Tracing Mistral

MLflow Tracing ensures observability for your interactions with Mistral AI models. When Mistral auto-tracing is enabled by calling the mlflow.mistral.autolog() function, usage of the Mistral SDK will automatically record generated traces during interactive development.

MLflow automatically captures the following information about Mistral calls:

• Prompts and completion responses
• Latencies
• Model name
• Token usage (input, output, and total tokens)
• Additional metadata such as temperature, max_tokens, if specified
• Function calling if returned in the response
• Any exception if raised

Getting Started

1

Install Dependencies

bash

pip install 'mlflow[genai]' mistralai

2

Start MLflow Server

Local (pip)

If you have a local Python environment >= 3.10, you can start the MLflow server locally using the mlflow CLI command.

bash

mlflow server

Local (docker)

MLflow also provides a Docker Compose file to start a local MLflow server with a postgres database and a minio server.

bash

git clone --depth 1 --filter=blob:none --sparse https://github.com/mlflow/mlflow.git
cd mlflow
git sparse-checkout set docker-compose
cd docker-compose
cp .env.dev.example .env
docker compose up -d

Refer to the instruction for more details, e.g., overriding the default environment variables.

3

Enable Tracing and Make API Calls

Enable tracing with mlflow.mistral.autolog() and make API calls as usual.

python

import os
from mistralai import Mistral
import mlflow

# Enable auto-tracing for Mistral
mlflow.mistral.autolog()

# Set a tracking URI and an experiment
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("Mistral")

# Configure your API key
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

# Use the chat complete method to create new chat
chat_response = client.chat.complete(
    model="mistral-small-latest",
    messages=[
        {
            "role": "user",
            "content": "Who is the best French painter? Answer in one short sentence.",
        },
    ],
)
print(chat_response.choices[0].message)

4

View Traces in MLflow UI

Browse to the MLflow UI at http://localhost:5000 (or your MLflow server URL) and you should see the traces for the Mistral API calls.

Supported APIs

MLflow supports automatic tracing for the following Mistral APIs:

Chat	Function Calling	Streaming	Async	Image	Embeddings	Agents
✅	✅	-	✅ (*1)	-	-	-

(*1) Async support was added in MLflow 3.5.0.

To request support for additional APIs, please open a feature request on GitHub.

Examples

Basic Example

python

import os

from mistralai import Mistral

import mlflow

# Turn on auto tracing for Mistral AI by calling mlflow.mistral.autolog()
mlflow.mistral.autolog()

# Configure your API key.
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

# Use the chat complete method to create new chat.
chat_response = client.chat.complete(
    model="mistral-small-latest",
    messages=[
        {
            "role": "user",
            "content": "Who is the best French painter? Answer in one short sentence.",
        },
    ],
)
print(chat_response.choices[0].message)

Tracking Token Usage and Cost

MLflow automatically tracks token usage and cost for Mistral. The token usage for each LLM call will be logged in each Trace/Span and the aggregated cost and time trend are displayed in the built-in dashboard. See the Token Usage and Cost Tracking documentation for details on accessing this information programmatically.

Disable auto-tracing

Auto tracing for Mistral can be disabled globally by calling mlflow.mistral.autolog(disable=True) or mlflow.autolog(disable=True).