fix: preserve provider-specific tool call metadata for Gemini

nanobot/agent/loop.py

@@ -208,14 +208,7 @@ class AgentLoop:
                     await on_progress(self._tool_hint(response.tool_calls), tool_hint=True)
 
                 tool_call_dicts = [
-                    {
-                        "id": tc.id,
-                        "type": "function",
-                        "function": {
-                            "name": tc.name,
-                            "arguments": json.dumps(tc.arguments, ensure_ascii=False)
-                        }
-                    }
+                    self._build_tool_call_message(tc)
                     for tc in response.tool_calls
                 ]
                 messages = self.context.add_assistant_message(
@@ -256,6 +249,22 @@ class AgentLoop:
 
         return final_content, tools_used, messages
 
+    @staticmethod
+    def _build_tool_call_message(tc: Any) -> dict[str, Any]:
+        tool_call = {
+                        "id": tc.id,
+                        "type": "function",
+                        "function": {
+                            "name": tc.name,
+                            "arguments": json.dumps(tc.arguments, ensure_ascii=False)
+                        }
+                    }
+        if getattr(tc, "provider_specific_fields", None):
+            tool_call["provider_specific_fields"] = tc.provider_specific_fields
+        if getattr(tc, "function_provider_specific_fields", None):
+            tool_call["function"]["provider_specific_fields"] = tc.function_provider_specific_fields
+        return tool_call
+
     async def run(self) -> None:
         """Run the agent loop, dispatching messages as tasks to stay responsive to /stop."""
         self._running = True

nanobot/agent/subagent.py

@@ -135,14 +135,7 @@ class SubagentManager:
                 if response.has_tool_calls:
                     # Add assistant message with tool calls
                     tool_call_dicts = [
-                        {
-                            "id": tc.id,
-                            "type": "function",
-                            "function": {
-                                "name": tc.name,
-                                "arguments": json.dumps(tc.arguments, ensure_ascii=False),
-                            },
-                        }
+                        self._build_tool_call_message(tc)
                         for tc in response.tool_calls
                     ]
                     messages.append({
@@ -230,6 +223,22 @@ Stay focused on the assigned task. Your final response will be reported back to
             parts.append(f"## Skills\n\nRead SKILL.md with read_file to use a skill.\n\n{skills_summary}")
 
         return "\n\n".join(parts)
+
+    @staticmethod
+    def _build_tool_call_message(tc: Any) -> dict[str, Any]:
+        tool_call = {
+            "id": tc.id,
+            "type": "function",
+            "function": {
+                "name": tc.name,
+                "arguments": json.dumps(tc.arguments, ensure_ascii=False),
+            },
+        }
+        if getattr(tc, "provider_specific_fields", None):
+            tool_call["provider_specific_fields"] = tc.provider_specific_fields
+        if getattr(tc, "function_provider_specific_fields", None):
+            tool_call["function"]["provider_specific_fields"] = tc.function_provider_specific_fields
+        return tool_call
     
     async def cancel_by_session(self, session_key: str) -> int:
         """Cancel all subagents for the given session. Returns count cancelled."""

nanobot/providers/base.py

@@ -14,6 +14,8 @@ class ToolCallRequest:
     id: str
     name: str
     arguments: dict[str, Any]
+    provider_specific_fields: dict[str, Any] | None = None
+    function_provider_specific_fields: dict[str, Any] | None = None
 
 
 @dataclass

nanobot/providers/litellm_provider.py

@@ -309,10 +309,17 @@ class LiteLLMProvider(LLMProvider):
             if isinstance(args, str):
                 args = json_repair.loads(args)
 
+            provider_specific_fields = getattr(tc, "provider_specific_fields", None) or None
+            function_provider_specific_fields = (
+                getattr(tc.function, "provider_specific_fields", None) or None
+            )
+
             tool_calls.append(ToolCallRequest(
                 id=_short_tool_id(),
                 name=tc.function.name,
                 arguments=args,
+                provider_specific_fields=provider_specific_fields,
+                function_provider_specific_fields=function_provider_specific_fields,
             ))
 
         usage = {}

tests/test_gemini_thought_signature.py

@@ -0,0 +1,54 @@
+from types import SimpleNamespace
+
+from nanobot.agent.loop import AgentLoop
+from nanobot.providers.base import ToolCallRequest
+from nanobot.providers.litellm_provider import LiteLLMProvider
+
+
+def test_litellm_parse_response_preserves_tool_call_provider_fields() -> None:
+    provider = LiteLLMProvider(default_model="gemini/gemini-3-flash")
+
+    response = SimpleNamespace(
+        choices=[
+            SimpleNamespace(
+                finish_reason="tool_calls",
+                message=SimpleNamespace(
+                    content=None,
+                    tool_calls=[
+                        SimpleNamespace(
+                            id="call_123",
+                            function=SimpleNamespace(
+                                name="read_file",
+                                arguments='{"path":"todo.md"}',
+                                provider_specific_fields={"inner": "value"},
+                            ),
+                            provider_specific_fields={"thought_signature": "signed-token"},
+                        )
+                    ],
+                ),
+            )
+        ],
+        usage=None,
+    )
+
+    parsed = provider._parse_response(response)
+
+    assert len(parsed.tool_calls) == 1
+    assert parsed.tool_calls[0].provider_specific_fields == {"thought_signature": "signed-token"}
+    assert parsed.tool_calls[0].function_provider_specific_fields == {"inner": "value"}
+
+
+def test_agent_loop_replays_tool_call_provider_fields() -> None:
+    tool_call = ToolCallRequest(
+        id="abc123xyz",
+        name="read_file",
+        arguments={"path": "todo.md"},
+        provider_specific_fields={"thought_signature": "signed-token"},
+        function_provider_specific_fields={"inner": "value"},
+    )
+
+    message = AgentLoop._build_tool_call_message(tool_call)
+
+    assert message["provider_specific_fields"] == {"thought_signature": "signed-token"}
+    assert message["function"]["provider_specific_fields"] == {"inner": "value"}
+    assert message["function"]["arguments"] == '{"path": "todo.md"}'