Autogen

The AI agent framework landscape has shifted significantly in the last 90 days. The framework you chose for your agentic project in January 2026 might not be the right choice in April — and one major player has effectively retired. This is the freshest comparison available as of this morning, covering 8 SDKs and 3 inter-agent protocols. The 8 Frameworks 1. Claude Agent SDK (Anthropic) Anthropic’s first-party SDK for building agents on Claude. Deep integration with Claude Code’s subagent architecture, native MCP tool support, and CLAUDE.md context injection. Best choice if you’re building Claude-native workflows and want tight model-SDK alignment. Not model-agnostic. ...

The biggest battle in AI right now isn’t about which model is most powerful. It’s about who controls the layer between models and the real world. This is the control layer — the orchestration and management tier of agentic AI that routes tasks, governs agent behavior, manages state, and connects models to tools, data, and each other. And every major tech company is racing to own it. What the Control Layer Actually Is Think of it like this: large language models are powerful engines, but they don’t drive themselves. To do useful work at scale, you need infrastructure that: ...

If you’ve tried to build a serious AI agent in 2025 or 2026, you’ve almost certainly hit the same wall: you pick a framework, go deep, and then discover you’re locked in. Want to move from LangChain to AutoGen? That’s not a refactor — that’s a rewrite. Choose Claude Code as your execution environment? Great, until you need to run the same agent in an OpenAI Assistants context. GitAgent is the project that calls this problem by its name and offers a structural solution: a framework-agnostic, git-native specification that lets you define an agent once and deploy it across any of the major orchestration layers without touching your core logic. ...

We’ve covered the “big three” AI agent frameworks — LangGraph, CrewAI, and Pydantic AI — in our 2026 framework decision guide. That guide remains the place to go for deep dives on those three. But the landscape has shifted. GitHub repositories for AI agent frameworks grew 535% between 2024 and 2025. Four more frameworks now have production deployments and genuine trade-offs worth understanding: the OpenAI Agents SDK, Claude SDK (Anthropic), Google ADK, and Dify. ...

Microsoft Agent Framework Reaches Release Candidate — AutoGen + Semantic Kernel Unified The fragmentation era of Microsoft’s agentic AI tooling is officially ending. On February 19, 2026, Microsoft announced that the Microsoft Agent Framework (MAF) has reached Release Candidate for both .NET and Python — unifying AutoGen and Semantic Kernel into a single, coherent open-source SDK. General Availability is targeted for Q1 2026. If you’ve been running AutoGen or Semantic Kernel in production, the clock is ticking. AutoGen is entering maintenance-only mode. ...

Microsoft Agent Framework Reaches Release Candidate — AutoGen + Semantic Kernel Now Unified Enterprise agentic AI just got a major milestone. Microsoft Agent Framework has reached Release Candidate status for both .NET and Python, officially merging two of the most widely-used agent SDKs — AutoGen and Semantic Kernel — into a single, stable platform. General availability is targeted for end of Q1 2026. This is a big deal for anyone building production AI agents at enterprise scale. ...