fix tool calls in non-native function calling messages

openhands/core/message.py

@@ -56,6 +56,7 @@ class Message(BaseModel):
     cache_enabled: bool = False
     vision_enabled: bool = False
     # function calling
+    function_calling_enabled: bool = False
     # - tool calls (from LLM)
     tool_calls: list[ChatCompletionMessageToolCall] | None = None
     # - tool execution result (to LLM)
@@ -72,22 +73,21 @@ def serialize_model(self) -> dict:
         # - into a single string: for providers that don't support list of content items (e.g. no vision, no tool calls)
         # - into a list of content items: the new APIs of providers with vision/prompt caching/tool calls
         # NOTE: remove this when litellm or providers support the new API
-        if (
-            self.cache_enabled
-            or self.vision_enabled
-            or self.tool_call_id is not None
-            or self.tool_calls is not None
-        ):
+        if self.cache_enabled or self.vision_enabled or self.function_calling_enabled:
             return self._list_serializer()
         return self._string_serializer()
 
-    def _string_serializer(self):
+    def _string_serializer(self) -> dict:
+        # convert content to a single string
         content = '\n'.join(
             item.text for item in self.content if isinstance(item, TextContent)
         )
-        return {'content': content, 'role': self.role}
+        message_dict: dict = {'content': content, 'role': self.role}
+
+        # add tool call keys if we have a tool call or response
+        return self._add_tool_call_keys(message_dict)
 
-    def _list_serializer(self):
+    def _list_serializer(self) -> dict:
         content: list[dict] = []
         role_tool_with_prompt_caching = False
         for item in self.content:
@@ -102,24 +102,42 @@ def _list_serializer(self):
             elif isinstance(item, ImageContent) and self.vision_enabled:
                 content.extend(d)
 
-        ret: dict = {'content': content, 'role': self.role}
+        message_dict: dict = {'content': content, 'role': self.role}
+
         # pop content if it's empty
         if not content or (
             len(content) == 1
             and content[0]['type'] == 'text'
             and content[0]['text'] == ''
         ):
-            ret.pop('content')
+            message_dict.pop('content')
+
+        # some providers, like HF and Groq/llama, don't support a list here, but a single string
+        # if not self.function_calling_enabled:
+        #    content_str = '\n'.join([item['text'] for item in content])
+        #    message_dict['content'] = [{'type': 'text', 'text': content_str}]
 
         if role_tool_with_prompt_caching:
-            ret['cache_control'] = {'type': 'ephemeral'}
+            message_dict['cache_control'] = {'type': 'ephemeral'}
+
+        # add tool call keys if we have a tool call or response
+        return self._add_tool_call_keys(message_dict)
 
+    def _add_tool_call_keys(self, message_dict: dict) -> dict:
+        """Add tool call keys if we have a tool call or response.
+
+        NOTE: this is necessary for both native and non-native tool calling"""
+
+        # an assistant message calling a tool
+        if self.tool_calls is not None:
+            message_dict['tool_calls'] = self.tool_calls
+
+        # an observation message with tool response
         if self.tool_call_id is not None:
             assert (
                 self.name is not None
             ), 'name is required when tool_call_id is not None'
-            ret['tool_call_id'] = self.tool_call_id
-            ret['name'] = self.name
-        if self.tool_calls:
-            ret['tool_calls'] = self.tool_calls
-        return ret
+            message_dict['tool_call_id'] = self.tool_call_id
+            message_dict['name'] = self.name
+
+        return message_dict

openhands/llm/fn_call_converter.py

@@ -449,7 +449,7 @@ def convert_fncall_messages_to_non_fncall_messages(
 
         # 4. TOOL MESSAGES (tool outputs)
         elif role == 'tool':
-            # Convert tool result as assistant message
+            # Convert tool result as user message
             tool_name = message.get('name', 'function')
             prefix = f'EXECUTION RESULT of [{tool_name}]:\n'
             # and omit "tool_call_id" AND "name"

openhands/llm/llm.py

@@ -567,6 +567,7 @@ def format_messages_for_llm(self, messages: Message | list[Message]) -> list[dic
         for message in messages:
             message.cache_enabled = self.is_caching_prompt_active()
             message.vision_enabled = self.vision_is_active()
+            message.function_calling_enabled = self.is_function_calling_active()
 
         # let pydantic handle the serialization
         return [message.model_dump() for message in messages]