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LangGraph vs CrewAI vs AutoGen: three different ways to build multi-agent AI

LangGraph, CrewAI, and AutoGen all let you wire multiple LLM calls together, but they solve orchestration in genuinely different ways - an explicit state graph, role-based "crews," and conversational actors. All three are open source and MIT-licensed, all three now ship a managed cloud layer, and Microsoft has moved AutoGen into maintenance mode in favor of its new Agent Framework. Below is what we verified, side by side, so you can match the tool to your own constraints.

At a glance

LangGraph

Maintained by LangChain, Inc.

Low-level graph orchestration framework for building durable, stateful agents.

LicenseMIT
GitHub stars~35k (approx., mid-2026)

CrewAI

Maintained by crewAI, Inc.

Role-based multi-agent "crews" framework built for fast, production agentic automation.

LicenseMIT
GitHub stars~54k (approx., mid-2026)

AutoGen

Maintained by Microsoft

Conversational multi-agent framework on an async actor runtime; now in maintenance mode.

LicenseMIT (code)
GitHub stars~50k (approx., mid-2026)

Side-by-side facts

LangGraph
CrewAI
AutoGen
Orchestration model
Explicit state graph - nodes and edges, conditional routing, subgraphs
Role-based crews (agents + tasks), plus event-driven "Flows" for deterministic control
Conversational multi-agent chat on an async actor-model runtime
Primary language
Python (also a JS/TS package, LangGraph.js)
Python
Python (Microsoft Agent Framework adds first-class .NET)
State & memory
Typed state object threaded through the graph; built-in checkpointer persists state at every step
Shared task context by default; Flows add an explicit, typed state class for structured runs
Conversation history plus actor-runtime state; newer builds support explicit state save/load
Multi-agent pattern
Any topology you draw - sequential, branching, cyclical, supervisor, or subgraph-of-agents
Sequential or hierarchical crews of role-playing agents collaborating on shared tasks
Group chat, two-agent chat, and nested conversations between addressable agents
Managed offering
LangGraph Platform (managed deployment) + LangSmith (tracing/observability)
CrewAI AMP (formerly Enterprise) - managed deployment, monitoring, governance
None dedicated; Microsoft points new managed work to Azure AI Foundry Agent Service / Microsoft Agent Framework
License
MIT, fully open source
MIT for the framework; AMP management layer is a separate commercial product
MIT for code, CC-BY 4.0 for docs
Learning curve
Steepest of the three - requires a graph-design mental model, more boilerplate for a simple agent
Easiest - the role/task abstraction reads close to plain English
Moderate to build with, but multi-party conversation loops can be hard to debug
Best-known for
Durable, production-grade stateful agents with checkpointing and human-in-the-loop
Fastest path from idea to a working role-based agent team
Pioneering conversational multi-agent research patterns; now in maintenance mode

Strengths & trade-offs

LangGraph

  • ✓Explicit graph control over branching, loops, and retries
  • ✓Built-in checkpointing and durable execution across long-running runs
  • ✓First-class human-in-the-loop and streaming primitives
  • ✓Deep observability via LangSmith tracing
  • ✕Steepest learning curve of the three - needs a graph-design mindset
  • ✕More boilerplate to stand up even a single simple agent

Best if you need production-grade reliability and fine control over exactly how state moves through a multi-step agent.

CrewAI

  • ✓Role/task abstraction that reads close to plain English
  • ✓Fastest of the three to prototype a working multi-agent team in
  • ✓Not tied to any one LLM framework's ecosystem
  • ✓Large certified-developer community and course library
  • ✕Standard print/log calls behave inconsistently inside a Task, complicating debugging
  • ✕Failure-recovery patterns are less mature than LangGraph's checkpointing

Best if you want a role-based agent team running with minimal setup and don't need deep low-level execution control.

AutoGen

  • ✓Flexible conversational patterns - two-agent, group chat, nested chats
  • ✓Mature async, actor-based runtime with typed message passing
  • ✓Strong research pedigree and existing integrations
  • ✕Entered maintenance mode in October 2025 - security/bug fixes only, no new features
  • ✕Microsoft is steering new projects to Microsoft Agent Framework instead
  • ✕Debugging multi-party conversation loops can get complex

Best if you're maintaining an existing AutoGen deployment or specifically need its conversational chat patterns - evaluate Microsoft Agent Framework first for anything new.

How to choose

  • You want explicit control over branching/looping logic plus durable checkpoints→LangGraph
  • You want the fastest way to stand up a role-based agent team→CrewAI
  • You're deep in the Azure/.NET stack or starting a new Microsoft-ecosystem project→Microsoft Agent Framework (AutoGen's successor)
  • You already run AutoGen and need flexible group conversations between agents→AutoGen (maintenance mode)
  • You want managed hosting and tracing bundled with the framework itself→LangGraph Platform + LangSmith, or CrewAI AMP
  • You're a small team optimizing for the lowest learning curve→CrewAI

Sources

GitHub - langchain-ai/langgraph GitHub - crewAIInc/crewAI GitHub - microsoft/autogen Microsoft Learn - Agent Framework overview

No overall winner is declared by design - the reader draws their own conclusion.

Facts current as of July 2026 - GitHub star counts are approximate and move constantly; verify current numbers and framework status before relying on them for a decision.