hardening.
+- **2026-04-01** 🔑 GitHub Copilot auth restored; stricter workspace paths; OpenRouter Claude caching fix.
+- **2026-03-31** 🛰️ WeChat multimodal alignment, Discord/Matrix polish, Python SDK facade, MCP and tool fixes.
+- **2026-03-30** 🧩 OpenAI-compatible API tightened; composable agent lifecycle hooks.
+- **2026-03-29** 💬 WeChat voice, typing, QR/media resilience; fixed-session OpenAI-compatible API.
+- **2026-03-28** 📚 Provider docs refresh; skill template wording fix.
 - **2026-03-27** 🚀 Released **v0.1.4.post6** — architecture decoupling, litellm removal, end-to-end streaming, WeChat channel, and a security fix. Please see [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.1.4.post6) for details.
 - **2026-03-26** 🏗️ Agent runner extracted and lifecycle hooks unified; stream delta coalescing at boundaries.
 - **2026-03-25** 🌏 StepFun provider, configurable timezone, Gemini thought signatures.
 - **2026-03-24** 🔧 WeChat compatibility, Feishu CardKit streaming, test suite restructured.
+
+<details>
+<summary>Earlier news</summary>
+
 - **2026-03-23** 🔧 Command routing refactored for plugins, WhatsApp/WeChat media, unified channel login CLI.
 - **2026-03-22** ⚡ End-to-end streaming, WeChat channel, Anthropic cache optimization, `/status` command.
 - **2026-03-21** 🔒 Replace `litellm` with native `openai` + `anthropic` SDKs. Please see [commit](https://github.com/HKUDS/nanobot/commit/3dfdab7).
@@ -34,10 +41,6 @@
 - **2026-03-19** 💬 Telegram gets more resilient under load; Feishu now renders code blocks properly.
 - **2026-03-18** 📷 Telegram can now send media via URL. Cron schedules show human-readable details.
 - **2026-03-17** ✨ Feishu formatting glow-up, Slack reacts when done, custom endpoints support extra headers, and image handling is more reliable.
-
-<details>
-<summary>Earlier news</summary>
-
 - **2026-03-16** 🚀 Released **v0.1.4.post5** — a refinement-focused release with stronger reliability and channel support, and a more dependable day-to-day experience. Please see [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.1.4.post5) for details.
 - **2026-03-15** 🧩 DingTalk rich media, smarter built-in skills, and cleaner model compatibility.
 - **2026-03-14** 💬 Channel plugins, Feishu replies, and steadier MCP, QQ, and media handling.

nanobot/agent/context.py

@@ -110,6 +110,20 @@ IMPORTANT: To send files (images, documents, audio, video) to the user, you MUST
             lines += [f"Channel: {channel}", f"Chat ID: {chat_id}"]
         return ContextBuilder._RUNTIME_CONTEXT_TAG + "\n" + "\n".join(lines)
 
+    @staticmethod
+    def _merge_message_content(left: Any, right: Any) -> str | list[dict[str, Any]]:
+        if isinstance(left, str) and isinstance(right, str):
+            return f"{left}\n\n{right}" if left else right
+
+        def _to_blocks(value: Any) -> list[dict[str, Any]]:
+            if isinstance(value, list):
+                return [item if isinstance(item, dict) else {"type": "text", "text": str(item)} for item in value]
+            if value is None:
+                return []
+            return [{"type": "text", "text": str(value)}]
+
+        return _to_blocks(left) + _to_blocks(right)
+
     def _load_bootstrap_files(self) -> str:
         """Load all bootstrap files from workspace."""
         parts = []
@@ -142,12 +156,17 @@ IMPORTANT: To send files (images, documents, audio, video) to the user, you MUST
             merged = f"{runtime_ctx}\n\n{user_content}"
         else:
             merged = [{"type": "text", "text": runtime_ctx}] + user_content
-
-        return [
+        messages = [
             {"role": "system", "content": self.build_system_prompt(skill_names)},
             *history,
-            {"role": current_role, "content": merged},
         ]
+        if messages[-1].get("role") == current_role:
+            last = dict(messages[-1])
+            last["content"] = self._merge_message_content(last.get("content"), merged)
+            messages[-1] = last
+            return messages
+        messages.append({"role": current_role, "content": merged})
+        return messages
 
     def _build_user_content(self, text: str, media: list[str] | None) -> str | list[dict[str, Any]]:
         """Build user message content with optional base64-encoded images."""

nanobot/agent/loop.py

@@ -29,8 +29,11 @@ from nanobot.agent.tools.web import WebFetchTool, WebSearchTool
 from nanobot.bus.events import InboundMessage, OutboundMessage
 from nanobot.command import CommandContext, CommandRouter, register_builtin_commands
 from nanobot.bus.queue import MessageBus
+from nanobot.config.schema import AgentDefaults
 from nanobot.providers.base import LLMProvider
 from nanobot.session.manager import Session, SessionManager
+from nanobot.utils.helpers import image_placeholder_text, truncate_text
+from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE
 
 if TYPE_CHECKING:
     from nanobot.config.schema import ChannelsConfig, ExecToolConfig, WebSearchConfig
@@ -38,11 +41,7 @@ if TYPE_CHECKING:
 
 
 class _LoopHook(AgentHook):
-    """Core lifecycle hook for the main agent loop.
-
-    Handles streaming delta relay, progress reporting, tool-call logging,
-    and think-tag stripping for the built-in agent path.
-    """
+    """Core hook for the main loop."""
 
     def __init__(
         self,
@@ -111,11 +110,7 @@ class _LoopHook(AgentHook):
 
 
 class _LoopHookChain(AgentHook):
-    """Run the core loop hook first, then best-effort extra hooks.
-
-    This preserves the historical failure behavior of ``_LoopHook`` while still
-    letting user-supplied hooks opt into ``CompositeHook`` isolation.
-    """
+    """Run the core hook before extra hooks."""
 
     __slots__ = ("_primary", "_extras")
 
@@ -163,7 +158,7 @@ class AgentLoop:
     5. Sends responses back
     """
 
-    _TOOL_RESULT_MAX_CHARS = 16_000
+    _RUNTIME_CHECKPOINT_KEY = "runtime_checkpoint"
 
     def __init__(
         self,
@@ -171,8 +166,11 @@ class AgentLoop:
         provider: LLMProvider,
         workspace: Path,
         model: str | None = None,
-        max_iterations: int = 40,
-        context_window_tokens: int = 65_536,
+        max_iterations: int | None = None,
+        context_window_tokens: int | None = None,
+        context_block_limit: int | None = None,
+        max_tool_result_chars: int | None = None,
+        provider_retry_mode: str = "standard",
         web_search_config: WebSearchConfig | None = None,
         web_proxy: str | None = None,
         exec_config: ExecToolConfig | None = None,
@@ -186,13 +184,27 @@ class AgentLoop:
     ):
         from nanobot.config.schema import ExecToolConfig, WebSearchConfig
 
+        defaults = AgentDefaults()
         self.bus = bus
         self.channels_config = channels_config
         self.provider = provider
         self.workspace = workspace
         self.model = model or provider.get_default_model()
-        self.max_iterations = max_iterations
-        self.context_window_tokens = context_window_tokens
+        self.max_iterations = (
+            max_iterations if max_iterations is not None else defaults.max_tool_iterations
+        )
+        self.context_window_tokens = (
+            context_window_tokens
+            if context_window_tokens is not None
+            else defaults.context_window_tokens
+        )
+        self.context_block_limit = context_block_limit
+        self.max_tool_result_chars = (
+            max_tool_result_chars
+            if max_tool_result_chars is not None
+            else defaults.max_tool_result_chars
+        )
+        self.provider_retry_mode = provider_retry_mode
         self.web_search_config = web_search_config or WebSearchConfig()
         self.web_proxy = web_proxy
         self.exec_config = exec_config or ExecToolConfig()
@@ -211,6 +223,7 @@ class AgentLoop:
             workspace=workspace,
             bus=bus,
             model=self.model,
+            max_tool_result_chars=self.max_tool_result_chars,
             web_search_config=self.web_search_config,
             web_proxy=web_proxy,
             exec_config=self.exec_config,
@@ -322,6 +335,7 @@ class AgentLoop:
         on_stream: Callable[[str], Awaitable[None]] | None = None,
         on_stream_end: Callable[..., Awaitable[None]] | None = None,
         *,
+        session: Session | None = None,
         channel: str = "cli",
         chat_id: str = "direct",
         message_id: str | None = None,
@@ -348,14 +362,27 @@ class AgentLoop:
             else loop_hook
         )
 
+        async def _checkpoint(payload: dict[str, Any]) -> None:
+            if session is None:
+                return
+            self._set_runtime_checkpoint(session, payload)
+
         result = await self.runner.run(AgentRunSpec(
             initial_messages=initial_messages,
             tools=self.tools,
             model=self.model,
             max_iterations=self.max_iterations,
+            max_tool_result_chars=self.max_tool_result_chars,
             hook=hook,
             error_message="Sorry, I encountered an error calling the AI model.",
             concurrent_tools=True,
+            workspace=self.workspace,
+            session_key=session.key if session else None,
+            context_window_tokens=self.context_window_tokens,
+            context_block_limit=self.context_block_limit,
+            provider_retry_mode=self.provider_retry_mode,
+            progress_callback=on_progress,
+            checkpoint_callback=_checkpoint,
         ))
         self._last_usage = result.usage
         if result.stop_reason == "max_iterations":
@@ -493,6 +520,8 @@ class AgentLoop:
             logger.info("Processing system message from {}", msg.sender_id)
             key = f"{channel}:{chat_id}"
             session = self.sessions.get_or_create(key)
+            if self._restore_runtime_checkpoint(session):
+                self.sessions.save(session)
             await self.memory_consolidator.maybe_consolidate_by_tokens(session)
             self._set_tool_context(channel, chat_id, msg.metadata.get("message_id"))
             history = session.get_history(max_messages=0)
@@ -503,10 +532,11 @@ class AgentLoop:
                 current_role=current_role,
             )
             final_content, _, all_msgs = await self._run_agent_loop(
-                messages, channel=channel, chat_id=chat_id,
+                messages, session=session, channel=channel, chat_id=chat_id,
                 message_id=msg.metadata.get("message_id"),
             )
             self._save_turn(session, all_msgs, 1 + len(history))
+            self._clear_runtime_checkpoint(session)
             self.sessions.save(session)
             self._schedule_background(self.memory_consolidator.maybe_consolidate_by_tokens(session))
             return OutboundMessage(channel=channel, chat_id=chat_id,
@@ -517,6 +547,8 @@ class AgentLoop:
 
         key = session_key or msg.session_key
         session = self.sessions.get_or_create(key)
+        if self._restore_runtime_checkpoint(session):
+            self.sessions.save(session)
 
         # Slash commands
         raw = msg.content.strip()
@@ -552,14 +584,16 @@ class AgentLoop:
             on_progress=on_progress or _bus_progress,
             on_stream=on_stream,
             on_stream_end=on_stream_end,
+            session=session,
             channel=msg.channel, chat_id=msg.chat_id,
             message_id=msg.metadata.get("message_id"),
         )
 
-        if final_content is None:
-            final_content = "I've completed processing but have no response to give."
+        if final_content is None or not final_content.strip():
+            final_content = EMPTY_FINAL_RESPONSE_MESSAGE
 
         self._save_turn(session, all_msgs, 1 + len(history))
+        self._clear_runtime_checkpoint(session)
         self.sessions.save(session)
         self._schedule_background(self.memory_consolidator.maybe_consolidate_by_tokens(session))
 
@@ -577,12 +611,6 @@ class AgentLoop:
             metadata=meta,
         )
 
-    @staticmethod
-    def _image_placeholder(block: dict[str, Any]) -> dict[str, str]:
-        """Convert an inline image block into a compact text placeholder."""
-        path = (block.get("_meta") or {}).get("path", "")
-        return {"type": "text", "text": f"[image: {path}]" if path else "[image]"}
-
     def _sanitize_persisted_blocks(
         self,
         content: list[dict[str, Any]],
@@ -609,13 +637,14 @@ class AgentLoop:
                 block.get("type") == "image_url"
                 and block.get("image_url", {}).get("url", "").startswith("data:image/")
             ):
-                filtered.append(self._image_placeholder(block))
+                path = (block.get("_meta") or {}).get("path", "")
+                filtered.append({"type": "text", "text": image_placeholder_text(path)})
                 continue
 
             if block.get("type") == "text" and isinstance(block.get("text"), str):
                 text = block["text"]
-                if truncate_text and len(text) > self._TOOL_RESULT_MAX_CHARS:
-                    text = text[:self._TOOL_RESULT_MAX_CHARS] + "\n... (truncated)"
+                if truncate_text and len(text) > self.max_tool_result_chars:
+                    text = truncate_text(text, self.max_tool_result_chars)
                 filtered.append({**block, "text": text})
                 continue
 
@@ -632,8 +661,8 @@ class AgentLoop:
             if role == "assistant" and not content and not entry.get("tool_calls"):
                 continue  # skip empty assistant messages — they poison session context
             if role == "tool":
-                if isinstance(content, str) and len(content) > self._TOOL_RESULT_MAX_CHARS:
-                    entry["content"] = content[:self._TOOL_RESULT_MAX_CHARS] + "\n... (truncated)"
+                if isinstance(content, str) and len(content) > self.max_tool_result_chars:
+                    entry["content"] = truncate_text(content, self.max_tool_result_chars)
                 elif isinstance(content, list):
                     filtered = self._sanitize_persisted_blocks(content, truncate_text=True)
                     if not filtered:
@@ -656,6 +685,78 @@ class AgentLoop:
             session.messages.append(entry)
         session.updated_at = datetime.now()
 
+    def _set_runtime_checkpoint(self, session: Session, payload: dict[str, Any]) -> None:
+        """Persist the latest in-flight turn state into session metadata."""
+        session.metadata[self._RUNTIME_CHECKPOINT_KEY] = payload
+        self.sessions.save(session)
+
+    def _clear_runtime_checkpoint(self, session: Session) -> None:
+        if self._RUNTIME_CHECKPOINT_KEY in session.metadata:
+            session.metadata.pop(self._RUNTIME_CHECKPOINT_KEY, None)
+
+    @staticmethod
+    def _checkpoint_message_key(message: dict[str, Any]) -> tuple[Any, ...]:
+        return (
+            message.get("role"),
+            message.get("content"),
+            message.get("tool_call_id"),
+            message.get("name"),
+            message.get("tool_calls"),
+            message.get("reasoning_content"),
+            message.get("thinking_blocks"),
+        )
+
+    def _restore_runtime_checkpoint(self, session: Session) -> bool:
+        """Materialize an unfinished turn into session history before a new request."""
+        from datetime import datetime
+
+        checkpoint = session.metadata.get(self._RUNTIME_CHECKPOINT_KEY)
+        if not isinstance(checkpoint, dict):
+            return False
+
+        assistant_message = checkpoint.get("assistant_message")
+        completed_tool_results = checkpoint.get("completed_tool_results") or []
+        pending_tool_calls = checkpoint.get("pending_tool_calls") or []
+
+        restored_messages: list[dict[str, Any]] = []
+        if isinstance(assistant_message, dict):
+            restored = dict(assistant_message)
+            restored.setdefault("timestamp", datetime.now().isoformat())
+            restored_messages.append(restored)
+        for message in completed_tool_results:
+            if isinstance(message, dict):
+                restored = dict(message)
+                restored.setdefault("timestamp", datetime.now().isoformat())
+                restored_messages.append(restored)
+        for tool_call in pending_tool_calls:
+            if not isinstance(tool_call, dict):
+                continue
+            tool_id = tool_call.get("id")
+            name = ((tool_call.get("function") or {}).get("name")) or "tool"
+            restored_messages.append({
+                "role": "tool",
+                "tool_call_id": tool_id,
+                "name": name,
+                "content": "Error: Task interrupted before this tool finished.",
+                "timestamp": datetime.now().isoformat(),
+            })
+
+        overlap = 0
+        max_overlap = min(len(session.messages), len(restored_messages))
+        for size in range(max_overlap, 0, -1):
+            existing = session.messages[-size:]
+            restored = restored_messages[:size]
+            if all(
+                self._checkpoint_message_key(left) == self._checkpoint_message_key(right)
+                for left, right in zip(existing, restored)
+            ):
+                overlap = size
+                break
+        session.messages.extend(restored_messages[overlap:])
+
+        self._clear_runtime_checkpoint(session)
+        return True
+
     async def process_direct(
         self,
         content: str,

nanobot/agent/runner.py

@@ -4,20 +4,36 @@ from __future__ import annotations
 
 import asyncio
 from dataclasses import dataclass, field
+from pathlib import Path
 from typing import Any
 
+from loguru import logger
+
 from nanobot.agent.hook import AgentHook, AgentHookContext
 from nanobot.agent.tools.registry import ToolRegistry
 from nanobot.providers.base import LLMProvider, ToolCallRequest
-from nanobot.utils.helpers import build_assistant_message
+from nanobot.utils.helpers import (
+    build_assistant_message,
+    estimate_message_tokens,
+    estimate_prompt_tokens_chain,
+    find_legal_message_start,
+    maybe_persist_tool_result,
+    truncate_text,
+)
+from nanobot.utils.runtime import (
+    EMPTY_FINAL_RESPONSE_MESSAGE,
+    build_finalization_retry_message,
+    ensure_nonempty_tool_result,
+    is_blank_text,
+    repeated_external_lookup_error,
+)
 
 _DEFAULT_MAX_ITERATIONS_MESSAGE = (
     "I reached the maximum number of tool call iterations ({max_iterations}) "
     "without completing the task. You can try breaking the task into smaller steps."
 )
 _DEFAULT_ERROR_MESSAGE = "Sorry, I encountered an error calling the AI model."
-
-
+_SNIP_SAFETY_BUFFER = 1024
 @dataclass(slots=True)
 class AgentRunSpec:
     """Configuration for a single agent execution."""
@@ -26,6 +42,7 @@ class AgentRunSpec:
     tools: ToolRegistry
     model: str
     max_iterations: int
+    max_tool_result_chars: int
     temperature: float | None = None
     max_tokens: int | None = None
     reasoning_effort: str | None = None
@@ -34,6 +51,13 @@ class AgentRunSpec:
     max_iterations_message: str | None = None
     concurrent_tools: bool = False
     fail_on_tool_error: bool = False
+    workspace: Path | None = None
+    session_key: str | None = None
+    context_window_tokens: int | None = None
+    context_block_limit: int | None = None
+    provider_retry_mode: str = "standard"
+    progress_callback: Any | None = None
+    checkpoint_callback: Any | None = None
 
 
 @dataclass(slots=True)
@@ -60,91 +84,142 @@ class AgentRunner:
         messages = list(spec.initial_messages)
         final_content: str | None = None
         tools_used: list[str] = []
-        usage: dict[str, int] = {}
+        usage: dict[str, int] = {"prompt_tokens": 0, "completion_tokens": 0}
         error: str | None = None
         stop_reason = "completed"
         tool_events: list[dict[str, str]] = []
+        external_lookup_counts: dict[str, int] = {}
 
         for iteration in range(spec.max_iterations):
+            try:
+                messages = self._apply_tool_result_budget(spec, messages)
+                messages_for_model = self._snip_history(spec, messages)
+            except Exception as exc:
+                logger.warning(
+                    "Context governance failed on turn {} for {}: {}; using raw messages",
+                    iteration,
+                    spec.session_key or "default",
+                    exc,
+                )
+                messages_for_model = messages
             context = AgentHookContext(iteration=iteration, messages=messages)
             await hook.before_iteration(context)
-            kwargs: dict[str, Any] = {
-                "messages": messages,
-                "tools": spec.tools.get_definitions(),
-                "model": spec.model,
-            }
-            if spec.temperature is not None:
-                kwargs["temperature"] = spec.temperature
-            if spec.max_tokens is not None:
-                kwargs["max_tokens"] = spec.max_tokens
-            if spec.reasoning_effort is not None:
-                kwargs["reasoning_effort"] = spec.reasoning_effort
-
-            if hook.wants_streaming():
-                async def _stream(delta: str) -> None:
-                    await hook.on_stream(context, delta)
-
-                response = await self.provider.chat_stream_with_retry(
-                    **kwargs,
-                    on_content_delta=_stream,
-                )
-            else:
-                response = await self.provider.chat_with_retry(**kwargs)
-
-            raw_usage = response.usage or {}
+            response = await self._request_model(spec, messages_for_model, hook, context)
+            raw_usage = self._usage_dict(response.usage)
             context.response = response
-            context.usage = raw_usage
+            context.usage = dict(raw_usage)
             context.tool_calls = list(response.tool_calls)
-            # Accumulate standard fields into result usage.
-            usage["prompt_tokens"] = usage.get("prompt_tokens", 0) + int(raw_usage.get("prompt_tokens", 0) or 0)
-            usage["completion_tokens"] = usage.get("completion_tokens", 0) + int(raw_usage.get("completion_tokens", 0) or 0)
-            cached = raw_usage.get("cached_tokens")
-            if cached:
-                usage["cached_tokens"] = usage.get("cached_tokens", 0) + int(cached)
+            self._accumulate_usage(usage, raw_usage)
 
             if response.has_tool_calls:
                 if hook.wants_streaming():
                     await hook.on_stream_end(context, resuming=True)
 
-                messages.append(build_assistant_message(
+                assistant_message = build_assistant_message(
                     response.content or "",
                     tool_calls=[tc.to_openai_tool_call() for tc in response.tool_calls],
                     reasoning_content=response.reasoning_content,
                     thinking_blocks=response.thinking_blocks,
-                ))
+                )
+                messages.append(assistant_message)
                 tools_used.extend(tc.name for tc in response.tool_calls)
+                await self._emit_checkpoint(
+                    spec,
+                    {
+                        "phase": "awaiting_tools",
+                        "iteration": iteration,
+                        "model": spec.model,
+                        "assistant_message": assistant_message,
+                        "completed_tool_results": [],
+                        "pending_tool_calls": [tc.to_openai_tool_call() for tc in response.tool_calls],
+                    },
+                )
 
                 await hook.before_execute_tools(context)
 
-                results, new_events, fatal_error = await self._execute_tools(spec, response.tool_calls)
+                results, new_events, fatal_error = await self._execute_tools(
+                    spec,
+                    response.tool_calls,
+                    external_lookup_counts,
+                )
                 tool_events.extend(new_events)
                 context.tool_results = list(results)
                 context.tool_events = list(new_events)
                 if fatal_error is not None:
                     error = f"Error: {type(fatal_error).__name__}: {fatal_error}"
+                    final_content = error
                     stop_reason = "tool_error"
+                    self._append_final_message(messages, final_content)
+                    context.final_content = final_content
                     context.error = error
                     context.stop_reason = stop_reason
                     await hook.after_iteration(context)
                     break
+                completed_tool_results: list[dict[str, Any]] = []
                 for tool_call, result in zip(response.tool_calls, results):
-                    messages.append({
+                    tool_message = {
                         "role": "tool",
                         "tool_call_id": tool_call.id,
                         "name": tool_call.name,
-                        "content": result,
-                    })
+                        "content": self._normalize_tool_result(
+                            spec,
+                            tool_call.id,
+                            tool_call.name,
+                            result,
+                        ),
+                    }
+                    messages.append(tool_message)
+                    completed_tool_results.append(tool_message)
+                await self._emit_checkpoint(
+                    spec,
+                    {
+                        "phase": "tools_completed",
+                        "iteration": iteration,
+                        "model": spec.model,
+                        "assistant_message": assistant_message,
+                        "completed_tool_results": completed_tool_results,
+                        "pending_tool_calls": [],
+                    },
+                )
                 await hook.after_iteration(context)
                 continue
 
+            clean = hook.finalize_content(context, response.content)
+            if response.finish_reason != "error" and is_blank_text(clean):
+                logger.warning(
+                    "Empty final response on turn {} for {}; retrying with explicit finalization prompt",
+                    iteration,
+                    spec.session_key or "default",
+                )
+                if hook.wants_streaming():
+                    await hook.on_stream_end(context, resuming=False)
+                response = await self._request_finalization_retry(spec, messages_for_model)
+                retry_usage = self._usage_dict(response.usage)
+                self._accumulate_usage(usage, retry_usage)
+                raw_usage = self._merge_usage(raw_usage, retry_usage)
+                context.response = response
+                context.usage = dict(raw_usage)
+                context.tool_calls = list(response.tool_calls)
+                clean = hook.finalize_content(context, response.content)
+
             if hook.wants_streaming():
                 await hook.on_stream_end(context, resuming=False)
 
-            clean = hook.finalize_content(context, response.content)
             if response.finish_reason == "error":
                 final_content = clean or spec.error_message or _DEFAULT_ERROR_MESSAGE
                 stop_reason = "error"
                 error = final_content
+                self._append_final_message(messages, final_content)
+                context.final_content = final_content
+                context.error = error
+                context.stop_reason = stop_reason
+                await hook.after_iteration(context)
+                break
+            if is_blank_text(clean):
+                final_content = EMPTY_FINAL_RESPONSE_MESSAGE
+                stop_reason = "empty_final_response"
+                error = final_content
+                self._append_final_message(messages, final_content)
                 context.final_content = final_content
                 context.error = error
                 context.stop_reason = stop_reason
@@ -156,6 +231,17 @@ class AgentRunner:
                 reasoning_content=response.reasoning_content,
                 thinking_blocks=response.thinking_blocks,
             ))
+            await self._emit_checkpoint(
+                spec,
+                {
+                    "phase": "final_response",
+                    "iteration": iteration,
+                    "model": spec.model,
+                    "assistant_message": messages[-1],
+                    "completed_tool_results": [],
+                    "pending_tool_calls": [],
+                },
+            )
             final_content = clean
             context.final_content = final_content
             context.stop_reason = stop_reason
@@ -165,6 +251,7 @@ class AgentRunner:
             stop_reason = "max_iterations"
             template = spec.max_iterations_message or _DEFAULT_MAX_ITERATIONS_MESSAGE
             final_content = template.format(max_iterations=spec.max_iterations)
+            self._append_final_message(messages, final_content)
 
         return AgentRunResult(
             final_content=final_content,
@@ -176,21 +263,101 @@ class AgentRunner:
             tool_events=tool_events,
         )
 
