> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Basic Agent as Judge

> Basic usage of Agent as Judge evaluation with numeric scoring and failure callbacks

This example demonstrates basic Agent as Judge evaluation with numeric scoring (1-10 scale) and an `on_fail` callback for handling evaluation failures.

<Steps>
  <Step title="Add the following code to your Python file">
    ```python agent_as_judge_basic.py theme={null}
    from agno.agent import Agent
    from agno.db.sqlite import SqliteDb
    from agno.eval.agent_as_judge import AgentAsJudgeEval, AgentAsJudgeEvaluation
    from agno.models.openai import OpenAIResponses


    def on_evaluation_failure(evaluation: AgentAsJudgeEvaluation):
        """Callback triggered when evaluation fails (score < threshold)."""
        print(f"Evaluation failed - Score: {evaluation.score}/10")
        print(f"Reason: {evaluation.reason[:100]}...")


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

    agent = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        instructions="You are a technical writer. Explain concepts clearly and concisely.",
        db=db,
    )

    response = agent.run("Explain what an API is")

    evaluation = AgentAsJudgeEval(
        name="Explanation Quality",
        criteria="Explanation should be clear, beginner-friendly, and use simple language",
        scoring_strategy="numeric",  # Score 1-10
        threshold=9,  # Pass if score >= 9
        on_fail=on_evaluation_failure,
        db=db,
    )

    result = evaluation.run(
        input="Explain what an API is",
        output=str(response.content),
        print_results=True,
        print_summary=True,
    )

    # Query database for stored results
    print("Database Results:")
    eval_runs = db.get_eval_runs()
    print(f"Total evaluations stored: {len(eval_runs)}")
    if eval_runs:
        latest = eval_runs[-1]
        print(f"Eval ID: {latest.run_id}")
        print(f"Name: {latest.name}")

    ```
  </Step>

  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
        export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
        $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    ```bash theme={null}
    python agent_as_judge_basic.py
    ```
  </Step>
</Steps>