feat(provider): preserve reasoning_details across turns and configure MiniMax reasoning_split via registry

nanobot/agent/context.py

@@ -151,6 +151,7 @@ def add_assistant_message(
         tool_calls: list[dict[str, Any]] | None = None,
         reasoning_content: str | None = None,
         thinking_blocks: list[dict] | None = None,
+        reasoning_details: Any | None = None,
     ) -> list[dict[str, Any]]:
         """Add an assistant message to the message list."""
         msg: dict[str, Any] = {"role": "assistant", "content": content}
@@ -160,5 +161,7 @@ def add_assistant_message(
             msg["reasoning_content"] = reasoning_content
         if thinking_blocks:
             msg["thinking_blocks"] = thinking_blocks
+        if reasoning_details is not None:
+            msg["reasoning_details"] = reasoning_details
         messages.append(msg)
         return messages

nanobot/agent/loop.py

@@ -222,6 +222,7 @@ async def _run_agent_loop(
                     messages, response.content, tool_call_dicts,
                     reasoning_content=response.reasoning_content,
                     thinking_blocks=response.thinking_blocks,
+                    reasoning_details=response.reasoning_details,
                 )
 
                 for tool_call in response.tool_calls:
@@ -243,6 +244,7 @@ async def _run_agent_loop(
                 messages = self.context.add_assistant_message(
                     messages, clean, reasoning_content=response.reasoning_content,
                     thinking_blocks=response.thinking_blocks,
+                    reasoning_details=response.reasoning_details,
                 )
                 final_content = clean
                 break

nanobot/agent/subagent.py

@@ -149,6 +149,18 @@ async def _run_subagent(
                         "role": "assistant",
                         "content": response.content or "",
                         "tool_calls": tool_call_dicts,
+                        **(
+                            {"reasoning_content": response.reasoning_content}
+                            if response.reasoning_content is not None else {}
+                        ),
+                        **(
+                            {"thinking_blocks": response.thinking_blocks}
+                            if response.thinking_blocks else {}
+                        ),
+                        **(
+                            {"reasoning_details": response.reasoning_details}
+                            if response.reasoning_details is not None else {}
+                        ),
                     })
 
                     # Execute tools

nanobot/providers/base.py

@@ -22,6 +22,7 @@ class LLMResponse:
     usage: dict[str, int] = field(default_factory=dict)
     reasoning_content: str | None = None  # Kimi, DeepSeek-R1 etc.
     thinking_blocks: list[dict] | None = None  # Anthropic extended thinking
+    reasoning_details: Any | None = None  # MiniMax interleaved CoT metadata
 
     @property
     def has_tool_calls(self) -> bool:

nanobot/providers/litellm_provider.py

@@ -13,7 +13,9 @@
 from nanobot.providers.registry import find_by_model, find_gateway
 
 # Standard chat-completion message keys.
-_ALLOWED_MSG_KEYS = frozenset({"role", "content", "tool_calls", "tool_call_id", "name", "reasoning_content"})
+_ALLOWED_MSG_KEYS = frozenset(
+    {"role", "content", "tool_calls", "tool_call_id", "name", "reasoning_content", "reasoning_details"}
+)
 _ANTHROPIC_EXTRA_KEYS = frozenset({"thinking_blocks"})
 _ALNUM = string.ascii_letters + string.digits
 
@@ -280,6 +282,11 @@ def _parse_response(self, response: Any) -> LLMResponse:
 
         reasoning_content = getattr(message, "reasoning_content", None) or None
         thinking_blocks = getattr(message, "thinking_blocks", None) or None
+        reasoning_details = getattr(message, "reasoning_details", None) or None
+        if reasoning_details is None:
+            psf = getattr(message, "provider_specific_fields", None) or {}
+            if isinstance(psf, dict):
+                reasoning_details = psf.get("reasoning_details")
 
         return LLMResponse(
             content=message.content,
@@ -288,6 +295,7 @@ def _parse_response(self, response: Any) -> LLMResponse:
             usage=usage,
             reasoning_content=reasoning_content,
             thinking_blocks=thinking_blocks,
+            reasoning_details=reasoning_details,
         )
 
     def get_default_model(self) -> str:

nanobot/providers/registry.py

@@ -352,7 +352,11 @@ def label(self) -> str:
         detect_by_base_keyword="",
         default_api_base="https://api.minimax.io/v1",
         strip_model_prefix=False,
-        model_overrides=(),
+        model_overrides=(
+            ("minimax-m2.5", {"reasoning_split": True}),
+            ("minimax-m2.1", {"reasoning_split": True}),
+            ("minimax-m2", {"reasoning_split": True}),
+        ),
     ),
 
     # === Local deployment (matched by config key, NOT by api_base) =========