+    def _build_request_kwargs(
+        self,
+        spec: AgentRunSpec,
+        messages: list[dict[str, Any]],
+        *,
+        tools: list[dict[str, Any]] | None,
+    ) -> dict[str, Any]:
+        kwargs: dict[str, Any] = {
+            "messages": messages,
+            "tools": tools,
+            "model": spec.model,
+            "retry_mode": spec.provider_retry_mode,
+            "on_retry_wait": spec.progress_callback,
+        }
+        if spec.temperature is not None:
+            kwargs["temperature"] = spec.temperature
+        if spec.max_tokens is not None:
+            kwargs["max_tokens"] = spec.max_tokens
+        if spec.reasoning_effort is not None:
+            kwargs["reasoning_effort"] = spec.reasoning_effort
+        return kwargs
+
+    async def _request_model(
+        self,
+        spec: AgentRunSpec,
+        messages: list[dict[str, Any]],
+        hook: AgentHook,
+        context: AgentHookContext,
+    ):
+        kwargs = self._build_request_kwargs(
+            spec,
+            messages,
+            tools=spec.tools.get_definitions(),
+        )
+        if hook.wants_streaming():
+            async def _stream(delta: str) -> None:
+                await hook.on_stream(context, delta)
+
+            return await self.provider.chat_stream_with_retry(
+                **kwargs,
+                on_content_delta=_stream,
+            )
+        return await self.provider.chat_with_retry(**kwargs)
+
+    async def _request_finalization_retry(
+        self,
+        spec: AgentRunSpec,
+        messages: list[dict[str, Any]],
+    ):
+        retry_messages = list(messages)
+        retry_messages.append(build_finalization_retry_message())
+        kwargs = self._build_request_kwargs(spec, retry_messages, tools=None)
+        return await self.provider.chat_with_retry(**kwargs)
+
+    @staticmethod
+    def _usage_dict(usage: dict[str, Any] | None) -> dict[str, int]:
+        if not usage:
+            return {}
+        result: dict[str, int] = {}
+        for key, value in usage.items():
+            try:
+                result[key] = int(value or 0)
+            except (TypeError, ValueError):
+                continue
+        return result
+
+    @staticmethod
+    def _accumulate_usage(target: dict[str, int], addition: dict[str, int]) -> None:
+        for key, value in addition.items():
+            target[key] = target.get(key, 0) + value
+
+    @staticmethod
+    def _merge_usage(left: dict[str, int], right: dict[str, int]) -> dict[str, int]:
+        merged = dict(left)
+        for key, value in right.items():
+            merged[key] = merged.get(key, 0) + value
+        return merged
+
     async def _execute_tools(
         self,
         spec: AgentRunSpec,
         tool_calls: list[ToolCallRequest],
+        external_lookup_counts: dict[str, int],
     ) -> tuple[list[Any], list[dict[str, str]], BaseException | None]:
-        if spec.concurrent_tools:
-            tool_results = await asyncio.gather(*(
-                self._run_tool(spec, tool_call)
-                for tool_call in tool_calls
-            ))
-        else:
-            tool_results = [
-                await self._run_tool(spec, tool_call)
-                for tool_call in tool_calls
-            ]
+        batches = self._partition_tool_batches(spec, tool_calls)
+        tool_results: list[tuple[Any, dict[str, str], BaseException | None]] = []
+        for batch in batches:
+            if spec.concurrent_tools and len(batch) > 1:
+                tool_results.extend(await asyncio.gather(*(
+                    self._run_tool(spec, tool_call, external_lookup_counts)
+                    for tool_call in batch
+                )))
+            else:
+                for tool_call in batch:
+                    tool_results.append(await self._run_tool(spec, tool_call, external_lookup_counts))
 
         results: list[Any] = []
         events: list[dict[str, str]] = []
@@ -206,9 +373,44 @@ class AgentRunner:
         self,
         spec: AgentRunSpec,
         tool_call: ToolCallRequest,
+        external_lookup_counts: dict[str, int],
     ) -> tuple[Any, dict[str, str], BaseException | None]:
+        _HINT = "\n\n[Analyze the error above and try a different approach.]"
+        lookup_error = repeated_external_lookup_error(
+            tool_call.name,
+            tool_call.arguments,
+            external_lookup_counts,
+        )
+        if lookup_error:
+            event = {
+                "name": tool_call.name,
+                "status": "error",
+                "detail": "repeated external lookup blocked",
+            }
+            if spec.fail_on_tool_error:
+                return lookup_error + _HINT, event, RuntimeError(lookup_error)
+            return lookup_error + _HINT, event, None
+        prepare_call = getattr(spec.tools, "prepare_call", None)
+        tool, params, prep_error = None, tool_call.arguments, None
+        if callable(prepare_call):
+            try:
+                prepared = prepare_call(tool_call.name, tool_call.arguments)
+                if isinstance(prepared, tuple) and len(prepared) == 3:
+                    tool, params, prep_error = prepared
+            except Exception:
+                pass
+        if prep_error:
+            event = {
+                "name": tool_call.name,
+                "status": "error",
+                "detail": prep_error.split(": ", 1)[-1][:120],
+            }
+            return prep_error + _HINT, event, RuntimeError(prep_error) if spec.fail_on_tool_error else None
         try:
-            result = await spec.tools.execute(tool_call.name, tool_call.arguments)
+            if tool is not None:
+                result = await tool.execute(**params)
+            else:
+                result = await spec.tools.execute(tool_call.name, params)
         except asyncio.CancelledError:
             raise
         except BaseException as exc:
@@ -221,14 +423,178 @@ class AgentRunner:
                 return f"Error: {type(exc).__name__}: {exc}", event, exc
             return f"Error: {type(exc).__name__}: {exc}", event, None
 
+        if isinstance(result, str) and result.startswith("Error"):
+            event = {
+                "name": tool_call.name,
+                "status": "error",
+                "detail": result.replace("\n", " ").strip()[:120],
+            }
+            if spec.fail_on_tool_error:
+                return result + _HINT, event, RuntimeError(result)
+            return result + _HINT, event, None
+
         detail = "" if result is None else str(result)
         detail = detail.replace("\n", " ").strip()
         if not detail:
             detail = "(empty)"
         elif len(detail) > 120:
             detail = detail[:120] + "..."
-        return result, {
-            "name": tool_call.name,
-            "status": "error" if isinstance(result, str) and result.startswith("Error") else "ok",
-            "detail": detail,
-        }, None
+        return result, {"name": tool_call.name, "status": "ok", "detail": detail}, None
+
+    async def _emit_checkpoint(
+        self,
+        spec: AgentRunSpec,
+        payload: dict[str, Any],
+    ) -> None:
+        callback = spec.checkpoint_callback
+        if callback is not None:
+            await callback(payload)
+
+    @staticmethod
+    def _append_final_message(messages: list[dict[str, Any]], content: str | None) -> None:
+        if not content:
+            return
+        if (
+            messages
+            and messages[-1].get("role") == "assistant"
+            and not messages[-1].get("tool_calls")
+        ):
+            if messages[-1].get("content") == content:
+                return
+            messages[-1] = build_assistant_message(content)
+            return
+        messages.append(build_assistant_message(content))
+
+    def _normalize_tool_result(
+        self,
+        spec: AgentRunSpec,
+        tool_call_id: str,
+        tool_name: str,
+        result: Any,
+    ) -> Any:
+        result = ensure_nonempty_tool_result(tool_name, result)
+        try:
+            content = maybe_persist_tool_result(
+                spec.workspace,
+                spec.session_key,
+                tool_call_id,
+                result,
+                max_chars=spec.max_tool_result_chars,
+            )
+        except Exception as exc:
+            logger.warning(
+                "Tool result persist failed for {} in {}: {}; using raw result",
+                tool_call_id,
+                spec.session_key or "default",
+                exc,
+            )
+            content = result
+        if isinstance(content, str) and len(content) > spec.max_tool_result_chars:
+            return truncate_text(content, spec.max_tool_result_chars)
+        return content
+
+    def _apply_tool_result_budget(
+        self,
+        spec: AgentRunSpec,
+        messages: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        updated = messages
+        for idx, message in enumerate(messages):
+            if message.get("role") != "tool":
+                continue
+            normalized = self._normalize_tool_result(
+                spec,
+                str(message.get("tool_call_id") or f"tool_{idx}"),
+                str(message.get("name") or "tool"),
+                message.get("content"),
+            )
+            if normalized != message.get("content"):
+                if updated is messages:
+                    updated = [dict(m) for m in messages]
+                updated[idx]["content"] = normalized
+        return updated
+
+    def _snip_history(
+        self,
+        spec: AgentRunSpec,
+        messages: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        if not messages or not spec.context_window_tokens:
+            return messages
+
+        provider_max_tokens = getattr(getattr(self.provider, "generation", None), "max_tokens", 4096)
+        max_output = spec.max_tokens if isinstance(spec.max_tokens, int) else (
+            provider_max_tokens if isinstance(provider_max_tokens, int) else 4096
+        )
+        budget = spec.context_block_limit or (
+            spec.context_window_tokens - max_output - _SNIP_SAFETY_BUFFER
+        )
+        if budget <= 0:
+            return messages
+
+        estimate, _ = estimate_prompt_tokens_chain(
+            self.provider,
+            spec.model,
+            messages,
+            spec.tools.get_definitions(),
+        )
+        if estimate <= budget:
+            return messages
+
+        system_messages = [dict(msg) for msg in messages if msg.get("role") == "system"]
+        non_system = [dict(msg) for msg in messages if msg.get("role") != "system"]
+        if not non_system:
+            return messages
+
+        system_tokens = sum(estimate_message_tokens(msg) for msg in system_messages)
+        remaining_budget = max(128, budget - system_tokens)
+        kept: list[dict[str, Any]] = []
+        kept_tokens = 0
+        for message in reversed(non_system):
+            msg_tokens = estimate_message_tokens(message)
+            if kept and kept_tokens + msg_tokens > remaining_budget:
+                break
+            kept.append(message)
+            kept_tokens += msg_tokens
+        kept.reverse()
+
+        if kept:
+            for i, message in enumerate(kept):
+                if message.get("role") == "user":
+                    kept = kept[i:]
+                    break
+            start = find_legal_message_start(kept)
+            if start:
+                kept = kept[start:]
+        if not kept:
+            kept = non_system[-min(len(non_system), 4) :]
+            start = find_legal_message_start(kept)
+            if start:
+                kept = kept[start:]
+        return system_messages + kept
+
+    def _partition_tool_batches(
+        self,
+        spec: AgentRunSpec,
+        tool_calls: list[ToolCallRequest],
+    ) -> list[list[ToolCallRequest]]:
+        if not spec.concurrent_tools:
+            return [[tool_call] for tool_call in tool_calls]
+
+        batches: list[list[ToolCallRequest]] = []
+        current: list[ToolCallRequest] = []
+        for tool_call in tool_calls:
+            get_tool = getattr(spec.tools, "get", None)
+            tool = get_tool(tool_call.name) if callable(get_tool) else None
+            can_batch = bool(tool and tool.concurrency_safe)
+            if can_batch:
+                current.append(tool_call)
+                continue
+            if current:
+                batches.append(current)
+                current = []
+            batches.append([tool_call])
+        if current:
+            batches.append(current)
+        return batches
+

nanobot/agent/subagent.py

@@ -44,6 +44,7 @@ class SubagentManager:
         provider: LLMProvider,
         workspace: Path,
         bus: MessageBus,
+        max_tool_result_chars: int,
         model: str | None = None,
         web_search_config: "WebSearchConfig | None" = None,
         web_proxy: str | None = None,
@@ -56,6 +57,7 @@ class SubagentManager:
         self.workspace = workspace
         self.bus = bus
         self.model = model or provider.get_default_model()
+        self.max_tool_result_chars = max_tool_result_chars
         self.web_search_config = web_search_config or WebSearchConfig()
         self.web_proxy = web_proxy
         self.exec_config = exec_config or ExecToolConfig()
@@ -136,6 +138,7 @@ class SubagentManager:
                 tools=tools,
                 model=self.model,
                 max_iterations=15,
+                max_tool_result_chars=self.max_tool_result_chars,
                 hook=_SubagentHook(task_id),
                 max_iterations_message="Task completed but no final response was generated.",
                 error_message=None,

nanobot/agent/tools/base.py

@@ -53,6 +53,21 @@ class Tool(ABC):
         """JSON Schema for tool parameters."""
         pass
 
+    @property
+    def read_only(self) -> bool:
+        """Whether this tool is side-effect free and safe to parallelize."""
+        return False
+
+    @property
+    def concurrency_safe(self) -> bool:
+        """Whether this tool can run alongside other concurrency-safe tools."""
+        return self.read_only and not self.exclusive
+
+    @property
+    def exclusive(self) -> bool:
+        """Whether this tool should run alone even if concurrency is enabled."""
+        return False
+
     @abstractmethod
     async def execute(self, **kwargs: Any) -> Any:
         """

nanobot/agent/tools/filesystem.py

@@ -73,6 +73,10 @@ class ReadFileTool(_FsTool):
             "Use offset and limit to paginate through large files."
         )
 
+    @property
+    def read_only(self) -> bool:
+        return True
+
     @property
     def parameters(self) -> dict[str, Any]:
         return {
@@ -344,6 +348,10 @@ class ListDirTool(_FsTool):
             "Common noise directories (.git, node_modules, __pycache__, etc.) are auto-ignored."
         )
 
+    @property
+    def read_only(self) -> bool:
+        return True
+
     @property
     def parameters(self) -> dict[str, Any]:
         return {

nanobot/agent/tools/message.py

@@ -84,6 +84,9 @@ class MessageTool(Tool):
         media: list[str] | None = None,
         **kwargs: Any
     ) -> str:
+        from nanobot.utils.helpers import strip_think
+        content = strip_think(content)
+        
         channel = channel or self._default_channel
         chat_id = chat_id or self._default_chat_id
         # Only inherit default message_id when targeting the same channel+chat.

nanobot/agent/tools/registry.py

@@ -35,22 +35,35 @@ class ToolRegistry:
         """Get all tool definitions in OpenAI format."""
         return [tool.to_schema() for tool in self._tools.values()]
 
+    def prepare_call(
+        self,
+        name: str,
+        params: dict[str, Any],
+    ) -> tuple[Tool | None, dict[str, Any], str | None]:
+        """Resolve, cast, and validate one tool call."""
+        tool = self._tools.get(name)
+        if not tool:
+            return None, params, (
+                f"Error: Tool '{name}' not found. Available: {', '.join(self.tool_names)}"
+            )
+
+        cast_params = tool.cast_params(params)
+        errors = tool.validate_params(cast_params)
+        if errors:
+            return tool, cast_params, (
+                f"Error: Invalid parameters for tool '{name}': " + "; ".join(errors)
+            )
+        return tool, cast_params, None
+
     async def execute(self, name: str, params: dict[str, Any]) -> Any:
         """Execute a tool by name with given parameters."""
         _HINT = "\n\n[Analyze the error above and try a different approach.]"
-
-        tool = self._tools.get(name)
-        if not tool:
-            return f"Error: Tool '{name}' not found. Available: {', '.join(self.tool_names)}"
+        tool, params, error = self.prepare_call(name, params)
+        if error:
+            return error + _HINT
 
         try:
-            # Attempt to cast parameters to match schema types
-            params = tool.cast_params(params)
-            
-            # Validate parameters
-            errors = tool.validate_params(params)
-            if errors:
-                return f"Error: Invalid parameters for tool '{name}': " + "; ".join(errors) + _HINT
+            assert tool is not None  # guarded by prepare_call()
             result = await tool.execute(**params)
             if isinstance(result, str) and result.startswith("Error"):
                 return result + _HINT

nanobot/agent/tools/shell.py

@@ -52,6 +52,10 @@ class ExecTool(Tool):
     def description(self) -> str:
         return "Execute a shell command and return its output. Use with caution."
 
+    @property
+    def exclusive(self) -> bool:
+        return True
+
     @property
     def parameters(self) -> dict[str, Any]:
         return {

nanobot/agent/tools/web.py

@@ -92,6 +92,10 @@ class WebSearchTool(Tool):
         self.config = config if config is not None else WebSearchConfig()
         self.proxy = proxy
 
+    @property
+    def read_only(self) -> bool:
+        return True
+
     async def execute(self, query: str, count: int | None = None, **kwargs: Any) -> str:
         provider = self.config.provider.strip().lower() or "brave"
         n = min(max(count or self.config.max_results, 1), 10)
@@ -234,6 +238,10 @@ class WebFetchTool(Tool):
         self.max_chars = max_chars
         self.proxy = proxy
 
+    @property
+    def read_only(self) -> bool:
+        return True
+
     async def execute(self, url: str, extractMode: str = "markdown", maxChars: int | None = None, **kwargs: Any) -> Any:
         max_chars = maxChars or self.max_chars
         is_valid, error_msg = _validate_url_safe(url)

nanobot/api/server.py

@@ -14,6 +14,8 @@ from typing import Any
 from aiohttp import web
 from loguru import logger
 
+from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE
+
 API_SESSION_KEY = "api:default"
 API_CHAT_ID = "default"
 
@@ -98,7 +100,7 @@ async def handle_chat_completions(request: web.Request) -> web.Response:
 
     logger.info("API request session_key={} content={}", session_key, user_content[:80])
 
-    _FALLBACK = "I've completed processing but have no response to give."
+    _FALLBACK = EMPTY_FINAL_RESPONSE_MESSAGE
 
     try:
         async with session_lock:

nanobot/channels/qq.py

@@ -134,6 +134,7 @@ class QQConfig(Base):
     secret: str = ""
     allow_from: list[str] = Field(default_factory=list)
     msg_format: Literal["plain", "markdown"] = "plain"
+    ack_message: str = "⏳ Processing..."
 
     # Optional: directory to save inbound attachments. If empty, use nanobot get_media_dir("qq").
     media_dir: str = ""
@@ -484,6 +485,17 @@ class QQChannel(BaseChannel):
         if not content and not media_paths:
             return
 
+        if self.config.ack_message:
+            try:
+                await self._send_text_only(
+                    chat_id=chat_id,
+                    is_group=is_group,
+                    msg_id=data.id,
+                    content=self.config.ack_message,
+                )
+            except Exception:
+                logger.debug("QQ ack message failed for chat_id={}", chat_id)
+
         await self._handle_message(
             sender_id=user_id,
             chat_id=chat_id,

nanobot/channels/telegram.py

@@ -275,13 +275,10 @@ class TelegramChannel(BaseChannel):
         self._app = builder.build()
         self._app.add_error_handler(self._on_error)
 
-        # Add command handlers
-        self._app.add_handler(CommandHandler("start", self._on_start))
-        self._app.add_handler(CommandHandler("new", self._forward_command))
-        self._app.add_handler(CommandHandler("stop", self._forward_command))
-        self._app.add_handler(CommandHandler("restart", self._forward_command))
-        self._app.add_handler(CommandHandler("status", self._forward_command))
-        self._app.add_handler(CommandHandler("help", self._on_help))
+        # Add command handlers (using Regex to support @username suffixes before bot initialization)
+        self._app.add_handler(MessageHandler(filters.Regex(r"^/start(?:@\w+)?$"), self._on_start))
+        self._app.add_handler(MessageHandler(filters.Regex(r"^/(new|stop|restart|status)(?:@\w+)?$"), self._forward_command))
+        self._app.add_handler(MessageHandler(filters.Regex(r"^/help(?:@\w+)?$"), self._on_help))
 
         # Add message handler for text, photos, voice, documents
         self._app.add_handler(
@@ -313,7 +310,7 @@ class TelegramChannel(BaseChannel):
         # Start polling (this runs until stopped)
         await self._app.updater.start_polling(
             allowed_updates=["message"],
-            drop_pending_updates=True  # Ignore old messages on startup
+            drop_pending_updates=False  # Process pending messages on startup
         )
 
         # Keep running until stopped
@@ -362,9 +359,14 @@ class TelegramChannel(BaseChannel):
             logger.warning("Telegram bot not running")
             return
 
-        # Only stop typing indicator for final responses
+        # Only stop typing indicator and remove reaction for final responses
         if not msg.metadata.get("_progress", False):
             self._stop_typing(msg.chat_id)
+            if reply_to_message_id := msg.metadata.get("message_id"):
+                try:
+                    await self._remove_reaction(msg.chat_id, int(reply_to_message_id))
+                except ValueError:
+                    pass
 
         try:
             chat_id = int(msg.chat_id)
@@ -435,7 +437,9 @@ class TelegramChannel(BaseChannel):
                 await self._send_text(chat_id, chunk, reply_params, thread_kwargs)
 
     async def _call_with_retry(self, fn, *args, **kwargs):
-        """Call an async Telegram API function with retry on pool/network timeout."""
+        """Call an async Telegram API function with retry on pool/network timeout and RetryAfter."""
+        from telegram.error import RetryAfter
+        
         for attempt in range(1, _SEND_MAX_RETRIES + 1):
             try:
                 return await fn(*args, **kwargs)
@@ -448,6 +452,15 @@ class TelegramChannel(BaseChannel):
                     attempt, _SEND_MAX_RETRIES, delay,
                 )
                 await asyncio.sleep(delay)
+            except RetryAfter as e:
+                if attempt == _SEND_MAX_RETRIES:
+                    raise
+                delay = float(e.retry_after)
+                logger.warning(
+                    "Telegram Flood Control (attempt {}/{}), retrying in {:.1f}s",
+                    attempt, _SEND_MAX_RETRIES, delay,
+                )
+                await asyncio.sleep(delay)
 
     async def _send_text(
         self,
@@ -498,6 +511,11 @@ class TelegramChannel(BaseChannel):
             if stream_id is not None and buf.stream_id is not None and buf.stream_id != stream_id:
                 return
             self._stop_typing(chat_id)
+            if reply_to_message_id := meta.get("message_id"):
+                try:
+                    await self._remove_reaction(chat_id, int(reply_to_message_id))
+                except ValueError:
+                    pass
             try:
                 html = _markdown_to_telegram_html(buf.text)
                 await self._call_with_retry(
@@ -619,8 +637,7 @@ class TelegramChannel(BaseChannel):
             "reply_to_message_id": getattr(reply_to, "message_id", None) if reply_to else None,
         }
 
-    @staticmethod
-    def _extract_reply_context(message) -> str | None:
+    async def _extract_reply_context(self, message) -> str | None:
         """Extract text from the message being replied to, if any."""
         reply = getattr(message, "reply_to_message", None)
         if not reply:
@@ -628,7 +645,21 @@ class TelegramChannel(BaseChannel):
         text = getattr(reply, "text", None) or getattr(reply, "caption", None) or ""
         if len(text) > TELEGRAM_REPLY_CONTEXT_MAX_LEN:
             text = text[:TELEGRAM_REPLY_CONTEXT_MAX_LEN] + "..."
-        return f"[Reply to: {text}]" if text else None
+            
+        if not text:
+            return None
+            
+        bot_id, _ = await self._ensure_bot_identity()
+        reply_user = getattr(reply, "from_user", None)
+        
+        if bot_id and reply_user and getattr(reply_user, "id", None) == bot_id:
+            return f"[Reply to bot: {text}]"
+        elif reply_user and getattr(reply_user, "username", None):
+            return f"[Reply to @{reply_user.username}: {text}]"
+        elif reply_user and getattr(reply_user, "first_name", None):
+            return f"[Reply to {reply_user.first_name}: {text}]"
+        else:
+            return f"[Reply to: {text}]"
 
     async def _download_message_media(
         self, msg, *, add_failure_content: bool = False
@@ -765,10 +796,18 @@ class TelegramChannel(BaseChannel):
         message = update.message
         user = update.effective_user
         self._remember_thread_context(message)
+        
+        # Strip @bot_username suffix if present
+        content = message.text or ""
+        if content.startswith("/") and "@" in content:
+            cmd_part, *rest = content.split(" ", 1)
+            cmd_part = cmd_part.split("@")[0]
+            content = f"{cmd_part} {rest[0]}" if rest else cmd_part
+            
         await self._handle_message(
             sender_id=self._sender_id(user),
             chat_id=str(message.chat_id),
-            content=message.text or "",
+            content=content,
             metadata=self._build_message_metadata(message, user),
             session_key=self._derive_topic_session_key(message),
         )
@@ -812,7 +851,7 @@ class TelegramChannel(BaseChannel):
         # Reply context: text and/or media from the replied-to message
         reply = getattr(message, "reply_to_message", None)
         if reply is not None:
-            reply_ctx = self._extract_reply_context(message)
+            reply_ctx = await self._extract_reply_context(message)
             reply_media, reply_media_parts = await self._download_message_media(reply)
             if reply_media:
                 media_paths = reply_media + media_paths
@@ -903,6 +942,19 @@ class TelegramChannel(BaseChannel):
         except Exception as e:
             logger.debug("Telegram reaction failed: {}", e)
 
