LangGraph vs CrewAI: explicit graphs or role-based crews?
Updated By Agent Radar6 min read
Short answer
LangGraph vs CrewAI compared from docs and repos checked 2026-11-02: graph state and checkpointers vs Crews and Flows, human review, persistence, license.
Top pick: LangGraph. LangChain Inc's MIT-licensed low-level orchestration framework for long-running, stateful agents: you define a graph of nodes over a shared state, and a checkpointer persists every step.
Pick LangGraph if you need to see and control every step of an agent: an explicit graph, state saved at each step, and runs you can pause for approval and resume after a crash. Pick CrewAI if your problem is a team of specialists, such as researcher, writer and reviewer, and you want that team running quickly with role-based agents, then add Flows for the fixed parts. Both are MIT-licensed. Sources checked 2026-11-02.
Quick comparison
| LangGraph | CrewAI | |
|---|---|---|
| Level | Low-level orchestration runtime | High-level agents, tasks, crews, plus Flows |
| Core abstraction | Graph of nodes and edges over shared state | Agents (role, goal, backstory) doing tasks in a Crew |
| Multi-agent | You wire it, or use Deep Agents on top | Built in: sequential or hierarchical process |
| Deterministic control | The graph itself | Flows (@start, @listen, @router) |
| Persistence | Checkpointer per thread_id | @persist on Flows (SQLite by default) |
| Human review | interrupt() + Command(resume=...) | @human_feedback in Flows |
| Languages | Python, plus LangGraph.js | Python >=3.10, <3.14 |
| Latest release checked | langgraph 1.2.19 (PyPI, October 30, 2026, UTC) | crewai 1.15.34 (November 1, 2026, UTC) |
| Paid layer | LangSmith, LangSmith Deployment | CrewAI AMP Suite |
| GitHub stars (2026-11-02) | about 44,300 | about 60,400 |
The column order follows the query wording, not a ranking.
How they differ
Who designs the process
The LangGraph README calls it a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents." You decide the nodes, the edges and the state schema. That is more code up front, and in exchange nothing about the control flow is hidden. For a higher-level start, the README points to Deep Agents, a package on top of LangGraph for planning, subagents and file systems.
CrewAI starts from the team. Its README describes Crews as role-based agents "optimized for autonomy and collaborative intelligence." With the sequential process, tasks run in order. With the hierarchical process, a manager model (manager_llm) or custom manager agent (manager_agent) delegates tasks, per the processes docs. You describe who does what, and CrewAI decides the hand-offs.
Fixed steps inside an agent system
In LangGraph, the graph is the fixed structure, and agentic behaviour lives inside nodes. In CrewAI, the fixed structure is a Flow: methods decorated with @start, @listen and @router, with typed or dict state. A Flow can call a whole Crew as one step. The README frames it as Crews for autonomy and Flows for control, used together.
Pausing, resuming and recovering
LangGraph's interrupts docs list three requirements: a checkpointer that persists graph state ("use a durable checkpointer in production"), a thread_id that tells it which state to load, and an interrupt() call where you want to pause. You resume with Command(resume=...), and the value comes back as the return value of interrupt(). The same checkpoints give durable execution: the README says agents "automatically resum[e] from exactly where they left off" after failures.
CrewAI Flows use @persist, at class or method level, with SQLite by default. kickoff(inputs={"id": ...}) resumes a saved flow, and kickoff(restore_from_state_id=...) forks it into a new run. Human review is the @human_feedback decorator, which needs CrewAI 1.8.0 or later.
Dependencies
LangGraph 1.2.19 depends on langchain-core, its own checkpoint, prebuilt and SDK packages, pydantic and xxhash. The README says it "can be used without LangChain." CrewAI 1.15.34 has a larger tree: openai, instructor, chromadb, lancedb, mcp and OpenTelemetry packages, among others. Neither langgraph nor langchain is in that list, so the old idea that CrewAI wraps LangChain is out of date.
Operations and telemetry
LangGraph pairs with LangSmith for tracing and evals and with LangSmith Deployment for hosting long-running, stateful workflows. CrewAI's AMP Suite adds managed deployment, observability, governance and enterprise support, on-premise or in the cloud. CrewAI collects anonymous telemetry by default. Its README says prompts and task data are not collected unless you set share_crew=True, and OTEL_SDK_DISABLED=true disables it.
