Master Microsoft Agent Framework with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Microsoft Agent Framework powerful for ai agent framework workflows.
Choose between agent orchestration (AutoGen-style LLM-driven agents that reason and collaborate dynamically) and workflow orchestration (Semantic Kernel-style deterministic pipelines with business logic). Use them separately or combine them — an agent can trigger a deterministic workflow, or a workflow can delegate a step to an autonomous agent.
A customer support system where a deterministic workflow handles ticket routing and SLA tracking, but delegates the actual response generation to an LLM-driven agent that reasons about the customer's issue.
Consistent APIs across both Python and C#/.NET — same concepts, same patterns, just different language idioms. This isn't a Python framework with a .NET wrapper; both are first-class citizens with dedicated SDKs.
An enterprise with a .NET backend that wants to add AI agents without introducing Python into their deployment pipeline. The .NET SDK lets them build agents that integrate natively with their existing services.
Built-in support for group chats (multiple agents discussing a problem), reflection (agents reviewing their own output), sequential handoffs, and parallel execution. Inherited from AutoGen's research-proven patterns.
A code review system where a 'reviewer' agent identifies issues, a 'fixer' agent proposes solutions, and a 'validator' agent checks the fixes — all coordinated through the framework's group chat pattern.
Save agent state at any point and restore it later. Useful for debugging long-running agent workflows, implementing retry logic, and creating reproducible test scenarios. 'Time-travel' lets you replay from any checkpoint.
Debugging why a multi-agent financial analysis went wrong by replaying from the checkpoint just before the error, with different model parameters or tool configurations.
Model Context Protocol (MCP) integration for connecting agents to external tools, and Agent-to-Agent (A2A) protocol support for cross-framework agent communication. This means your agents can use tools from the broader MCP ecosystem and communicate with agents built on other frameworks.
An agent built with Microsoft Agent Framework calling tools exposed by a LangChain-based service through MCP, or communicating with a Google ADK agent via A2A protocol.
For new projects, the public preview is stable enough for development and testing. Azure AI Foundry Agent Service (which uses this framework) reached GA in May 2025, so the production infrastructure is proven. For mission-critical deployments, consider waiting for framework GA in Q1 2026. For learning and development, start now.
AutoGen is in maintenance mode — it'll get security patches but no new features. You should plan to migrate, but there's no urgent deadline. The Agent Framework preserves AutoGen's core concepts (agents, group chats, tool use), so migration is more about namespace changes than architectural rewrites.
LangChain has a much larger ecosystem (more integrations, tutorials, community examples) and is more mature for Python developers. Microsoft Agent Framework wins on .NET support (LangChain has none), multi-agent orchestration patterns, and Azure integration. For Python-only teams, both are viable; evaluate based on your cloud provider and orchestration needs.
Yes. The framework supports any model provider through its model client abstraction — OpenAI, Anthropic, local models via Ollama, etc. Azure OpenAI gets the tightest integration, but the framework is not locked to Microsoft models.
AF Labs is the experimental package that ships alongside the main framework, containing cutting-edge features that aren't yet stable enough for the core SDK. Think of it as a staging area for new capabilities. Use it for experimentation, not production.
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Tutorial updated March 2026