+    async def _remove_reaction(self, chat_id: str, message_id: int) -> None:
+        """Remove emoji reaction from a message (best-effort, non-blocking)."""
+        if not self._app:
+            return
+        try:
+            await self._app.bot.set_message_reaction(
+                chat_id=int(chat_id),
+                message_id=message_id,
+                reaction=[],
+            )
+        except Exception as e:
+            logger.debug("Telegram reaction removal failed: {}", e)
+
     async def _typing_loop(self, chat_id: str) -> None:
         """Repeatedly send 'typing' action until cancelled."""
         try:

nanobot/cli/commands.py

@@ -542,6 +542,9 @@ def serve(
         model=runtime_config.agents.defaults.model,
         max_iterations=runtime_config.agents.defaults.max_tool_iterations,
         context_window_tokens=runtime_config.agents.defaults.context_window_tokens,
+        context_block_limit=runtime_config.agents.defaults.context_block_limit,
+        max_tool_result_chars=runtime_config.agents.defaults.max_tool_result_chars,
+        provider_retry_mode=runtime_config.agents.defaults.provider_retry_mode,
         web_search_config=runtime_config.tools.web.search,
         web_proxy=runtime_config.tools.web.proxy or None,
         exec_config=runtime_config.tools.exec,
@@ -629,6 +632,9 @@ def gateway(
         model=config.agents.defaults.model,
         max_iterations=config.agents.defaults.max_tool_iterations,
         context_window_tokens=config.agents.defaults.context_window_tokens,
+        context_block_limit=config.agents.defaults.context_block_limit,
+        max_tool_result_chars=config.agents.defaults.max_tool_result_chars,
+        provider_retry_mode=config.agents.defaults.provider_retry_mode,
         web_search_config=config.tools.web.search,
         web_proxy=config.tools.web.proxy or None,
         exec_config=config.tools.exec,
@@ -835,6 +841,9 @@ def agent(
         model=config.agents.defaults.model,
         max_iterations=config.agents.defaults.max_tool_iterations,
         context_window_tokens=config.agents.defaults.context_window_tokens,
+        context_block_limit=config.agents.defaults.context_block_limit,
+        max_tool_result_chars=config.agents.defaults.max_tool_result_chars,
+        provider_retry_mode=config.agents.defaults.provider_retry_mode,
         web_search_config=config.tools.web.search,
         web_proxy=config.tools.web.proxy or None,
         exec_config=config.tools.exec,
@@ -1023,12 +1032,18 @@ app.add_typer(channels_app, name="channels")
 
 
 @channels_app.command("status")
-def channels_status():
+def channels_status(
+    config_path: str | None = typer.Option(None, "--config", "-c", help="Path to config file"),
+):
     """Show channel status."""
     from nanobot.channels.registry import discover_all
-    from nanobot.config.loader import load_config
+    from nanobot.config.loader import load_config, set_config_path
 
-    config = load_config()
+    resolved_config_path = Path(config_path).expanduser().resolve() if config_path else None
+    if resolved_config_path is not None:
+        set_config_path(resolved_config_path)
+
+    config = load_config(resolved_config_path)
 
     table = Table(title="Channel Status")
     table.add_column("Channel", style="cyan")
@@ -1115,12 +1130,17 @@ def _get_bridge_dir() -> Path:
 def channels_login(
     channel_name: str = typer.Argument(..., help="Channel name (e.g. weixin, whatsapp)"),
     force: bool = typer.Option(False, "--force", "-f", help="Force re-authentication even if already logged in"),
+    config_path: str | None = typer.Option(None, "--config", "-c", help="Path to config file"),
 ):
     """Authenticate with a channel via QR code or other interactive login."""
     from nanobot.channels.registry import discover_all
-    from nanobot.config.loader import load_config
+    from nanobot.config.loader import load_config, set_config_path
 
-    config = load_config()
+    resolved_config_path = Path(config_path).expanduser().resolve() if config_path else None
+    if resolved_config_path is not None:
+        set_config_path(resolved_config_path)
+
+    config = load_config(resolved_config_path)
     channel_cfg = getattr(config.channels, channel_name, None) or {}
 
     # Validate channel exists

nanobot/command/builtin.py

@@ -26,7 +26,10 @@ async def cmd_stop(ctx: CommandContext) -> OutboundMessage:
     sub_cancelled = await loop.subagents.cancel_by_session(msg.session_key)
     total = cancelled + sub_cancelled
     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)
+    return OutboundMessage(
+        channel=msg.channel, chat_id=msg.chat_id, content=content,
+        metadata=dict(msg.metadata or {})
+    )
 
 
 async def cmd_restart(ctx: CommandContext) -> OutboundMessage:
@@ -38,7 +41,10 @@ async def cmd_restart(ctx: CommandContext) -> OutboundMessage:
         os.execv(sys.executable, [sys.executable, "-m", "nanobot"] + sys.argv[1:])
 
     asyncio.create_task(_do_restart())
-    return OutboundMessage(channel=msg.channel, chat_id=msg.chat_id, content="Restarting...")
+    return OutboundMessage(
+        channel=msg.channel, chat_id=msg.chat_id, content="Restarting...",
+        metadata=dict(msg.metadata or {})
+    )
 
 
 async def cmd_status(ctx: CommandContext) -> OutboundMessage:
@@ -62,7 +68,7 @@ async def cmd_status(ctx: CommandContext) -> OutboundMessage:
             session_msg_count=len(session.get_history(max_messages=0)),
             context_tokens_estimate=ctx_est,
         ),
-        metadata={"render_as": "text"},
+        metadata={**dict(ctx.msg.metadata or {}), "render_as": "text"},
     )
 
 
@@ -79,6 +85,7 @@ async def cmd_new(ctx: CommandContext) -> OutboundMessage:
     return OutboundMessage(
         channel=ctx.msg.channel, chat_id=ctx.msg.chat_id,
         content="New session started.",
+        metadata=dict(ctx.msg.metadata or {})
     )
 
 
@@ -88,7 +95,7 @@ async def cmd_help(ctx: CommandContext) -> OutboundMessage:
         channel=ctx.msg.channel,
         chat_id=ctx.msg.chat_id,
         content=build_help_text(),
-        metadata={"render_as": "text"},
+        metadata={**dict(ctx.msg.metadata or {}), "render_as": "text"},
     )
 
 

nanobot/config/schema.py

@@ -38,8 +38,11 @@ class AgentDefaults(Base):
     )
     max_tokens: int = 8192
     context_window_tokens: int = 65_536
+    context_block_limit: int | None = None
     temperature: float = 0.1
-    max_tool_iterations: int = 40
+    max_tool_iterations: int = 200
+    max_tool_result_chars: int = 16_000
+    provider_retry_mode: Literal["standard", "persistent"] = "standard"
     reasoning_effort: str | None = None  # low / medium / high - enables LLM thinking mode
     timezone: str = "UTC"  # IANA timezone, e.g. "Asia/Shanghai", "America/New_York"
 

nanobot/nanobot.py

@@ -73,6 +73,9 @@ class Nanobot:
             model=defaults.model,
             max_iterations=defaults.max_tool_iterations,
             context_window_tokens=defaults.context_window_tokens,
+            context_block_limit=defaults.context_block_limit,
+            max_tool_result_chars=defaults.max_tool_result_chars,
+            provider_retry_mode=defaults.provider_retry_mode,
             web_search_config=config.tools.web.search,
             web_proxy=config.tools.web.proxy or None,
             exec_config=config.tools.exec,

nanobot/providers/anthropic_provider.py

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+import asyncio
+import os
 import re
 import secrets
 import string
@@ -434,13 +436,33 @@ class AnthropicProvider(LLMProvider):
             messages, tools, model, max_tokens, temperature,
             reasoning_effort, tool_choice,
         )
+        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:
-                    async for text in stream.text_stream:
+                    stream_iter = stream.text_stream.__aiter__()
+                    while True:
+                        try:
+                            text = await asyncio.wait_for(
+                                stream_iter.__anext__(),
+                                timeout=idle_timeout_s,
+                            )
+                        except StopAsyncIteration:
+                            break
                         await on_content_delta(text)
-                response = await stream.get_final_message()
+                response = await asyncio.wait_for(
+                    stream.get_final_message(),
+                    timeout=idle_timeout_s,
+                )
             return self._parse_response(response)
+        except asyncio.TimeoutError:
+            return LLMResponse(
+                content=(
+                    f"Error calling LLM: stream stalled for more than "
+                    f"{idle_timeout_s} seconds"
+                ),
+                finish_reason="error",
+            )
         except Exception as e:
             return LLMResponse(content=f"Error calling LLM: {e}", finish_reason="error")
 

nanobot/providers/azure_openai_provider.py

@@ -1,31 +1,36 @@
-"""Azure OpenAI provider implementation with API version 2024-10-21."""
+"""Azure OpenAI provider using the OpenAI SDK Responses API.
+
+Uses ``AsyncOpenAI`` pointed at ``https://{endpoint}/openai/v1/`` which
+routes to the Responses API (``/responses``).  Reuses shared conversion
+helpers from :mod:`nanobot.providers.openai_responses`.
+"""
 
 from __future__ import annotations
 
-import json
 import uuid
 from collections.abc import Awaitable, Callable
 from typing import Any
-from urllib.parse import urljoin
 
-import httpx
-import json_repair
+from openai import AsyncOpenAI
 
-from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
-
-_AZURE_MSG_KEYS = frozenset({"role", "content", "tool_calls", "tool_call_id", "name"})
+from nanobot.providers.base import LLMProvider, LLMResponse
+from nanobot.providers.openai_responses import (
+    consume_sdk_stream,
+    convert_messages,
+    convert_tools,
+    parse_response_output,
+)
 
 
 class AzureOpenAIProvider(LLMProvider):
-    """
-    Azure OpenAI provider with API version 2024-10-21 compliance.
-    
+    """Azure OpenAI provider backed by the Responses API.
+
     Features:
-    - Hardcoded API version 2024-10-21
-    - Uses model field as Azure deployment name in URL path
-    - Uses api-key header instead of Authorization Bearer
-    - Uses max_completion_tokens instead of max_tokens
-    - Direct HTTP calls, bypasses LiteLLM
+    - Uses the OpenAI Python SDK (``AsyncOpenAI``) with
+      ``base_url = {endpoint}/openai/v1/``
+    - Calls ``client.responses.create()`` (Responses API)
+    - Reuses shared message/tool/SSE conversion from
+      ``openai_responses``
     """
 
     def __init__(
@@ -36,40 +41,28 @@ class AzureOpenAIProvider(LLMProvider):
     ):
         super().__init__(api_key, api_base)
         self.default_model = default_model
-        self.api_version = "2024-10-21"
-        
-        # Validate required parameters
+
         if not api_key:
             raise ValueError("Azure OpenAI api_key is required")
         if not api_base:
             raise ValueError("Azure OpenAI api_base is required")
-        
-        # Ensure api_base ends with /
-        if not api_base.endswith('/'):
-            api_base += '/'
+
+        # Normalise: ensure trailing slash
+        if not api_base.endswith("/"):
+            api_base += "/"
         self.api_base = api_base
 
-    def _build_chat_url(self, deployment_name: str) -> str:
-        """Build the Azure OpenAI chat completions URL."""
-        # Azure OpenAI URL format:
-        # https://{resource}.openai.azure.com/openai/deployments/{deployment}/chat/completions?api-version={version}
-        base_url = self.api_base
-        if not base_url.endswith('/'):
-            base_url += '/'
-        
-        url = urljoin(
-            base_url, 
-            f"openai/deployments/{deployment_name}/chat/completions"
+        # SDK client targeting the Azure Responses API endpoint
+        base_url = f"{api_base.rstrip('/')}/openai/v1/"
+        self._client = AsyncOpenAI(
+            api_key=api_key,
+            base_url=base_url,
+            default_headers={"x-session-affinity": uuid.uuid4().hex},
         )
-        return f"{url}?api-version={self.api_version}"
 
-    def _build_headers(self) -> dict[str, str]:
-        """Build headers for Azure OpenAI API with api-key header."""
-        return {
-            "Content-Type": "application/json",
-            "api-key": self.api_key,  # Azure OpenAI uses api-key header, not Authorization
-            "x-session-affinity": uuid.uuid4().hex,  # For cache locality
-        }
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
 
     @staticmethod
     def _supports_temperature(
@@ -82,36 +75,51 @@ class AzureOpenAIProvider(LLMProvider):
         name = deployment_name.lower()
         return not any(token in name for token in ("gpt-5", "o1", "o3", "o4"))
 
-    def _prepare_request_payload(
+    def _build_body(
         self,
-        deployment_name: str,
         messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]] | None = None,
-        max_tokens: int = 4096,
-        temperature: float = 0.7,
-        reasoning_effort: str | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[dict[str, Any]] | None,
+        model: str | None,
+        max_tokens: int,
+        temperature: float,
+        reasoning_effort: str | None,
+        tool_choice: str | dict[str, Any] | None,
     ) -> dict[str, Any]:
-        """Prepare the request payload with Azure OpenAI 2024-10-21 compliance."""
-        payload: dict[str, Any] = {
-            "messages": self._sanitize_request_messages(
-                self._sanitize_empty_content(messages),
-                _AZURE_MSG_KEYS,
-            ),
-            "max_completion_tokens": max(1, max_tokens),  # Azure API 2024-10-21 uses max_completion_tokens
+        """Build the Responses API request body from Chat-Completions-style args."""
+        deployment = model or self.default_model
+        instructions, input_items = convert_messages(self._sanitize_empty_content(messages))
+
+        body: dict[str, Any] = {
+            "model": deployment,
+            "instructions": instructions or None,
+            "input": input_items,
+            "max_output_tokens": max(1, max_tokens),
+            "store": False,
+            "stream": False,
         }
 
-        if self._supports_temperature(deployment_name, reasoning_effort):
-            payload["temperature"] = temperature
+        if self._supports_temperature(deployment, reasoning_effort):
+            body["temperature"] = temperature
 
         if reasoning_effort:
-            payload["reasoning_effort"] = reasoning_effort
+            body["reasoning"] = {"effort": reasoning_effort}
+            body["include"] = ["reasoning.encrypted_content"]
 
         if tools:
-            payload["tools"] = tools
-            payload["tool_choice"] = tool_choice or "auto"
+            body["tools"] = convert_tools(tools)
+            body["tool_choice"] = tool_choice or "auto"
 
-        return payload
+        return body
+
+    @staticmethod
+    def _handle_error(e: Exception) -> LLMResponse:
+        body = getattr(e, "body", None) or getattr(getattr(e, "response", None), "text", None)
+        msg = f"Error: {str(body).strip()[:500]}" if body else f"Error calling Azure OpenAI: {e}"
+        return LLMResponse(content=msg, finish_reason="error")
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
 
     async def chat(
         self,
@@ -123,92 +131,15 @@ class AzureOpenAIProvider(LLMProvider):
         reasoning_effort: str | None = None,
         tool_choice: str | dict[str, Any] | None = None,
     ) -> LLMResponse:
-        """
-        Send a chat completion request to Azure OpenAI.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            tools: Optional list of tool definitions in OpenAI format.
-            model: Model identifier (used as deployment name).
-            max_tokens: Maximum tokens in response (mapped to max_completion_tokens).
-            temperature: Sampling temperature.
-            reasoning_effort: Optional reasoning effort parameter.
-
-        Returns:
-            LLMResponse with content and/or tool calls.
-        """
-        deployment_name = model or self.default_model
-        url = self._build_chat_url(deployment_name)
-        headers = self._build_headers()
-        payload = self._prepare_request_payload(
-            deployment_name, messages, tools, max_tokens, temperature, reasoning_effort,
-            tool_choice=tool_choice,
+        body = self._build_body(
+            messages, tools, model, max_tokens, temperature,
+            reasoning_effort, tool_choice,
         )
-
         try:
-            async with httpx.AsyncClient(timeout=60.0, verify=True) as client:
-                response = await client.post(url, headers=headers, json=payload)
-                if response.status_code != 200:
-                    return LLMResponse(
-                        content=f"Azure OpenAI API Error {response.status_code}: {response.text}",
-                        finish_reason="error",
-                    )
-                
-                response_data = response.json()
-                return self._parse_response(response_data)
-
+            response = await self._client.responses.create(**body)
+            return parse_response_output(response)
         except Exception as e:
-            return LLMResponse(
-                content=f"Error calling Azure OpenAI: {repr(e)}",
-                finish_reason="error",
-            )
-
-    def _parse_response(self, response: dict[str, Any]) -> LLMResponse:
-        """Parse Azure OpenAI response into our standard format."""
-        try:
-            choice = response["choices"][0]
-            message = choice["message"]
-
-            tool_calls = []
-            if message.get("tool_calls"):
-                for tc in message["tool_calls"]:
-                    # Parse arguments from JSON string if needed
-                    args = tc["function"]["arguments"]
-                    if isinstance(args, str):
-                        args = json_repair.loads(args)
-
-                    tool_calls.append(
-                        ToolCallRequest(
-                            id=tc["id"],
-                            name=tc["function"]["name"],
-                            arguments=args,
-                        )
-                    )
-
-            usage = {}
-            if response.get("usage"):
-                usage_data = response["usage"]
-                usage = {
-                    "prompt_tokens": usage_data.get("prompt_tokens", 0),
-                    "completion_tokens": usage_data.get("completion_tokens", 0),
-                    "total_tokens": usage_data.get("total_tokens", 0),
-                }
-
-            reasoning_content = message.get("reasoning_content") or None
-
-            return LLMResponse(
-                content=message.get("content"),
-                tool_calls=tool_calls,
-                finish_reason=choice.get("finish_reason", "stop"),
-                usage=usage,
-                reasoning_content=reasoning_content,
-            )
-
-        except (KeyError, IndexError) as e:
-            return LLMResponse(
-                content=f"Error parsing Azure OpenAI response: {str(e)}",
-                finish_reason="error",
-            )
+            return self._handle_error(e)
 
     async def chat_stream(
         self,
@@ -221,89 +152,26 @@ class AzureOpenAIProvider(LLMProvider):
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
     ) -> LLMResponse:
-        """Stream a chat completion via Azure OpenAI SSE."""
-        deployment_name = model or self.default_model
-        url = self._build_chat_url(deployment_name)
-        headers = self._build_headers()
-        payload = self._prepare_request_payload(
-            deployment_name, messages, tools, max_tokens, temperature,
-            reasoning_effort, tool_choice=tool_choice,
+        body = self._build_body(
+            messages, tools, model, max_tokens, temperature,
+            reasoning_effort, tool_choice,
         )
-        payload["stream"] = True
+        body["stream"] = True
 
         try:
-            async with httpx.AsyncClient(timeout=60.0, verify=True) as client:
-                async with client.stream("POST", url, headers=headers, json=payload) as response:
-                    if response.status_code != 200:
-                        text = await response.aread()
-                        return LLMResponse(
-                            content=f"Azure OpenAI API Error {response.status_code}: {text.decode('utf-8', 'ignore')}",
-                            finish_reason="error",
-                        )
-                    return await self._consume_stream(response, on_content_delta)
-        except Exception as e:
-            return LLMResponse(content=f"Error calling Azure OpenAI: {repr(e)}", finish_reason="error")
-
-    async def _consume_stream(
-        self,
-        response: httpx.Response,
-        on_content_delta: Callable[[str], Awaitable[None]] | None,
-    ) -> LLMResponse:
-        """Parse Azure OpenAI SSE stream into an LLMResponse."""
-        content_parts: list[str] = []
-        tool_call_buffers: dict[int, dict[str, str]] = {}
-        finish_reason = "stop"
-
-        async for line in response.aiter_lines():
-            if not line.startswith("data: "):
-                continue
-            data = line[6:].strip()
-            if data == "[DONE]":
-                break
-            try:
-                chunk = json.loads(data)
-            except Exception:
-                continue
-
-            choices = chunk.get("choices") or []
-            if not choices:
-                continue
-            choice = choices[0]
-            if choice.get("finish_reason"):
-                finish_reason = choice["finish_reason"]
-            delta = choice.get("delta") or {}
-
-            text = delta.get("content")
-            if text:
-                content_parts.append(text)
-                if on_content_delta:
-                    await on_content_delta(text)
-
-            for tc in delta.get("tool_calls") or []:
-                idx = tc.get("index", 0)
-                buf = tool_call_buffers.setdefault(idx, {"id": "", "name": "", "arguments": ""})
-                if tc.get("id"):
-                    buf["id"] = tc["id"]
-                fn = tc.get("function") or {}
-                if fn.get("name"):
-                    buf["name"] = fn["name"]
-                if fn.get("arguments"):
-                    buf["arguments"] += fn["arguments"]
-
-        tool_calls = [
-            ToolCallRequest(
-                id=buf["id"], name=buf["name"],
-                arguments=json_repair.loads(buf["arguments"]) if buf["arguments"] else {},
+            stream = await self._client.responses.create(**body)
+            content, tool_calls, finish_reason, usage, reasoning_content = (
+                await consume_sdk_stream(stream, on_content_delta)
             )
-            for buf in tool_call_buffers.values()
-        ]
-
-        return LLMResponse(
-            content="".join(content_parts) or None,
-            tool_calls=tool_calls,
-            finish_reason=finish_reason,
-        )
+            return LLMResponse(
+                content=content or None,
+                tool_calls=tool_calls,
+                finish_reason=finish_reason,
+                usage=usage,
+                reasoning_content=reasoning_content,
+            )
+        except Exception as e:
+            return self._handle_error(e)
 
     def get_default_model(self) -> str:
-        """Get the default model (also used as default deployment name)."""
         return self.default_model

nanobot/providers/base.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import json
+import re
 from abc import ABC, abstractmethod
 from collections.abc import Awaitable, Callable
 from dataclasses import dataclass, field
@@ -9,6 +10,8 @@ from typing import Any
 
 from loguru import logger
 
+from nanobot.utils.helpers import image_placeholder_text
+
 
 @dataclass
 class ToolCallRequest:
@@ -57,13 +60,7 @@ class LLMResponse:
 
 @dataclass(frozen=True)
 class GenerationSettings:
-    """Default generation parameters for LLM calls.
-
-    Stored on the provider so every call site inherits the same defaults
-    without having to pass temperature / max_tokens / reasoning_effort
-    through every layer.  Individual call sites can still override by
-    passing explicit keyword arguments to chat() / chat_with_retry().
-    """
+    """Default generation settings."""
 
     temperature: float = 0.7
     max_tokens: int = 4096
@@ -71,14 +68,12 @@ class GenerationSettings:
 
 
 class LLMProvider(ABC):
-    """
-    Abstract base class for LLM providers.
-    
-    Implementations should handle the specifics of each provider's API
-    while maintaining a consistent interface.
-    """
+    """Base class for LLM providers."""
 
     _CHAT_RETRY_DELAYS = (1, 2, 4)
+    _PERSISTENT_MAX_DELAY = 60
+    _PERSISTENT_IDENTICAL_ERROR_LIMIT = 10
+    _RETRY_HEARTBEAT_CHUNK = 30
     _TRANSIENT_ERROR_MARKERS = (
         "429",
         "rate limit",
@@ -208,7 +203,7 @@ class LLMProvider(ABC):
                 for b in content:
                     if isinstance(b, dict) and b.get("type") == "image_url":
                         path = (b.get("_meta") or {}).get("path", "")
-                        placeholder = f"[image: {path}]" if path else "[image omitted]"
+                        placeholder = image_placeholder_text(path, empty="[image omitted]")
                         new_content.append({"type": "text", "text": placeholder})
                         found = True
                     else:
@@ -273,6 +268,8 @@ class LLMProvider(ABC):
         reasoning_effort: object = _SENTINEL,
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
+        retry_mode: str = "standard",
+        on_retry_wait: Callable[[str], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         """Call chat_stream() with retry on transient provider failures."""
         if max_tokens is self._SENTINEL:
@@ -288,28 +285,13 @@ class LLMProvider(ABC):
             reasoning_effort=reasoning_effort, tool_choice=tool_choice,
             on_content_delta=on_content_delta,
         )
-
-        for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
-            response = await self._safe_chat_stream(**kw)
-
-            if response.finish_reason != "error":
-                return response
-
-            if not self._is_transient_error(response.content):
-                stripped = self._strip_image_content(messages)
-                if stripped is not None:
-                    logger.warning("Non-transient LLM error with image content, retrying without images")
-                    return await self._safe_chat_stream(**{**kw, "messages": stripped})
-                return response
-
-            logger.warning(
-                "LLM transient error (attempt {}/{}), retrying in {}s: {}",
-                attempt, len(self._CHAT_RETRY_DELAYS), delay,
-                (response.content or "")[:120].lower(),
-            )
-            await asyncio.sleep(delay)
-
-        return await self._safe_chat_stream(**kw)
+        return await self._run_with_retry(
+            self._safe_chat_stream,
+            kw,
+            messages,
+            retry_mode=retry_mode,
+            on_retry_wait=on_retry_wait,
+        )
 
     async def chat_with_retry(
         self,
@@ -320,6 +302,8 @@ class LLMProvider(ABC):
         temperature: object = _SENTINEL,
         reasoning_effort: object = _SENTINEL,
         tool_choice: str | dict[str, Any] | None = None,
+        retry_mode: str = "standard",
+        on_retry_wait: Callable[[str], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         """Call chat() with retry on transient provider failures.
 
