What AI agent framework should I use?

It depends on what you’re building. Here’s a practical way to choose:

Best general-purpose picks

• LangGraph: Best if you want reliable, stateful, multi-step agents with branching, loops, and human-in-the-loop control.
• AutoGen: Best for multi-agent collaboration and conversational agent workflows.
• CrewAI: Best for simple role-based agent teams and faster prototyping.
• Semantic Kernel: Best if you’re in .NET/C# or want strong integration with enterprise apps.
• LlamaIndex: Best if your agent is heavily data/retrieval/document-centric.

Simple recommendation by use case

• Production agent with complex workflow → LangGraph
• Multiple agents discussing/working together → AutoGen
• Fast MVP with easy abstractions → CrewAI
• RAG-heavy assistant over docs/knowledge base → LlamaIndex
• Microsoft ecosystem / .NET → Semantic Kernel

If you want the shortest answer

• Choose LangGraph unless you have a very specific reason not to.
• Choose CrewAI if you want something easier and more “out of the box.”
• Choose AutoGen if your app is genuinely multi-agent.

What matters most when deciding

1. Workflow complexity
   Do you need loops, branching, retries, tool calling, and checkpoints?
2. State management
   Will the agent need memory across steps/sessions?
3. Reliability
   Are you building something that must behave predictably in production?
4. Multi-agent vs single-agent
   Most apps do not need multiple agents.
5. Ecosystem fit
   Python, JavaScript, .NET, cloud stack, vector DB, etc.

My default recommendation

If you’re unsure, start with:

• LangGraph for a robust base
• Add LlamaIndex if retrieval over your own data is a major part
• Use AutoGen only if you specifically need agent-to-agent collaboration

If you tell me:

• your language/framework,
• whether it’s production or prototype,
• and what the agent needs to do,

I can recommend one framework very specifically.