What Are the Best Frameworks for Developing AI Agents?

There is no best AI agent framework. There is only the framework whose lock-in you can live with — and the honest answer most vendor roundups won't give you is that the orchestration you build today is the part you will have to throw away tomorrow.

Every framework on the market does the same five jobs: run the loop, call tools, keep memory, plan, and leave a trace you can inspect. What actually differs is how much of the loop they seize from you — and how painful the exit is when you outgrow them. Gartner says 40% of enterprise AI deployments will be multi-agent by 2027. That long tail is exactly why the choice matters more than the demo.

The verdict, framework by framework

• LangGraph is the control freak's pick — and the honest one. You model the agent as an explicit graph of nodes and edges with typed state, and its durable execution is the real deal: checkpoint after every super-step, resume from a thread ID after a crash, human-in-the-loop via interrupt(). Version 1.2.20, ~44,700 stars, MIT. The catch nobody mentions: the dev server is Elastic-2.0, so you can't offer it as a managed service. Control has a price, and this one is printed in the licence.
• CrewAI sells you a team; AutoGen sells you a conversation. CrewAI's roles, goals and backstories map neatly onto human teams — 60,700 stars, the biggest crowd here — while AutoGen's agents argue it out in structured chat. But here's the plot twist the article buries: Microsoft already moved on. Microsoft Agent Framework hit 1.0 in April 2026 as the successor to both, and AutoGen now gets bug fixes and security patches only. If you're starting fresh on AutoGen, you're building on a framework its own maker has retired.
• Semantic Kernel is the proven incumbent, not the future. Microsoft's SDK for C#, Python and Java is genuinely good at wrapping existing enterprise APIs as plugins with a few lines of code. But Microsoft has told you, plainly, that Agent Framework is the path for new work. Treat Semantic Kernel as the safe choice for a .NET shop with legacy systems — not the default.
• The OpenAI Agents SDK is refreshingly thin — and deliberately unfinished. A handful of primitives, a Runner that handles the loop, tracing on by default. Version 0.24.0, below 1.0, interface not yet stable. Fine for a small self-hosted agent on OpenAI models; don't build your company's agent platform on a pre-1.0 API and act surprised when it moves.
• Google ADK is the dark horse. Code-first, model-agnostic through LiteLLM, speaks the Agent-to-Agent protocol natively, ships a real evaluation harness, deploys to Cloud Run or anywhere Python runs — not tied to Google Cloud despite the name. Version 2.14.1, Apache 2.0. The most complete package here that nobody is hyping.
• LlamaIndex is the data-first answer — and only that. If your agent's job is answering over your own documents, its retrieval-grounded agents and FaithfulnessEvaluator are the right tool. If the job is mostly acting, you're using a screwdriver as a hammer.

The part everyone skips: price the exit

Prompts, plain-function tools and evaluation cases will probably carry over. The orchestration won't — a LangGraph graph, a CrewAI crew and an AutoGen group chat are built on different models, and checkpoint formats don't travel. Assess each part for reuse before you commit, not after you're locked in.

Match the framework to the job: solo assistant, go OpenAI Agents SDK or LangGraph for long jobs that must survive restarts. Team of specialists, CrewAI for roles or Microsoft Agent Framework for critique-and-revise. Enterprise workflow, Semantic Kernel for .NET, Google ADK for Google Cloud, LlamaIndex for retrieval over internal docs.

Go deeper

Read the full comparison — including the six decision criteria, the deployment matrix, and working code skeletons for LangGraph, CrewAI and the OpenAI Agents SDK — on Agentic Affairs:

What Are the Best Frameworks for Developing AI Agents?

Originally published on Agentic Affairs (https://agentic-affairs.com)