A practical comparison of the four frameworks developers actually reach for when building autonomous AI agents — LangChain/LangGraph, CrewAI, AutoGPT and Microsoft AutoGen. Pick one based on how your problem is shaped, not on stars.
| Framework | Language | Multi-agent | Tool use | Deployment |
|---|---|---|---|---|
| LangChain / LangGraph | Python, JS/TS | Yes (LangGraph) | Built-in, hundreds of integrations | Self-host or LangSmith / LangGraph Cloud |
| CrewAI | Python | First-class (roles, tasks, processes) | Yes, native + LangChain tools | Self-host or CrewAI Enterprise |
| AutoGPT | Python | Limited | Yes via blocks / plugins | Self-host (Platform UI) or cloud beta |
| Microsoft AutoGen | Python, .NET | First-class (conversable agents) | Yes, including code execution | Self-host; integrates with Azure AI |
The most widely-used agent framework. LangGraph adds a state machine for multi-step, multi-agent flows on top of LangChain's runnables and integrations.
Best for: General-purpose RAG + tool-using agents with deep ecosystem
Designed around a 'crew' of specialized agents with defined roles and goals. Cleaner mental model than LangChain when the problem is naturally collaborative.
Best for: Role-based multi-agent crews that collaborate on tasks
Pioneered the autonomous-agent loop. The modern platform exposes a block-based visual builder for chaining tools and LLM steps without code.
Best for: Autonomous long-running goal-seeking agents with a visual builder
Microsoft Research's framework for agents that talk to each other. Strong at having agents write, execute and critique code in a loop.
Best for: Research-grade multi-agent conversations and code-executing agents
A library or platform for building LLM-powered programs that plan, call tools, and act autonomously — not just generate one response.
LangChain/LangGraph for general use, CrewAI for multi-agent collaboration, AutoGen for code-executing research agents, AutoGPT for goal-seeking automation with a visual builder.
No — simple tool-using agents are fine with raw API calls. Frameworks pay off once you need memory, retries, multi-agent coordination, or production tracing.
The open-source cores are free. Hosted tiers (LangGraph Cloud, CrewAI Enterprise, AutoGPT Cloud) are paid.