---
title: "Mistral"
description: "Learn about using Sentry for Mistral."
url: https://docs.sentry.io/platforms/python/agent-tracing/mistral/
---

# Mistral | Sentry for Python

This integration connects Sentry with the [Mistral Python SDK](https://github.com/mistralai/client-python).

Once you've installed this SDK, you can use the Sentry Agents Tracing, a Sentry dashboard that helps you understand what's going on with your AI requests.

Sentry AI Observability will automatically collect information about prompts, tokens, and models. Learn more about the [Agents Dashboard](https://docs.sentry.io/product/agents/dashboards.md).

## [Install](https://docs.sentry.io/platforms/python/agent-tracing/mistral.md#install)

Install `sentry-sdk` from PyPI:

```bash
pip install sentry-sdk
```

*Other available variations of the above snippet: uv*

## [Configure](https://docs.sentry.io/platforms/python/agent-tracing/mistral.md#configure)

Add `MistralIntegration()` to your `integrations` list:

```python
import sentry_sdk
from sentry_sdk.integrations.mistral import MistralIntegration

sentry_sdk.init(
    dsn="https://<key>@o<orgId>.ingest.sentry.io/<projectId>",
    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for tracing.
    traces_sample_rate=1.0,
    # Add data like inputs and responses;
    # see https://docs.sentry.io/platforms/python/data-management/data-collected/ for more info
    send_default_pii=True,
    integrations=[
        MistralIntegration(),
    ],
)
```

## [Verify](https://docs.sentry.io/platforms/python/agent-tracing/mistral.md#verify)

Verify that the integration works by making a chat request to Mistral.

```python
import sentry_sdk
from sentry_sdk.integrations.mistral import MistralIntegration

from mistralai.client import Mistral

sentry_sdk.init(
    dsn="https://<key>@o<orgId>.ingest.sentry.io/<projectId>",
    traces_sample_rate=1.0,
    send_default_pii=True,
    integrations=[
        MistralIntegration(),
    ],
)

client = Mistral(api_key="(your Mistral key)")

response = client.chat.complete(
    model="mistral-medium-latest",
    messages=[
        {"role": "user", "content": "What is the best French cheese?"}
    ],
)
```

To try the async variant, use `client.chat.complete_async(...)` with `await`.

After running this script, the resulting data should show up in the `AI Spans` tab on the `Explore > Traces > Trace` page on Sentry.io.

If you manually created an [Invoke Agent Span](https://docs.sentry.io/platforms/python/agent-tracing/manual-instrumentation.md#invoke-agent-span) (not done in the example above) the data will also show up in the [Agents Dashboard](https://docs.sentry.io/product/agents/dashboards.md).

It may take a couple of moments for the data to appear in [sentry.io](https://sentry.io).

## [Behavior](https://docs.sentry.io/platforms/python/agent-tracing/mistral.md#behavior)

* The Mistral integration will connect Sentry with the supported Mistral methods automatically.

* The supported functions are currently `Chat.complete` and `Chat.complete_async` (sync and async). Streaming completions (`stream=True`) are not instrumented.

* Sentry considers LLM inputs/outputs as PII (Personally identifiable information) and doesn't include PII data by default. If you want to include the data, set `send_default_pii=True` in the `sentry_sdk.init()` call.

## [Supported Versions](https://docs.sentry.io/platforms/python/agent-tracing/mistral.md#supported-versions)

* Mistral AI (`mistralai`): 2.0.5+