Which one to choose
- Approval steps, long jobs that must survive restarts, or audited control flow: LangGraph. Checkpoints and interrupts are its core features, not add-ons.
- Content, research or analysis pipelines with clear roles: CrewAI.
crewai create crewscaffolds the agents and tasks as YAML, and roles map straight onto the pipeline. - A mostly fixed pipeline with one agentic step: either one. A LangGraph graph with one agent node, or a CrewAI Flow that calls one Crew.
- A TypeScript codebase: LangGraph, through LangGraph.js. CrewAI is Python only.
- Python 3.14: LangGraph (
>=3.10). CrewAI currently caps at<3.14.
How we compared
We read both READMEs, the PyPI metadata for langgraph 1.2.19 and crewai 1.15.34, the LangGraph interrupts docs, the CrewAI Flows and Processes docs and the CrewAI 1.15.34 release notes, and we pulled repository metadata from the GitHub API. All were checked on 2026-11-02. We did not install, run or benchmark either framework.
Limitations
There are no benchmarks here. Throughput, token use and reliability depend on your models and prompts. LangSmith and CrewAI AMP pricing is not stated in the sources. Star counts change daily.
Related: LangChain vs CrewAI, LangChain alternatives, Agno vs LangGraph, Agno vs CrewAI, LangGraph vs Google ADK and Pydantic AI vs LangGraph. Browse the LangGraph and CrewAI hubs and the frameworks category.
The tools
LangChain Inc's MIT-licensed low-level orchestration framework for long-running, stateful agents: you define a graph of nodes over a shared state, and a checkpointer persists every step.
Pros
- Durable execution: runs resume from the last checkpoint after a failure
- interrupt() pauses any node for human review, resumed with Command(resume=...)
- Python and JavaScript/TypeScript (LangGraph.js)
- Usable without LangChain, per the README
Cons
- Low-level by design: roles, delegation and planning are yours to build, or you add Deep Agents on top
- Hosted deployment and tracing live in LangSmith, a separate product whose pricing is not stated in the cited sources
CrewAI's MIT-licensed Python framework for multi-agent workflows: Crews of role-based agents for autonomy and event-driven Flows for precise control, used alone or together.
Pros
- Agents with role, goal and backstory, tasks and crews are built in
- Sequential or hierarchical processes; hierarchical adds a manager model or agent
- Flows add state, @start/@listen/@router control and @persist resume or fork
- CLI scaffolds, runs and deploys projects (crewai create, run, deploy)
Cons
- Python only, limited to Python 3.10 to 3.13
- Anonymous telemetry is on by default; OTEL_SDK_DISABLED=true turns it off
- Managed deployment and governance are in the commercial CrewAI AMP Suite, whose pricing is not stated in the cited sources
Questions
What is the main difference between LangGraph and CrewAI?
LangGraph is a low-level runtime: you write the graph of nodes and edges, and it gives you state, checkpoints, interrupts and resumption. CrewAI is higher level: you describe agents by role and goal, give them tasks, and a Crew runs them sequentially or under a manager. Flows add explicit control when you need it.
Is LangGraph or CrewAI better for multi-agent systems?
CrewAI if you want multi-agent collaboration without designing it, because roles, delegation and hierarchical managers are built in. LangGraph if you need to control exactly which agent runs when, and to pause, inspect or resume that process at any step.
Is CrewAI built on LangGraph or LangChain?
No. Neither langgraph nor langchain appears in the crewai 1.15.34 dependencies on PyPI. CrewAI has its own runtime, with LiteLLM as an optional extra for model access. LangGraph, for its part, can be used without LangChain.
Do LangGraph and CrewAI support human-in-the-loop?
Yes. In LangGraph you call interrupt() inside a node, which needs a checkpointer and a thread_id, and resume with Command(resume=...). In CrewAI, Flows have a @human_feedback decorator (CrewAI 1.8.0 or later), and @persist lets a flow resume from its saved state by ID.
Are LangGraph and CrewAI free?
Both frameworks are MIT licensed according to the GitHub API. The paid layers are separate: LangSmith (observability and LangSmith Deployment) for LangGraph, and the CrewAI AMP Suite for CrewAI. Neither price is stated in the cited sources.