Simplify fn calling usage

openhands/agenthub/codeact_agent/codeact_agent.py

@@ -90,15 +90,7 @@ def __init__(
         self.pending_actions: deque[Action] = deque()
         self.reset()
 
-        self.mock_function_calling = False
-        if not self.llm.is_function_calling_active():
-            logger.info(
-                f'Function calling not enabled for model {self.llm.config.model}. '
-                'Mocking function calling via prompting.'
-            )
-            self.mock_function_calling = True
-
-        # Function calling mode
+        # Retrieve the enabled tools
         self.tools = codeact_function_calling.get_tools(
             codeact_enable_browsing=self.config.codeact_enable_browsing,
             codeact_enable_jupyter=self.config.codeact_enable_jupyter,
@@ -311,10 +303,7 @@ def get_observation_message(
                 and len(obs.set_of_marks) > 0
                 and self.config.enable_som_visual_browsing
                 and self.llm.vision_is_active()
-                and (
-                    self.mock_function_calling
-                    or self.llm.is_visual_browser_tool_active()
-                )
+                and self.llm.is_visual_browser_tool_active()
             ):
                 text += 'Image: Current webpage screenshot (Note that only visible portion of webpage is present in the screenshot. You may need to scroll to view the remaining portion of the web-page.)\n'
                 message = Message(
@@ -400,8 +389,6 @@ def step(self, state: State) -> Action:
             'messages': self.llm.format_messages_for_llm(messages),
         }
         params['tools'] = self.tools
-        if self.mock_function_calling:
-            params['mock_function_calling'] = True
         response = self.llm.completion(**params)
         actions = codeact_function_calling.response_to_actions(response)
         for action in actions:

openhands/llm/llm.py

@@ -197,7 +197,7 @@ def wrapper(*args, **kwargs):
             from openhands.core.utils import json
 
             messages: list[dict[str, Any]] | dict[str, Any] = []
-            mock_function_calling = kwargs.pop('mock_function_calling', False)
+            mock_function_calling = not self.is_function_calling_active()
 
             # some callers might send the model and messages directly
             # litellm allows positional args, like completion(model, messages, **kwargs)
@@ -216,20 +216,22 @@ def wrapper(*args, **kwargs):
 
             # ensure we work with a list of messages
             messages = messages if isinstance(messages, list) else [messages]
+
+            # handle conversion of to non-function calling messages if needed
             original_fncall_messages = copy.deepcopy(messages)
             mock_fncall_tools = None
-            if mock_function_calling:
-                assert (
-                    'tools' in kwargs
-                ), "'tools' must be in kwargs when mock_function_calling is True"
+            # if the agent or caller has defined tools, and we mock via prompting, convert the messages
+            if mock_function_calling and 'tools' in kwargs:
                 messages = convert_fncall_messages_to_non_fncall_messages(
                     messages, kwargs['tools']
                 )
                 kwargs['messages'] = messages
-                if self.config.model not in MODELS_WITHOUT_STOP_WORDS:
-                    kwargs['stop'] = STOP_WORDS
                 mock_fncall_tools = kwargs.pop('tools')
 
+            # add stop words if the model supports it
+            if self.config.model not in MODELS_WITHOUT_STOP_WORDS:
+                kwargs['stop'] = STOP_WORDS
+
             # if we have no messages, something went very wrong
             if not messages:
                 raise ValueError(
@@ -256,9 +258,10 @@ def wrapper(*args, **kwargs):
             self.metrics.add_response_latency(latency, response_id)
 
             non_fncall_response = copy.deepcopy(resp)
-            if mock_function_calling:
+
+            # if we mocked function calling, and we have tools, convert the response back to function calling format
+            if mock_function_calling and mock_fncall_tools is not None:
                 assert len(resp.choices) == 1
-                assert mock_fncall_tools is not None
                 non_fncall_response_message = resp.choices[0].message
                 fn_call_messages_with_response = (
                     convert_non_fncall_messages_to_fncall_messages(

tests/unit/test_codeact_agent.py

@@ -464,16 +464,6 @@ def test_browser_tool():
     assert 'description' in BrowserTool['function']['parameters']['properties']['code']
 
 
-def test_mock_function_calling():
-    # Test mock function calling when LLM doesn't support it
-    llm = Mock()
-    llm.is_function_calling_active = lambda: False
-    config = AgentConfig()
-    config.enable_prompt_extensions = False
-    agent = CodeActAgent(llm=llm, config=config)
-    assert agent.mock_function_calling is True
-
-
 def test_response_to_actions_invalid_tool():
     # Test response with invalid tool call
     mock_response = Mock()