Mistral AI Integration

Updated on Aug 14, 2026

Use this integration when your app is implemented in Python.

Setup

1. Install SDK

bash

pip install progress-observability

2. Instrument your app

python

Observability.instrument(
    app_name=os.getenv("OBSERVABILITY_APP_NAME"),
    api_key=os.getenv("OBSERVABILITY_API_KEY")
)

3. Complete example

python

import os
from dotenv import load_dotenv

from mistralai import Mistral
from progress.observability import Observability

load_dotenv()

Observability.instrument(
    app_name=os.getenv("OBSERVABILITY_APP_NAME"),
    api_key=os.getenv("OBSERVABILITY_API_KEY"),
)

client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
model = os.getenv("MISTRAL_MODEL", "mistral-large-latest")

response = client.chat.complete(
    model=model,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    temperature=0.0,
    max_tokens=64,
)


Observability.shutdown()

To get a richer trace tree when building agents on top of this provider, use the @agent, @tool, @workflow, or @task decorators. See the Python SDK for details.