comet-ml--opik/apps/opik-documentation/documentation/fern/docs/tracing/integrations/mistral.mdx

---
description: Start here to integrate Opik into your Mistral AI-based genai application
  for end-to-end LLM observability, unit testing, and optimization.
headline: Mistral AI | Opik Documentation
og:description: Learn to integrate Mistral AI with Opik via LiteLLM to track and evaluate
  API calls effectively in your projects.
og:site_name: Opik Documentation
og:title: Integrate Mistral AI with Opik - Streamline Your Workflow
title: Observability for Mistral AI with Opik
canonical-url: https://www.comet.com/docs/opik/integrations/mistral
---

[Mistral AI](https://mistral.ai/) provides cutting-edge large language models with excellent performance for text generation, reasoning, and specialized tasks like code generation.

This guide explains how to integrate Opik with Mistral AI via LiteLLM. By using the LiteLLM integration provided by Opik, you can easily track and evaluate your Mistral API calls within your Opik projects as Opik will automatically log the input prompt, model used, token usage, and response generated.

## Getting Started

### Configuring Opik

To start tracking your Mistral AI LLM calls, you'll need to have both `opik` and `litellm` installed. You can install them using pip:

```bash
pip install opik litellm
```

In addition, you can configure Opik using the `opik configure` command which will prompt you for the correct local server address or if you are using the Cloud platform your API key:

```bash
opik configure
```

### Configuring Mistral AI

You'll need to set your Mistral AI API key as an environment variable:

```bash
export MISTRAL_API_KEY="YOUR_API_KEY"
```

## Logging LLM calls

In order to log the LLM calls to Opik, you will need to create the OpikLogger callback. Once the OpikLogger callback is created and added to LiteLLM, you can make calls to LiteLLM as you normally would:

```python
from litellm.integrations.opik.opik import OpikLogger
import litellm

opik_logger = OpikLogger()
litellm.callbacks = [opik_logger]

response = litellm.completion(
    model="mistral/mistral-large-2407",
    messages=[
        {"role": "user", "content": "Why is tracking and evaluation of LLMs important?"}
    ]
)
```

## Logging LLM calls within a tracked function

If you are using LiteLLM within a function tracked with the [`@track`](/v1/tracing/log_traces#using-function-decorators) decorator, you will need to pass the `current_span_data` as metadata to the `litellm.completion` call:

```python
from opik import track, opik_context
import litellm

@track
def generate_story(prompt):
    response = litellm.completion(
        model="mistral/mistral-large-2407",
        messages=[{"role": "user", "content": prompt}],
        metadata={
            "opik": {
                "current_span_data": opik_context.get_current_span_data(),
            },
        },
    )
    return response.choices[0].message.content


@track
def generate_topic():
    prompt = "Generate a topic for a story about Opik."
    response = litellm.completion(
        model="mistral/mistral-medium-2312",
        messages=[{"role": "user", "content": prompt}],
        metadata={
            "opik": {
                "current_span_data": opik_context.get_current_span_data(),
            },
        },
    )
    return response.choices[0].message.content


@track
def generate_opik_story():
    topic = generate_topic()
    story = generate_story(topic)
    return story


generate_opik_story()
```