nanobot/providers/anthropic_oauth.py

@@ -68,21 +68,87 @@ class AnthropicOAuthProvider(LLMProvider):
         self,
         messages: list[dict[str, Any]]
     ) -> tuple[str | None, list[dict[str, Any]]]:
-        """Prepare messages, extracting system prompt and adding Claude Code identity.
+        """Prepare messages: extract system prompt and convert OpenAI format to Anthropic.
 
-        Returns (system_prompt, messages_without_system)
+        The agent loop produces messages in OpenAI format:
+          - assistant msgs with tool_calls [{type:"function", function:{name, arguments}}]
+          - tool role msgs with tool_call_id, name, content
+
+        Anthropic API expects:
+          - assistant msgs with content blocks [{type:"tool_use", id, name, input}]
+          - user msgs with content blocks [{type:"tool_result", tool_use_id, content}]
+
+        Returns (system_prompt, anthropic_messages)
         """
         system_parts = [get_claude_code_system_prefix()]
-        filtered_messages = []
+        converted: list[dict[str, Any]] = []
 
         for msg in messages:
-            if msg.get("role") == "system":
+            role = msg.get("role")
+
+            if role == "system":
                 system_parts.append(msg.get("content", ""))
-            else:
+                continue
-                filtered_messages.append(msg)
+
+            if role == "assistant" and msg.get("tool_calls"):
+                # Convert OpenAI tool_calls to Anthropic content blocks
+                content_blocks: list[dict[str, Any]] = []
+                text = msg.get("content")
+                if text:
+                    content_blocks.append({"type": "text", "text": text})
+                for tc in msg["tool_calls"]:
+                    func = tc.get("function", {})
+                    args = func.get("arguments", "{}")
+                    if isinstance(args, str):
+                        try:
+                            args = json.loads(args)
+                        except (json.JSONDecodeError, TypeError):
+                            args = {}
+                    content_blocks.append({
+                        "type": "tool_use",
+                        "id": tc.get("id", ""),
+                        "name": func.get("name", ""),
+                        "input": args,
+                    })
+                converted.append({"role": "assistant", "content": content_blocks})
+                continue
+
+            if role == "tool":
+                # Convert tool result to Anthropic user message with tool_result block
+                tool_result_block = {
+                    "type": "tool_result",
+                    "tool_use_id": msg.get("tool_call_id", ""),
+                    "content": msg.get("content", ""),
+                }
+                # Merge into previous user message if it already has tool_result blocks
+                if converted and converted[-1].get("role") == "user":
+                    prev_content = converted[-1].get("content")
+                    if isinstance(prev_content, list):
+                        prev_content.append(tool_result_block)
+                        continue
+                converted.append({"role": "user", "content": [tool_result_block]})
+                continue
+
+            if role == "user":
+                content = msg.get("content", "")
+                # Merge text into previous user message if it has tool_result blocks
+                # (handles the "Reflect on the results" interleaved message)
+                if converted and converted[-1].get("role") == "user":
+                    prev_content = converted[-1].get("content")
+                    if isinstance(prev_content, list):
+                        if isinstance(content, str):
+                            prev_content.append({"type": "text", "text": content})
+                        elif isinstance(content, list):
+                            prev_content.extend(content)
+                        continue
+                converted.append({"role": role, "content": content})
+                continue
+
+            # Pass through other messages (assistant without tool_calls, etc.)
+            converted.append(msg)
 
         system_prompt = "\n\n".join(system_parts)
-        return system_prompt, filtered_messages
+        return system_prompt, converted
 
     def _convert_tools_to_anthropic(
         self,