CrewAI vs AutoGen vs LangGraph
Three approaches to multi-agent orchestration.
/ quick answer
CrewAI is role-driven, AutoGen is conversation-driven, LangGraph is graph-driven. Pick the metaphor that matches how you think about the problem. Three approaches to multi-agent orchestration.
| Dimension | Option A | Option B |
|---|---|---|
| Metaphor | Roles + tasks (CrewAI) | Chats (AutoGen) / State graph (LangGraph) |
| Control | Medium (CrewAI) | Emergent (AutoGen) / Explicit (LangGraph) |
| Debuggability | OK (CrewAI) | Hard (AutoGen) / Best (LangGraph) |
- →Fast start, role decomposition → CrewAI
- →Research / emergent behavior → AutoGen
- →Production, deterministic → LangGraph
/ frequently asked
What is the difference in CrewAI vs AutoGen vs LangGraph?
CrewAI is role-driven, AutoGen is conversation-driven, LangGraph is graph-driven. Pick the metaphor that matches how you think about the problem.
What are the main points of comparison?
Metaphor: Roles + tasks (CrewAI) vs Chats (AutoGen) / State graph (LangGraph) · Control: Medium (CrewAI) vs Emergent (AutoGen) / Explicit (LangGraph) · Debuggability: OK (CrewAI) vs Hard (AutoGen) / Best (LangGraph)
Which one should I choose?
LangGraph for anything you'll ship to real users. CrewAI to prototype fast. AutoGen for research.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
- →Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
- →Agentic Workflow
A workflow where an LLM decides the next step instead of a hard-coded path.
- →Tool Use
An LLM invoking external functions to act beyond text generation.
Related workflows
Turn this into a repeatable process.
- →Autonomous QA Testing
This workflow utilizes AutoGen to create a multi-agent system that autonomously generates test cases, executes them, identifies bugs, and suggests fixes for software applications, enhancing development efficiency.
- →Multi-Agent Code Review
This workflow details how AutoGen can orchestrate a team of AI agents to perform autonomous code reviews, identify issues, and suggest improvements, enhancing code quality and accelerating development cycles.
Related tool stacks
The tools that run it in production.
- →A2A Orchestration Stack
This stack comprises the essential tools and frameworks for designing, deploying, and managing Agent-to-Agent (A2A) communication protocols and multi-agent system orchestration. It provides the infrastructure for complex AI agent collaboration.
- →Multi-Agent Orchestration Stack (CrewAI)
This stack outlines the essential components for deploying a robust multi-agent system using CrewAI, focusing on agent definition, tool integration, and monitoring for collaborative task execution.
Comparisons & alternatives
Pick between the options.
- →OpenAI vs Anthropic for Agents
Which provider builds better production agents in 2026.
- →Cursor Agents vs Devin vs Lovable
Three ways AI writes code for you in 2026.
- →LangGraph vs CrewAI vs AI SDK for Agents
LangGraph for stateful control, CrewAI for fast role-based teams, AI SDK for shipping agents inside a product.
- →OpenAI vs Gemini for Agent Building
Both ship strong models and SDKs — differences are in tool calling, context and pricing.