[AGENT][LiteLLM FIX] [API FIX]

api/agent_api_test.py

@@ -3,9 +3,8 @@
 import time
 from typing import Dict, Optional, Tuple
 from uuid import UUID
-import sys
 
-BASE_URL = "http://localhost:8000/v1"
+BASE_URL = "http://0.0.0.0:8000/v1"
 
 
 def check_api_server() -> bool:
@@ -199,6 +198,7 @@ def test_completion(session: TestSession, agent_id: UUID) -> bool:
 
         if response.status_code == 200:
             completion_data = response.json()
+            print(completion_data)
             logger.success(
                 f"Got completion, used {completion_data['token_usage']['total_tokens']} tokens"
             )
@@ -317,4 +317,4 @@ def run_test_workflow():
 
 if __name__ == "__main__":
     success = run_test_workflow()
-    sys.exit(0 if success else 1)
+    print(success)

api/test_api.py

@@ -2,7 +2,7 @@
 import json
 from time import sleep
 
-BASE_URL = "http://api.swarms.ai:8000"
+BASE_URL = "http://swarms-api-893767232.us-east-2.elb.amazonaws.com"
 
 
 def make_request(method, endpoint, data=None):

async_workflow_example.py

@@ -1,4 +1,3 @@
-
 import asyncio
 from typing import List
 
@@ -18,16 +17,16 @@
 
 async def create_specialized_agents() -> List[Agent]:
     """Create a set of specialized agents for financial analysis"""
-    
+
     # Base model configuration
     model = OpenAIChat(model_name="gpt-4o")
-    
+
     # Financial Analysis Agent
     financial_agent = Agent(
         agent_name="Financial-Analysis-Agent",
         agent_description="Personal finance advisor agent",
-        system_prompt=FINANCIAL_AGENT_SYS_PROMPT + 
-            "Output the <DONE> token when you're done creating a portfolio of etfs, index, funds, and more for AI",
+        system_prompt=FINANCIAL_AGENT_SYS_PROMPT
+        + "Output the <DONE> token when you're done creating a portfolio of etfs, index, funds, and more for AI",
         max_loops=1,
         llm=model,
         dynamic_temperature_enabled=True,
@@ -42,7 +41,7 @@ async def create_specialized_agents() -> List[Agent]:
         saved_state_path="financial_agent.json",
         interactive=False,
     )
-    
+
     # Risk Assessment Agent
     risk_agent = Agent(
         agent_name="Risk-Assessment-Agent",
@@ -60,7 +59,7 @@ async def create_specialized_agents() -> List[Agent]:
         saved_state_path="risk_agent.json",
         interactive=False,
     )
-    
+
     # Market Research Agent
     research_agent = Agent(
         agent_name="Market-Research-Agent",
@@ -78,49 +77,50 @@ async def create_specialized_agents() -> List[Agent]:
         saved_state_path="research_agent.json",
         interactive=False,
     )
-    
+
     return [financial_agent, risk_agent, research_agent]
 
+
 async def main():
     # Create specialized agents
     agents = await create_specialized_agents()
-    
+
     # Create workflow with group chat enabled
     workflow = create_default_workflow(
         agents=agents,
         name="AI-Investment-Analysis-Workflow",
-        enable_group_chat=True
+        enable_group_chat=True,
     )
-    
+
     # Configure speaker roles
     workflow.speaker_system.add_speaker(
         SpeakerConfig(
             role=SpeakerRole.COORDINATOR,
             agent=agents[0],  # Financial agent as coordinator
             priority=1,
             concurrent=False,
-            required=True
+            required=True,
         )
     )
-    
+
     workflow.speaker_system.add_speaker(
         SpeakerConfig(
             role=SpeakerRole.CRITIC,
             agent=agents[1],  # Risk agent as critic
             priority=2,
-            concurrent=True
+            concurrent=True,
         )
     )
-    
+
     workflow.speaker_system.add_speaker(
         SpeakerConfig(
             role=SpeakerRole.EXECUTOR,
             agent=agents[2],  # Research agent as executor
             priority=2,
-            concurrent=True
+            concurrent=True,
         )
     )
-    
+
     # Investment analysis task
     investment_task = """
     Create a comprehensive investment analysis for a $40k portfolio focused on AI growth opportunities:
@@ -130,32 +130,30 @@ async def main():
     4. Provide market trend analysis
     Present the results in a structured markdown format.
     """
-    
+
     try:
         # Run workflow with retry
         result = await run_workflow_with_retry(
-            workflow=workflow,
-            task=investment_task,
-            max_retries=3
+            workflow=workflow, task=investment_task, max_retries=3
         )
-        
+
         print("\nWorkflow Results:")
         print("================")
-        
+
         # Process and display agent outputs
         for output in result.agent_outputs:
             print(f"\nAgent: {output.agent_name}")
             print("-" * (len(output.agent_name) + 8))
             print(output.output)
-            
+
         # Display group chat history if enabled
         if workflow.enable_group_chat:
             print("\nGroup Chat Discussion:")
             print("=====================")
             for msg in workflow.speaker_system.message_history:
                 print(f"\n{msg.role} ({msg.agent_name}):")
                 print(msg.content)
-        
+
         # Save detailed results
         if result.metadata.get("shared_memory_keys"):
             print("\nShared Insights:")
@@ -165,13 +163,14 @@ async def main():
                 if value:
                     print(f"\n{key}:")
                     print(value)
-        
+
     except Exception as e:
         print(f"Workflow failed: {str(e)}")
-        
+
     finally:
         await workflow.cleanup()
 
+
 if __name__ == "__main__":
     # Run the example
-    asyncio.run(main())
+    asyncio.run(main())