@@ -339,28 +323,118 @@ class LLMProvider(ABC):
             max_tokens=max_tokens, temperature=temperature,
             reasoning_effort=reasoning_effort, tool_choice=tool_choice,
         )
+        return await self._run_with_retry(
+            self._safe_chat,
+            kw,
+            messages,
+            retry_mode=retry_mode,
+            on_retry_wait=on_retry_wait,
+        )
 
-        for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
-            response = await self._safe_chat(**kw)
+    @classmethod
+    def _extract_retry_after(cls, content: str | None) -> float | None:
+        text = (content or "").lower()
+        match = re.search(r"retry after\s+(\d+(?:\.\d+)?)\s*(ms|milliseconds|s|sec|secs|seconds|m|min|minutes)?", text)
+        if not match:
+            return None
+        value = float(match.group(1))
+        unit = (match.group(2) or "s").lower()
+        if unit in {"ms", "milliseconds"}:
+            return max(0.1, value / 1000.0)
+        if unit in {"m", "min", "minutes"}:
+            return value * 60.0
+        return value
 
+    async def _sleep_with_heartbeat(
+        self,
+        delay: float,
+        *,
+        attempt: int,
+        persistent: bool,
+        on_retry_wait: Callable[[str], Awaitable[None]] | None = None,
+    ) -> None:
+        remaining = max(0.0, delay)
+        while remaining > 0:
+            if on_retry_wait:
+                kind = "persistent retry" if persistent else "retry"
+                await on_retry_wait(
+                    f"Model request failed, {kind} in {max(1, int(round(remaining)))}s "
+                    f"(attempt {attempt})."
+                )
+            chunk = min(remaining, self._RETRY_HEARTBEAT_CHUNK)
+            await asyncio.sleep(chunk)
+            remaining -= chunk
+
+    async def _run_with_retry(
+        self,
+        call: Callable[..., Awaitable[LLMResponse]],
+        kw: dict[str, Any],
+        original_messages: list[dict[str, Any]],
+        *,
+        retry_mode: str,
+        on_retry_wait: Callable[[str], Awaitable[None]] | None,
+    ) -> LLMResponse:
+        attempt = 0
+        delays = list(self._CHAT_RETRY_DELAYS)
+        persistent = retry_mode == "persistent"
+        last_response: LLMResponse | None = None
+        last_error_key: str | None = None
+        identical_error_count = 0
+        while True:
+            attempt += 1
+            response = await call(**kw)
             if response.finish_reason != "error":
                 return response
+            last_response = response
+            error_key = ((response.content or "").strip().lower() or None)
+            if error_key and error_key == last_error_key:
+                identical_error_count += 1
+            else:
+                last_error_key = error_key
+                identical_error_count = 1 if error_key else 0
 
             if not self._is_transient_error(response.content):
-                stripped = self._strip_image_content(messages)
-                if stripped is not None:
-                    logger.warning("Non-transient LLM error with image content, retrying without images")
-                    return await self._safe_chat(**{**kw, "messages": stripped})
+                stripped = self._strip_image_content(original_messages)
+                if stripped is not None and stripped != kw["messages"]:
+                    logger.warning(
+                        "Non-transient LLM error with image content, retrying without images"
+                    )
+                    retry_kw = dict(kw)
+                    retry_kw["messages"] = stripped
+                    return await call(**retry_kw)
                 return response
 
+            if persistent and identical_error_count >= self._PERSISTENT_IDENTICAL_ERROR_LIMIT:
+                logger.warning(
+                    "Stopping persistent retry after {} identical transient errors: {}",
+                    identical_error_count,
+                    (response.content or "")[:120].lower(),
+                )
+                return response
+
+            if not persistent and attempt > len(delays):
+                break
+
+            base_delay = delays[min(attempt - 1, len(delays) - 1)]
+            delay = self._extract_retry_after(response.content) or base_delay
+            if persistent:
+                delay = min(delay, self._PERSISTENT_MAX_DELAY)
+
             logger.warning(
-                "LLM transient error (attempt {}/{}), retrying in {}s: {}",
-                attempt, len(self._CHAT_RETRY_DELAYS), delay,
+                "LLM transient error (attempt {}{}), retrying in {}s: {}",
+                attempt,
+                "+" if persistent and attempt > len(delays) else f"/{len(delays)}",
+                int(round(delay)),
                 (response.content or "")[:120].lower(),
             )
-            await asyncio.sleep(delay)
+            await self._sleep_with_heartbeat(
+                delay,
+                attempt=attempt,
+                persistent=persistent,
+                on_retry_wait=on_retry_wait,
+            )
 
-        return await self._safe_chat(**kw)
+        return last_response if last_response is not None else await call(**kw)
 
     @abstractmethod
     def get_default_model(self) -> str:

nanobot/providers/openai_codex_provider.py

@@ -6,13 +6,18 @@ import asyncio
 import hashlib
 import json
 from collections.abc import Awaitable, Callable
-from typing import Any, AsyncGenerator
+from typing import Any
 
 import httpx
 from loguru import logger
 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,
+    convert_messages,
+    convert_tools,
+)
 
 DEFAULT_CODEX_URL = "https://chatgpt.com/backend-api/codex/responses"
 DEFAULT_ORIGINATOR = "nanobot"
@@ -36,7 +41,7 @@ class OpenAICodexProvider(LLMProvider):
     ) -> LLMResponse:
         """Shared request logic for both chat() and chat_stream()."""
         model = model or self.default_model
-        system_prompt, input_items = _convert_messages(messages)
+        system_prompt, input_items = convert_messages(messages)
 
         token = await asyncio.to_thread(get_codex_token)
         headers = _build_headers(token.account_id, token.access)
@@ -56,7 +61,7 @@ class OpenAICodexProvider(LLMProvider):
         if reasoning_effort:
             body["reasoning"] = {"effort": reasoning_effort}
         if tools:
-            body["tools"] = _convert_tools(tools)
+            body["tools"] = convert_tools(tools)
 
         try:
             try:
@@ -127,96 +132,7 @@ async def _request_codex(
             if response.status_code != 200:
                 text = await response.aread()
                 raise RuntimeError(_friendly_error(response.status_code, text.decode("utf-8", "ignore")))
-            return await _consume_sse(response, on_content_delta)
-
-
-def _convert_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    """Convert OpenAI function-calling schema to Codex flat format."""
-    converted: list[dict[str, Any]] = []
-    for tool in tools:
-        fn = (tool.get("function") or {}) if tool.get("type") == "function" else tool
-        name = fn.get("name")
-        if not name:
-            continue
-        params = fn.get("parameters") or {}
-        converted.append({
-            "type": "function",
-            "name": name,
-            "description": fn.get("description") or "",
-            "parameters": params if isinstance(params, dict) else {},
-        })
-    return converted
-
-
-def _convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
-    system_prompt = ""
-    input_items: list[dict[str, Any]] = []
-
-    for idx, msg in enumerate(messages):
-        role = msg.get("role")
-        content = msg.get("content")
-
-        if role == "system":
-            system_prompt = content if isinstance(content, str) else ""
-            continue
-
-        if role == "user":
-            input_items.append(_convert_user_message(content))
-            continue
-
-        if role == "assistant":
-            if isinstance(content, str) and content:
-                input_items.append({
-                    "type": "message", "role": "assistant",
-                    "content": [{"type": "output_text", "text": content}],
-                    "status": "completed", "id": f"msg_{idx}",
-                })
-            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"))
-                input_items.append({
-                    "type": "function_call",
-                    "id": item_id or f"fc_{idx}",
-                    "call_id": call_id or f"call_{idx}",
-                    "name": fn.get("name"),
-                    "arguments": fn.get("arguments") or "{}",
-                })
-            continue
-
-        if role == "tool":
-            call_id, _ = _split_tool_call_id(msg.get("tool_call_id"))
-            output_text = content if isinstance(content, str) else json.dumps(content, ensure_ascii=False)
-            input_items.append({"type": "function_call_output", "call_id": call_id, "output": output_text})
-
-    return system_prompt, input_items
-
-
-def _convert_user_message(content: Any) -> dict[str, Any]:
-    if isinstance(content, str):
-        return {"role": "user", "content": [{"type": "input_text", "text": content}]}
-    if isinstance(content, list):
-        converted: list[dict[str, Any]] = []
-        for item in content:
-            if not isinstance(item, dict):
-                continue
-            if item.get("type") == "text":
-                converted.append({"type": "input_text", "text": item.get("text", "")})
-            elif item.get("type") == "image_url":
-                url = (item.get("image_url") or {}).get("url")
-                if url:
-                    converted.append({"type": "input_image", "image_url": url, "detail": "auto"})
-        if converted:
-            return {"role": "user", "content": converted}
-    return {"role": "user", "content": [{"type": "input_text", "text": ""}]}
-
-
-def _split_tool_call_id(tool_call_id: Any) -> tuple[str, str | None]:
-    if isinstance(tool_call_id, str) and tool_call_id:
-        if "|" in tool_call_id:
-            call_id, item_id = tool_call_id.split("|", 1)
-            return call_id, item_id or None
-        return tool_call_id, None
-    return "call_0", None
+            return await consume_sse(response, on_content_delta)
 
 
 def _prompt_cache_key(messages: list[dict[str, Any]]) -> str:
@@ -224,96 +140,6 @@ def _prompt_cache_key(messages: list[dict[str, Any]]) -> str:
     return hashlib.sha256(raw.encode("utf-8")).hexdigest()
 
 
-async def _iter_sse(response: httpx.Response) -> AsyncGenerator[dict[str, Any], None]:
-    buffer: list[str] = []
-    async for line in response.aiter_lines():
-        if line == "":
-            if buffer:
-                data_lines = [l[5:].strip() for l in buffer if l.startswith("data:")]
-                buffer = []
-                if not data_lines:
-                    continue
-                data = "\n".join(data_lines).strip()
-                if not data or data == "[DONE]":
-                    continue
-                try:
-                    yield json.loads(data)
-                except Exception:
-                    continue
-            continue
-        buffer.append(line)
-
-
-async def _consume_sse(
-    response: httpx.Response,
-    on_content_delta: Callable[[str], Awaitable[None]] | None = None,
-) -> tuple[str, list[ToolCallRequest], str]:
-    content = ""
-    tool_calls: list[ToolCallRequest] = []
-    tool_call_buffers: dict[str, dict[str, Any]] = {}
-    finish_reason = "stop"
-
-    async for event in _iter_sse(response):
-        event_type = event.get("type")
-        if event_type == "response.output_item.added":
-            item = event.get("item") or {}
-            if item.get("type") == "function_call":
-                call_id = item.get("call_id")
-                if not call_id:
-                    continue
-                tool_call_buffers[call_id] = {
-                    "id": item.get("id") or "fc_0",
-                    "name": item.get("name"),
-                    "arguments": item.get("arguments") or "",
-                }
-        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.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 ""
-        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 ""
-        elif event_type == "response.output_item.done":
-            item = event.get("item") or {}
-            if item.get("type") == "function_call":
-                call_id = item.get("call_id")
-                if not call_id:
-                    continue
-                buf = tool_call_buffers.get(call_id) or {}
-                args_raw = buf.get("arguments") or item.get("arguments") or "{}"
-                try:
-                    args = json.loads(args_raw)
-                except Exception:
-                    args = {"raw": args_raw}
-                tool_calls.append(
-                    ToolCallRequest(
-                        id=f"{call_id}|{buf.get('id') or item.get('id') or 'fc_0'}",
-                        name=buf.get("name") or item.get("name"),
-                        arguments=args,
-                    )
-                )
-        elif event_type == "response.completed":
-            status = (event.get("response") or {}).get("status")
-            finish_reason = _map_finish_reason(status)
-        elif event_type in {"error", "response.failed"}:
-            raise RuntimeError("Codex response failed")
-
-    return content, tool_calls, finish_reason
-
-
-_FINISH_REASON_MAP = {"completed": "stop", "incomplete": "length", "failed": "error", "cancelled": "error"}
-
-
-def _map_finish_reason(status: str | None) -> str:
-    return _FINISH_REASON_MAP.get(status or "completed", "stop")
-
-
 def _friendly_error(status_code: int, raw: str) -> str:
     if status_code == 429:
         return "ChatGPT usage quota exceeded or rate limit triggered. Please try again later."

nanobot/providers/openai_compat_provider.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import hashlib
 import os
 import secrets
@@ -20,7 +21,6 @@ if TYPE_CHECKING:
 
 _ALLOWED_MSG_KEYS = frozenset({
     "role", "content", "tool_calls", "tool_call_id", "name",
-    "reasoning_content", "extra_content",
 })
 _ALNUM = string.ascii_letters + string.digits
 
@@ -615,16 +615,33 @@ class OpenAICompatProvider(LLMProvider):
         )
         kwargs["stream"] = True
         kwargs["stream_options"] = {"include_usage": True}
+        idle_timeout_s = int(os.environ.get("NANOBOT_STREAM_IDLE_TIMEOUT_S", "90"))
         try:
             stream = await self._client.chat.completions.create(**kwargs)
             chunks: list[Any] = []
-            async for chunk in stream:
+            stream_iter = stream.__aiter__()
+            while True:
+                try:
+                    chunk = await asyncio.wait_for(
+                        stream_iter.__anext__(),
+                        timeout=idle_timeout_s,
+                    )
+                except StopAsyncIteration:
+                    break
                 chunks.append(chunk)
                 if on_content_delta and chunk.choices:
                     text = getattr(chunk.choices[0].delta, "content", None)
                     if text:
                         await on_content_delta(text)
             return self._parse_chunks(chunks)
+        except asyncio.TimeoutError:
+            return LLMResponse(
+                content=(
+                    f"Error calling LLM: stream stalled for more than "
+                    f"{idle_timeout_s} seconds"
+                ),
+                finish_reason="error",
+            )
         except Exception as e:
             return self._handle_error(e)
 

nanobot/providers/openai_responses/__init__.py

@@ -0,0 +1,29 @@
+"""Shared helpers for OpenAI Responses API providers (Codex, Azure OpenAI)."""
+
+from nanobot.providers.openai_responses.converters import (
+    convert_messages,
+    convert_tools,
+    convert_user_message,
+    split_tool_call_id,
+)
+from nanobot.providers.openai_responses.parsing import (
+    FINISH_REASON_MAP,
+    consume_sdk_stream,
+    consume_sse,
+    iter_sse,
+    map_finish_reason,
+    parse_response_output,
+)
+
+__all__ = [
+    "convert_messages",
+    "convert_tools",
+    "convert_user_message",
+    "split_tool_call_id",
+    "iter_sse",
+    "consume_sse",
+    "consume_sdk_stream",
+    "map_finish_reason",
+    "parse_response_output",
+    "FINISH_REASON_MAP",
+]

nanobot/providers/openai_responses/converters.py

@@ -0,0 +1,110 @@
+"""Convert Chat Completions messages/tools to Responses API format."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+
+def convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
+    """Convert Chat Completions messages to Responses API input items.
+
+    Returns ``(system_prompt, input_items)`` where *system_prompt* is extracted
+    from any ``system`` role message and *input_items* is the Responses API
+    ``input`` array.
+    """
+    system_prompt = ""
+    input_items: list[dict[str, Any]] = []
+
+    for idx, msg in enumerate(messages):
+        role = msg.get("role")
+        content = msg.get("content")
+
+        if role == "system":
+            system_prompt = content if isinstance(content, str) else ""
+            continue
+
+        if role == "user":
+            input_items.append(convert_user_message(content))
+            continue
+
+        if role == "assistant":
+            if isinstance(content, str) and content:
+                input_items.append({
+                    "type": "message", "role": "assistant",
+                    "content": [{"type": "output_text", "text": content}],
+                    "status": "completed", "id": f"msg_{idx}",
+                })
+            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"))
+                input_items.append({
+                    "type": "function_call",
+                    "id": item_id or f"fc_{idx}",
+                    "call_id": call_id or f"call_{idx}",
+                    "name": fn.get("name"),
+                    "arguments": fn.get("arguments") or "{}",
+                })
+            continue
+
+        if role == "tool":
+            call_id, _ = split_tool_call_id(msg.get("tool_call_id"))
+            output_text = content if isinstance(content, str) else json.dumps(content, ensure_ascii=False)
+            input_items.append({"type": "function_call_output", "call_id": call_id, "output": output_text})
+
+    return system_prompt, input_items
+
+
+def convert_user_message(content: Any) -> dict[str, Any]:
+    """Convert a user message's content to Responses API format.
+
+    Handles plain strings, ``text`` blocks -> ``input_text``, and
+    ``image_url`` blocks -> ``input_image``.
+    """
+    if isinstance(content, str):
+        return {"role": "user", "content": [{"type": "input_text", "text": content}]}
+    if isinstance(content, list):
+        converted: list[dict[str, Any]] = []
+        for item in content:
+            if not isinstance(item, dict):
+                continue
+            if item.get("type") == "text":
+                converted.append({"type": "input_text", "text": item.get("text", "")})
+            elif item.get("type") == "image_url":
+                url = (item.get("image_url") or {}).get("url")
+                if url:
+                    converted.append({"type": "input_image", "image_url": url, "detail": "auto"})
+        if converted:
+            return {"role": "user", "content": converted}
+    return {"role": "user", "content": [{"type": "input_text", "text": ""}]}
+
+
+def convert_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Convert OpenAI function-calling tool schema to Responses API flat format."""
+    converted: list[dict[str, Any]] = []
+    for tool in tools:
+        fn = (tool.get("function") or {}) if tool.get("type") == "function" else tool
+        name = fn.get("name")
+        if not name:
+            continue
+        params = fn.get("parameters") or {}
+        converted.append({
+            "type": "function",
+            "name": name,
+            "description": fn.get("description") or "",
+            "parameters": params if isinstance(params, dict) else {},
+        })
+    return converted
+
+
+def split_tool_call_id(tool_call_id: Any) -> tuple[str, str | None]:
+    """Split a compound ``call_id|item_id`` string.
+
+    Returns ``(call_id, item_id)`` where *item_id* may be ``None``.
+    """
+    if isinstance(tool_call_id, str) and tool_call_id:
+        if "|" in tool_call_id:
+            call_id, item_id = tool_call_id.split("|", 1)
+            return call_id, item_id or None
+        return tool_call_id, None
+    return "call_0", None

nanobot/providers/openai_responses/parsing.py

@@ -0,0 +1,297 @@
+"""Parse Responses API SSE streams and SDK response objects."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Awaitable, Callable
+from typing import Any, AsyncGenerator
+
+import httpx
+import json_repair
+from loguru import logger
+
+from nanobot.providers.base import LLMResponse, ToolCallRequest
+
+FINISH_REASON_MAP = {
+    "completed": "stop",
+    "incomplete": "length",
+    "failed": "error",
+    "cancelled": "error",
+}
+
+
+def map_finish_reason(status: str | None) -> str:
+    """Map a Responses API status string to a Chat-Completions-style finish_reason."""
+    return FINISH_REASON_MAP.get(status or "completed", "stop")
+
+
+async def iter_sse(response: httpx.Response) -> AsyncGenerator[dict[str, Any], None]:
+    """Yield parsed JSON events from a Responses API SSE stream."""
+    buffer: list[str] = []
+
+    def _flush() -> dict[str, Any] | None:
+        data_lines = [l[5:].strip() for l in buffer if l.startswith("data:")]
+        buffer.clear()
+        if not data_lines:
+            return None
+        data = "\n".join(data_lines).strip()
+        if not data or data == "[DONE]":
+            return None
+        try:
+            return json.loads(data)
+        except Exception:
+            logger.warning("Failed to parse SSE event JSON: {}", data[:200])
+            return None
+
+    async for line in response.aiter_lines():
+        if line == "":
+            if buffer:
+                event = _flush()
+                if event is not None:
+                    yield event
+            continue
+        buffer.append(line)
+
+    # Flush any remaining buffer at EOF (#10)
+    if buffer:
+        event = _flush()
+        if event is not None:
+            yield event
+
+
+async def consume_sse(
+    response: httpx.Response,
+    on_content_delta: Callable[[str], Awaitable[None]] | None = None,
+) -> tuple[str, list[ToolCallRequest], str]:
+    """Consume a Responses API SSE stream into ``(content, tool_calls, finish_reason)``."""
+    content = ""
+    tool_calls: list[ToolCallRequest] = []
+    tool_call_buffers: dict[str, dict[str, Any]] = {}
+    finish_reason = "stop"
+
+    async for event in iter_sse(response):
+        event_type = event.get("type")
+        if event_type == "response.output_item.added":
+            item = event.get("item") or {}
+            if item.get("type") == "function_call":
+                call_id = item.get("call_id")
+                if not call_id:
+                    continue
+                tool_call_buffers[call_id] = {
+                    "id": item.get("id") or "fc_0",
+                    "name": item.get("name"),
+                    "arguments": item.get("arguments") or "",
+                }
+        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.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 ""
+        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 ""
+        elif event_type == "response.output_item.done":
+            item = event.get("item") or {}
+            if item.get("type") == "function_call":
+                call_id = item.get("call_id")
+                if not call_id:
+                    continue
+                buf = tool_call_buffers.get(call_id) or {}
+                args_raw = buf.get("arguments") or item.get("arguments") or "{}"
+                try:
+                    args = json.loads(args_raw)
+                except Exception:
+                    logger.warning(
+                        "Failed to parse tool call arguments for '{}': {}",
+                        buf.get("name") or item.get("name"),
+                        args_raw[:200],
+                    )
+                    args = json_repair.loads(args_raw)
+                    if not isinstance(args, dict):
+                        args = {"raw": args_raw}
+                tool_calls.append(
+                    ToolCallRequest(
+                        id=f"{call_id}|{buf.get('id') or item.get('id') or 'fc_0'}",
+                        name=buf.get("name") or item.get("name") or "",
+                        arguments=args,
+                    )
+                )
+        elif event_type == "response.completed":
+            status = (event.get("response") or {}).get("status")
+            finish_reason = map_finish_reason(status)
+        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
+
+
+def parse_response_output(response: Any) -> LLMResponse:
+    """Parse an SDK ``Response`` object into an ``LLMResponse``."""
+    if not isinstance(response, dict):
+        dump = getattr(response, "model_dump", None)
+        response = dump() if callable(dump) else vars(response)
+
+    output = response.get("output") or []
+    content_parts: list[str] = []
+    tool_calls: list[ToolCallRequest] = []
+    reasoning_content: str | None = None
+
+    for item in output:
+        if not isinstance(item, dict):
+            dump = getattr(item, "model_dump", None)
+            item = dump() if callable(dump) else vars(item)
+
+        item_type = item.get("type")
+        if item_type == "message":
+            for block in item.get("content") or []:
+                if not isinstance(block, dict):
+                    dump = getattr(block, "model_dump", None)
+                    block = dump() if callable(dump) else vars(block)
+                if block.get("type") == "output_text":
+                    content_parts.append(block.get("text") or "")
+        elif item_type == "reasoning":
+            for s in item.get("summary") or []:
+                if not isinstance(s, dict):
+                    dump = getattr(s, "model_dump", None)
+                    s = dump() if callable(dump) else vars(s)
+                if s.get("type") == "summary_text" and s.get("text"):
+                    reasoning_content = (reasoning_content or "") + s["text"]
+        elif item_type == "function_call":
+            call_id = item.get("call_id") or ""
+            item_id = item.get("id") or "fc_0"
+            args_raw = item.get("arguments") or "{}"
+            try:
+                args = json.loads(args_raw) if isinstance(args_raw, str) else args_raw
+            except Exception:
+                logger.warning(
+                    "Failed to parse tool call arguments for '{}': {}",
+                    item.get("name"),
+                    str(args_raw)[:200],
+                )
+                args = json_repair.loads(args_raw) if isinstance(args_raw, str) else args_raw
+                if not isinstance(args, dict):
+                    args = {"raw": args_raw}
+            tool_calls.append(ToolCallRequest(
+                id=f"{call_id}|{item_id}",
+                name=item.get("name") or "",
+                arguments=args if isinstance(args, dict) else {},
+            ))
+
+    usage_raw = response.get("usage") or {}
+    if not isinstance(usage_raw, dict):
+        dump = getattr(usage_raw, "model_dump", None)
+        usage_raw = dump() if callable(dump) else vars(usage_raw)
+    usage = {}
+    if usage_raw:
+        usage = {
+            "prompt_tokens": int(usage_raw.get("input_tokens") or 0),
+            "completion_tokens": int(usage_raw.get("output_tokens") or 0),
+            "total_tokens": int(usage_raw.get("total_tokens") or 0),
+        }
+
+    status = response.get("status")
+    finish_reason = map_finish_reason(status)
+
+    return LLMResponse(
+        content="".join(content_parts) or None,
+        tool_calls=tool_calls,
+        finish_reason=finish_reason,
+        usage=usage,
+        reasoning_content=reasoning_content if isinstance(reasoning_content, str) else None,
+    )
+
+
+async def consume_sdk_stream(
+    stream: Any,
+    on_content_delta: Callable[[str], 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]] = {}
+    finish_reason = "stop"
+    usage: dict[str, int] = {}
+    reasoning_content: str | None = None
+
+    async for event in stream:
+        event_type = getattr(event, "type", None)
+        if event_type == "response.output_item.added":
+            item = getattr(event, "item", None)
+            if item and getattr(item, "type", None) == "function_call":
+                call_id = getattr(item, "call_id", None)
+                if not call_id:
+                    continue
+                tool_call_buffers[call_id] = {
+                    "id": getattr(item, "id", None) or "fc_0",
+                    "name": getattr(item, "name", None),
+                    "arguments": getattr(item, "arguments", None) or "",
+                }
+        elif event_type == "response.output_text.delta":
+            delta_text = getattr(event, "delta", "") or ""
+            content += delta_text
+            if on_content_delta and delta_text:
+                await on_content_delta(delta_text)
+        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 ""
+        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 ""
+        elif event_type == "response.output_item.done":
+            item = getattr(event, "item", None)
+            if item and getattr(item, "type", None) == "function_call":
+                call_id = getattr(item, "call_id", None)
+                if not call_id:
+                    continue
+                buf = tool_call_buffers.get(call_id) or {}
+                args_raw = buf.get("arguments") or getattr(item, "arguments", None) or "{}"
+                try:
+                    args = json.loads(args_raw)
+                except Exception:
+                    logger.warning(
+                        "Failed to parse tool call arguments for '{}': {}",
+                        buf.get("name") or getattr(item, "name", None),
+                        str(args_raw)[:200],
+                    )
+                    args = json_repair.loads(args_raw)
+                    if not isinstance(args, dict):
+                        args = {"raw": args_raw}
+                tool_calls.append(
+                    ToolCallRequest(
+                        id=f"{call_id}|{buf.get('id') or getattr(item, 'id', None) or 'fc_0'}",
+                        name=buf.get("name") or getattr(item, "name", None) or "",
+                        arguments=args,
+                    )
+                )
+        elif event_type == "response.completed":
+            resp = getattr(event, "response", None)
+            status = getattr(resp, "status", None) if resp else None
+            finish_reason = map_finish_reason(status)
+            if resp:
+                usage_obj = getattr(resp, "usage", None)
+                if usage_obj:
+                    usage = {
+                        "prompt_tokens": int(getattr(usage_obj, "input_tokens", 0) or 0),
+                        "completion_tokens": int(getattr(usage_obj, "output_tokens", 0) or 0),
+                        "total_tokens": int(getattr(usage_obj, "total_tokens", 0) or 0),
+                    }
+                for out_item in getattr(resp, "output", None) or []:
+                    if getattr(out_item, "type", None) == "reasoning":
+                        for s in getattr(out_item, "summary", None) or []:
+                            if getattr(s, "type", None) == "summary_text":
+                                text = getattr(s, "text", None)
+                                if text:
+                                    reasoning_content = (reasoning_content or "") + text
+        elif event_type in {"error", "response.failed"}:
+            detail = getattr(event, "error", None) or getattr(event, "message", None) or event
+            raise RuntimeError(f"Response failed: {str(detail)[:500]}")
+
+    return content, tool_calls, finish_reason, usage, reasoning_content

nanobot/session/manager.py

@@ -10,20 +10,12 @@ from typing import Any
 from loguru import logger
 
 from nanobot.config.paths import get_legacy_sessions_dir
-from nanobot.utils.helpers import ensure_dir, safe_filename
+from nanobot.utils.helpers import ensure_dir, find_legal_message_start, safe_filename
 
