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MistralAI is a leading provider for state-of-the-art LLMs. The MistralAI SDK can be instrumented using the openinference-instrumentation-mistralai package.

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Launch Phoenix

  • Phoenix Cloud
  • Command Line
  • Docker
  • Notebook
Sign up for Phoenix:
  1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
  2. Click Create Space, then follow the prompts to create and launch your space.
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint and API Key:From your new Phoenix Space
  1. Create your API key from the Settings page
  2. Copy your Hostname from the Settings page
  3. In your code, set your endpoint and API key: 
import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"
Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

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Install

pip install openinference-instrumentation-mistralai mistralai

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Setup

Set the MISTRAL_API_KEY environment variable to authenticate calls made using the SDK. 
export MISTRAL_API_KEY=[your_key_here]
Connect to your Phoenix instance using the register function. 
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

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Run Mistral

import os

from mistralai import Mistral
from mistralai.models import UserMessage

api_key = os.environ["MISTRAL_API_KEY"]
model = "mistral-tiny"

client = Mistral(api_key=api_key)

chat_response = client.chat.complete(
    model=model,
    messages=[UserMessage(content="What is the best French cheese?")],
)
print(chat_response.choices[0].message.content)

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Observe

Now that you have tracing setup, all invocations of Mistral (completions, chat completions, embeddings) will be streamed to your running Phoenix for observability and evaluation.

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