refactor: centralize tool call serialization in ToolCallRequest

nanobot/agent/loop.py

@@ -208,7 +208,7 @@ class AgentLoop:
                     await on_progress(self._tool_hint(response.tool_calls), tool_hint=True)
 
                 tool_call_dicts = [
-                    self._build_tool_call_message(tc)
+                    tc.to_openai_tool_call()
                     for tc in response.tool_calls
                 ]
                 messages = self.context.add_assistant_message(
@@ -249,22 +249,6 @@ 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

@@ -135,7 +135,7 @@ class SubagentManager:
                 if response.has_tool_calls:
                     # Add assistant message with tool calls
                     tool_call_dicts = [
-                        self._build_tool_call_message(tc)
+                        tc.to_openai_tool_call()
                         for tc in response.tool_calls
                     ]
                     messages.append({
@@ -224,22 +224,6 @@ Stay focused on the assigned task. Your final response will be reported back to
 
         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."""
         tasks = [self._running_tasks[tid] for tid in self._session_tasks.get(session_key, [])

nanobot/providers/base.py

@@ -1,6 +1,7 @@
 """Base LLM provider interface."""
 
 import asyncio
+import json
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import Any
@@ -17,6 +18,22 @@ class ToolCallRequest:
     provider_specific_fields: dict[str, Any] | None = None
     function_provider_specific_fields: dict[str, Any] | None = None
 
+    def to_openai_tool_call(self) -> dict[str, Any]:
+        """Serialize to an OpenAI-style tool_call payload."""
+        tool_call = {
+            "id": self.id,
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "arguments": json.dumps(self.arguments, ensure_ascii=False),
+            },
+        }
+        if self.provider_specific_fields:
+            tool_call["provider_specific_fields"] = self.provider_specific_fields
+        if self.function_provider_specific_fields:
+            tool_call["function"]["provider_specific_fields"] = self.function_provider_specific_fields
+        return tool_call
+
 
 @dataclass
 class LLMResponse:

tests/test_gemini_thought_signature.py

@@ -1,6 +1,5 @@
 from types import SimpleNamespace
 
-from nanobot.agent.loop import AgentLoop
 from nanobot.providers.base import ToolCallRequest
 from nanobot.providers.litellm_provider import LiteLLMProvider
 
@@ -38,7 +37,7 @@ def test_litellm_parse_response_preserves_tool_call_provider_fields() -> None:
     assert parsed.tool_calls[0].function_provider_specific_fields == {"inner": "value"}
 
 
-def test_agent_loop_replays_tool_call_provider_fields() -> None:
+def test_tool_call_request_serializes_provider_fields() -> None:
     tool_call = ToolCallRequest(
         id="abc123xyz",
         name="read_file",
@@ -47,7 +46,7 @@ def test_agent_loop_replays_tool_call_provider_fields() -> None:
         function_provider_specific_fields={"inner": "value"},
     )
 
-    message = AgentLoop._build_tool_call_message(tool_call)
+    message = tool_call.to_openai_tool_call()
 
     assert message["provider_specific_fields"] == {"thought_signature": "signed-token"}
     assert message["function"]["provider_specific_fields"] == {"inner": "value"}