> ## 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.

# MLflow Via OpenInference

> Demonstrates instrumenting an Agno agent with OpenInference and sending traces to MLflow.

```python mlflow_via_openinference.py theme={null}
"""
MLflow Via OpenInference
========================

Demonstrates instrumenting an Agno agent with OpenInference and sending traces to MLflow.

Requirements:
    pip install -U mlflow opentelemetry-exporter-otlp-proto-http openinference-instrumentation-agno

Start MLflow with OTLP tracing enabled:
    mlflow server --host 127.0.0.1 --port 5000
"""

import asyncio
import os

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.yfinance import YFinanceTools
from openinference.instrumentation.agno import AgnoInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
MLFLOW_TRACKING_URI = os.getenv("MLFLOW_TRACKING_URI", "http://127.0.0.1:5000")

endpoint = f"{MLFLOW_TRACKING_URI}/api/2.0/mlflow/traces"

tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    SimpleSpanProcessor(
        OTLPSpanExporter(
            endpoint=endpoint,
            headers={"x-mlflow-experiment-id": "0"},
        )
    )
)
# Start instrumenting agno
AgnoInstrumentor().instrument(tracer_provider=tracer_provider)


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    name="Stock Price Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools()],
    instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
)


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------
async def main() -> None:
    await agent.aprint_response(
        "What is the current price of Tesla? Then find the current price of NVIDIA",
        stream=True,
    )


if __name__ == "__main__":
    asyncio.run(main())
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai openinference-instrumentation-agno opentelemetry-exporter-otlp opentelemetry-sdk yfinance
    ```
  </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">
    Save the code above as `mlflow_via_openinference.py`, then run:

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

Full source: [cookbook/observability/mlflow\_via\_openinference.py](https://github.com/agno-agi/agno/blob/main/cookbook/observability/mlflow_via_openinference.py)