> ## 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.

# Performance on Agent Instantiation with Tool

> Example showing how to analyze the runtime and memory usage of an Agent that is using tools.

**1. Create a Python file**

```python performance_instantiation_with_tool.py theme={null}
"""Run `uv pip install agno openai` to install dependencies."""

from typing import Literal

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


def get_weather(city: Literal["nyc", "sf"]):
    """Use this to get weather information."""
    if city == "nyc":
        return "It might be cloudy in nyc"
    elif city == "sf":
        return "It's always sunny in sf"


tools = [get_weather]


def instantiate_agent():
    return Agent(model=OpenAIResponses(id="gpt-5.2"), tools=tools)  # type: ignore


instantiation_perf = PerformanceEval(
    name="Tool Instantiation Performance", func=instantiate_agent, num_iterations=1000
)

if __name__ == "__main__":
    instantiation_perf.run(print_results=True, print_summary=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_instantiation_with_tool.py
```


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