refactor(llm): share transient retry across agent paths

nanobot/agent/loop.py

@@ -159,33 +159,6 @@ class AgentLoop:
                 if hasattr(tool, "set_context"):
                     tool.set_context(channel, chat_id, *([message_id] if name == "message" else []))
 
-    _RETRY_DELAYS = (1, 2, 4)  # seconds — exponential backoff for transient LLM errors
-
-    async def _chat_with_retry(self, **kwargs: Any) -> Any:
-        """Call provider.chat() with retry on transient errors (429, 5xx, network)."""
-        from nanobot.providers.base import LLMResponse
-
-        last_response: LLMResponse | None = None
-        for attempt, delay in enumerate(self._RETRY_DELAYS):
-            response = await self.provider.chat(**kwargs)
-            if response.finish_reason != "error":
-                return response
-            # Check if the error looks transient (rate limit, server error, network)
-            err = (response.content or "").lower()
-            is_transient = any(kw in err for kw in (
-                "429", "rate limit", "500", "502", "503", "504",
-                "overloaded", "timeout", "connection", "server error",
-            ))
-            if not is_transient:
-                return response  # permanent error (400, 401, etc.) — don't retry
-            last_response = response
-            logger.warning("LLM transient error (attempt {}/{}), retrying in {}s: {}",
-                           attempt + 1, len(self._RETRY_DELAYS), delay, err[:120])
-            await asyncio.sleep(delay)
-        # All retries exhausted — make one final attempt
-        response = await self.provider.chat(**kwargs)
-        return response if response.finish_reason != "error" else (last_response or response)
-
     @staticmethod
     def _strip_think(text: str | None) -> str | None:
         """Remove <think>…</think> blocks that some models embed in content."""
@@ -218,7 +191,7 @@ class AgentLoop:
         while iteration < self.max_iterations:
             iteration += 1
 
-            response = await self._chat_with_retry(
+            response = await self.provider.chat_with_retry(
                 messages=messages,
                 tools=self.tools.get_definitions(),
                 model=self.model,