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LangGraph vs CrewAI vs AutoGen

LangGraph vs CrewAI vs AutoGen is a multi-agent framework comparison of three open-source Python libraries for building agents that call tools and work together. LangGraph models an agent as a state graph, CrewAI as a team of role-based agents with tasks, and AutoGen as a conversation between agents.

LangGraph: A graph framework from the LangChain team, with explicit nodes, edges and a shared state object.
CrewAI: A role-based framework, where agents with goals complete tasks inside a crew.
AutoGen: A Microsoft framework, where agents exchange messages in a group chat until a termination condition is met.
Control: The main difference is how much of the execution order the developer defines explicitly. The contrast is sharpest in LangGraph vs AutoGen: LangGraph fixes the order in a graph, while AutoGen lets it emerge from the conversation.
Common ground: All three AI agent frameworks support tool calling, several model providers and some form of human input.

LangGraph vs CrewAI vs AutoGen: three control styles

For example, the same incident triage agent uses read_logs, get_metrics and list_recent_deploys to find the cause of a 5xx spike on the checkout service in each framework. In every version, a production rollback needs human approval.

Quick Answer

LangGraph suits production agents that need explicit control flow, durable state and approval pauses. CrewAI suits workflows that divide naturally into roles and can be prototyped quickly. AutoGen suits research and experiments with conversational multi-agent patterns, although new projects should first confirm its maintenance status.

LangGraph vs CrewAI vs AutoGen: Comparison Table

Aspect	LangGraph	CrewAI	AutoGen
Control model	Explicit graph of nodes and edges, including conditional edges and loops	Tasks run by a process, either sequential or hierarchical with a manager	Agents take turns in a conversation, chosen by a team strategy
State	Typed state object, saved by a checkpointer after each step	Task outputs passed forward through context, plus optional memory	Message history shared inside the team
Human-in-the-loop	Built-in interrupts pause a run and resume it later	Task-level human input, or approval code around the crew	A user proxy agent or a handoff that returns control to a person
Multi-agent style	Supervisor, network or subgraph patterns built from nodes	Role-based crews with delegation between agents	Group chats such as round-robin or model-selected speakers
Learning curve	Steeper, because the graph must be designed first	Gentle, because roles and tasks read like a team plan	Moderate, and the API changed between major versions
Typical use	Long-running production agents with audits and approvals	Content, research and triage pipelines with clear roles	Research prototypes and conversational agent experiments
Observability	Tracing through LangSmith or standard logging	Verbose logs and integrations with tracing tools	Message logs and event streams from the runtime

When to Use LangGraph

Explicit flow: Every transition must be visible in code, reviewed and tested like the rest of a service.
Durable runs: A run must survive restarts or wait hours for an approval without losing state.
Branching: The agent needs retries, loops and conditional routes that a linear process cannot express.
Guidance: The LangGraph tutorial builds the incident agent step by step.

When to Use CrewAI

Clear roles: The work splits into specialists, such as analysts, reviewers and writers.
Fast prototypes: A team wants a working multi-agent pipeline with little orchestration code.
Readable configuration: Roles, goals and tasks should be understandable to reviewers who do not write the code.
Guidance: The CrewAI tutorial builds the same triage crew with an approval gate.

When to Use AutoGen

Conversational patterns: The design depends on agents debating, critiquing or handing tasks to each other through messages.
Research: The goal is to test multi-agent systems ideas rather than ship a long-lived service.
Existing projects: A codebase already uses AutoGen and a migration is not yet justified.

Example: One Incident Agent, Three Frameworks

LangGraph: Nodes gather_evidence, diagnose and rollback share one state; an interrupt before rollback saves the state and waits for the on-call engineer.
CrewAI: A log analyst, a deploy analyst and an incident lead run three tasks in sequence. The lead only recommends, and plain code asks a person before the rollback.
AutoGen: An assistant agent with the three tools talks to a user proxy agent that stands for the on-call engineer. The rollback runs only after that person approves.
Shared rule: In all three, no agent owns a rollback tool that runs without a human-in-the-loop decision.

Single agent: A single tool-calling agent without orchestration fits the Claude Agent SDK tutorial better.
Low-code: A visual workflow with approval steps is covered in the n8n AI agent lesson.
Coding agent: Interactive investigation inside a repository is covered in the Claude Code tutorial.

Quick Quiz

Pick an answer to check yourself. Nothing is saved.

Question 1 / 3

1. Which framework models an agent as explicit nodes and edges over a shared state?
2. In the incident example, how does CrewAI divide the work?
3. Where does the production rollback approval sit in all three versions of the incident agent?

Frequently Asked Questions

Which is easier to learn, LangGraph or CrewAI?

CrewAI is usually quicker to start, because agents, tasks and crews map directly to a team and its assignments. LangGraph asks the developer to design states, nodes and edges first, which takes longer but gives finer control over each step.

Can LangGraph and CrewAI be used together?

Yes. A LangGraph node can call a CrewAI crew as one step, or a crew can use tools that wrap other code. Mixing frameworks adds dependencies and debugging effort, so it is worth doing only when each part clearly benefits.

Is AutoGen still maintained?

Microsoft has moved new development towards a successor framework, and AutoGen receives mainly maintenance updates. Teams starting a new project should check the project's repository for its current status before choosing it.

Which framework is better for human approval steps?

LangGraph has the most explicit support, because a run can pause at a node, save its state and resume after a person responds. CrewAI and AutoGen can also involve a person, but a production approval is often placed in ordinary code around the agents.

Do these frameworks work with Claude and other model providers?

All three can call models from several providers through provider packages or a shared model interface. The provider key is read from configuration, so the choice of framework and the choice of model are largely independent.