Async Team Post-Hook Agent as Judge

Automatic async evaluation of team outputs using post-hooks

This example demonstrates using Agent as Judge as an async post-hook on a Team to automatically evaluate team responses.

1. Add the following code to your Python file

   agent_as_judge_team_post_hook_async.py

   import asyncio
   
   from agno.agent import Agent
   from agno.db.sqlite import AsyncSqliteDb
   from agno.eval.agent_as_judge import AgentAsJudgeEval
   from agno.models.openai import OpenAIResponses
   from agno.team.team import Team
   
   
   async def main():
       # Setup database to persist eval results
       db = AsyncSqliteDb(db_file="tmp/agent_as_judge_team_post_hook_async.db")
   
       # Eval runs as post-hook, results saved to database
       agent_as_judge_eval = AgentAsJudgeEval(
           name="Team Response Quality",
           model=OpenAIResponses(id="gpt-5.2"),
           criteria="Response should be well-researched, clear, comprehensive, and show good collaboration between team members",
           scoring_strategy="numeric",
           threshold=7,
           db=db,
       )
   
       # Setup a team with researcher and writer
       researcher = Agent(
           name="Researcher",
           role="Research and gather information",
           model=OpenAIResponses(id="gpt-5.2"),
       )
   
       writer = Agent(
           name="Writer",
           role="Write clear and concise summaries",
           model=OpenAIResponses(id="gpt-5.2"),
       )
   
       research_team = Team(
           name="Research Team",
           model=OpenAIResponses(id="gpt-5.2"),
           members=[researcher, writer],
           instructions=["First research the topic thoroughly, then write a clear summary."],
           post_hooks=[agent_as_judge_eval],
           db=db,
       )
   
       response = await research_team.arun("Explain quantum computing")
       print(response.content)
   
       # Query database for eval results
       print("Evaluation Results:")
       eval_runs = await db.get_eval_runs()
       if eval_runs:
           latest = eval_runs[-1]
           if latest.eval_data and "results" in latest.eval_data:
               result = latest.eval_data["results"][0]
               print(f"Score: {result.get('score', 'N/A')}/10")
               print(f"Status: {'PASSED' if result.get('passed') else 'FAILED'}")
               print(f"Reason: {result.get('reason', 'N/A')[:200]}...")
   
   
   if __name__ == "__main__":
       asyncio.run(main())
   
   
2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U agno openai
   
4. Export your OpenAI API key

   Mac/Linux

     export OPENAI_API_KEY="your_openai_api_key_here"
   

     $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run the example

   python agent_as_judge_team_post_hook_async.py