[fix] support chat interface

runtime/node/agent/providers/openai_provider.py

@@ -4,7 +4,7 @@ import base64
 
 import binascii
 import os
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 from urllib.parse import unquote_to_bytes
 
 import openai
@@ -46,7 +46,6 @@ class OpenAIProvider(ModelProvider):
             )
         else:
             return OpenAI(
-                base_url="https://api.openai.com/v1",
                 api_key=self.api_key,
             )
 
@@ -60,26 +59,43 @@ class OpenAIProvider(ModelProvider):
     ) -> ModelResponse:
         """
         Call the OpenAI model with the given messages and parameters.
-        
-        Args:
-            client: OpenAI client instance
-            conversation: List of messages in the conversation
-            tool_specs: Optional tool specifications
-            **kwargs: Additional parameters for the model call
-            
-        Returns:
-            ModelResponse containing content and potentially tool calls
         """
+        # 1. Determine if we should use Chat Completions directly
+        is_chat = self._is_chat_completions_mode(client)
+        
+        if is_chat:
+            request_payload = self._build_chat_payload(timeline, tool_specs, kwargs)
+            response = client.chat.completions.create(**request_payload)
+            self._track_token_usage(response)
+            self._append_chat_response_output(timeline, response)
+            message = self._deserialize_chat_response(response)
+            return ModelResponse(message=message, raw_response=response)
+
+        # 2. Try Responses API with fallback
         request_payload = self._build_request_payload(timeline, tool_specs, kwargs)
-        # print(request_payload)
+        try:
+            response = client.responses.create(**request_payload)
+            self._track_token_usage(response)
+            self._append_response_output(timeline, response)
+            message = self._deserialize_response(response)
+            return ModelResponse(message=message, raw_response=response)
+        except Exception as e:
+            new_request_payload = self._build_chat_payload(timeline, tool_specs, kwargs)
+            response = client.chat.completions.create(**new_request_payload)
+            self._track_token_usage(response)
+            self._append_chat_response_output(timeline, response)
+            message = self._deserialize_chat_response(response)
+            return ModelResponse(message=message, raw_response=response)
 
-        response = client.responses.create(**request_payload)
-        # print(response)
-        self._track_token_usage(response)
-        self._append_response_output(timeline, response)
-
-        message = self._deserialize_response(response)
-        return ModelResponse(message=message, raw_response=response)
+    def _is_chat_completions_mode(self, client: Any) -> bool:
+        """Determine if we should use standard chat completions instead of responses API."""
+        protocol = self.params.get("protocol")
+        if protocol == "chat":
+            return True
+        if protocol == "responses":
+            return False
+        # Default to Responses API only if it exists on the client
+        return not hasattr(client, "responses")
 
     def extract_token_usage(self, response: Any) -> TokenUsage:
         """
@@ -208,6 +224,185 @@ class OpenAIProvider(ModelProvider):
         payload.update(params)
         return payload
 
