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

# LangGraph Instantiation Performance Evaluation

> Measure LangGraph create_react_agent instantiation over 1000 iterations with PerformanceEval, using ChatOpenAI gpt-4o and a weather tool.

Demonstrates agent instantiation benchmarking with LangGraph.

```python langgraph_instantiation.py theme={null}
"""
LangGraph Instantiation Performance Evaluation
==============================================

Demonstrates agent instantiation benchmarking with LangGraph.
"""

from typing import Literal

from agno.eval.performance import PerformanceEval
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent


# ---------------------------------------------------------------------------
# Create Benchmark Tool
# ---------------------------------------------------------------------------
@tool
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"
    else:
        raise AssertionError("Unknown city")


tools = [get_weather]


# ---------------------------------------------------------------------------
# Create Benchmark Function
# ---------------------------------------------------------------------------
def instantiate_agent():
    return create_react_agent(model=ChatOpenAI(model="gpt-4o"), tools=tools)


# ---------------------------------------------------------------------------
# Create Evaluation
# ---------------------------------------------------------------------------
langgraph_instantiation = PerformanceEval(func=instantiate_agent, num_iterations=1000)

# ---------------------------------------------------------------------------
# Run Evaluation
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    langgraph_instantiation.run(print_results=True, print_summary=True)
```

## Run the Example

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

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

  <Step title="Export your API keys">
    <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">
    Save the code above as `langgraph_instantiation.py`, then run:

    ```bash theme={null}
    python langgraph_instantiation.py
    ```
  </Step>
</Steps>

Full source: [cookbook/09\_evals/performance/comparison/langgraph\_instantiation.py](https://github.com/agno-agi/agno/blob/main/cookbook/09_evals/performance/comparison/langgraph_instantiation.py)