Merge pull request #611 from sambhavnoobcoder/Longterm-Memory-Access-Issue

Ensure tool execution regardless of long-term memory usage

swarms/structs/agent.py

@@ -799,51 +799,61 @@ class Agent:
                 while attempt < self.retry_attempts and not success:
                     try:
                         if self.long_term_memory is not None:
-                            logger.info(
-                                "Querying long term memory..."
-                            )
+                            logger.info("Querying long term memory...")
                             self.memory_query(task_prompt)
-
+                        
+                        # Generate response using LLM
+                        response_args = (
+                            (task_prompt, *args) if img is None else (task_prompt, img, *args)
+                        )
+                        response = self.call_llm(*response_args, **kwargs)
+                        
+                        # Check if response is a dictionary and has 'choices' key
+                        if isinstance(response, dict) and 'choices' in response:
+                            response = response['choices'][0]['message']['content']
+                        elif isinstance(response, str):
+                            # If response is already a string, use it as is
+                            pass
                         else:
-                            response_args = (
-                                (task_prompt, *args)
-                                if img is None
-                                else (task_prompt, img, *args)
-                            )
-                            response = self.call_llm(
-                                *response_args, **kwargs
-                            )
-
-                            # Log the step metadata
-                            logged = self.log_step_metadata(
-                                loop_count, task_prompt, response
-                            )
-                            logger.info(logged)
-
-                            # Conver to a str if the response is not a str
-                            response = self.llm_output_parser(
-                                response
+                            raise ValueError(
+                                f"Unexpected response format: {type(response)}"
                             )
-
-                            # Print
-                            if self.streaming_on is True:
-                                self.stream_response(response)
-                            else:
-                                print(response)
-
-                            # Add the response to the memory
-                            self.short_memory.add(
-                                role=self.agent_name, content=response
-                            )
-
-                            # Add to all responses
-                            all_responses.append(response)
-
-                        # TODO: Implement reliablity check
+                        
+                        # Check and execute tools
+                        if self.tools is not None:
+                            print(f"self.tools is not None: {response}")
+                            self.parse_and_execute_tools(response)
+                        
+                        # Log the step metadata
+                        logged = self.log_step_metadata(
+                            loop_count, 
+                            task_prompt, 
+                            response
+                        )
+                        logger.info(logged)
+                        
+                        # Convert to a str if the response is not a str
+                        response = self.llm_output_parser(response)
+                        
+                        # Print
+                        if self.streaming_on is True:
+                            self.stream_response(response)
+                        else:
+                            print(response)
+                        
+                        # Add the response to the memory
+                        self.short_memory.add(
+                            role=self.agent_name,
+                            content=response
+                        )
+                        
+                        # Add to all responses
+                        all_responses.append(response)
+                        
+                        # TODO: Implement reliability check
                         if self.tools is not None:
                             # self.parse_function_call_and_execute(response)
                             self.parse_and_execute_tools(response)
-
                         # if self.code_interpreter is True:
                         #     # Parse the code and execute
                         #     logger.info("Parsing code and executing...")
@@ -1846,20 +1856,21 @@ class Agent:
 
         return response
 
-    def llm_output_parser(self, response: Any) -> str:
-        """
-        Parses the response from the LLM (Low-Level Monitor) and returns it as a string.
-
-        Args:
-            response (Any): The response from the LLM.
-
-        Returns:
-            str: The parsed response as a string.
-        """
-        if response is not str:
-            response = str(response)
-
-        return response
+    def llm_output_parser(self, response):
+        """Parse the output from the LLM"""
+        try:
+            if isinstance(response, dict):
+                if 'choices' in response:
+                    return response['choices'][0]['message']['content']
+                else:
+                    return json.dumps(response)  # Convert dict to string
+            elif isinstance(response, str):
+                return response
+            else:
+                return str(response)  # Convert any other type to string
+        except Exception as e:
+            logger.error(f"Error parsing LLM output: {e}")
+            return str(response)  # Return string representation as fallback
 
     def log_step_metadata(
         self, loop: int, task: str, response: str