+    def _build_chat_payload(
+        self,
+        timeline: List[Any],
+        tool_specs: Optional[List[ToolSpec]],
+        raw_params: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """Construct standard Chat Completions API payload."""
+        params = dict(raw_params)
+        max_output_tokens = params.pop("max_output_tokens", None)
+        max_tokens = params.pop("max_tokens", None)
+        if max_tokens is None and max_output_tokens is not None:
+            max_tokens = max_output_tokens
+
+        messages: List[Any] = []
+        for item in timeline:
+            serialized = self._serialize_timeline_item_for_chat(item)
+            if serialized is not None:
+                messages.append(serialized)
+
+        if not messages:
+            messages = [{"role": "user", "content": ""}]
+
+        payload: Dict[str, Any] = {
+            "model": self.model_name,
+            "messages": messages,
+            "temperature": params.pop("temperature", 0.7),
+        }
+        if max_tokens is not None:
+            payload["max_tokens"] = max_tokens
+        elif self.params.get("max_tokens"):
+             payload["max_tokens"] = self.params["max_tokens"]
+
+        user_tools = params.pop("tools", None)
+        merged_tools: List[Any] = []
+        if isinstance(user_tools, list):
+            merged_tools.extend(user_tools)
+
+        if tool_specs:
+            for spec in tool_specs:
+                merged_tools.append({
+                    "type": "function",
+                    "function": {
+                        "name": spec.name,
+                        "description": spec.description,
+                        "parameters": spec.parameters or {"type": "object", "properties": {}},
+                    }
+                })
+
+        if merged_tools:
+            payload["tools"] = merged_tools
+
+        tool_choice = params.pop("tool_choice", None)
+        if tool_choice is not None:
+            payload["tool_choice"] = tool_choice
+        elif tool_specs:
+            payload.setdefault("tool_choice", "auto")
+
+        payload.update(params)
+        return payload
+
+    def _serialize_timeline_item_for_chat(self, item: Any) -> Optional[Any]:
+        if isinstance(item, Message):
+            return self._serialize_message_for_chat(item)
+        if isinstance(item, FunctionCallOutputEvent):
+            return self._serialize_function_call_output_event_for_chat(item)
+        if isinstance(item, dict):
+             # basic conversion if it looks like a Responses output
+             role = item.get("role")
+             content = item.get("content")
+             tool_calls = item.get("tool_calls")
+             if role and (content or tool_calls):
+                  return {
+                       "role": role,
+                       "content": self._transform_blocks_for_chat(content) if isinstance(content, list) else content,
+                       "tool_calls": tool_calls
+                  }
+        return None
+
+    def _serialize_message_for_chat(self, message: Message) -> Dict[str, Any]:
+        """Convert internal Message to standard Chat Completions schema."""
+        role_value = message.role.value
+        blocks = message.blocks()
+        if not blocks:
+            content = message.text_content()
+        else:
+            content = self._transform_blocks_for_chat(self._serialize_blocks(blocks, message.role))
+
+        payload: Dict[str, Any] = {
+            "role": role_value,
+            "content": content,
+        }
+        if message.name:
+            payload["name"] = message.name
+        if message.tool_call_id:
+            payload["tool_call_id"] = message.tool_call_id
+        if message.tool_calls:
+            payload["tool_calls"] = [tc.to_openai_dict() for tc in message.tool_calls]
+        return payload
+
+    def _serialize_function_call_output_event_for_chat(self, event: FunctionCallOutputEvent) -> Dict[str, Any]:
+        """Convert tool result to standard Chat Completions schema."""
+        text = event.output_text or ""
+        if event.output_blocks:
+             # simple concatenation for tool output in chat mode
+             text = "\n".join(b.describe() for b in event.output_blocks)
+        
+        return {
+            "role": "tool",
+            "tool_call_id": event.call_id or "tool_call",
+            "content": text,
+        }
+
+    def _transform_blocks_for_chat(self, blocks: List[Dict[str, Any]]) -> Union[str, List[Dict[str, Any]]]:
+        """Convert Responses block types to Chat block types (e.g., input_text -> text)."""
+        transformed: List[Dict[str, Any]] = []
+        for block in blocks:
+            b_type = block.get("type", "")
+            if b_type in ("input_text", "output_text"):
+                transformed.append({"type": "text", "text": block.get("text", "")})
+            elif b_type in ("input_image", "output_image"):
+                transformed.append({"type": "image_url", "image_url": {"url": block.get("image_url", "")}})
+            else:
+                # Keep as is or drop if complex
+                transformed.append(block)
+        
+        # If only one text block, return as string for better compatibility
+        if len(transformed) == 1 and transformed[0]["type"] == "text":
+            return transformed[0]["text"]
+        return transformed
+
+    def _deserialize_chat_response(self, response: Any) -> Message:
+        """Convert Chat Completions output to internal Message."""
+        choices = self._get_attr(response, "choices") or []
+        if not choices:
+            return Message(role=MessageRole.ASSISTANT, content="")
+            
+        choice = choices[0]
+        msg = self._get_attr(choice, "message")
+        
+        tool_calls: List[ToolCallPayload] = []
+        tc_data = self._get_attr(msg, "tool_calls")
+        if tc_data:
+            for tc in tc_data:
+                f_data = self._get_attr(tc, "function") or {}
+                tool_calls.append(ToolCallPayload(
+                    id=self._get_attr(tc, "id"),
+                    function_name=self._get_attr(f_data, "name"),
+                    arguments=self._get_attr(f_data, "arguments"),
+                    type="function"
+                ))
+        
+        return Message(
+            role=MessageRole.ASSISTANT,
+            content=self._get_attr(msg, "content") or "",
+            tool_calls=tool_calls
+        )
+
+    def _append_chat_response_output(self, timeline: List[Any], response: Any) -> None:
+        """Add chat response to timeline, preserving tool_calls (Chat API compatible)."""
+        msg = response.choices[0].message
+        assistant_msg = {
+            "role": "assistant",
+            "content": msg.content or ""
+        }
+
+        if getattr(msg, "tool_calls", None):
+            assistant_msg["tool_calls"] = []
+            for tc in msg.tool_calls:
+                assistant_msg["tool_calls"].append({
+                    "id": tc.id,
+                    "type": "function",
+                    "function": {
+                        "name": tc.function.name,
+                        "arguments": tc.function.arguments,
+                    },
+                })
+
+        timeline.append(assistant_msg)
+
     def _serialize_timeline_item(self, item: Any) -> Optional[Any]:
         if isinstance(item, Message):
             return self._serialize_message_for_responses(item)

runtime/node/executor/agent_executor.py

@@ -283,7 +283,6 @@ class AgentNodeExecutor(NodeExecutor):
         self._record_model_call(node, last_input, None, CallStage.BEFORE)
         response = self._execute_with_retry(node, retry_policy, _call_provider)
         self.log_manager.debug(response.str_raw_response())
-        # print(timeline)
         self._record_model_call(node, last_input, response, CallStage.AFTER)
         return response