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This example demonstrates using Agent as Judge evaluation as a post-hook, automatically evaluating every agent response.
1

Add the following code to your Python file

agent_as_judge_post_hook.py
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.eval.agent_as_judge import AgentAsJudgeEval
from agno.models.openai import OpenAIChat

# Setup database to persist eval results
db = SqliteDb(db_file="tmp/agent_as_judge_post_hook.db")

# Eval runs as post-hook, results saved to database
agent_as_judge_eval = AgentAsJudgeEval(
    name="Response Quality Check",
    model=OpenAIChat(id="gpt-4o-mini"),
    criteria="Response should be professional, well-structured, and provide balanced perspectives",
    scoring_strategy="numeric",
    threshold=7,
    db=db,
)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    instructions="Provide professional and well-reasoned answers.",
    post_hooks=[agent_as_judge_eval],
    db=db,
)

response = agent.run("What are the benefits of renewable energy?")
print(response.content)

# Query database for eval results
print("Evaluation Results:")
eval_runs = 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]}...")
2

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
3

Install libraries

pip install -U agno openai
4

Export your OpenAI API key

  export OPENAI_API_KEY="your_openai_api_key_here"
5

Run the example

python agent_as_judge_post_hook.py