 
 @dataclass
 class Session:
-    """
-    A conversation session.
-
-    Stores messages in JSONL format for easy reading and persistence.
-
-    Important: Messages are append-only for LLM cache efficiency.
-    The consolidation process writes summaries to MEMORY.md/HISTORY.md
-    but does NOT modify the messages list or get_history() output.
-    """
+    """A conversation session."""
 
     key: str  # channel:chat_id
     messages: list[dict[str, Any]] = field(default_factory=list)
@@ -43,43 +35,19 @@ class Session:
         self.messages.append(msg)
         self.updated_at = datetime.now()
 
-    @staticmethod
-    def _find_legal_start(messages: list[dict[str, Any]]) -> int:
-        """Find first index where every tool result has a matching assistant tool_call."""
-        declared: set[str] = set()
-        start = 0
-        for i, msg in enumerate(messages):
-            role = msg.get("role")
-            if role == "assistant":
-                for tc in msg.get("tool_calls") or []:
-                    if isinstance(tc, dict) and tc.get("id"):
-                        declared.add(str(tc["id"]))
-            elif role == "tool":
-                tid = msg.get("tool_call_id")
-                if tid and str(tid) not in declared:
-                    start = i + 1
-                    declared.clear()
-                    for prev in messages[start:i + 1]:
-                        if prev.get("role") == "assistant":
-                            for tc in prev.get("tool_calls") or []:
-                                if isinstance(tc, dict) and tc.get("id"):
-                                    declared.add(str(tc["id"]))
-        return start
-
     def get_history(self, max_messages: int = 500) -> list[dict[str, Any]]:
         """Return unconsolidated messages for LLM input, aligned to a legal tool-call boundary."""
         unconsolidated = self.messages[self.last_consolidated:]
         sliced = unconsolidated[-max_messages:]
 
-        # Drop leading non-user messages to avoid starting mid-turn when possible.
+        # Avoid starting mid-turn when possible.
         for i, message in enumerate(sliced):
             if message.get("role") == "user":
                 sliced = sliced[i:]
                 break
 
-        # Some providers reject orphan tool results if the matching assistant
-        # tool_calls message fell outside the fixed-size history window.
-        start = self._find_legal_start(sliced)
+        # Drop orphan tool results at the front.
+        start = find_legal_message_start(sliced)
         if start:
             sliced = sliced[start:]
 
@@ -115,7 +83,7 @@ class Session:
         retained = self.messages[start_idx:]
 
         # Mirror get_history(): avoid persisting orphan tool results at the front.
-        start = self._find_legal_start(retained)
+        start = find_legal_message_start(retained)
         if start:
             retained = retained[start:]
 

nanobot/utils/helpers.py

@@ -3,12 +3,15 @@
 import base64
 import json
 import re
+import shutil
 import time
+import uuid
 from datetime import datetime
 from pathlib import Path
 from typing import Any
 
 import tiktoken
+from loguru import logger
 
 
 def strip_think(text: str) -> str:
@@ -56,11 +59,7 @@ def timestamp() -> str:
 
 
 def current_time_str(timezone: str | None = None) -> str:
-    """Human-readable current time with weekday and UTC offset.
-
-    When *timezone* is a valid IANA name (e.g. ``"Asia/Shanghai"``), the time
-    is converted to that zone.  Otherwise falls back to the host local time.
-    """
+    """Return the current time string."""
     from zoneinfo import ZoneInfo
 
     try:
@@ -76,12 +75,164 @@ def current_time_str(timezone: str | None = None) -> str:
 
 
 _UNSAFE_CHARS = re.compile(r'[<>:"/\\|?*]')
+_TOOL_RESULT_PREVIEW_CHARS = 1200
+_TOOL_RESULTS_DIR = ".nanobot/tool-results"
+_TOOL_RESULT_RETENTION_SECS = 7 * 24 * 60 * 60
+_TOOL_RESULT_MAX_BUCKETS = 32
 
 def safe_filename(name: str) -> str:
     """Replace unsafe path characters with underscores."""
     return _UNSAFE_CHARS.sub("_", name).strip()
 
 
+def image_placeholder_text(path: str | None, *, empty: str = "[image]") -> str:
+    """Build an image placeholder string."""
+    return f"[image: {path}]" if path else empty
+
+
+def truncate_text(text: str, max_chars: int) -> str:
+    """Truncate text with a stable suffix."""
+    if max_chars <= 0 or len(text) <= max_chars:
+        return text
+    return text[:max_chars] + "\n... (truncated)"
+
+
+def find_legal_message_start(messages: list[dict[str, Any]]) -> int:
+    """Find the first index whose tool results have matching assistant calls."""
+    declared: set[str] = set()
+    start = 0
+    for i, msg in enumerate(messages):
+        role = msg.get("role")
+        if role == "assistant":
+            for tc in msg.get("tool_calls") or []:
+                if isinstance(tc, dict) and tc.get("id"):
+                    declared.add(str(tc["id"]))
+        elif role == "tool":
+            tid = msg.get("tool_call_id")
+            if tid and str(tid) not in declared:
+                start = i + 1
+                declared.clear()
+                for prev in messages[start : i + 1]:
+                    if prev.get("role") == "assistant":
+                        for tc in prev.get("tool_calls") or []:
+                            if isinstance(tc, dict) and tc.get("id"):
+                                declared.add(str(tc["id"]))
+    return start
+
+
+def stringify_text_blocks(content: list[dict[str, Any]]) -> str | None:
+    parts: list[str] = []
+    for block in content:
+        if not isinstance(block, dict):
+            return None
+        if block.get("type") != "text":
+            return None
+        text = block.get("text")
+        if not isinstance(text, str):
+            return None
+        parts.append(text)
+    return "\n".join(parts)
+
+
+def _render_tool_result_reference(
+    filepath: Path,
+    *,
+    original_size: int,
+    preview: str,
+    truncated_preview: bool,
+) -> str:
+    result = (
+        f"[tool output persisted]\n"
+        f"Full output saved to: {filepath}\n"
+        f"Original size: {original_size} chars\n"
+        f"Preview:\n{preview}"
+    )
+    if truncated_preview:
+        result += "\n...\n(Read the saved file if you need the full output.)"
+    return result
+
+
+def _bucket_mtime(path: Path) -> float:
+    try:
+        return path.stat().st_mtime
+    except OSError:
+        return 0.0
+
+
+def _cleanup_tool_result_buckets(root: Path, current_bucket: Path) -> None:
+    siblings = [path for path in root.iterdir() if path.is_dir() and path != current_bucket]
+    cutoff = time.time() - _TOOL_RESULT_RETENTION_SECS
+    for path in siblings:
+        if _bucket_mtime(path) < cutoff:
+            shutil.rmtree(path, ignore_errors=True)
+    keep = max(_TOOL_RESULT_MAX_BUCKETS - 1, 0)
+    siblings = [path for path in siblings if path.exists()]
+    if len(siblings) <= keep:
+        return
+    siblings.sort(key=_bucket_mtime, reverse=True)
+    for path in siblings[keep:]:
+        shutil.rmtree(path, ignore_errors=True)
+
+
+def _write_text_atomic(path: Path, content: str) -> None:
+    tmp = path.with_name(f".{path.name}.{uuid.uuid4().hex}.tmp")
+    try:
+        tmp.write_text(content, encoding="utf-8")
+        tmp.replace(path)
+    finally:
+        if tmp.exists():
+            tmp.unlink(missing_ok=True)
+
+
+def maybe_persist_tool_result(
+    workspace: Path | None,
+    session_key: str | None,
+    tool_call_id: str,
+    content: Any,
+    *,
+    max_chars: int,
+) -> Any:
+    """Persist oversized tool output and replace it with a stable reference string."""
+    if workspace is None or max_chars <= 0:
+        return content
+
+    text_payload: str | None = None
+    suffix = "txt"
+    if isinstance(content, str):
+        text_payload = content
+    elif isinstance(content, list):
+        text_payload = stringify_text_blocks(content)
+        if text_payload is None:
+            return content
+        suffix = "json"
+    else:
+        return content
+
+    if len(text_payload) <= max_chars:
+        return content
+
+    root = ensure_dir(workspace / _TOOL_RESULTS_DIR)
+    bucket = ensure_dir(root / safe_filename(session_key or "default"))
+    try:
+        _cleanup_tool_result_buckets(root, bucket)
+    except Exception as exc:
+        logger.warning("Failed to clean stale tool result buckets in {}: {}", root, exc)
+    path = bucket / f"{safe_filename(tool_call_id)}.{suffix}"
+    if not path.exists():
+        if suffix == "json" and isinstance(content, list):
+            _write_text_atomic(path, json.dumps(content, ensure_ascii=False, indent=2))
+        else:
+            _write_text_atomic(path, text_payload)
+
+    preview = text_payload[:_TOOL_RESULT_PREVIEW_CHARS]
+    return _render_tool_result_reference(
+        path,
+        original_size=len(text_payload),
+        preview=preview,
+        truncated_preview=len(text_payload) > _TOOL_RESULT_PREVIEW_CHARS,
+    )
+
+
 def split_message(content: str, max_len: int = 2000) -> list[str]:
     """
     Split content into chunks within max_len, preferring line breaks.

nanobot/utils/runtime.py

@@ -0,0 +1,88 @@
+"""Runtime-specific helper functions and constants."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from loguru import logger
+
+from nanobot.utils.helpers import stringify_text_blocks
+
+_MAX_REPEAT_EXTERNAL_LOOKUPS = 2
+
+EMPTY_FINAL_RESPONSE_MESSAGE = (
+    "I completed the tool steps but couldn't produce a final answer. "
+    "Please try again or narrow the task."
+)
+
+FINALIZATION_RETRY_PROMPT = (
+    "You have already finished the tool work. Do not call any more tools. "
+    "Using only the conversation and tool results above, provide the final answer for the user now."
+)
+
+
+def empty_tool_result_message(tool_name: str) -> str:
+    """Short prompt-safe marker for tools that completed without visible output."""
+    return f"({tool_name} completed with no output)"
+
+
+def ensure_nonempty_tool_result(tool_name: str, content: Any) -> Any:
+    """Replace semantically empty tool results with a short marker string."""
+    if content is None:
+        return empty_tool_result_message(tool_name)
+    if isinstance(content, str) and not content.strip():
+        return empty_tool_result_message(tool_name)
+    if isinstance(content, list):
+        if not content:
+            return empty_tool_result_message(tool_name)
+        text_payload = stringify_text_blocks(content)
+        if text_payload is not None and not text_payload.strip():
+            return empty_tool_result_message(tool_name)
+    return content
+
+
+def is_blank_text(content: str | None) -> bool:
+    """True when *content* is missing or only whitespace."""
+    return content is None or not content.strip()
+
+
+def build_finalization_retry_message() -> dict[str, str]:
+    """A short no-tools-allowed prompt for final answer recovery."""
+    return {"role": "user", "content": FINALIZATION_RETRY_PROMPT}
+
+
+def external_lookup_signature(tool_name: str, arguments: dict[str, Any]) -> str | None:
+    """Stable signature for repeated external lookups we want to throttle."""
+    if tool_name == "web_fetch":
+        url = str(arguments.get("url") or "").strip()
+        if url:
+            return f"web_fetch:{url.lower()}"
+    if tool_name == "web_search":
+        query = str(arguments.get("query") or arguments.get("search_term") or "").strip()
+        if query:
+            return f"web_search:{query.lower()}"
+    return None
+
+
+def repeated_external_lookup_error(
+    tool_name: str,
+    arguments: dict[str, Any],
+    seen_counts: dict[str, int],
+) -> str | None:
+    """Block repeated external lookups after a small retry budget."""
+    signature = external_lookup_signature(tool_name, arguments)
+    if signature is None:
+        return None
+    count = seen_counts.get(signature, 0) + 1
+    seen_counts[signature] = count
+    if count <= _MAX_REPEAT_EXTERNAL_LOOKUPS:
+        return None
+    logger.warning(
+        "Blocking repeated external lookup {} on attempt {}",
+        signature[:160],
+        count,
+    )
+    return (
+        "Error: repeated external lookup blocked. "
+        "Use the results you already have to answer, or try a meaningfully different source."
+    )

tests/agent/test_context_prompt_cache.py

@@ -71,3 +71,19 @@ def test_runtime_context_is_separate_untrusted_user_message(tmp_path) -> None:
     assert "Channel: cli" in user_content
     assert "Chat ID: direct" in user_content
     assert "Return exactly: OK" in user_content
+
+
+def test_subagent_result_does_not_create_consecutive_assistant_messages(tmp_path) -> None:
+    workspace = _make_workspace(tmp_path)
+    builder = ContextBuilder(workspace)
+
+    messages = builder.build_messages(
+        history=[{"role": "assistant", "content": "previous result"}],
+        current_message="subagent result",
+        channel="cli",
+        chat_id="direct",
+        current_role="assistant",
+    )
+
+    for left, right in zip(messages, messages[1:]):
+        assert not (left.get("role") == right.get("role") == "assistant")

tests/agent/test_loop_save_turn.py

@@ -5,7 +5,9 @@ from nanobot.session.manager import Session
 
 def _mk_loop() -> AgentLoop:
     loop = AgentLoop.__new__(AgentLoop)
-    loop._TOOL_RESULT_MAX_CHARS = AgentLoop._TOOL_RESULT_MAX_CHARS
+    from nanobot.config.schema import AgentDefaults
+
+    loop.max_tool_result_chars = AgentDefaults().max_tool_result_chars
     return loop
 
 
@@ -72,3 +74,129 @@ def test_save_turn_keeps_tool_results_under_16k() -> None:
     )
 
     assert session.messages[0]["content"] == content
+
+
+def test_restore_runtime_checkpoint_rehydrates_completed_and_pending_tools() -> None:
+    loop = _mk_loop()
+    session = Session(
+        key="test:checkpoint",
+        metadata={
+            AgentLoop._RUNTIME_CHECKPOINT_KEY: {
+                "assistant_message": {
+                    "role": "assistant",
+                    "content": "working",
+                    "tool_calls": [
+                        {
+                            "id": "call_done",
+                            "type": "function",
+                            "function": {"name": "read_file", "arguments": "{}"},
+                        },
+                        {
+                            "id": "call_pending",
+                            "type": "function",
+                            "function": {"name": "exec", "arguments": "{}"},
+                        },
+                    ],
+                },
+                "completed_tool_results": [
+                    {
+                        "role": "tool",
+                        "tool_call_id": "call_done",
+                        "name": "read_file",
+                        "content": "ok",
+                    }
+                ],
+                "pending_tool_calls": [
+                    {
+                        "id": "call_pending",
+                        "type": "function",
+                        "function": {"name": "exec", "arguments": "{}"},
+                    }
+                ],
+            }
+        },
+    )
+
+    restored = loop._restore_runtime_checkpoint(session)
+
+    assert restored is True
+    assert session.metadata.get(AgentLoop._RUNTIME_CHECKPOINT_KEY) is None
+    assert session.messages[0]["role"] == "assistant"
+    assert session.messages[1]["tool_call_id"] == "call_done"
+    assert session.messages[2]["tool_call_id"] == "call_pending"
+    assert "interrupted before this tool finished" in session.messages[2]["content"].lower()
+
+
+def test_restore_runtime_checkpoint_dedupes_overlapping_tail() -> None:
+    loop = _mk_loop()
+    session = Session(
+        key="test:checkpoint-overlap",
+        messages=[
+            {
+                "role": "assistant",
+                "content": "working",
+                "tool_calls": [
+                    {
+                        "id": "call_done",
+                        "type": "function",
+                        "function": {"name": "read_file", "arguments": "{}"},
+                    },
+                    {
+                        "id": "call_pending",
+                        "type": "function",
+                        "function": {"name": "exec", "arguments": "{}"},
+                    },
+                ],
+            },
+            {
+                "role": "tool",
+                "tool_call_id": "call_done",
+                "name": "read_file",
+                "content": "ok",
+            },
+        ],
+        metadata={
+            AgentLoop._RUNTIME_CHECKPOINT_KEY: {
+                "assistant_message": {
+                    "role": "assistant",
+                    "content": "working",
+                    "tool_calls": [
+                        {
+                            "id": "call_done",
+                            "type": "function",
+                            "function": {"name": "read_file", "arguments": "{}"},
+                        },
+                        {
+                            "id": "call_pending",
+                            "type": "function",
+                            "function": {"name": "exec", "arguments": "{}"},
+                        },
+                    ],
+                },
+                "completed_tool_results": [
+                    {
+                        "role": "tool",
+                        "tool_call_id": "call_done",
+                        "name": "read_file",
+                        "content": "ok",
+                    }
+                ],
+                "pending_tool_calls": [
+                    {
+                        "id": "call_pending",
+                        "type": "function",
+                        "function": {"name": "exec", "arguments": "{}"},
+                    }
+                ],
+            }
+        },
+    )
+
+    restored = loop._restore_runtime_checkpoint(session)
+
+    assert restored is True
+    assert session.metadata.get(AgentLoop._RUNTIME_CHECKPOINT_KEY) is None
+    assert len(session.messages) == 3
+    assert session.messages[0]["role"] == "assistant"
+    assert session.messages[1]["tool_call_id"] == "call_done"
+    assert session.messages[2]["tool_call_id"] == "call_pending"

tests/agent/test_runner.py

@@ -2,12 +2,20 @@
 
 from __future__ import annotations
 
+import asyncio
+import os
+import time
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from nanobot.config.schema import AgentDefaults
+from nanobot.agent.tools.base import Tool
+from nanobot.agent.tools.registry import ToolRegistry
 from nanobot.providers.base import LLMResponse, ToolCallRequest
 
+_MAX_TOOL_RESULT_CHARS = AgentDefaults().max_tool_result_chars
+
 
 def _make_loop(tmp_path):
     from nanobot.agent.loop import AgentLoop
@@ -60,6 +68,7 @@ async def test_runner_preserves_reasoning_fields_and_tool_results():
         tools=tools,
         model="test-model",
         max_iterations=3,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
     ))
 
     assert result.final_content == "done"
@@ -135,6 +144,7 @@ async def test_runner_calls_hooks_in_order():
         tools=tools,
         model="test-model",
         max_iterations=3,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
         hook=RecordingHook(),
     ))
 
@@ -191,6 +201,7 @@ async def test_runner_streaming_hook_receives_deltas_and_end_signal():
         tools=tools,
         model="test-model",
         max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
         hook=StreamingHook(),
     ))
 
@@ -219,6 +230,7 @@ async def test_runner_returns_max_iterations_fallback():
         tools=tools,
         model="test-model",
         max_iterations=2,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
     ))
 
     assert result.stop_reason == "max_iterations"
@@ -226,7 +238,8 @@ async def test_runner_returns_max_iterations_fallback():
         "I reached the maximum number of tool call iterations (2) "
         "without completing the task. You can try breaking the task into smaller steps."
     )
-
+    assert result.messages[-1]["role"] == "assistant"
+    assert result.messages[-1]["content"] == result.final_content
 
 @pytest.mark.asyncio
 async def test_runner_returns_structured_tool_error():
@@ -248,6 +261,7 @@ async def test_runner_returns_structured_tool_error():
         tools=tools,
         model="test-model",
         max_iterations=2,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
         fail_on_tool_error=True,
     ))
 
@@ -258,6 +272,457 @@ async def test_runner_returns_structured_tool_error():
     ]
 
