> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Batch Agent-as-Judge Evaluation

> Judge three pre-supplied customer-service cases with binary scoring, persist the evaluation runs in SqliteDb, and report the result pass rate.

Demonstrates evaluating multiple cases in one run.

```python agent_as_judge_batch.py theme={null}
"""
Batch Agent-as-Judge Evaluation
===============================

Demonstrates evaluating multiple cases in one run.
"""

from agno.db.sqlite import SqliteDb
from agno.eval.agent_as_judge import AgentAsJudgeEval

# ---------------------------------------------------------------------------
# Create Database
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/agent_as_judge_batch.db")

# ---------------------------------------------------------------------------
# Create Evaluation
# ---------------------------------------------------------------------------
evaluation = AgentAsJudgeEval(
    name="Customer Service Quality",
    criteria="Response should be empathetic, professional, and helpful",
    scoring_strategy="binary",
    db=db,
)

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    result = evaluation.run(
        cases=[
            {
                "input": "My order is delayed and I'm very upset!",
                "output": "I sincerely apologize for the delay. I understand how frustrating this must be. Let me check your order status right away and see how we can make this right for you.",
            },
            {
                "input": "Can you help me with a refund?",
                "output": "Of course! I'd be happy to help with your refund. Could you please provide your order number so I can process this quickly for you?",
            },
            {
                "input": "Your product is terrible!",
                "output": "I'm sorry to hear you're disappointed. Your feedback is valuable to us. Could you share more details about what went wrong so we can improve?",
            },
        ],
        print_results=True,
        print_summary=True,
    )

    print(f"Pass rate: {result.pass_rate:.1f}%")
    print(f"Passed: {sum(1 for r in result.results if r.passed)}/{len(result.results)}")

    print("Database Results:")
    eval_runs = db.get_eval_runs()
    print(f"Total evaluations stored: {len(eval_runs)}")
    if eval_runs:
        latest = eval_runs[0]
        print(f"Run ID: {latest.run_id}")
        print(f"Cases evaluated: {len(result.results)}")
```

## Run the Example

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

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno openai sqlalchemy
```

**2. Export your API keys**

```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"
```

**3. Run the example**

Save the code above as `agent_as_judge_batch.py`, then run:

```bash theme={null}
python agent_as_judge_batch.py
```

Full source: [cookbook/09\_evals/agent\_as\_judge/agent\_as\_judge\_batch.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/09_evals/agent_as_judge/agent_as_judge_batch.py)


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