Short answer: Evaluate LangGraph when explicit control over stateful, long-running workflows matters most; CrewAI when agents, crews, and flows match how you want to model the work; and AutoGen mainly when you already have an application to maintain or migrate. Microsoft says new users should start with Microsoft Agent Framework, AutoGen’s successor. This is a comparison of documented capabilities and project status—not a six-month hands-on test or a performance ranking.
What separates LangGraph, CrewAI, and AutoGen?
The main difference is how each framework asks you to think about orchestration. LangGraph puts the workflow graph and its state at the center. CrewAI organizes work around agents and crews coordinated by flows and task processes. AutoGen provides conversational agents through AgentChat and an event-driven Core runtime, but its maintenance status makes lifecycle and migration central to a new-project decision.
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| Framework | Core model | Consider it when | Documented lifecycle position |
|---|---|---|---|
| LangGraph | Explicit graph and state, combining deterministic nodes with model-driven steps. | You need fine-grained workflow control, branching, persistence, or human checkpoints. | Its current documentation presents it as a framework and runtime for long-running, stateful agents. |
| CrewAI | Agents and crews coordinated through flows and task processes. | Role-based agents, tasks, and flows are a natural way to express the work. | Its current documentation covers agents, crews, flows, and enterprise features. |
| AutoGen | Conversational AgentChat abstractions and an event-driven Core runtime. | You are supporting an existing application and have assessed maintenance and migration implications. | Microsoft says AutoGen is in maintenance mode and recommends Microsoft Agent Framework to new users. |
This is a selection guide, not a ranking. The documented capabilities do not establish that one framework is faster, more reliable, or easier to operate for your workload.
How does each framework handle control and state?
LangGraph: make the workflow explicit
LangGraph describes itself as a low-level orchestration framework and runtime for long-running, stateful agents. Its graph model lets you combine deterministic steps with model-driven steps, which is useful when application logic needs defined transitions rather than an agent deciding every next action. Its overview describes persistence through failures, human oversight, memory, streaming, and deployment.
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Those are capabilities to build with, not guarantees that an application will recover correctly. The application still depends on sound state design, durable infrastructure, and careful handling of interrupts and retries. Evaluate whether your team wants this degree of explicit control and is prepared to implement and operate it.
CrewAI: express collaboration with agents, crews, and flows
CrewAI’s documentation centers on agents, crews, and flows. Its task processes include sequential, hierarchical, and hybrid patterns, while flows provide stateful workflow coordination. Documentation also describes persistence, resumption of long-running workflows, and human-in-the-loop triggers.
This model may fit teams whose work is naturally described through agent roles and coordinated tasks. Before committing, inspect how the exact version you plan to use handles state persistence, resumption, guardrails, and deployment; feature names alone do not tell you how much application-specific work remains.
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AutoGen: account for maintenance mode
AutoGen combines conversational AgentChat abstractions with an event-driven Core runtime. Microsoft’s repository states: “AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward.” Microsoft also says: “New users should start with Microsoft Agent Framework.”
That status does not mean an existing AutoGen system must stop working. It does change the decision: assess compatibility with the versions and integrations you rely on, your support expectations, and the effort to maintain or migrate framework-specific code. Do not assume that broad feature descriptions prove parity between AutoGen and its successor.
Which framework fits your project?
- Choose LangGraph for evaluation when the workflow has meaningful branches, explicit state transitions, long-running execution, durable recovery, or approval checkpoints—and when you want those transitions represented directly.
- Choose CrewAI for evaluation when agent roles, crews, and flows map cleanly to the work your application performs, and those abstractions clarify rather than obscure orchestration.
- Assess AutoGen for an existing system by weighing maintenance-mode implications against the cost and risk of migration. For a new project in Microsoft’s ecosystem, evaluate Microsoft Agent Framework as the stated successor.
Microsoft offers a useful, vendor-specific heuristic for its Agent Framework: use agents for open-ended or conversational work and workflows when steps are defined and execution order needs control. Its guidance also says to use a regular function instead of an agent when a function can do the job. Treat this as Microsoft’s design advice, not a universal rule for every architecture.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you test before choosing?
Compare the frameworks using the same representative workflow and the same model, tools, and operating conditions. Measure implementation effort and operational behavior for your application rather than expecting a documentation comparison to predict either.
- Workflow fit: Implement a typical path plus a branch, a failed step, and an approval pause. Check whether the framework makes the intended control flow clear.
- State and recovery: Interrupt a long-running workflow, restart it, and verify what state is preserved and what work is repeated. Confirm how persistence is configured in the version under consideration.
- Human approvals: Test how an operator inspects or changes state, approves or rejects a step, and resumes execution.
- Integrations and operations: Verify language and runtime fit, model and tool integrations, observability, hosting, security controls, and deployment requirements.
- Ownership cost: Review licensing and support expectations, the amount of framework-specific code, upgrade obligations, and—if using AutoGen—the migration path you would accept.
The available documentation establishes neither comparative prices nor benchmark results for these three frameworks. A hosted or enterprise service wrapped around a framework is also a separate choice: evaluate its packaging, availability, and operational terms independently rather than treating them as properties of the open-source framework.
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What does ecosystem research tell you—and what does it not?
A 2026 study by Liu, Upadhyay, Chhetri, Siddique, and Farooq analyzed eight selected open-source projects, covering 42,267 unique commits and 4,731 resolved issues. Across the analyzed projects, it classified 40.83% of maintenance as perfective, 27.36% as corrective, and 24.30% as adaptive; 22% of normalized issue labels were bugs, 14% infrastructure, and 10% agent issues.
These are ecosystem-level figures, not head-to-head scores for LangGraph, CrewAI, and AutoGen. They do not establish framework quality, current activity, or comparative reliability. The study also notes that GitHub open-source results may not generalize to proprietary systems and that repository metrics do not directly measure code quality or design rationale.
How should you decide?
Start with the shape of the workflow and the lifecycle you are willing to own. If explicit state transitions and recovery dominate, put LangGraph on the shortlist; if agent-and-crew abstractions fit the work, evaluate CrewAI; if you already run AutoGen, make maintenance and migration a deliberate operational decision. Then validate the fit with a small representative implementation, including failure recovery and human approval—not a feature checklist alone.
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