 
+@pytest.mark.asyncio
+async def test_runner_persists_large_tool_results_for_follow_up_calls(tmp_path):
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    captured_second_call: list[dict] = []
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(
+                content="working",
+                tool_calls=[ToolCallRequest(id="call_big", name="list_dir", arguments={"path": "."})],
+                usage={"prompt_tokens": 5, "completion_tokens": 3},
+            )
+        captured_second_call[:] = messages
+        return LLMResponse(content="done", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="x" * 20_000)
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "do task"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=2,
+        workspace=tmp_path,
+        session_key="test:runner",
+        max_tool_result_chars=2048,
+    ))
+
+    assert result.final_content == "done"
+    tool_message = next(msg for msg in captured_second_call if msg.get("role") == "tool")
+    assert "[tool output persisted]" in tool_message["content"]
+    assert "tool-results" in tool_message["content"]
+    assert (tmp_path / ".nanobot" / "tool-results" / "test_runner" / "call_big.txt").exists()
+
+
+def test_persist_tool_result_prunes_old_session_buckets(tmp_path):
+    from nanobot.utils.helpers import maybe_persist_tool_result
+
+    root = tmp_path / ".nanobot" / "tool-results"
+    old_bucket = root / "old_session"
+    recent_bucket = root / "recent_session"
+    old_bucket.mkdir(parents=True)
+    recent_bucket.mkdir(parents=True)
+    (old_bucket / "old.txt").write_text("old", encoding="utf-8")
+    (recent_bucket / "recent.txt").write_text("recent", encoding="utf-8")
+
+    stale = time.time() - (8 * 24 * 60 * 60)
+    os.utime(old_bucket, (stale, stale))
+    os.utime(old_bucket / "old.txt", (stale, stale))
+
+    persisted = maybe_persist_tool_result(
+        tmp_path,
+        "current:session",
+        "call_big",
+        "x" * 5000,
+        max_chars=64,
+    )
+
+    assert "[tool output persisted]" in persisted
+    assert not old_bucket.exists()
+    assert recent_bucket.exists()
+    assert (root / "current_session" / "call_big.txt").exists()
+
+
+def test_persist_tool_result_leaves_no_temp_files(tmp_path):
+    from nanobot.utils.helpers import maybe_persist_tool_result
+
+    root = tmp_path / ".nanobot" / "tool-results"
+    maybe_persist_tool_result(
+        tmp_path,
+        "current:session",
+        "call_big",
+        "x" * 5000,
+        max_chars=64,
+    )
+
+    assert (root / "current_session" / "call_big.txt").exists()
+    assert list((root / "current_session").glob("*.tmp")) == []
+
+
+def test_persist_tool_result_logs_cleanup_failures(monkeypatch, tmp_path):
+    from nanobot.utils.helpers import maybe_persist_tool_result
+
+    warnings: list[str] = []
+
+    monkeypatch.setattr(
+        "nanobot.utils.helpers._cleanup_tool_result_buckets",
+        lambda *_args, **_kwargs: (_ for _ in ()).throw(OSError("busy")),
+    )
+    monkeypatch.setattr(
+        "nanobot.utils.helpers.logger.warning",
+        lambda message, *args: warnings.append(message.format(*args)),
+    )
+
+    persisted = maybe_persist_tool_result(
+        tmp_path,
+        "current:session",
+        "call_big",
+        "x" * 5000,
+        max_chars=64,
+    )
+
+    assert "[tool output persisted]" in persisted
+    assert warnings and "Failed to clean stale tool result buckets" in warnings[0]
+
+
+@pytest.mark.asyncio
+async def test_runner_replaces_empty_tool_result_with_marker():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    captured_second_call: list[dict] = []
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(
+                content="working",
+                tool_calls=[ToolCallRequest(id="call_1", name="noop", arguments={})],
+                usage={},
+            )
+        captured_second_call[:] = messages
+        return LLMResponse(content="done", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="")
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "do task"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=2,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert result.final_content == "done"
+    tool_message = next(msg for msg in captured_second_call if msg.get("role") == "tool")
+    assert tool_message["content"] == "(noop completed with no output)"
+
+
+@pytest.mark.asyncio
+async def test_runner_uses_raw_messages_when_context_governance_fails():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    captured_messages: list[dict] = []
+
+    async def chat_with_retry(*, messages, **kwargs):
+        captured_messages[:] = messages
+        return LLMResponse(content="done", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    initial_messages = [
+        {"role": "system", "content": "system"},
+        {"role": "user", "content": "hello"},
+    ]
+
+    runner = AgentRunner(provider)
+    runner._snip_history = MagicMock(side_effect=RuntimeError("boom"))  # type: ignore[method-assign]
+    result = await runner.run(AgentRunSpec(
+        initial_messages=initial_messages,
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert result.final_content == "done"
+    assert captured_messages == initial_messages
+
+
+@pytest.mark.asyncio
+async def test_runner_retries_empty_final_response_with_summary_prompt():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    calls: list[dict] = []
+
+    async def chat_with_retry(*, messages, tools=None, **kwargs):
+        calls.append({"messages": messages, "tools": tools})
+        if len(calls) == 1:
+            return LLMResponse(
+                content=None,
+                tool_calls=[],
+                usage={"prompt_tokens": 10, "completion_tokens": 1},
+            )
+        return LLMResponse(
+            content="final answer",
+            tool_calls=[],
+            usage={"prompt_tokens": 3, "completion_tokens": 7},
+        )
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "do task"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert result.final_content == "final answer"
+    assert len(calls) == 2
+    assert calls[1]["tools"] is None
+    assert "Do not call any more tools" in calls[1]["messages"][-1]["content"]
+    assert result.usage["prompt_tokens"] == 13
+    assert result.usage["completion_tokens"] == 8
+
+
+@pytest.mark.asyncio
+async def test_runner_uses_specific_message_after_empty_finalization_retry():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+    from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE
+
+    provider = MagicMock()
+
+    async def chat_with_retry(*, messages, **kwargs):
+        return LLMResponse(content=None, tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "do task"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert result.final_content == EMPTY_FINAL_RESPONSE_MESSAGE
+    assert result.stop_reason == "empty_final_response"
+
+
+def test_snip_history_drops_orphaned_tool_results_from_trimmed_slice(monkeypatch):
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    runner = AgentRunner(provider)
+    messages = [
+        {"role": "system", "content": "system"},
+        {"role": "user", "content": "old user"},
+        {
+            "role": "assistant",
+            "content": "tool call",
+            "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "ls", "arguments": "{}"}}],
+        },
+        {"role": "tool", "tool_call_id": "call_1", "content": "tool output"},
+        {"role": "assistant", "content": "after tool"},
+    ]
+    spec = AgentRunSpec(
+        initial_messages=messages,
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        context_window_tokens=2000,
+        context_block_limit=100,
+    )
+
+    monkeypatch.setattr("nanobot.agent.runner.estimate_prompt_tokens_chain", lambda *_args, **_kwargs: (500, None))
+    token_sizes = {
+        "old user": 120,
+        "tool call": 120,
+        "tool output": 40,
+        "after tool": 40,
+        "system": 0,
+    }
+    monkeypatch.setattr(
+        "nanobot.agent.runner.estimate_message_tokens",
+        lambda msg: token_sizes.get(str(msg.get("content")), 40),
+    )
+
+    trimmed = runner._snip_history(spec, messages)
+
+    assert trimmed == [
+        {"role": "system", "content": "system"},
+        {"role": "assistant", "content": "after tool"},
+    ]
+
+
+@pytest.mark.asyncio
+async def test_runner_keeps_going_when_tool_result_persistence_fails():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    captured_second_call: list[dict] = []
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(
+                content="working",
+                tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
+                usage={"prompt_tokens": 5, "completion_tokens": 3},
+            )
+        captured_second_call[:] = messages
+        return LLMResponse(content="done", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="tool result")
+
+    runner = AgentRunner(provider)
+    with patch("nanobot.agent.runner.maybe_persist_tool_result", side_effect=RuntimeError("disk full")):
+        result = await runner.run(AgentRunSpec(
+            initial_messages=[{"role": "user", "content": "do task"}],
+            tools=tools,
+            model="test-model",
+            max_iterations=2,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        ))
+
+    assert result.final_content == "done"
+    tool_message = next(msg for msg in captured_second_call if msg.get("role") == "tool")
+    assert tool_message["content"] == "tool result"
+
+
+class _DelayTool(Tool):
+    def __init__(self, name: str, *, delay: float, read_only: bool, shared_events: list[str]):
+        self._name = name
+        self._delay = delay
+        self._read_only = read_only
+        self._shared_events = shared_events
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def description(self) -> str:
+        return self._name
+
+    @property
+    def parameters(self) -> dict:
+        return {"type": "object", "properties": {}, "required": []}
+
+    @property
+    def read_only(self) -> bool:
+        return self._read_only
+
+    async def execute(self, **kwargs):
+        self._shared_events.append(f"start:{self._name}")
+        await asyncio.sleep(self._delay)
+        self._shared_events.append(f"end:{self._name}")
+        return self._name
+
+
+@pytest.mark.asyncio
+async def test_runner_batches_read_only_tools_before_exclusive_work():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    tools = ToolRegistry()
+    shared_events: list[str] = []
+    read_a = _DelayTool("read_a", delay=0.05, read_only=True, shared_events=shared_events)
+    read_b = _DelayTool("read_b", delay=0.05, read_only=True, shared_events=shared_events)
+    write_a = _DelayTool("write_a", delay=0.01, read_only=False, shared_events=shared_events)
+    tools.register(read_a)
+    tools.register(read_b)
+    tools.register(write_a)
+
+    runner = AgentRunner(MagicMock())
+    await runner._execute_tools(
+        AgentRunSpec(
+            initial_messages=[],
+            tools=tools,
+            model="test-model",
+            max_iterations=1,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+            concurrent_tools=True,
+        ),
+        [
+            ToolCallRequest(id="ro1", name="read_a", arguments={}),
+            ToolCallRequest(id="ro2", name="read_b", arguments={}),
+            ToolCallRequest(id="rw1", name="write_a", arguments={}),
+        ],
+        {},
+    )
+
+    assert shared_events[0:2] == ["start:read_a", "start:read_b"]
+    assert "end:read_a" in shared_events and "end:read_b" in shared_events
+    assert shared_events.index("end:read_a") < shared_events.index("start:write_a")
+    assert shared_events.index("end:read_b") < shared_events.index("start:write_a")
+    assert shared_events[-2:] == ["start:write_a", "end:write_a"]
+
+
+@pytest.mark.asyncio
+async def test_runner_blocks_repeated_external_fetches():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    captured_final_call: list[dict] = []
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] <= 3:
+            return LLMResponse(
+                content="working",
+                tool_calls=[ToolCallRequest(id=f"call_{call_count['n']}", name="web_fetch", arguments={"url": "https://example.com"})],
+                usage={},
+            )
+        captured_final_call[:] = messages
+        return LLMResponse(content="done", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="page content")
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "research task"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=4,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert result.final_content == "done"
+    assert tools.execute.await_count == 2
+    blocked_tool_message = [
+        msg for msg in captured_final_call
+        if msg.get("role") == "tool" and msg.get("tool_call_id") == "call_3"
+    ][0]
+    assert "repeated external lookup blocked" in blocked_tool_message["content"]
+
+
 @pytest.mark.asyncio
 async def test_loop_max_iterations_message_stays_stable(tmp_path):
     loop = _make_loop(tmp_path)
@@ -307,6 +772,57 @@ async def test_loop_stream_filter_handles_think_only_prefix_without_crashing(tmp
     assert endings == [False]
 
 
+@pytest.mark.asyncio
+async def test_loop_retries_think_only_final_response(tmp_path):
+    loop = _make_loop(tmp_path)
+    call_count = {"n": 0}
+
+    async def chat_with_retry(**kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(content="<think>hidden</think>", tool_calls=[], usage={})
+        return LLMResponse(content="Recovered answer", tool_calls=[], usage={})
+
+    loop.provider.chat_with_retry = chat_with_retry
+
+    final_content, _, _ = await loop._run_agent_loop([])
+
+    assert final_content == "Recovered answer"
+    assert call_count["n"] == 2
+
+
+@pytest.mark.asyncio
+async def test_runner_tool_error_sets_final_content():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+
+    async def chat_with_retry(*, messages, **kwargs):
+        return LLMResponse(
+            content="working",
+            tool_calls=[ToolCallRequest(id="call_1", name="read_file", arguments={"path": "x"})],
+            usage={},
+        )
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(side_effect=RuntimeError("boom"))
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "do task"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        fail_on_tool_error=True,
+    ))
+
+    assert result.final_content == "Error: RuntimeError: boom"
+    assert result.stop_reason == "tool_error"
+
+
 @pytest.mark.asyncio
 async def test_subagent_max_iterations_announces_existing_fallback(tmp_path, monkeypatch):
     from nanobot.agent.subagent import SubagentManager
@@ -317,15 +833,20 @@ async def test_subagent_max_iterations_announces_existing_fallback(tmp_path, mon
     provider.get_default_model.return_value = "test-model"
     provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
         content="working",
-        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
     ))
-    mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+    mgr = SubagentManager(
+        provider=provider,
+        workspace=tmp_path,
+        bus=bus,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    )
     mgr._announce_result = AsyncMock()
 
-    async def fake_execute(self, name, arguments):
+    async def fake_execute(self, **kwargs):
         return "tool result"
 
-    monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+    monkeypatch.setattr("nanobot.agent.tools.filesystem.ListDirTool.execute", fake_execute)
 
     await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
 
@@ -369,6 +890,7 @@ async def test_runner_accumulates_usage_and_preserves_cached_tokens():
         tools=tools,
         model="test-model",
         max_iterations=3,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
     ))
 
     # Usage should be accumulated across iterations
@@ -407,6 +929,7 @@ async def test_runner_passes_cached_tokens_to_hook_context():
         tools=tools,
         model="test-model",
         max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
         hook=UsageHook(),
     ))
 

tests/agent/test_task_cancel.py

@@ -8,6 +8,10 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from nanobot.config.schema import AgentDefaults
+
+_MAX_TOOL_RESULT_CHARS = AgentDefaults().max_tool_result_chars
+
 
 def _make_loop(*, exec_config=None):
     """Create a minimal AgentLoop with mocked dependencies."""
@@ -186,7 +190,12 @@ class TestSubagentCancellation:
         bus = MessageBus()
         provider = MagicMock()
         provider.get_default_model.return_value = "test-model"
-        mgr = SubagentManager(provider=provider, workspace=MagicMock(), bus=bus)
+        mgr = SubagentManager(
+            provider=provider,
+            workspace=MagicMock(),
+            bus=bus,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        )
 
         cancelled = asyncio.Event()
 
@@ -214,7 +223,12 @@ class TestSubagentCancellation:
         bus = MessageBus()
         provider = MagicMock()
         provider.get_default_model.return_value = "test-model"
-        mgr = SubagentManager(provider=provider, workspace=MagicMock(), bus=bus)
+        mgr = SubagentManager(
+            provider=provider,
+            workspace=MagicMock(),
+            bus=bus,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        )
         assert await mgr.cancel_by_session("nonexistent") == 0
 
     @pytest.mark.asyncio
@@ -236,19 +250,24 @@ class TestSubagentCancellation:
             if call_count["n"] == 1:
                 return LLMResponse(
                     content="thinking",
-                    tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+                    tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
                     reasoning_content="hidden reasoning",
                     thinking_blocks=[{"type": "thinking", "thinking": "step"}],
                 )
             captured_second_call[:] = messages
             return LLMResponse(content="done", tool_calls=[])
         provider.chat_with_retry = scripted_chat_with_retry
-        mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+        mgr = SubagentManager(
+            provider=provider,
+            workspace=tmp_path,
+            bus=bus,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        )
 
-        async def fake_execute(self, name, arguments):
+        async def fake_execute(self, **kwargs):
             return "tool result"
 
-        monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+        monkeypatch.setattr("nanobot.agent.tools.filesystem.ListDirTool.execute", fake_execute)
 
         await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
 
@@ -273,6 +292,7 @@ class TestSubagentCancellation:
             provider=provider,
             workspace=tmp_path,
             bus=bus,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
             exec_config=ExecToolConfig(enable=False),
         )
         mgr._announce_result = AsyncMock()
@@ -304,20 +324,25 @@ class TestSubagentCancellation:
         provider.get_default_model.return_value = "test-model"
         provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
             content="thinking",
-            tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+            tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
         ))
-        mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+        mgr = SubagentManager(
+            provider=provider,
+            workspace=tmp_path,
+            bus=bus,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        )
         mgr._announce_result = AsyncMock()
 
         calls = {"n": 0}
 
-        async def fake_execute(self, name, arguments):
+        async def fake_execute(self, **kwargs):
             calls["n"] += 1
             if calls["n"] == 1:
                 return "first result"
             raise RuntimeError("boom")
 
-        monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+        monkeypatch.setattr("nanobot.agent.tools.filesystem.ListDirTool.execute", fake_execute)
 
         await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
 
@@ -340,15 +365,20 @@ class TestSubagentCancellation:
         provider.get_default_model.return_value = "test-model"
         provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
             content="thinking",
-            tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+            tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
         ))
-        mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+        mgr = SubagentManager(
+            provider=provider,
+            workspace=tmp_path,
+            bus=bus,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        )
         mgr._announce_result = AsyncMock()
 
         started = asyncio.Event()
         cancelled = asyncio.Event()
 
-        async def fake_execute(self, name, arguments):
+        async def fake_execute(self, **kwargs):
             started.set()
             try:
                 await asyncio.sleep(60)
@@ -356,7 +386,7 @@ class TestSubagentCancellation:
                 cancelled.set()
                 raise
 
-        monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+        monkeypatch.setattr("nanobot.agent.tools.filesystem.ListDirTool.execute", fake_execute)
 
         task = asyncio.create_task(
             mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
@@ -364,7 +394,7 @@ class TestSubagentCancellation:
         mgr._running_tasks["sub-1"] = task
         mgr._session_tasks["test:c1"] = {"sub-1"}
 
-        await started.wait()
+        await asyncio.wait_for(started.wait(), timeout=1.0)
 
         count = await mgr.cancel_by_session("test:c1")
 

tests/channels/test_channel_plugins.py

@@ -208,7 +208,7 @@ def test_channels_login_uses_discovered_plugin_class(monkeypatch):
             seen["config"] = self.config
             return True
 
-    monkeypatch.setattr("nanobot.config.loader.load_config", lambda: Config())
+    monkeypatch.setattr("nanobot.config.loader.load_config", lambda config_path=None: Config())
     monkeypatch.setattr(
         "nanobot.channels.registry.discover_all",
         lambda: {"fakeplugin": _LoginPlugin},
@@ -220,6 +220,57 @@ def test_channels_login_uses_discovered_plugin_class(monkeypatch):
     assert seen["force"] is True
 
 
+def test_channels_login_sets_custom_config_path(monkeypatch, tmp_path):
+    from nanobot.cli.commands import app
+    from nanobot.config.schema import Config
+    from typer.testing import CliRunner
+
+    runner = CliRunner()
+    seen: dict[str, object] = {}
+    config_path = tmp_path / "custom-config.json"
+
+    class _LoginPlugin(_FakePlugin):
+        async def login(self, force: bool = False) -> bool:
+            return True
+
+    monkeypatch.setattr("nanobot.config.loader.load_config", lambda config_path=None: Config())
+    monkeypatch.setattr(
+        "nanobot.config.loader.set_config_path",
+        lambda path: seen.__setitem__("config_path", path),
+    )
+    monkeypatch.setattr(
+        "nanobot.channels.registry.discover_all",
+        lambda: {"fakeplugin": _LoginPlugin},
+    )
+
+    result = runner.invoke(app, ["channels", "login", "fakeplugin", "--config", str(config_path)])
+
+    assert result.exit_code == 0
+    assert seen["config_path"] == config_path.resolve()
+
+
+def test_channels_status_sets_custom_config_path(monkeypatch, tmp_path):
+    from nanobot.cli.commands import app
+    from nanobot.config.schema import Config
+    from typer.testing import CliRunner
+
+    runner = CliRunner()
+    seen: dict[str, object] = {}
+    config_path = tmp_path / "custom-config.json"
+
+    monkeypatch.setattr("nanobot.config.loader.load_config", lambda config_path=None: Config())
+    monkeypatch.setattr(
+        "nanobot.config.loader.set_config_path",
+        lambda path: seen.__setitem__("config_path", path),
+    )
+    monkeypatch.setattr("nanobot.channels.registry.discover_all", lambda: {})
+
+    result = runner.invoke(app, ["channels", "status", "--config", str(config_path)])
+
+    assert result.exit_code == 0
+    assert seen["config_path"] == config_path.resolve()
+
+
 @pytest.mark.asyncio
 async def test_manager_skips_disabled_plugin():
     fake_config = SimpleNamespace(

tests/channels/test_discord_channel.py

@@ -594,7 +594,7 @@ async def test_send_stops_typing_after_send() -> None:
     typing_channel.typing_enter_hook = slow_typing
 
     await channel._start_typing(typing_channel)
-    await start.wait()
+    await asyncio.wait_for(start.wait(), timeout=1.0)
 
     await channel.send(OutboundMessage(channel="discord", chat_id="123", content="hello"))
     release.set()
@@ -614,7 +614,7 @@ async def test_send_stops_typing_after_send() -> None:
     typing_channel.typing_enter_hook = slow_typing_progress
 
     await channel._start_typing(typing_channel)
-    await start.wait()
+    await asyncio.wait_for(start.wait(), timeout=1.0)
 
     await channel.send(
         OutboundMessage(
@@ -665,7 +665,7 @@ async def test_start_typing_uses_typing_context_when_trigger_typing_missing() ->
 
     typing_channel = _NoTriggerChannel(channel_id=123)
     await channel._start_typing(typing_channel)  # type: ignore[arg-type]
-    await entered.wait()
+    await asyncio.wait_for(entered.wait(), timeout=1.0)
 
     assert "123" in channel._typing_tasks
 

tests/channels/test_matrix_channel.py

@@ -3,16 +3,14 @@ from pathlib import Path
 from types import SimpleNamespace
 
 import pytest
+
+pytest.importorskip("nio")
+pytest.importorskip("nh3")
+pytest.importorskip("mistune")
 from nio import RoomSendResponse
 
 from nanobot.channels.matrix import _build_matrix_text_content
 
-# Check optional matrix dependencies before importing
-try:
-    import nh3  # noqa: F401
-except ImportError:
-    pytest.skip("Matrix dependencies not installed (nh3)", allow_module_level=True)
-
 import nanobot.channels.matrix as matrix_module
 from nanobot.bus.events import OutboundMessage
 from nanobot.bus.queue import MessageBus

tests/channels/test_qq_ack_message.py

@@ -0,0 +1,172 @@
+"""Tests for QQ channel ack_message feature.
+
+Covers the four verification points from the PR:
+1. C2C message: ack appears instantly
+2. Group message: ack appears instantly
+3. ack_message set to "": no ack sent
+4. Custom ack_message text: correct text delivered
+Each test also verifies that normal message processing is not blocked.
+"""
+
+from types import SimpleNamespace
+
+import pytest
+
+try:
+    from nanobot.channels import qq
+
+    QQ_AVAILABLE = getattr(qq, "QQ_AVAILABLE", False)
+except ImportError:
+    QQ_AVAILABLE = False
+
+if not QQ_AVAILABLE:
+    pytest.skip("QQ dependencies not installed (qq-botpy)", allow_module_level=True)
+
+from nanobot.bus.queue import MessageBus
+from nanobot.channels.qq import QQChannel, QQConfig
+
+
+class _FakeApi:
+    def __init__(self) -> None:
+        self.c2c_calls: list[dict] = []
+        self.group_calls: list[dict] = []
+
+    async def post_c2c_message(self, **kwargs) -> None:
+        self.c2c_calls.append(kwargs)
+
+    async def post_group_message(self, **kwargs) -> None:
+        self.group_calls.append(kwargs)
+
+
+class _FakeClient:
+    def __init__(self) -> None:
+        self.api = _FakeApi()
+
+
+@pytest.mark.asyncio
+async def test_ack_sent_on_c2c_message() -> None:
+    """Ack is sent immediately for C2C messages, then normal processing continues."""
+    channel = QQChannel(
+        QQConfig(
+            app_id="app",
+            secret="secret",
+            allow_from=["*"],
+            ack_message="⏳ Processing...",
+        ),
+        MessageBus(),
+    )
+    channel._client = _FakeClient()
+
+    data = SimpleNamespace(
+        id="msg1",
+        content="hello",
+        author=SimpleNamespace(user_openid="user1"),
+        attachments=[],
+    )
+    await channel._on_message(data, is_group=False)
+
+    assert len(channel._client.api.c2c_calls) >= 1
+    ack_call = channel._client.api.c2c_calls[0]
+    assert ack_call["content"] == "⏳ Processing..."
+    assert ack_call["openid"] == "user1"
+    assert ack_call["msg_id"] == "msg1"
+    assert ack_call["msg_type"] == 0
+
+    msg = await channel.bus.consume_inbound()
+    assert msg.content == "hello"
+    assert msg.sender_id == "user1"
+
+
+@pytest.mark.asyncio
+async def test_ack_sent_on_group_message() -> None:
+    """Ack is sent immediately for group messages, then normal processing continues."""
+    channel = QQChannel(
+        QQConfig(
+            app_id="app",
+            secret="secret",
+            allow_from=["*"],
+            ack_message="⏳ Processing...",
+        ),
+        MessageBus(),
+    )
+    channel._client = _FakeClient()
+
+    data = SimpleNamespace(
+        id="msg2",
+        content="hello group",
+        group_openid="group123",
+        author=SimpleNamespace(member_openid="user1"),
+        attachments=[],
+    )
+    await channel._on_message(data, is_group=True)
+
+    assert len(channel._client.api.group_calls) >= 1
+    ack_call = channel._client.api.group_calls[0]
+    assert ack_call["content"] == "⏳ Processing..."
+    assert ack_call["group_openid"] == "group123"
+    assert ack_call["msg_id"] == "msg2"
+    assert ack_call["msg_type"] == 0
+
+    msg = await channel.bus.consume_inbound()
+    assert msg.content == "hello group"
+    assert msg.chat_id == "group123"
+
+
+@pytest.mark.asyncio
+async def test_no_ack_when_ack_message_empty() -> None:
+    """Setting ack_message to empty string disables the ack entirely."""
+    channel = QQChannel(
+        QQConfig(
+            app_id="app",
+            secret="secret",
+            allow_from=["*"],
+            ack_message="",
+        ),
+        MessageBus(),
+    )
+    channel._client = _FakeClient()
+
+    data = SimpleNamespace(
+        id="msg3",
+        content="hello",
+        author=SimpleNamespace(user_openid="user1"),
+        attachments=[],
+    )
+    await channel._on_message(data, is_group=False)
+
+    assert len(channel._client.api.c2c_calls) == 0
+    assert len(channel._client.api.group_calls) == 0
+
+    msg = await channel.bus.consume_inbound()
+    assert msg.content == "hello"
+
+
+@pytest.mark.asyncio
+async def test_custom_ack_message_text() -> None:
+    """Custom Chinese ack_message text is delivered correctly."""
+    custom = "正在处理中，请稍候..."
+    channel = QQChannel(
+        QQConfig(
+            app_id="app",
+            secret="secret",
+            allow_from=["*"],
+            ack_message=custom,
+        ),
+        MessageBus(),
+    )
+    channel._client = _FakeClient()
+
+    data = SimpleNamespace(
+        id="msg4",
+        content="test input",
+        author=SimpleNamespace(user_openid="user1"),
+        attachments=[],
+    )
+    await channel._on_message(data, is_group=False)
+
+    assert len(channel._client.api.c2c_calls) >= 1
+    ack_call = channel._client.api.c2c_calls[0]
+    assert ack_call["content"] == custom
+
+    msg = await channel.bus.consume_inbound()
+    assert msg.content == "test input"

tests/channels/test_telegram_channel.py

@@ -647,43 +647,56 @@ async def test_group_policy_open_accepts_plain_group_message() -> None:
     assert channel._app.bot.get_me_calls == 0
 
 
-def test_extract_reply_context_no_reply() -> None:
+@pytest.mark.asyncio
+async def test_extract_reply_context_no_reply() -> None:
     """When there is no reply_to_message, _extract_reply_context returns None."""
+    channel = TelegramChannel(TelegramConfig(enabled=True, token="123:abc"), MessageBus())
     message = SimpleNamespace(reply_to_message=None)
-    assert TelegramChannel._extract_reply_context(message) is None
+    assert await channel._extract_reply_context(message) is None
 
 
-def test_extract_reply_context_with_text() -> None:
+@pytest.mark.asyncio
+async def test_extract_reply_context_with_text() -> None:
     """When reply has text, return prefixed string."""
-    reply = SimpleNamespace(text="Hello world", caption=None)
+    channel = TelegramChannel(TelegramConfig(enabled=True, token="123:abc"), MessageBus())
+    channel._app = _FakeApp(lambda: None)
+    reply = SimpleNamespace(text="Hello world", caption=None, from_user=SimpleNamespace(id=2, username="testuser", first_name="Test"))
     message = SimpleNamespace(reply_to_message=reply)
-    assert TelegramChannel._extract_reply_context(message) == "[Reply to: Hello world]"
+    assert await channel._extract_reply_context(message) == "[Reply to @testuser: Hello world]"
 
 
-def test_extract_reply_context_with_caption_only() -> None:
+@pytest.mark.asyncio
+async def test_extract_reply_context_with_caption_only() -> None:
     """When reply has only caption (no text), caption is used."""
-    reply = SimpleNamespace(text=None, caption="Photo caption")
+    channel = TelegramChannel(TelegramConfig(enabled=True, token="123:abc"), MessageBus())
+    channel._app = _FakeApp(lambda: None)
+    reply = SimpleNamespace(text=None, caption="Photo caption", from_user=SimpleNamespace(id=2, username=None, first_name="Test"))
     message = SimpleNamespace(reply_to_message=reply)
-    assert TelegramChannel._extract_reply_context(message) == "[Reply to: Photo caption]"
+    assert await channel._extract_reply_context(message) == "[Reply to Test: Photo caption]"
 
 
-def test_extract_reply_context_truncation() -> None:
+@pytest.mark.asyncio
+async def test_extract_reply_context_truncation() -> None:
     """Reply text is truncated at TELEGRAM_REPLY_CONTEXT_MAX_LEN."""
+    channel = TelegramChannel(TelegramConfig(enabled=True, token="123:abc"), MessageBus())
+    channel._app = _FakeApp(lambda: None)
     long_text = "x" * (TELEGRAM_REPLY_CONTEXT_MAX_LEN + 100)
-    reply = SimpleNamespace(text=long_text, caption=None)
+    reply = SimpleNamespace(text=long_text, caption=None, from_user=SimpleNamespace(id=2, username=None, first_name=None))
     message = SimpleNamespace(reply_to_message=reply)
-    result = TelegramChannel._extract_reply_context(message)
+    result = await channel._extract_reply_context(message)
     assert result is not None
     assert result.startswith("[Reply to: ")
     assert result.endswith("...]")
     assert len(result) == len("[Reply to: ]") + TELEGRAM_REPLY_CONTEXT_MAX_LEN + len("...")
 
