CrewAI vs LangGraph: Which Multi-Agent Framework to Pick

Why this comparison matters

If you are building a system where several LLM-driven agents split up a task, two open source Python frameworks will come up first: CrewAI and LangGraph. Both carry an MIT licence. Both have big GitHub followings: crewAIInc/crewAI showed about 59,190 stars and LangGraph about 42,439 when scored.tools last scored them on 29 September 2026. Both also sell a hosted platform on top of the free library.

The difference is how much they decide for you. CrewAI has you describe agents by role, goal and task, then group them into a crew that works through the job. LangGraph has you draw the workflow yourself as a graph of nodes and edges over a shared state object, with a checkpointer saving that state as the run goes. The first gets you a working team of agents with less code. The second makes you spell out every step and branch, and in return you can pause, inspect, replay and resume any part of the run.

That trade decides how fast you ship the first version and how much control you have when the hundredth version misbehaves in production. The sections below set out what each vendor documents, what each costs, and which one to pick for specific jobs.

Feature comparison

How the scores break down

These are the criteria scores from the scored.tools rubric. They come from public evidence (repos, docs, pricing pages), not hands-on use.

The ease-of-use row may look backwards, since CrewAI is the higher-level tool. It reflects evidence, not difficulty. CrewAI's ease-of-use claims rest on vendor statements, with no install or onboarding material in the evidence used for scoring. LangGraph has a documented pip install with optional provider extras, which counts for something even though the framework has more concepts to learn. The same pattern drives the API and problem-fit gaps: LangGraph has reference docs, a JavaScript port and named production users (Klarna and Replit, per its GitHub README). CrewAI's enterprise claims have had fewer independent checks.

Abstraction level

CrewAI's model maps onto how people already think about delegating work. You define a researcher agent, a writer agent and an editor agent, give each a goal and some tools, and assign tasks. The framework handles the hand-offs. For workflows that really do look like a small team passing work along, that is a short path to something running. CrewAI also offers a visual editor that exports to Python, so a prototype built in the browser can move into a repo.

LangGraph sits lower. Its own docs call it a low-level orchestration framework. You define a state schema, write each node as a function that reads and updates that state, and wire edges between them, including conditional edges for branching and loops. Nothing is hidden, so nothing is decided for you. The tool data lists the cost plainly: a steep learning curve, documentation that can feel like a lot, and a need to understand agent concepts before you get far.

State, failure and recovery

This is where the two diverge most for production work. LangGraph saves graph state through a checkpointer, so a long-running job can survive a crash and resume where it stopped. Interrupts let you pause the graph for human approval before a risky step, and time travel lets you rewind to an earlier checkpoint, edit state and run forward again. If your agents send emails, move money or write to a database, those controls matter.

CrewAI's hosted platform lists human-in-the-loop, guardrails, testing and training on both its plans, per its pricing page. The evidence available does not show the same level of explicit, developer-controlled state management that LangGraph documents. If you need to know exactly what state the system held at step 7 and replay from there, LangGraph is the better documented choice.

Distribution and integration

CrewAI has a neat trick here: you can export a workflow as an MCP server or as a UI component. If you want other agents or apps to call your crew as a tool, that is a short route. LangGraph's integration story runs through the LangChain family: LangChain model and tool integrations, LangSmith for tracing and evaluation, and LangGraph.js for TypeScript teams. You don't need LangChain to use LangGraph, but the tooling fits together best when you stay inside it.

Pricing comparison

Both libraries are free and MIT licensed. You can self-host either one and pay only for your model API calls and your own infrastructure. The paid layer is the hosted platform.

CrewAI

• Basic (free): visual editor and AI copilot, GitHub integration, shared infrastructure, tracing with OpenTelemetry, and 50 workflow executions a month.
• Enterprise (custom): SSO, RBAC, workload identity, PII redaction and policies, deployment on CrewAI's cloud, your VPC or your own infrastructure, enterprise connectors, a 45-day onboarding, and forward deployed engineering sold a la carte.

There is no published middle tier. Fifty executions a month is enough to prove a workflow works and not enough to run anything real on CrewAI's hosting, so the jump goes straight from free to a sales call. If you self-host the open source framework, that cap doesn't apply.

LangGraph (via LangSmith)

• Developer ($0): one seat, up to 5k base traces a month then pay as you go, community support, no deployment included.
• Plus ($39 per seat per month): unlimited seats at that rate, up to 10k base traces a month, one free small serverless deployment, email support.
• Enterprise (custom): self-hosted and hybrid deployment, custom SSO, ABAC and RBAC, SLA and Slack support.

On top of seats, LangChain meters usage in LangChain Standard Units at $1 per LSU. Per its pricing page, deployment runtime compute costs 0.0675 LSU per vCPU-hour and database compute 0.177 LSU per vCPU-hour. That makes costs predictable to model if you know your load, though the bill has more lines than a flat plan.

On price, LangGraph wins for small teams that want managed hosting: a published $39 seat with an included deployment beats a custom quote. For self-hosters the two cost the same, which is nothing beyond compute and tokens.

Use case scenarios

Prototype a content or research pipeline this week: CrewAI

A researcher, an analyst and a writer handing work down a line is the shape CrewAI was built around. Roles and tasks in plain language, plus a visual editor that exports to Python, give you the shortest route from idea to demo. LangGraph can model the same thing, but you will write more code to get there.

Long-running agents that must survive failure: LangGraph

Checkpointing, durable execution and resume-from-failure are documented first-class features in LangGraph. If a job runs for hours, calls flaky external APIs or waits on a human, pick LangGraph.

Workflows with approval gates: LangGraph

Interrupts let you halt before a sensitive action and wait for sign-off, and time travel lets you correct state and rerun. CrewAI lists human-in-the-loop on its platform, but LangGraph documents finer control over where and how the pause happens.

Exposing an agent team to other tools: CrewAI

Export as an MCP server means your crew can become a tool that Claude, Cursor or any MCP client calls. If that is the end goal, CrewAI gets you there with fewer steps.

TypeScript or mixed-language teams: LangGraph

LangGraph.js gives JavaScript and TypeScript developers the same graph model. CrewAI is Python only.

Enterprise with strict governance: closer call, lean LangGraph

Both sell SSO, RBAC and self-hosted or VPC deployment at the enterprise tier. CrewAI adds PII redaction and policy controls and a 45-day onboarding with optional forward deployed engineers, which suits a company that wants hand-holding. LangGraph has named production users (Klarna and Replit) and a public price for its lower tiers. Ask CrewAI for references to back its enterprise claims before you sign.

Verdict

LangGraph is the stronger choice for production multi-agent systems and scores 8 out of 10 on scored.tools. You get explicit state, documented persistence and recovery, a JavaScript port, published hosted pricing and named production users. The cost is a real learning curve: you will design the graph yourself, and the docs assume you know what an agent loop is.

CrewAI scores 6.8. It has the larger GitHub following, a gentler mental model, a visual editor and a neat MCP export, which makes it the faster way to get a role-based team of agents working. The score is lower because so much of its case rests on CrewAI's own statements, the hosted free tier stops at 50 executions, and the enterprise price is unpublished.

Pick CrewAI for fast prototypes, linear role-based pipelines and agents you want to ship as MCP tools. Pick LangGraph for anything long-running, stateful, approval-gated or bound for production at scale. Plenty of teams can prototype in CrewAI and rebuild in LangGraph once the workflow shape settles, so the choice doesn't have to be permanent.