Packages
Builds and orchestrates AI agents powered by OpenAI and Azure OpenAI APIs, with support for multi-agent workflows, tool calling, and agent-to-agent collaboration patterns.
Enables communication with remote A2A-compliant agents and allows local agents to be hosted and accessed via the standardized A2A protocol.
However, this is a beta release, so expect potential API changes.
Connects Agent Framework agents and workflows to AG-UI's web interface and streaming protocol, enabling FastAPI-based servers and async clients for agent-driven applications.
Install it if you need to expose Agent Framework agents or workflows through a web interface with streaming and interrupt support.
Connects Agent Framework applications to Anthropic's API, providing client implementations for direct Anthropic endpoints and hosted transports via Microsoft Foundry, Amazon Bedrock, and Google Vertex AI.
Integrates Azure AI Foundry memory capabilities into Microsoft Agent Framework, enabling agents to store, retrieve, and maintain semantic memories across conversations.
Provides RAG (Retrieval Augmented Generation) context providers for Azure AI Search, supporting both semantic hybrid search and agentic multi-hop reasoning modes within the Microsoft Agent Framework.
Provides Azure Cosmos DB integration for the Microsoft Agent Framework, enabling persistent storage of conversation history and workflow checkpoints.
Hosts Microsoft Agent Framework agents on Azure Durable Functions, enabling stateful agent execution with automatic conversation history replay and failure recovery.
However, it is in beta (1.0.0b260730), so expect potential API changes; verify that your use case aligns with the Durable Functions model and that the upstream…
Connects Microsoft Agent Framework applications to Amazon Bedrock models, enabling chat interactions and tool/function calling through a unified interface.
Bridges Microsoft Agent Framework agents with OpenAI ChatKit, converting between Agent Framework message formats and ChatKit thread events for integrated agentic chat applications.
However, be aware that ChatKit's frontend requires internet connectivity to OpenAI's CDN—this is a hard blocker for air-gapped or highly regulated environments.
Integrates Claude with Microsoft Agent Framework to enable building production-grade AI agents and multi-agent workflows in Python using Claude as the LLM provider.
Integrates Microsoft Copilot Studio published copilots into applications via the Agent Framework, enabling async interaction with copilots through a unified agent interface.
Build, orchestrate, and deploy AI agents and multi-agent systems with support for multiple LLM providers, function calling, and orchestration patterns.
Enables building agents from YAML specifications using Microsoft's Agent Framework, with stable workflow support and experimental agent configuration loading.
However, avoid the experimental AgentFactory and YAML agent loading APIs in production code until they stabilize, as they carry no backward-compatibility guarantee.
Provides a lightweight web UI and OpenAI-compatible API server for discovering, registering, and running agents and workflows built with the Microsoft Agent Framework.
However, do not use it for production—it is explicitly a sample app, and the documentation recommends building your own custom interface for production deployments.
Integrates Microsoft Agent Framework agents with the Durable Task framework to enable state persistence, conversation history replay, and automatic failure recovery.
Provides Microsoft Foundry integrations for the Agent Framework, including chat clients, preconfigured agents, embedding clients, memory providers, and toolbox management for building AI agents on Azure.
Integrates Agent Framework agents and workflows with the Foundry Agent Server, providing durable state persistence (sessions, checkpoints, approvals) with automatic user isolation when hosted on Foundry infrastructure.
Integrates Microsoft Agent Framework with Foundry Local, enabling local development and testing of production-grade AI agents and multi-agent workflows using Microsoft Foundry as the LLM provider.
Integrates GitHub Copilot's agentic capabilities into the Microsoft Agent Framework, enabling tool use and permission handling through Copilot's native SDK.
Install it if you are already using the Microsoft Agent Framework and want to leverage GitHub Copilot's native agentic capabilities with built-in permission handling.
Integrates sandboxed code execution into Microsoft Agent Framework agents, allowing them to run Python code safely inside a Hyperlight sandbox with controlled tool access and network permissions.