 
-def test_extract_reply_context_no_text_returns_none() -> None:
+@pytest.mark.asyncio
+async def test_extract_reply_context_no_text_returns_none() -> None:
     """When reply has no text/caption, _extract_reply_context returns None (media handled separately)."""
+    channel = TelegramChannel(TelegramConfig(enabled=True, token="123:abc"), MessageBus())
     reply = SimpleNamespace(text=None, caption=None)
     message = SimpleNamespace(reply_to_message=reply)
-    assert TelegramChannel._extract_reply_context(message) is None
+    assert await channel._extract_reply_context(message) is None
 
 
 @pytest.mark.asyncio

tests/providers/test_azure_openai_provider.py

@@ -1,6 +1,6 @@
-"""Test Azure OpenAI provider implementation (updated for model-based deployment names)."""
+"""Test Azure OpenAI provider (Responses API via OpenAI SDK)."""
 
-from unittest.mock import AsyncMock, Mock, patch
+from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
@@ -8,392 +8,401 @@ from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
 from nanobot.providers.base import LLMResponse
 
 
-def test_azure_openai_provider_init():
-    """Test AzureOpenAIProvider initialization without deployment_name."""
+# ---------------------------------------------------------------------------
+# Init & validation
+# ---------------------------------------------------------------------------
+
+
+def test_init_creates_sdk_client():
+    """Provider creates an AsyncOpenAI client with correct base_url."""
     provider = AzureOpenAIProvider(
         api_key="test-key",
         api_base="https://test-resource.openai.azure.com",
         default_model="gpt-4o-deployment",
     )
-    
     assert provider.api_key == "test-key"
     assert provider.api_base == "https://test-resource.openai.azure.com/"
     assert provider.default_model == "gpt-4o-deployment"
-    assert provider.api_version == "2024-10-21"
+    # SDK client base_url ends with /openai/v1/
+    assert str(provider._client.base_url).rstrip("/").endswith("/openai/v1")
 
 
-def test_azure_openai_provider_init_validation():
-    """Test AzureOpenAIProvider initialization validation."""
-    # Missing api_key
+def test_init_base_url_no_trailing_slash():
+    """Trailing slashes are normalised before building base_url."""
+    provider = AzureOpenAIProvider(
+        api_key="k", api_base="https://res.openai.azure.com",
+    )
+    assert str(provider._client.base_url).rstrip("/").endswith("/openai/v1")
+
+
+def test_init_base_url_with_trailing_slash():
+    provider = AzureOpenAIProvider(
+        api_key="k", api_base="https://res.openai.azure.com/",
+    )
+    assert str(provider._client.base_url).rstrip("/").endswith("/openai/v1")
+
+
+def test_init_validation_missing_key():
     with pytest.raises(ValueError, match="Azure OpenAI api_key is required"):
         AzureOpenAIProvider(api_key="", api_base="https://test.com")
-    
-    # Missing api_base
+
+
+def test_init_validation_missing_base():
     with pytest.raises(ValueError, match="Azure OpenAI api_base is required"):
         AzureOpenAIProvider(api_key="test", api_base="")
 
 
-def test_build_chat_url():
-    """Test Azure OpenAI URL building with different deployment names."""
+def test_no_api_version_in_base_url():
+    """The /openai/v1/ path should NOT contain an api-version query param."""
+    provider = AzureOpenAIProvider(api_key="k", api_base="https://res.openai.azure.com")
+    base = str(provider._client.base_url)
+    assert "api-version" not in base
+
+
+# ---------------------------------------------------------------------------
+# _supports_temperature
+# ---------------------------------------------------------------------------
+
+
+def test_supports_temperature_standard_model():
+    assert AzureOpenAIProvider._supports_temperature("gpt-4o") is True
+
+
+def test_supports_temperature_reasoning_model():
+    assert AzureOpenAIProvider._supports_temperature("o3-mini") is False
+    assert AzureOpenAIProvider._supports_temperature("gpt-5-chat") is False
+    assert AzureOpenAIProvider._supports_temperature("o4-mini") is False
+
+
+def test_supports_temperature_with_reasoning_effort():
+    assert AzureOpenAIProvider._supports_temperature("gpt-4o", reasoning_effort="medium") is False
+
+
+# ---------------------------------------------------------------------------
+# _build_body — Responses API body construction
+# ---------------------------------------------------------------------------
+
+
+def test_build_body_basic():
     provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
+        api_key="k", api_base="https://res.openai.azure.com", default_model="gpt-4o",
     )
-    
-    # Test various deployment names
-    test_cases = [
-        ("gpt-4o-deployment", "https://test-resource.openai.azure.com/openai/deployments/gpt-4o-deployment/chat/completions?api-version=2024-10-21"),
-        ("gpt-35-turbo", "https://test-resource.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-10-21"),
-        ("custom-model", "https://test-resource.openai.azure.com/openai/deployments/custom-model/chat/completions?api-version=2024-10-21"),
-    ]
-    
-    for deployment_name, expected_url in test_cases:
-        url = provider._build_chat_url(deployment_name)
-        assert url == expected_url
+    messages = [{"role": "system", "content": "You are helpful."}, {"role": "user", "content": "Hi"}]
+    body = provider._build_body(messages, None, None, 4096, 0.7, None, None)
 
-
-def test_build_chat_url_api_base_without_slash():
-    """Test URL building when api_base doesn't end with slash."""
-    provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",  # No trailing slash
-        default_model="gpt-4o",
+    assert body["model"] == "gpt-4o"
+    assert body["instructions"] == "You are helpful."
+    assert body["temperature"] == 0.7
+    assert body["max_output_tokens"] == 4096
+    assert body["store"] is False
+    assert "reasoning" not in body
+    # input should contain the converted user message only (system extracted)
+    assert any(
+        item.get("role") == "user"
+        for item in body["input"]
     )
-    
-    url = provider._build_chat_url("test-deployment")
-    expected = "https://test-resource.openai.azure.com/openai/deployments/test-deployment/chat/completions?api-version=2024-10-21"
-    assert url == expected
 
 
-def test_build_headers():
-    """Test Azure OpenAI header building with api-key authentication."""
-    provider = AzureOpenAIProvider(
-        api_key="test-api-key-123",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
-    )
-    
-    headers = provider._build_headers()
-    assert headers["Content-Type"] == "application/json"
-    assert headers["api-key"] == "test-api-key-123"  # Azure OpenAI specific header
-    assert "x-session-affinity" in headers
+def test_build_body_max_tokens_minimum():
+    """max_output_tokens should never be less than 1."""
+    provider = AzureOpenAIProvider(api_key="k", api_base="https://r.com", default_model="gpt-4o")
+    body = provider._build_body([{"role": "user", "content": "x"}], None, None, 0, 0.7, None, None)
+    assert body["max_output_tokens"] == 1
 
 
-def test_prepare_request_payload():
-    """Test request payload preparation with Azure OpenAI 2024-10-21 compliance."""
-    provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
-    )
-    
-    messages = [{"role": "user", "content": "Hello"}]
-    payload = provider._prepare_request_payload("gpt-4o", messages, max_tokens=1500, temperature=0.8)
-    
-    assert payload["messages"] == messages
-    assert payload["max_completion_tokens"] == 1500  # Azure API 2024-10-21 uses max_completion_tokens
-    assert payload["temperature"] == 0.8
-    assert "tools" not in payload
-    
-    # Test with tools
+def test_build_body_with_tools():
+    provider = AzureOpenAIProvider(api_key="k", api_base="https://r.com", default_model="gpt-4o")
     tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
-    payload_with_tools = provider._prepare_request_payload("gpt-4o", messages, tools=tools)
-    assert payload_with_tools["tools"] == tools
-    assert payload_with_tools["tool_choice"] == "auto"
-    
-    # Test with reasoning_effort
-    payload_with_reasoning = provider._prepare_request_payload(
-        "gpt-5-chat", messages, reasoning_effort="medium"
+    body = provider._build_body(
+        [{"role": "user", "content": "weather?"}], tools, None, 4096, 0.7, None, None,
     )
-    assert payload_with_reasoning["reasoning_effort"] == "medium"
-    assert "temperature" not in payload_with_reasoning
+    assert body["tools"] == [{"type": "function", "name": "get_weather", "description": "", "parameters": {}}]
+    assert body["tool_choice"] == "auto"
 
 
-def test_prepare_request_payload_sanitizes_messages():
-    """Test Azure payload strips non-standard message keys before sending."""
-    provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
+def test_build_body_with_reasoning():
+    provider = AzureOpenAIProvider(api_key="k", api_base="https://r.com", default_model="gpt-5-chat")
+    body = provider._build_body(
+        [{"role": "user", "content": "think"}], None, "gpt-5-chat", 4096, 0.7, "medium", None,
     )
+    assert body["reasoning"] == {"effort": "medium"}
+    assert "reasoning.encrypted_content" in body.get("include", [])
+    # temperature omitted for reasoning models
+    assert "temperature" not in body
 
-    messages = [
-        {
-            "role": "assistant",
-            "tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
-            "reasoning_content": "hidden chain-of-thought",
-        },
-        {
-            "role": "tool",
-            "tool_call_id": "call_123",
-            "name": "x",
-            "content": "ok",
-            "extra_field": "should be removed",
-        },
-    ]
 
-    payload = provider._prepare_request_payload("gpt-4o", messages)
+def test_build_body_image_conversion():
+    """image_url content blocks should be converted to input_image."""
+    provider = AzureOpenAIProvider(api_key="k", api_base="https://r.com", default_model="gpt-4o")
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "What's in this image?"},
+            {"type": "image_url", "image_url": {"url": "https://example.com/img.png"}},
+        ],
+    }]
+    body = provider._build_body(messages, None, None, 4096, 0.7, None, None)
+    user_item = body["input"][0]
+    content_types = [b["type"] for b in user_item["content"]]
+    assert "input_text" in content_types
+    assert "input_image" in content_types
+    image_block = next(b for b in user_item["content"] if b["type"] == "input_image")
+    assert image_block["image_url"] == "https://example.com/img.png"
 
-    assert payload["messages"] == [
-        {
-            "role": "assistant",
-            "content": None,
-            "tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
+
+def test_build_body_sanitizes_single_dict_content_block():
+    """Single content dicts should be preserved via shared message sanitization."""
+    provider = AzureOpenAIProvider(api_key="k", api_base="https://r.com", default_model="gpt-4o")
+    messages = [{
+        "role": "user",
+        "content": {"type": "text", "text": "Hi from dict content"},
+    }]
+
+    body = provider._build_body(messages, None, None, 4096, 0.7, None, None)
+
+    assert body["input"][0]["content"] == [{"type": "input_text", "text": "Hi from dict content"}]
+
+
+# ---------------------------------------------------------------------------
+# chat() — non-streaming
+# ---------------------------------------------------------------------------
+
+
+def _make_sdk_response(
+    content="Hello!", tool_calls=None, status="completed",
+    usage=None,
+):
+    """Build a mock that quacks like an openai Response object."""
+    resp = MagicMock()
+    resp.model_dump = MagicMock(return_value={
+        "output": [
+            {"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": content}]},
+            *([{
+                "type": "function_call",
+                "call_id": tc["call_id"], "id": tc["id"],
+                "name": tc["name"], "arguments": tc["arguments"],
+            } for tc in (tool_calls or [])]),
+        ],
+        "status": status,
+        "usage": {
+            "input_tokens": (usage or {}).get("input_tokens", 10),
+            "output_tokens": (usage or {}).get("output_tokens", 5),
+            "total_tokens": (usage or {}).get("total_tokens", 15),
         },
-        {
-            "role": "tool",
-            "tool_call_id": "call_123",
-            "name": "x",
-            "content": "ok",
-        },
-    ]
+    })
+    return resp
 
 
 @pytest.mark.asyncio
 async def test_chat_success():
-    """Test successful chat request using model as deployment name."""
     provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o-deployment",
+        api_key="test-key", api_base="https://test.openai.azure.com", default_model="gpt-4o",
     )
-    
-    # Mock response data
-    mock_response_data = {
-        "choices": [{
-            "message": {
-                "content": "Hello! How can I help you today?",
-                "role": "assistant"
-            },
-            "finish_reason": "stop"
-        }],
-        "usage": {
-            "prompt_tokens": 12,
-            "completion_tokens": 18,
-            "total_tokens": 30
-        }
-    }
-    
-    with patch("httpx.AsyncClient") as mock_client:
-        mock_response = AsyncMock()
-        mock_response.status_code = 200
-        mock_response.json = Mock(return_value=mock_response_data)
-        
-        mock_context = AsyncMock()
-        mock_context.post = AsyncMock(return_value=mock_response)
-        mock_client.return_value.__aenter__.return_value = mock_context
-        
-        # Test with specific model (deployment name)
-        messages = [{"role": "user", "content": "Hello"}]
-        result = await provider.chat(messages, model="custom-deployment")
-        
-        assert isinstance(result, LLMResponse)
-        assert result.content == "Hello! How can I help you today?"
-        assert result.finish_reason == "stop"
-        assert result.usage["prompt_tokens"] == 12
-        assert result.usage["completion_tokens"] == 18
-        assert result.usage["total_tokens"] == 30
-        
-        # Verify URL was built with the provided model as deployment name
-        call_args = mock_context.post.call_args
-        expected_url = "https://test-resource.openai.azure.com/openai/deployments/custom-deployment/chat/completions?api-version=2024-10-21"
-        assert call_args[0][0] == expected_url
+    mock_resp = _make_sdk_response(content="Hello!")
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_resp)
+
+    result = await provider.chat([{"role": "user", "content": "Hi"}])
+
+    assert isinstance(result, LLMResponse)
+    assert result.content == "Hello!"
+    assert result.finish_reason == "stop"
+    assert result.usage["prompt_tokens"] == 10
 
 
 @pytest.mark.asyncio
-async def test_chat_uses_default_model_when_no_model_provided():
-    """Test that chat uses default_model when no model is specified."""
+async def test_chat_uses_default_model():
     provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="default-deployment",
+        api_key="k", api_base="https://test.openai.azure.com", default_model="my-deployment",
     )
-    
-    mock_response_data = {
-        "choices": [{
-            "message": {"content": "Response", "role": "assistant"},
-            "finish_reason": "stop"
-        }],
-        "usage": {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
-    }
-    
-    with patch("httpx.AsyncClient") as mock_client:
-        mock_response = AsyncMock()
-        mock_response.status_code = 200
-        mock_response.json = Mock(return_value=mock_response_data)
-        
-        mock_context = AsyncMock()
-        mock_context.post = AsyncMock(return_value=mock_response)
-        mock_client.return_value.__aenter__.return_value = mock_context
-        
-        messages = [{"role": "user", "content": "Test"}]
-        await provider.chat(messages)  # No model specified
-        
-        # Verify URL was built with default model as deployment name
-        call_args = mock_context.post.call_args
-        expected_url = "https://test-resource.openai.azure.com/openai/deployments/default-deployment/chat/completions?api-version=2024-10-21"
-        assert call_args[0][0] == expected_url
+    mock_resp = _make_sdk_response(content="ok")
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_resp)
+
+    await provider.chat([{"role": "user", "content": "test"}])
+
+    call_kwargs = provider._client.responses.create.call_args[1]
+    assert call_kwargs["model"] == "my-deployment"
+
+
+@pytest.mark.asyncio
+async def test_chat_custom_model():
+    provider = AzureOpenAIProvider(
+        api_key="k", api_base="https://test.openai.azure.com", default_model="gpt-4o",
+    )
+    mock_resp = _make_sdk_response(content="ok")
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_resp)
+
+    await provider.chat([{"role": "user", "content": "test"}], model="custom-deploy")
+
+    call_kwargs = provider._client.responses.create.call_args[1]
+    assert call_kwargs["model"] == "custom-deploy"
 
 
 @pytest.mark.asyncio
 async def test_chat_with_tool_calls():
-    """Test chat request with tool calls in response."""
     provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
+        api_key="k", api_base="https://test.openai.azure.com", default_model="gpt-4o",
     )
-    
-    # Mock response with tool calls
-    mock_response_data = {
-        "choices": [{
-            "message": {
-                "content": None,
-                "role": "assistant",
-                "tool_calls": [{
-                    "id": "call_12345",
-                    "function": {
-                        "name": "get_weather",
-                        "arguments": '{"location": "San Francisco"}'
-                    }
-                }]
-            },
-            "finish_reason": "tool_calls"
+    mock_resp = _make_sdk_response(
+        content=None,
+        tool_calls=[{
+            "call_id": "call_123", "id": "fc_1",
+            "name": "get_weather", "arguments": '{"location": "SF"}',
         }],
-        "usage": {
-            "prompt_tokens": 20,
-            "completion_tokens": 15,
-            "total_tokens": 35
-        }
-    }
-    
-    with patch("httpx.AsyncClient") as mock_client:
-        mock_response = AsyncMock()
-        mock_response.status_code = 200
-        mock_response.json = Mock(return_value=mock_response_data)
-        
-        mock_context = AsyncMock()
-        mock_context.post = AsyncMock(return_value=mock_response)
-        mock_client.return_value.__aenter__.return_value = mock_context
-        
-        messages = [{"role": "user", "content": "What's the weather?"}]
-        tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
-        result = await provider.chat(messages, tools=tools, model="weather-model")
-        
-        assert isinstance(result, LLMResponse)
-        assert result.content is None
-        assert result.finish_reason == "tool_calls"
-        assert len(result.tool_calls) == 1
-        assert result.tool_calls[0].name == "get_weather"
-        assert result.tool_calls[0].arguments == {"location": "San Francisco"}
+    )
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_resp)
+
+    result = await provider.chat(
+        [{"role": "user", "content": "Weather?"}],
+        tools=[{"type": "function", "function": {"name": "get_weather", "parameters": {}}}],
+    )
+
+    assert len(result.tool_calls) == 1
+    assert result.tool_calls[0].name == "get_weather"
+    assert result.tool_calls[0].arguments == {"location": "SF"}
 
 
 @pytest.mark.asyncio
-async def test_chat_api_error():
-    """Test chat request API error handling."""
+async def test_chat_error_handling():
     provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
+        api_key="k", api_base="https://test.openai.azure.com", default_model="gpt-4o",
     )
-    
-    with patch("httpx.AsyncClient") as mock_client:
-        mock_response = AsyncMock()
-        mock_response.status_code = 401
-        mock_response.text = "Invalid authentication credentials"
-        
-        mock_context = AsyncMock()
-        mock_context.post = AsyncMock(return_value=mock_response)
-        mock_client.return_value.__aenter__.return_value = mock_context
-        
-        messages = [{"role": "user", "content": "Hello"}]
-        result = await provider.chat(messages)
-        
-        assert isinstance(result, LLMResponse)
-        assert "Azure OpenAI API Error 401" in result.content
-        assert "Invalid authentication credentials" in result.content
-        assert result.finish_reason == "error"
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(side_effect=Exception("Connection failed"))
 
+    result = await provider.chat([{"role": "user", "content": "Hi"}])
 
-@pytest.mark.asyncio
-async def test_chat_connection_error():
-    """Test chat request connection error handling."""
-    provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
-    )
-    
-    with patch("httpx.AsyncClient") as mock_client:
-        mock_context = AsyncMock()
-        mock_context.post = AsyncMock(side_effect=Exception("Connection failed"))
-        mock_client.return_value.__aenter__.return_value = mock_context
-        
-        messages = [{"role": "user", "content": "Hello"}]
-        result = await provider.chat(messages)
-        
-        assert isinstance(result, LLMResponse)
-        assert "Error calling Azure OpenAI: Exception('Connection failed')" in result.content
-        assert result.finish_reason == "error"
-
-
-def test_parse_response_malformed():
-    """Test response parsing with malformed data."""
-    provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o",
-    )
-    
-    # Test with missing choices
-    malformed_response = {"usage": {"prompt_tokens": 10}}
-    result = provider._parse_response(malformed_response)
-    
     assert isinstance(result, LLMResponse)
-    assert "Error parsing Azure OpenAI response" in result.content
+    assert "Connection failed" in result.content
     assert result.finish_reason == "error"
 
 
+@pytest.mark.asyncio
+async def test_chat_reasoning_param_format():
+    """reasoning_effort should be sent as reasoning={effort: ...} not a flat string."""
+    provider = AzureOpenAIProvider(
+        api_key="k", api_base="https://test.openai.azure.com", default_model="gpt-5-chat",
+    )
+    mock_resp = _make_sdk_response(content="thought")
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_resp)
+
+    await provider.chat(
+        [{"role": "user", "content": "think"}], reasoning_effort="medium",
+    )
+
+    call_kwargs = provider._client.responses.create.call_args[1]
+    assert call_kwargs["reasoning"] == {"effort": "medium"}
+    assert "reasoning_effort" not in call_kwargs
+
+
+# ---------------------------------------------------------------------------
+# chat_stream()
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_chat_stream_success():
+    """Streaming should call on_content_delta and return combined response."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key", api_base="https://test.openai.azure.com", default_model="gpt-4o",
+    )
+
+    # Build mock SDK stream events
+    events = []
+    ev1 = MagicMock(type="response.output_text.delta", delta="Hello")
+    ev2 = MagicMock(type="response.output_text.delta", delta=" world")
+    resp_obj = MagicMock(status="completed")
+    ev3 = MagicMock(type="response.completed", response=resp_obj)
+    events = [ev1, ev2, ev3]
+
+    async def mock_stream():
+        for e in events:
+            yield e
+
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_stream())
+
+    deltas: list[str] = []
+
+    async def on_delta(text: str) -> None:
+        deltas.append(text)
+
+    result = await provider.chat_stream(
+        [{"role": "user", "content": "Hi"}], on_content_delta=on_delta,
+    )
+
+    assert result.content == "Hello world"
+    assert result.finish_reason == "stop"
+    assert deltas == ["Hello", " world"]
+
+
+@pytest.mark.asyncio
+async def test_chat_stream_with_tool_calls():
+    """Streaming tool calls should be accumulated correctly."""
+    provider = AzureOpenAIProvider(
+        api_key="k", api_base="https://test.openai.azure.com", default_model="gpt-4o",
+    )
+
+    item_added = MagicMock(type="function_call", call_id="call_1", id="fc_1", arguments="")
+    item_added.name = "get_weather"
+    ev_added = MagicMock(type="response.output_item.added", item=item_added)
+    ev_args_delta = MagicMock(type="response.function_call_arguments.delta", call_id="call_1", delta='{"loc')
+    ev_args_done = MagicMock(
+        type="response.function_call_arguments.done",
+        call_id="call_1", arguments='{"location":"SF"}',
+    )
+    item_done = MagicMock(
+        type="function_call", call_id="call_1", id="fc_1",
+        arguments='{"location":"SF"}',
+    )
+    item_done.name = "get_weather"
+    ev_item_done = MagicMock(type="response.output_item.done", item=item_done)
+    resp_obj = MagicMock(status="completed")
+    ev_completed = MagicMock(type="response.completed", response=resp_obj)
+
+    async def mock_stream():
+        for e in [ev_added, ev_args_delta, ev_args_done, ev_item_done, ev_completed]:
+            yield e
+
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(return_value=mock_stream())
+
+    result = await provider.chat_stream(
+        [{"role": "user", "content": "weather?"}],
+        tools=[{"type": "function", "function": {"name": "get_weather", "parameters": {}}}],
+    )
+
+    assert len(result.tool_calls) == 1
+    assert result.tool_calls[0].name == "get_weather"
+    assert result.tool_calls[0].arguments == {"location": "SF"}
+
+
+@pytest.mark.asyncio
+async def test_chat_stream_error():
+    """Streaming should return error when SDK raises."""
+    provider = AzureOpenAIProvider(
+        api_key="k", api_base="https://test.openai.azure.com", default_model="gpt-4o",
+    )
+    provider._client.responses = MagicMock()
+    provider._client.responses.create = AsyncMock(side_effect=Exception("Connection failed"))
+
+    result = await provider.chat_stream([{"role": "user", "content": "Hi"}])
+
+    assert "Connection failed" in result.content
+    assert result.finish_reason == "error"
+
+
+# ---------------------------------------------------------------------------
+# get_default_model
+# ---------------------------------------------------------------------------
+
+
 def test_get_default_model():
-    """Test get_default_model method."""
     provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="my-custom-deployment",
+        api_key="k", api_base="https://r.com", default_model="my-deploy",
     )
-    
-    assert provider.get_default_model() == "my-custom-deployment"
-
-
-if __name__ == "__main__":
-    # Run basic tests
-    print("Running basic Azure OpenAI provider tests...")
-    
-    # Test initialization
-    provider = AzureOpenAIProvider(
-        api_key="test-key",
-        api_base="https://test-resource.openai.azure.com",
-        default_model="gpt-4o-deployment",
-    )
-    print("✅ Provider initialization successful")
-    
-    # Test URL building
-    url = provider._build_chat_url("my-deployment")
-    expected = "https://test-resource.openai.azure.com/openai/deployments/my-deployment/chat/completions?api-version=2024-10-21"
-    assert url == expected
-    print("✅ URL building works correctly")
-    
-    # Test headers
-    headers = provider._build_headers()
-    assert headers["api-key"] == "test-key"
-    assert headers["Content-Type"] == "application/json"
-    print("✅ Header building works correctly")
-    
-    # Test payload preparation
-    messages = [{"role": "user", "content": "Test"}]
-    payload = provider._prepare_request_payload("gpt-4o-deployment", messages, max_tokens=1000)
-    assert payload["max_completion_tokens"] == 1000  # Azure 2024-10-21 format
-    print("✅ Payload preparation works correctly")
-    
-    print("✅ All basic tests passed! Updated test file is working correctly.")
+    assert provider.get_default_model() == "my-deploy"

tests/providers/test_litellm_kwargs.py

@@ -8,6 +8,7 @@ Validates that:
 
 from __future__ import annotations
 
+import asyncio
 from types import SimpleNamespace
 from unittest.mock import AsyncMock, patch
 
