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MistralAI

Mistral AI develops open-weight large language models, focusing on efficiency, customization, and cost-effective AI solutions.

Website: https://mistral.ai/

MistralAI Tracing

Instrument LLM calls made using MistralAI's SDK via the MistralAIInstrumentor

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

Launch Phoenix

Install

pip install openinference-instrumentation-mistralai mistralai

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
)

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)

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.

Resources

MistralAI Evals

Configure and run MistralAI for evals

MistralAIModel

Need to install extra dependency mistralai

class MistralAIModel(BaseModel):
    model: str = "mistral-large-latest"
    temperature: float = 0
    top_p: Optional[float] = None
    random_seed: Optional[int] = None
    response_format: Optional[Dict[str, str]] = None
    safe_mode: bool = False
    safe_prompt: bool = False

Usag

# model = Instantiate your MistralAIModel here
model("Hello there, how are you?")
# Output: "As an artificial intelligence, I don't have feelings, 
#          but I'm here and ready to assist you. How can I help you today?"

MistralAI Tracing

Instrument LLM calls made using MistralAI's SDK via the MistralAIInstrumentor

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

Launch Phoenix

Install

pip install openinference-instrumentation-mistralai mistralai

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
)

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)

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.

Resources

MistralAI Evals

Configure and run MistralAI for evals

MistralAIModel

Need to install extra dependency mistralai

class MistralAIModel(BaseModel):
    model: str = "mistral-large-latest"
    temperature: float = 0
    top_p: Optional[float] = None
    random_seed: Optional[int] = None
    response_format: Optional[Dict[str, str]] = None
    safe_mode: bool = False
    safe_prompt: bool = False

Usag

# model = Instantiate your MistralAIModel here
model("Hello there, how are you?")
# Output: "As an artificial intelligence, I don't have feelings, 
#          but I'm here and ready to assist you. How can I help you today?"

Sign up for Phoenix:

Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
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

Create your API key from the Settings page
Copy your Hostname from the Settings page
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

Launch your local Phoenix instance:

pip install arize-phoenix
phoenix serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

See Terminal for more details.

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

For more info on using Phoenix with Docker, see Docker.

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See self-hosting or use one of the other deployment options to retain traces.