Get Started with Microsoft Agent Framework Mistral AI

Please install this package:

pip install agent-framework-mistral --pre

and see the README for more information.

See the Mistral agent sample and the Mistral embedding sample for runnable examples.

Chat Client

The MistralChatClient provides chat completions using Mistral AI models, with support for streaming, function tools, and structured output.

Quick Start

from agent_framework import Agent
from agent_framework.mistral import MistralChatClient

# Using environment variables (MISTRAL_API_KEY, MISTRAL_CHAT_MODEL)
# Parameters can also be passed directly:
# MistralChatClient(model="mistral-large-latest", api_key="your-api-key")
client = MistralChatClient()
try:
    agent = Agent(client=client, instructions="You are a helpful assistant.")
    response = await agent.run("Hello!")
    print(response.text)
finally:
    await client.close()

Configuration

Chat connection settings belong on the client, not on individual requests. Both MistralChatClient and RawMistralChatClient reject server_url, http_headers, retries, and timeout_ms in per-call options or client_kwargs. Configure the endpoint with the constructor's server_url, or inject a configured SDK client or http_client. Normal model generation options remain per-call.

Embedding Client

The MistralEmbeddingClient provides embedding generation using Mistral AI models.

Quick Start

from agent_framework.mistral import MistralEmbeddingClient

# Using environment variables (MISTRAL_API_KEY, MISTRAL_EMBEDDING_MODEL)
client = MistralEmbeddingClient()

try:
    # Parameters can also be passed directly:
    # MistralEmbeddingClient(model="mistral-embed", api_key="your-api-key")
    result = await client.get_embeddings(["Hello, world!", "How are you?"])
    for embedding in result:
        print(f"Dimensions: {embedding.dimensions}")
        print(f"Vector: {embedding.vector[:5]}...")
finally:
    await client.close()

Configuration