The main gotcha is platform availability of the Wasm backend—verify it is published for your target platform before relying on it in production.
Provides experimental lab modules (GAIA, TAU2, Lightning) built on Microsoft Agent Framework for benchmarking, evaluating, and training agents with research prototypes and incubating features.
Integrates Mem0 persistent memory into Microsoft Agent Framework agents, enabling agents to remember user preferences and conversation context across sessions and threads.
Integrates Ollama LLM provider support into Microsoft Agent Framework, enabling agents to run local or remote Ollama models as their reasoning backbone.
Provides OpenAI and Azure OpenAI integration for Microsoft Agent Framework, with chat clients using the Responses API or Chat Completions API, plus an embedding client.
Install it if you are building agents with OpenAI models; skip it if you do not use the Agent Framework or prefer direct OpenAI SDK calls.
Provides high-level builder patterns for orchestrating multi-agent workflows in Microsoft Agent Framework, including sequential, concurrent, handoff, group chat, and Magentic One orchestration strategies.
Install it if you are composing multiple agents and want to avoid reimplementing coordination logic.
Adds Microsoft Purview data governance and DLP policy enforcement to the Microsoft Agent Framework, blocking or allowing prompts and model responses based on centrally managed security policies.
However, the Beta status and recent release date mean you should expect API changes and test thoroughly before production use.
Adds Redis-backed persistent memory and conversation history to Microsoft Agent Framework agents, enabling them to remember context and chat history across sessions and application restarts.
Unified installer that bundles policy enforcement, agent identity and trust infrastructure, execution supervision, and reliability monitoring into a single governance stack for production AI agents.
However, the package is in public preview with potential API changes; evaluate your tolerance for breaking changes before general availability.
Provides CLI tools, SRE observability, and sandbox isolation for managing and governing AI agents, consolidating agent-sre, agt-sandbox, and agentmesh-mcp-trust into a single distribution.
Provides a unified runtime kernel and trust layer for building, executing, and governing multi-agent systems with audit trails, privilege isolation, and identity management.
Agent Lifecycle Toolkit provides modular components that integrate into agent pipelines to improve performance across reasoning, tool calling, error detection, and output validation stages.
However, the unclear license status requires clarification before production deployment, and the large dependency footprint (23 runtime packages) means careful…
A Python SDK for accessing sandbox, shell, file, Jupyter, Node.js, and MCP services through an All-in-One Sandbox API, with both sync and async support.
A batteries-included harness for building Pydantic-AI agents with an in-process knowledge graph, orchestration, memory, and tools—deployable as a library, MCP server, or REST gateway.
The main gotcha is the Python 3.11+ requirement and the need to configure a model provider API key.
Provides evaluators and utilities to assess agent trajectories—the intermediate steps LLM-based agents take while solving problems—using both rule-based matching and LLM-as-judge approaches.
However, the aging maintenance status (386 days since last release) means you should verify that it remains compatible with your LLM client versions and check whether…
Provides a typed Python REST client for the Agentex API with both synchronous and asynchronous interfaces, automatically generated with full request and response type definitions.
Install it if you are building on the Agentex platform.
Agent Lightning optimizes AI agents using reinforcement learning, prompt optimization, and fine-tuning without requiring code rewrites to your existing agent framework.
However, the unclear license, aging maintenance (233 days since last release), and 20 heavy dependencies make this a research-stage tool best suited for teams with…
Agentmail provides a Python client library for accessing the Agentmail API, supporting both synchronous and asynchronous operations, including real-time websocket connections.
AgentOps provides observability and monitoring for AI agents, capturing execution traces, LLM costs, and session replays with minimal code integration.
Install it if you are building or debugging AI agents and want production observability without writing custom instrumentation—the decorator-based API is lightweight…
AgentQL is a Python client for interacting with web pages using AI-driven queries instead of traditional selectors, built on Playwright for browser automation.