cleanup

examples/multi_agent/auto_swarm_builder_example.py

@@ -15,4 +15,4 @@ out = swarm.run(
     task="Create an accounting team to analyze crypto transactions, there must be 5 agents in the team with extremely extensive prompts. Make the prompts extremely detailed and specific and long and comprehensive. Make sure to include all the details of the task in the prompts."
 )
 
-print(json.dumps(out, indent=4))
+print(json.dumps(out, indent=4))

heavy_swarm.py

@@ -1,4 +1,3 @@
-from swarms_tools import exa_search
 
 from swarms import HeavySwarm
 
@@ -10,7 +9,7 @@ swarm = HeavySwarm(
     question_agent_model_name="gpt-4.1",
     loops_per_agent=1,
     agent_prints_on=False,
-    worker_tools=[exa_search],
+    # worker_tools=[exa_search],
     random_loops_per_agent=False,
 )
 

swarms/utils/litellm_wrapper.py

@@ -411,7 +411,7 @@ class LiteLLM:
     #             response_dict = response.model_dump()
     #         else:
     #             response_dict = response
-                
+
     #         print(f"Response dict: {response_dict}")
 
     #         # Check if tool_calls exists and is not None
@@ -458,7 +458,7 @@ class LiteLLM:
     #             }
     #     except Exception as e:
     #         logger.error(f"Error processing tool calls: {str(e)} Traceback: {traceback.format_exc()}")
-    
+
     def output_for_tools(self, response: any):
         """
         Process and extract tool call information from the LLM response.