Complete pricing guide for Microsoft Agent Framework. Compare all plans, analyze costs, and find the perfect tier for your needs.
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Pricing sourced from Microsoft Agent Framework · Last verified March 2026
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.
AI builders and operators use Microsoft Agent Framework to streamline their workflow.
Try Microsoft Agent Framework Now →Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
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