rename our completion as a drop-in replacement of litellm completion (#2509)

agenthub/SWE_agent/agent.py

@@ -38,7 +38,7 @@ class SWEAgent(Agent):
         self.cur_line: int = 0
 
     def _think_act(self, messages: list[dict]) -> tuple[Action, str]:
-        resp = self.llm.do_completion(
+        resp = self.llm.completion(
             messages=messages,
             temperature=0.05,
         )

agenthub/browsing_agent/browsing_agent.py

@@ -208,7 +208,7 @@ class BrowsingAgent(Agent):
         prompt = get_prompt(error_prefix, cur_axtree_txt, prev_action_str)
         messages.append({'role': 'user', 'content': prompt})
         logger.info(prompt)
-        response = self.llm.do_completion(
+        response = self.llm.completion(
             messages=messages,
             temperature=0.0,
             stop=[')```', ')\n```'],

agenthub/codeact_agent/codeact_agent.py

@@ -221,7 +221,7 @@ class CodeActAgent(Agent):
                 f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task.'
             )
 
-        response = self.llm.do_completion(
+        response = self.llm.completion(
             messages=messages,
             stop=[
                 '</execute_ipython>',

agenthub/codeact_swe_agent/codeact_swe_agent.py

@@ -173,7 +173,7 @@ class CodeActSWEAgent(Agent):
                 f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task.'
             )
 
-        response = self.llm.do_completion(
+        response = self.llm.completion(
             messages=messages,
             stop=[
                 '</execute_ipython>',

agenthub/micro/agent.py

@@ -64,7 +64,7 @@ class MicroAgent(Agent):
             latest_user_message=state.get_current_user_intent(),
         )
         messages = [{'content': prompt, 'role': 'user'}]
-        resp = self.llm.do_completion(messages=messages)
+        resp = self.llm.completion(messages=messages)
         action_resp = resp['choices'][0]['message']['content']
         state.num_of_chars += len(prompt) + len(action_resp)
         action = parse_response(action_resp)

agenthub/monologue_agent/agent.py

@@ -181,7 +181,7 @@ class MonologueAgent(Agent):
         ]
 
         # format all as a single message, a monologue
-        resp = self.llm.do_completion(messages=messages)
+        resp = self.llm.completion(messages=messages)
 
         # keep track of max_chars fallback option
         state.num_of_chars += len(prompt) + len(

agenthub/planner_agent/agent.py

@@ -47,7 +47,7 @@ class PlannerAgent(Agent):
             return AgentFinishAction()
         prompt = get_prompt(state)
         messages = [{'content': prompt, 'role': 'user'}]
-        resp = self.llm.do_completion(messages=messages)
+        resp = self.llm.completion(messages=messages)
         state.num_of_chars += len(prompt) + len(
             resp['choices'][0]['message']['content']
         )

opendevin/llm/llm.py

@@ -189,17 +189,31 @@ class LLM:
             after=attempt_on_error,
         )
         def wrapper(*args, **kwargs):
+            """
+            Wrapper for the litellm completion function. Logs the input and output of the completion function.
+            """
+
+            # some callers might just send the messages directly
             if 'messages' in kwargs:
                 messages = kwargs['messages']
             else:
                 messages = args[1]
+
+            # log the prompt
             debug_message = ''
             for message in messages:
                 debug_message += message_separator + message['content']
             llm_prompt_logger.debug(debug_message)
+
+            # call the completion function
             resp = completion_unwrapped(*args, **kwargs)
+
+            # log the response
             message_back = resp['choices'][0]['message']['content']
             llm_response_logger.debug(message_back)
+
+            # post-process to log costs
+            self._post_completion(resp)
             return resp
 
         self._completion = wrapper  # type: ignore
@@ -208,20 +222,12 @@ class LLM:
     def completion(self):
         """
         Decorator for the litellm completion function.
-        """
-        return self._completion
-
-    def do_completion(self, *args, **kwargs):
-        """
-        Wrapper for the litellm completion function.
 
         Check the complete documentation at https://litellm.vercel.app/docs/completion
         """
-        resp = self._completion(*args, **kwargs)
-        self.post_completion(resp)
-        return resp
+        return self._completion
 
-    def post_completion(self, response: str) -> None:
+    def _post_completion(self, response: str) -> None:
         """
         Post-process the completion response.
         """

opendevin/memory/condenser.py

@@ -16,7 +16,7 @@ class MemoryCondenser:
 
         try:
             messages = [{'content': summarize_prompt, 'role': 'user'}]
-            resp = llm.do_completion(messages=messages)
+            resp = llm.completion(messages=messages)
             summary_response = resp['choices'][0]['message']['content']
             return summary_response
         except Exception as e:

tests/unit/test_micro_agents.py

@@ -35,9 +35,7 @@ def test_coder_agent_with_summary():
     """
     mock_llm = MagicMock()
     content = json.dumps({'action': 'finish', 'args': {}})
-    mock_llm.do_completion.return_value = {
-        'choices': [{'message': {'content': content}}]
-    }
+    mock_llm.completion.return_value = {'choices': [{'message': {'content': content}}]}
 
     coder_agent = Agent.get_cls('CoderAgent')(llm=mock_llm)
     assert coder_agent is not None
@@ -49,8 +47,8 @@ def test_coder_agent_with_summary():
     state = State(history=history, inputs={'summary': summary})
     coder_agent.step(state)
 
-    mock_llm.do_completion.assert_called_once()
-    _, kwargs = mock_llm.do_completion.call_args
+    mock_llm.completion.assert_called_once()
+    _, kwargs = mock_llm.completion.call_args
     prompt = kwargs['messages'][0]['content']
     assert task in prompt
     assert "Here's a summary of the codebase, as it relates to this task" in prompt
@@ -64,9 +62,7 @@ def test_coder_agent_without_summary():
     """
     mock_llm = MagicMock()
     content = json.dumps({'action': 'finish', 'args': {}})
-    mock_llm.do_completion.return_value = {
-        'choices': [{'message': {'content': content}}]
-    }
+    mock_llm.completion.return_value = {'choices': [{'message': {'content': content}}]}
 
     coder_agent = Agent.get_cls('CoderAgent')(llm=mock_llm)
     assert coder_agent is not None
@@ -77,8 +73,8 @@ def test_coder_agent_without_summary():
     state = State(history=history)
     coder_agent.step(state)
 
-    mock_llm.do_completion.assert_called_once()
-    _, kwargs = mock_llm.do_completion.call_args
+    mock_llm.completion.assert_called_once()
+    _, kwargs = mock_llm.completion.call_args
     prompt = kwargs['messages'][0]['content']
     assert task in prompt
     assert "Here's a summary of the codebase, as it relates to this task" not in prompt