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Choose based on how your system should be orchestrated: LangGraph gives you an explicit graph and state model; CrewAI centers work on agents, crews, and flows; AutoGen remains usable for existing applications, but its maintainers say it is in maintenance mode and direct new users to Microsoft Agent Framework. This is a documentation-based comparison, not a six-month hands-on test or a performance ranking.
How the three frameworks organize agent work
| Framework | Core model | Documented capabilities | Lifecycle guidance |
|---|---|---|---|
| LangGraph | An explicit graph with state, combining deterministic nodes and model-driven steps. | Its overview describes persistence through failures, human oversight, memory, streaming, and deployment. | Its official overview presents it as a low-level orchestration framework and runtime for long-running, stateful agents. |
| CrewAI | Agents and crews coordinated through flows and task processes. | Its documentation describes sequential, hierarchical, and hybrid processes, plus stateful persistent flows, resumption, and human-in-the-loop triggers. | Its current documentation includes flows and enterprise features; check the exact version and offering you plan to use. |
| AutoGen | Conversational AgentChat abstractions and an event-driven Core runtime. | Evaluate the specific version and migration target rather than assuming feature parity from broad comparisons. | Microsoft says the project is in maintenance mode, will receive no new features or enhancements, and is community-managed. It recommends Microsoft Agent Framework for new users. |
These are different orchestration models, not interchangeable labels for the same design. Microsoft describes Agent Framework as the direct successor to AutoGen and Semantic Kernel. Its guidance distinguishes agents for open-ended or conversational work from workflows for defined steps that need explicit execution control; it also recommends a regular function when one can handle the task.
When LangGraph is a better fit
You need to make control flow explicit
Consider LangGraph when branching, state transitions, and the boundary between deterministic code and model-driven decisions are central to the design. The graph model makes those transitions part of the orchestration rather than leaving the whole process implicit in a conversational loop.
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Recovery and human checkpoints matter
LangGraph’s overview documents persistence, resumption after failures, and human oversight that can include inspecting or modifying agent state. Those features are relevant when work spans time, needs approval, or must recover from interruption. They do not by themselves guarantee reliability: the application still depends on sound state design, implementation, and infrastructure.
The framework also documents memory, streaming, and deployment capabilities. Treat those as framework capabilities, not proof that a particular application will meet its operational or availability requirements.
When CrewAI is a better fit
The work maps naturally to roles and teams
CrewAI is worth evaluating when people can describe the process in terms of agents with roles, tasks assigned to a crew, and flows coordinating the work. Its documented process options include sequential, hierarchical, and hybrid arrangements.
You need persistent flows or human triggers
CrewAI’s documentation describes stateful, persistent flows that can resume long-running work, as well as human-in-the-loop triggers in task processes. Confirm how the exact version you are considering handles persistence, guardrails, and deployment; the broad feature description alone does not establish that it meets your requirements.
What AutoGen’s maintenance status means
For an existing application
Maintenance mode does not mean an existing AutoGen application must stop working. It does change the planning question: weigh the application’s current-version compatibility and support expectations against the cost and risk of migration. Microsoft’s repository says AutoGen will receive no new features or enhancements and will be community-managed, and it provides a migration path for existing users.
For a new Microsoft-stack project
Start by evaluating Microsoft Agent Framework, the successor Microsoft recommends to new users, rather than choosing AutoGen simply because it is familiar. Compare the migration or adoption work against your required agent and workflow behavior; the successor relationship does not, by itself, establish drop-in compatibility or identical implementation details.
How to choose for a real project
Map the framework to the work and the operating model—not to a generic feature checklist. Use this sequence to make the decision concrete:
- Describe the workflow. Identify which steps are fixed, where model judgment is needed, where branches occur, and whether the work is conversational or open-ended. If a regular function can do the job, Microsoft advises using one instead of an agent.
- Specify state and recovery. Decide what must persist, how a run resumes after interruption, and whether operators need to inspect or change state. Test those behaviors in the exact version you intend to deploy.
- Mark approval points. Identify where a person must review, approve, or modify a result, then verify that the framework’s documented human-oversight model supports the intended interaction.
- Check integration and operations. Compare language and runtime fit, model and tool integrations, observability, hosting, security, licensing, and the maintenance burden of framework-specific code. Keep the framework separate from any hosted or enterprise service wrapped around it.
- Run a representative evaluation. Implement the same real workflow in the shortlisted options. Record implementation effort, state durability, interrupt-and-resume behavior, observability, and operational burden. Keep model settings, tasks, and evaluation criteria consistent if you want a meaningful comparison.
No controlled head-to-head performance results or comparative prices are established here, so claims that one is fastest, most reliable, or least expensive would be unsupported. A project-specific evaluation is more useful than a universal ranking.
What ecosystem statistics can—and cannot—tell you
A 2026 study by Liu, Upadhyay, Chhetri, Siddique, and Farooq analyzed 42,267 unique commits and 4,731 resolved issues across eight selected open-source projects. Across that study’s dataset, 40.83% of maintenance was classified as perfective, 27.36% as corrective, and 24.30% as adaptive; normalized issue labels classified 22% as bugs, 14% as infrastructure, and 10% as agent issues.
These are ecosystem-level measurements, not a head-to-head score for LangGraph, CrewAI, and AutoGen, and they do not establish framework quality or current activity. The authors also caution that GitHub open-source findings may not generalize to proprietary systems and that repository metrics do not directly measure code quality or design rationale.
Freshness and scope
Project status and documentation described here were checked against sources current on October 7, 2026. Framework documentation and product packaging can change, so verify the current version, deployment model, and migration guidance before committing to a design. This comparison does not establish comparative licensing terms, prices, or benchmark performance.
Quick Recap
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