Evan Mattson
ae7fa3389c
* Bump Python package versions for 1.14.0 release Bump the CHANGELOG-selected packages for the 1.14.0 release: minor versions for root/core, AG-UI, Foundry, OpenAI, and orchestrations due to additive public APIs; patch versions for declarative and GitHub Copilot fixes; and Pacific-date prerelease stamps only for changed alpha/beta packages. No beta cohort bump was applied. Core dependency floors follow the strict policy and remain unchanged because no dependent package requires a new 1.14 API. Release validation also identified and corrected missing AG-UI and Copilot Studio runtime dependencies and aligned GitHub Copilot metadata with its Python 3.11 SDK requirement. Lab is intentionally skipped because its changes are development-only, and the moved Azure Functions and Durable Task packages are documented but no longer versioned here. * Raise AG-UI core dependency floor
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
| Environment Variable | Description |
|---|---|
MISTRAL_API_KEY |
Your Mistral AI API key |
MISTRAL_CHAT_MODEL |
Chat model name (e.g., mistral-large-latest) |
MISTRAL_SERVER_URL |
Optional server URL override |
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
| Environment Variable | Description |
|---|---|
MISTRAL_API_KEY |
Your Mistral AI API key |
MISTRAL_EMBEDDING_MODEL |
Embedding model name (e.g., mistral-embed) |
MISTRAL_SERVER_URL |
Optional server URL override |