@@ -53,6 +54,15 @@ def _fake_tool_call_response() -> SimpleNamespace:
     return SimpleNamespace(choices=[choice], usage=usage)
 
 
+class _StalledStream:
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        await asyncio.sleep(3600)
+        raise StopAsyncIteration
+
+
 def test_openrouter_spec_is_gateway() -> None:
     spec = find_by_name("openrouter")
     assert spec is not None
@@ -214,3 +224,54 @@ def test_openai_model_passthrough() -> None:
             spec=spec,
         )
     assert provider.get_default_model() == "gpt-4o"
+
+
+def test_openai_compat_strips_message_level_reasoning_fields() -> None:
+    with patch("nanobot.providers.openai_compat_provider.AsyncOpenAI"):
+        provider = OpenAICompatProvider()
+
+    sanitized = provider._sanitize_messages([
+        {
+            "role": "assistant",
+            "content": "done",
+            "reasoning_content": "hidden",
+            "extra_content": {"debug": True},
+            "tool_calls": [
+                {
+                    "id": "call_1",
+                    "type": "function",
+                    "function": {"name": "fn", "arguments": "{}"},
+                    "extra_content": {"google": {"thought_signature": "sig"}},
+                }
+            ],
+        }
+    ])
+
+    assert "reasoning_content" not in sanitized[0]
+    assert "extra_content" not in sanitized[0]
+    assert sanitized[0]["tool_calls"][0]["extra_content"] == {"google": {"thought_signature": "sig"}}
+
+
+@pytest.mark.asyncio
+async def test_openai_compat_stream_watchdog_returns_error_on_stall(monkeypatch) -> None:
+    monkeypatch.setenv("NANOBOT_STREAM_IDLE_TIMEOUT_S", "0")
+    mock_create = AsyncMock(return_value=_StalledStream())
+    spec = find_by_name("openai")
+
+    with patch("nanobot.providers.openai_compat_provider.AsyncOpenAI") as MockClient:
+        client_instance = MockClient.return_value
+        client_instance.chat.completions.create = mock_create
+
+        provider = OpenAICompatProvider(
+            api_key="sk-test-key",
+            default_model="gpt-4o",
+            spec=spec,
+        )
+        result = await provider.chat_stream(
+            messages=[{"role": "user", "content": "hello"}],
+            model="gpt-4o",
+        )
+
+    assert result.finish_reason == "error"
+    assert result.content is not None
+    assert "stream stalled" in result.content

tests/providers/test_openai_responses.py

@@ -0,0 +1,522 @@
+"""Tests for the shared openai_responses converters and parsers."""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from nanobot.providers.base import LLMResponse, ToolCallRequest
+from nanobot.providers.openai_responses.converters import (
+    convert_messages,
+    convert_tools,
+    convert_user_message,
+    split_tool_call_id,
+)
+from nanobot.providers.openai_responses.parsing import (
+    consume_sdk_stream,
+    map_finish_reason,
+    parse_response_output,
+)
+
+
+# ======================================================================
+# converters - split_tool_call_id
+# ======================================================================
+
+
+class TestSplitToolCallId:
+    def test_plain_id(self):
+        assert split_tool_call_id("call_abc") == ("call_abc", None)
+
+    def test_compound_id(self):
+        assert split_tool_call_id("call_abc|fc_1") == ("call_abc", "fc_1")
+
+    def test_compound_empty_item_id(self):
+        assert split_tool_call_id("call_abc|") == ("call_abc", None)
+
+    def test_none(self):
+        assert split_tool_call_id(None) == ("call_0", None)
+
+    def test_empty_string(self):
+        assert split_tool_call_id("") == ("call_0", None)
+
+    def test_non_string(self):
+        assert split_tool_call_id(42) == ("call_0", None)
+
+
+# ======================================================================
+# converters - convert_user_message
+# ======================================================================
+
+
+class TestConvertUserMessage:
+    def test_string_content(self):
+        result = convert_user_message("hello")
+        assert result == {"role": "user", "content": [{"type": "input_text", "text": "hello"}]}
+
+    def test_text_block(self):
+        result = convert_user_message([{"type": "text", "text": "hi"}])
+        assert result["content"] == [{"type": "input_text", "text": "hi"}]
+
+    def test_image_url_block(self):
+        result = convert_user_message([
+            {"type": "image_url", "image_url": {"url": "https://img.example/a.png"}},
+        ])
+        assert result["content"] == [
+            {"type": "input_image", "image_url": "https://img.example/a.png", "detail": "auto"},
+        ]
+
+    def test_mixed_text_and_image(self):
+        result = convert_user_message([
+            {"type": "text", "text": "what's this?"},
+            {"type": "image_url", "image_url": {"url": "https://img.example/b.png"}},
+        ])
+        assert len(result["content"]) == 2
+        assert result["content"][0]["type"] == "input_text"
+        assert result["content"][1]["type"] == "input_image"
+
+    def test_empty_list_falls_back(self):
+        result = convert_user_message([])
+        assert result["content"] == [{"type": "input_text", "text": ""}]
+
+    def test_none_falls_back(self):
+        result = convert_user_message(None)
+        assert result["content"] == [{"type": "input_text", "text": ""}]
+
+    def test_image_without_url_skipped(self):
+        result = convert_user_message([{"type": "image_url", "image_url": {}}])
+        assert result["content"] == [{"type": "input_text", "text": ""}]
+
+    def test_meta_fields_not_leaked(self):
+        """_meta on content blocks must never appear in converted output."""
+        result = convert_user_message([
+            {"type": "text", "text": "hi", "_meta": {"path": "/tmp/x"}},
+        ])
+        assert "_meta" not in result["content"][0]
+
+    def test_non_dict_items_skipped(self):
+        result = convert_user_message(["just a string", 42])
+        assert result["content"] == [{"type": "input_text", "text": ""}]
+
+
+# ======================================================================
+# converters - convert_messages
+# ======================================================================
+
+
+class TestConvertMessages:
+    def test_system_extracted_as_instructions(self):
+        msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Hi"},
+        ]
+        instructions, items = convert_messages(msgs)
+        assert instructions == "You are helpful."
+        assert len(items) == 1
+        assert items[0]["role"] == "user"
+
+    def test_multiple_system_messages_last_wins(self):
+        msgs = [
+            {"role": "system", "content": "first"},
+            {"role": "system", "content": "second"},
+            {"role": "user", "content": "x"},
+        ]
+        instructions, _ = convert_messages(msgs)
+        assert instructions == "second"
+
+    def test_user_message_converted(self):
+        _, items = convert_messages([{"role": "user", "content": "hello"}])
+        assert items[0]["role"] == "user"
+        assert items[0]["content"][0]["type"] == "input_text"
+
+    def test_assistant_text_message(self):
+        _, items = convert_messages([
+            {"role": "assistant", "content": "I'll help"},
+        ])
+        assert items[0]["type"] == "message"
+        assert items[0]["role"] == "assistant"
+        assert items[0]["content"][0]["type"] == "output_text"
+        assert items[0]["content"][0]["text"] == "I'll help"
+
+    def test_assistant_empty_content_skipped(self):
+        _, items = convert_messages([{"role": "assistant", "content": ""}])
+        assert len(items) == 0
+
+    def test_assistant_with_tool_calls(self):
+        _, items = convert_messages([{
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{
+                "id": "call_abc|fc_1",
+                "function": {"name": "get_weather", "arguments": '{"city":"SF"}'},
+            }],
+        }])
+        assert items[0]["type"] == "function_call"
+        assert items[0]["call_id"] == "call_abc"
+        assert items[0]["id"] == "fc_1"
+        assert items[0]["name"] == "get_weather"
+
+    def test_assistant_with_tool_calls_no_id(self):
+        """Fallback IDs when tool_call.id is missing."""
+        _, items = convert_messages([{
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{"function": {"name": "f1", "arguments": "{}"}}],
+        }])
+        assert items[0]["call_id"] == "call_0"
+        assert items[0]["id"].startswith("fc_")
+
+    def test_tool_message(self):
+        _, items = convert_messages([{
+            "role": "tool",
+            "tool_call_id": "call_abc",
+            "content": "result text",
+        }])
+        assert items[0]["type"] == "function_call_output"
+        assert items[0]["call_id"] == "call_abc"
+        assert items[0]["output"] == "result text"
+
+    def test_tool_message_dict_content(self):
+        _, items = convert_messages([{
+            "role": "tool",
+            "tool_call_id": "call_1",
+            "content": {"key": "value"},
+        }])
+        assert items[0]["output"] == '{"key": "value"}'
+
+    def test_non_standard_keys_not_leaked(self):
+        """Extra keys on messages must not appear in converted items."""
+        _, items = convert_messages([{
+            "role": "user",
+            "content": "hi",
+            "extra_field": "should vanish",
+            "_meta": {"path": "/tmp"},
+        }])
+        item = items[0]
+        assert "extra_field" not in str(item)
+        assert "_meta" not in str(item)
+
+    def test_full_conversation_roundtrip(self):
+        """System + user + assistant(tool_call) + tool -> correct structure."""
+        msgs = [
+            {"role": "system", "content": "Be concise."},
+            {"role": "user", "content": "Weather in SF?"},
+            {
+                "role": "assistant", "content": None,
+                "tool_calls": [{
+                    "id": "c1|fc1",
+                    "function": {"name": "get_weather", "arguments": '{"city":"SF"}'},
+                }],
+            },
+            {"role": "tool", "tool_call_id": "c1", "content": '{"temp":72}'},
+        ]
+        instructions, items = convert_messages(msgs)
+        assert instructions == "Be concise."
+        assert len(items) == 3  # user, function_call, function_call_output
+        assert items[0]["role"] == "user"
+        assert items[1]["type"] == "function_call"
+        assert items[2]["type"] == "function_call_output"
+
+
+# ======================================================================
+# converters - convert_tools
+# ======================================================================
+
+
+class TestConvertTools:
+    def test_standard_function_tool(self):
+        tools = [{"type": "function", "function": {
+            "name": "get_weather",
+            "description": "Get weather",
+            "parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
+        }}]
+        result = convert_tools(tools)
+        assert len(result) == 1
+        assert result[0]["type"] == "function"
+        assert result[0]["name"] == "get_weather"
+        assert result[0]["description"] == "Get weather"
+        assert "properties" in result[0]["parameters"]
+
+    def test_tool_without_name_skipped(self):
+        tools = [{"type": "function", "function": {"parameters": {}}}]
+        assert convert_tools(tools) == []
+
+    def test_tool_without_function_wrapper(self):
+        """Direct dict without type=function wrapper."""
+        tools = [{"name": "f1", "description": "d", "parameters": {}}]
+        result = convert_tools(tools)
+        assert result[0]["name"] == "f1"
+
+    def test_missing_optional_fields_default(self):
+        tools = [{"type": "function", "function": {"name": "f"}}]
+        result = convert_tools(tools)
+        assert result[0]["description"] == ""
+        assert result[0]["parameters"] == {}
+
+    def test_multiple_tools(self):
+        tools = [
+            {"type": "function", "function": {"name": "a", "parameters": {}}},
+            {"type": "function", "function": {"name": "b", "parameters": {}}},
+        ]
+        assert len(convert_tools(tools)) == 2
+
+
+# ======================================================================
+# parsing - map_finish_reason
+# ======================================================================
+
+
+class TestMapFinishReason:
+    def test_completed(self):
+        assert map_finish_reason("completed") == "stop"
+
+    def test_incomplete(self):
+        assert map_finish_reason("incomplete") == "length"
+
+    def test_failed(self):
+        assert map_finish_reason("failed") == "error"
+
+    def test_cancelled(self):
+        assert map_finish_reason("cancelled") == "error"
+
+    def test_none_defaults_to_stop(self):
+        assert map_finish_reason(None) == "stop"
+
+    def test_unknown_defaults_to_stop(self):
+        assert map_finish_reason("some_new_status") == "stop"
+
+
+# ======================================================================
+# parsing - parse_response_output
+# ======================================================================
+
+
+class TestParseResponseOutput:
+    def test_text_response(self):
+        resp = {
+            "output": [{"type": "message", "role": "assistant",
+                         "content": [{"type": "output_text", "text": "Hello!"}]}],
+            "status": "completed",
+            "usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
+        }
+        result = parse_response_output(resp)
+        assert result.content == "Hello!"
+        assert result.finish_reason == "stop"
+        assert result.usage == {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
+        assert result.tool_calls == []
+
+    def test_tool_call_response(self):
+        resp = {
+            "output": [{
+                "type": "function_call",
+                "call_id": "call_1", "id": "fc_1",
+                "name": "get_weather",
+                "arguments": '{"city": "SF"}',
+            }],
+            "status": "completed",
+            "usage": {},
+        }
+        result = parse_response_output(resp)
+        assert result.content is None
+        assert len(result.tool_calls) == 1
+        assert result.tool_calls[0].name == "get_weather"
+        assert result.tool_calls[0].arguments == {"city": "SF"}
+        assert result.tool_calls[0].id == "call_1|fc_1"
+
+    def test_malformed_tool_arguments_logged(self):
+        """Malformed JSON arguments should log a warning and fallback."""
+        resp = {
+            "output": [{
+                "type": "function_call",
+                "call_id": "c1", "id": "fc1",
+                "name": "f", "arguments": "{bad json",
+            }],
+            "status": "completed", "usage": {},
+        }
+        with patch("nanobot.providers.openai_responses.parsing.logger") as mock_logger:
+            result = parse_response_output(resp)
+        assert result.tool_calls[0].arguments == {"raw": "{bad json"}
+        mock_logger.warning.assert_called_once()
+        assert "Failed to parse tool call arguments" in str(mock_logger.warning.call_args)
+
+    def test_reasoning_content_extracted(self):
+        resp = {
+            "output": [
+                {"type": "reasoning", "summary": [
+                    {"type": "summary_text", "text": "I think "},
+                    {"type": "summary_text", "text": "therefore I am."},
+                ]},
+                {"type": "message", "role": "assistant",
+                 "content": [{"type": "output_text", "text": "42"}]},
+            ],
+            "status": "completed", "usage": {},
+        }
+        result = parse_response_output(resp)
+        assert result.content == "42"
+        assert result.reasoning_content == "I think therefore I am."
+
+    def test_empty_output(self):
+        resp = {"output": [], "status": "completed", "usage": {}}
+        result = parse_response_output(resp)
+        assert result.content is None
+        assert result.tool_calls == []
+
+    def test_incomplete_status(self):
+        resp = {"output": [], "status": "incomplete", "usage": {}}
+        result = parse_response_output(resp)
+        assert result.finish_reason == "length"
+
+    def test_sdk_model_object(self):
+        """parse_response_output should handle SDK objects with model_dump()."""
+        mock = MagicMock()
+        mock.model_dump.return_value = {
+            "output": [{"type": "message", "role": "assistant",
+                         "content": [{"type": "output_text", "text": "sdk"}]}],
+            "status": "completed",
+            "usage": {"input_tokens": 1, "output_tokens": 2, "total_tokens": 3},
+        }
+        result = parse_response_output(mock)
+        assert result.content == "sdk"
+        assert result.usage["prompt_tokens"] == 1
+
+    def test_usage_maps_responses_api_keys(self):
+        """Responses API uses input_tokens/output_tokens, not prompt_tokens/completion_tokens."""
+        resp = {
+            "output": [],
+            "status": "completed",
+            "usage": {"input_tokens": 100, "output_tokens": 50, "total_tokens": 150},
+        }
+        result = parse_response_output(resp)
+        assert result.usage["prompt_tokens"] == 100
+        assert result.usage["completion_tokens"] == 50
+        assert result.usage["total_tokens"] == 150
+
+
+# ======================================================================
+# parsing - consume_sdk_stream
+# ======================================================================
+
+
+class TestConsumeSdkStream:
+    @pytest.mark.asyncio
+    async def test_text_stream(self):
+        ev1 = MagicMock(type="response.output_text.delta", delta="Hello")
+        ev2 = MagicMock(type="response.output_text.delta", delta=" world")
+        resp_obj = MagicMock(status="completed", usage=None, output=[])
+        ev3 = MagicMock(type="response.completed", response=resp_obj)
+
+        async def stream():
+            for e in [ev1, ev2, ev3]:
+                yield e
+
+        content, tool_calls, finish_reason, usage, reasoning = await consume_sdk_stream(stream())
+        assert content == "Hello world"
+        assert tool_calls == []
+        assert finish_reason == "stop"
+
+    @pytest.mark.asyncio
+    async def test_on_content_delta_called(self):
+        ev1 = MagicMock(type="response.output_text.delta", delta="hi")
+        resp_obj = MagicMock(status="completed", usage=None, output=[])
+        ev2 = MagicMock(type="response.completed", response=resp_obj)
+        deltas = []
+
+        async def cb(text):
+            deltas.append(text)
+
+        async def stream():
+            for e in [ev1, ev2]:
+                yield e
+
+        await consume_sdk_stream(stream(), on_content_delta=cb)
+        assert deltas == ["hi"]
+
+    @pytest.mark.asyncio
+    async def test_tool_call_stream(self):
+        item_added = MagicMock(type="function_call", call_id="c1", id="fc1", arguments="")
+        item_added.name = "get_weather"
+        ev1 = MagicMock(type="response.output_item.added", item=item_added)
+        ev2 = MagicMock(type="response.function_call_arguments.delta", call_id="c1", delta='{"ci')
+        ev3 = MagicMock(type="response.function_call_arguments.done", call_id="c1", arguments='{"city":"SF"}')
+        item_done = MagicMock(type="function_call", call_id="c1", id="fc1", arguments='{"city":"SF"}')
+        item_done.name = "get_weather"
+        ev4 = MagicMock(type="response.output_item.done", item=item_done)
+        resp_obj = MagicMock(status="completed", usage=None, output=[])
+        ev5 = MagicMock(type="response.completed", response=resp_obj)
+
+        async def stream():
+            for e in [ev1, ev2, ev3, ev4, ev5]:
+                yield e
+
+        content, tool_calls, finish_reason, usage, reasoning = await consume_sdk_stream(stream())
+        assert content == ""
+        assert len(tool_calls) == 1
+        assert tool_calls[0].name == "get_weather"
+        assert tool_calls[0].arguments == {"city": "SF"}
+
+    @pytest.mark.asyncio
+    async def test_usage_extracted(self):
+        usage_obj = MagicMock(input_tokens=10, output_tokens=5, total_tokens=15)
+        resp_obj = MagicMock(status="completed", usage=usage_obj, output=[])
+        ev = MagicMock(type="response.completed", response=resp_obj)
+
+        async def stream():
+            yield ev
+
+        _, _, _, usage, _ = await consume_sdk_stream(stream())
+        assert usage == {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
+
+    @pytest.mark.asyncio
+    async def test_reasoning_extracted(self):
+        summary_item = MagicMock(type="summary_text", text="thinking...")
+        reasoning_item = MagicMock(type="reasoning", summary=[summary_item])
+        resp_obj = MagicMock(status="completed", usage=None, output=[reasoning_item])
+        ev = MagicMock(type="response.completed", response=resp_obj)
+
+        async def stream():
+            yield ev
+
+        _, _, _, _, reasoning = await consume_sdk_stream(stream())
+        assert reasoning == "thinking..."
+
+    @pytest.mark.asyncio
+    async def test_error_event_raises(self):
+        ev = MagicMock(type="error", error="rate_limit_exceeded")
+
+        async def stream():
+            yield ev
+
+        with pytest.raises(RuntimeError, match="Response failed.*rate_limit_exceeded"):
+            await consume_sdk_stream(stream())
+
+    @pytest.mark.asyncio
+    async def test_failed_event_raises(self):
+        ev = MagicMock(type="response.failed", error="server_error")
+
+        async def stream():
+            yield ev
+
+        with pytest.raises(RuntimeError, match="Response failed.*server_error"):
+            await consume_sdk_stream(stream())
+
+    @pytest.mark.asyncio
+    async def test_malformed_tool_args_logged(self):
+        """Malformed JSON in streaming tool args should log a warning."""
+        item_added = MagicMock(type="function_call", call_id="c1", id="fc1", arguments="")
+        item_added.name = "f"
+        ev1 = MagicMock(type="response.output_item.added", item=item_added)
+        ev2 = MagicMock(type="response.function_call_arguments.done", call_id="c1", arguments="{bad")
+        item_done = MagicMock(type="function_call", call_id="c1", id="fc1", arguments="{bad")
+        item_done.name = "f"
+        ev3 = MagicMock(type="response.output_item.done", item=item_done)
+        resp_obj = MagicMock(status="completed", usage=None, output=[])
+        ev4 = MagicMock(type="response.completed", response=resp_obj)
+
+        async def stream():
+            for e in [ev1, ev2, ev3, ev4]:
+                yield e
+
+        with patch("nanobot.providers.openai_responses.parsing.logger") as mock_logger:
+            _, tool_calls, _, _, _ = await consume_sdk_stream(stream())
+        assert tool_calls[0].arguments == {"raw": "{bad"}
+        mock_logger.warning.assert_called_once()
+        assert "Failed to parse tool call arguments" in str(mock_logger.warning.call_args)

tests/providers/test_provider_retry.py

@@ -211,3 +211,56 @@ async def test_image_fallback_without_meta_uses_default_placeholder() -> None:
         content = msg.get("content")
         if isinstance(content, list):
             assert any("[image omitted]" in (b.get("text") or "") for b in content)
+
+
+@pytest.mark.asyncio
+async def test_chat_with_retry_uses_retry_after_and_emits_wait_progress(monkeypatch) -> None:
+    provider = ScriptedProvider([
+        LLMResponse(content="429 rate limit, retry after 7s", finish_reason="error"),
+        LLMResponse(content="ok"),
+    ])
+    delays: list[float] = []
+    progress: list[str] = []
+
+    async def _fake_sleep(delay: float) -> None:
+        delays.append(delay)
+
+    async def _progress(msg: str) -> None:
+        progress.append(msg)
+
+    monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
+
+    response = await provider.chat_with_retry(
+        messages=[{"role": "user", "content": "hello"}],
+        on_retry_wait=_progress,
+    )
+
+    assert response.content == "ok"
+    assert delays == [7.0]
+    assert progress and "7s" in progress[0]
+
+
+@pytest.mark.asyncio
+async def test_persistent_retry_aborts_after_ten_identical_transient_errors(monkeypatch) -> None:
+    provider = ScriptedProvider([
+        *[LLMResponse(content="429 rate limit", finish_reason="error") for _ in range(10)],
+        LLMResponse(content="ok"),
+    ])
+    delays: list[float] = []
+
+    async def _fake_sleep(delay: float) -> None:
+        delays.append(delay)
+
+    monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
+
+    response = await provider.chat_with_retry(
+        messages=[{"role": "user", "content": "hello"}],
+        retry_mode="persistent",
+    )
+
+    assert response.finish_reason == "error"
+    assert response.content == "429 rate limit"
+    assert provider.calls == 10
+    assert delays == [1, 2, 4, 4, 4, 4, 4, 4, 4]
+
+

tests/test_openai_api.py

@@ -347,6 +347,8 @@ async def test_empty_response_retry_then_success(aiohttp_client) -> None:
 @pytest.mark.skipif(not HAS_AIOHTTP, reason="aiohttp not installed")
 @pytest.mark.asyncio
 async def test_empty_response_falls_back(aiohttp_client) -> None:
+    from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE
+
     call_count = 0
 
     async def always_empty(content, session_key="", channel="", chat_id=""):
@@ -367,5 +369,5 @@ async def test_empty_response_falls_back(aiohttp_client) -> None:
     )
     assert resp.status == 200
     body = await resp.json()
-    assert body["choices"][0]["message"]["content"] == "I've completed processing but have no response to give."
+    assert body["choices"][0]["message"]["content"] == EMPTY_FINAL_RESPONSE_MESSAGE
     assert call_count == 2

tests/tools/test_mcp_tool.py

@@ -196,7 +196,7 @@ async def test_execute_re_raises_external_cancellation() -> None:
 
     wrapper = _make_wrapper(SimpleNamespace(call_tool=call_tool), timeout=10)
     task = asyncio.create_task(wrapper.execute())
-    await started.wait()
+    await asyncio.wait_for(started.wait(), timeout=1.0)
 
     task.cancel()