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
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.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
Related tool stacks
The tools that run it in production.
- →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.