> ## 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.

# Async Performance Evaluation

> Example showing how to run performance evaluations on async functions.

**1. Create a Python file**

```python performance_async.py theme={null}
"""This example shows how to run a Performance evaluation on an async function."""

import asyncio

from agno.agent import Agent
from agno.eval.performance import PerformanceEval
from agno.models.openai import OpenAIResponses


# Simple async function to run an Agent.
async def arun_agent():
    agent = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        system_message="Be concise, reply with one sentence.",
    )
    response = await agent.arun("What is the capital of France?")
    return response


performance_eval = PerformanceEval(func=arun_agent, num_iterations=10)

# Because we are evaluating an async function, we use the arun method.
asyncio.run(performance_eval.arun(print_summary=True, print_results=True))
```

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

**2. Install dependencies**

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

**3. Export your OpenAI API key**

```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"
```

**4. Run Agent**

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


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