> ## 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.

# MLflow Via OpenInference

> Send OpenInference spans from an async YFinance agent to an MLflow tracking server over the OTLP HTTP trace endpoint.

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 mlflow 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="Start MLflow">
    Start the local MLflow receiver on port 5000:

    ```bash theme={null}
    mlflow server --host 127.0.0.1 --port 5000
    ```
  </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)