fix: preserve reasoning_content in conversation history for thinking models

README.md

@@ -16,7 +16,7 @@
 
 ⚡️ Delivers core agent functionality in just **~4,000** lines of code — **99% smaller** than Clawdbot's 430k+ lines.
 
-📏 Real-time line count: **3,429 lines** (run `bash core_agent_lines.sh` to verify anytime)
+📏 Real-time line count: **3,437 lines** (run `bash core_agent_lines.sh` to verify anytime)
 
 ## 📢 News
 

nanobot/agent/context.py

@@ -207,7 +207,8 @@ When remembering something, write to {workspace_path}/memory/MEMORY.md"""
         self,
         messages: list[dict[str, Any]],
         content: str | None,
-        tool_calls: list[dict[str, Any]] | None = None
+        tool_calls: list[dict[str, Any]] | None = None,
+        reasoning_content: str | None = None,
     ) -> list[dict[str, Any]]:
         """
         Add an assistant message to the message list.
@@ -216,6 +217,7 @@ When remembering something, write to {workspace_path}/memory/MEMORY.md"""
             messages: Current message list.
             content: Message content.
             tool_calls: Optional tool calls.
+            reasoning_content: Thinking output (Kimi, DeepSeek-R1, etc.).
         
         Returns:
             Updated message list.
@@ -225,5 +227,9 @@ When remembering something, write to {workspace_path}/memory/MEMORY.md"""
         if tool_calls:
             msg["tool_calls"] = tool_calls
         
+        # Thinking models reject history without this
+        if reasoning_content:
+            msg["reasoning_content"] = reasoning_content
+        
         messages.append(msg)
         return messages

nanobot/agent/loop.py

@@ -213,7 +213,8 @@ class AgentLoop:
                     for tc in response.tool_calls
                 ]
                 messages = self.context.add_assistant_message(
-                    messages, response.content, tool_call_dicts
+                    messages, response.content, tool_call_dicts,
+                    reasoning_content=response.reasoning_content,
                 )
                 
                 # Execute tools
@@ -317,7 +318,8 @@ class AgentLoop:
                     for tc in response.tool_calls
                 ]
                 messages = self.context.add_assistant_message(
-                    messages, response.content, tool_call_dicts
+                    messages, response.content, tool_call_dicts,
+                    reasoning_content=response.reasoning_content,
                 )
                 
                 for tool_call in response.tool_calls:

nanobot/providers/base.py

@@ -20,6 +20,7 @@ class LLMResponse:
     tool_calls: list[ToolCallRequest] = field(default_factory=list)
     finish_reason: str = "stop"
     usage: dict[str, int] = field(default_factory=dict)
+    reasoning_content: str | None = None  # Kimi, DeepSeek-R1 etc.
     
     @property
     def has_tool_calls(self) -> bool:

nanobot/providers/litellm_provider.py

@@ -183,11 +183,14 @@ class LiteLLMProvider(LLMProvider):
                 "total_tokens": response.usage.total_tokens,
             }
         
+        reasoning_content = getattr(message, "reasoning_content", None)
+        
         return LLMResponse(
             content=message.content,
             tool_calls=tool_calls,
             finish_reason=choice.finish_reason or "stop",
             usage=usage,
+            reasoning_content=reasoning_content,
         )
     
     def get_default_model(self) -> str: