LangGraph vs CrewAI.
LangGraph when you want to control every step, state change and pause of an agent as an explicit graph. CrewAI when you want to describe agents by role and goal, let them collaborate on tasks, and wrap them in an event-driven flow. Both are open-source Python frameworks under the MIT licence, and both sell a paid platform for tracing and deployment. Below is each one on programming model, state and persistence, human-in-the-loop, observability, deployment, cost, licence and maturity, read on the vendors' own docs, pricing pages and GitHub repos on 24 October 2026, and when a plain script or an n8n workflow beats both.
Twelve questions, two agent frameworks.
LangGraph is the open-source library from LangChain Inc; LangSmith is its paid tracing, evaluation and deployment platform. CrewAI is the open-source framework from CrewAI; AMP is its paid platform.
| Question | LangGraph | CrewAI |
|---|---|---|
| What it is | A “low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents”, in LangChain's words. It mixes hand-coded steps and model-driven steps in one graph. | An open-source framework for orchestrating autonomous AI agents, in CrewAI's words. It combines Crews (teams of agents) with Flows (event-driven control of the application). |
| Programming model | A state machine you draw in code: define a state schema, add nodes (functions) and edges between them, compile the graph and invoke it. Conditional edges and loops are explicit, so you decide what runs next. | Agents with a role, goal and tools; tasks assigned to them; a crew that runs the tasks in a sequential or hierarchical process. Flows wrap crews with @start, @listen and @router methods for branching and loops. |
| Multi-agent patterns | You compose them yourself from nodes, edges and subgraphs, so the routing between agents is code you can read. Deep Agents, a higher-level package built on LangGraph, adds planning and subagents. | Built in. A sequential process passes each task's output to the next; a hierarchical process needs a manager_llm or manager_agent that delegates tasks, reviews outputs and decides when they are done. |
| State and persistence | Checkpointers save the graph's state per thread (thread_id) for conversation continuity, resuming after failures and time travel: in memory, SQLite for development, Postgres for production. A store keeps long-term memory across threads. | Flow state is a free-form dict or a typed Pydantic model. The @persist decorator saves it, by default to a local SQLite database, so a run can resume by ID or fork from an earlier state. Custom backends are supported. |
| Human-in-the-loop | interrupt() pauses the graph inside a node or tool, surfaces a payload, and waits; Command(resume=...) continues with the human's answer. Patterns in the docs: approve or reject, edit state, review a tool call. Needs a checkpointer and the same thread ID. | @human_feedback on a Flow method pauses for approval or review (CrewAI 1.8.0 or higher). With emit=[...] a model maps free-form feedback to an outcome that triggers the next step. Feedback history is kept on the flow. |
| Observability | LangSmith traces execution paths, state transitions and runtime metrics, with evaluations. LangSmith also traces other frameworks, CrewAI among them. | Built-in tracing (tracing=True or CREWAI_TRACING_ENABLED) sends agent decisions, tool calls, LLM calls, tokens and errors to CrewAI AMP, after a first-run consent prompt. The docs also list Langfuse, Arize Phoenix, MLflow, OpenLIT, Opik, Weave and others. |
| Deployment options | Run the library inside your own service, anywhere Python or TypeScript runs. Or LangSmith Deployment: Cloud (Plus and above), Hybrid, self-hosted with your own control plane (Enterprise), or a standalone Agent Server with your own Postgres, Redis and a LangSmith licence. | Run the library inside your own service, anywhere Python runs. Or CrewAI AMP: the free Basic plan, or Enterprise on CrewAI's cloud, your own VPC or your own infrastructure. |
| What costs money | The library is free. LangSmith: Developer $0 (1 seat, 5k base traces a month); Plus $39 a seat a month (10k base traces, one free small serverless deployment, extra deployments metered by compute and memory); Enterprise quoted. | The framework is free. AMP: Basic free with 50 workflow executions a month and community support; Enterprise quoted, with SSO, role-based access, PII redaction and dedicated support. No Enterprise prices or execution allowances are published. |
| Licence | MIT, per the LICENSE file in the langchain-ai/langgraph repository. LangSmith is a closed-source commercial product. | MIT, per the LICENSE file in the crewAIInc/crewAI repository. AMP is a commercial product. |
| Languages and models | Python, plus LangGraph.js for TypeScript. Works standalone or with LangChain's model and tool integrations; you call whatever model you choose inside a node. | Python. Agents use the OpenAI API by default and connect to other providers and local models (Ollama, LM Studio) through its LLM settings. A standalone framework, not built on LangChain. |
| Maturity signals | Repository created August 2023; 43,824 GitHub stars and release 1.2.17 on 24 October 2026, with several releases in October. From the company behind LangChain. | Repository created October 2023; 60,060 GitHub stars and release 1.15.30 on 24 October 2026, with several releases in October. Anonymous usage telemetry is on by default (opt out with CREWAI_DISABLE_TELEMETRY). |
| What each is best at (our view) | Long-running, stateful agents where you must say exactly what happens next, pause for approval mid-run and resume after a crash: support, operations and approval workflows in production. | Getting a multi-agent prototype working quickly, research and content pipelines where agents with clear roles hand work along, and teams that like describing work as roles and tasks. |
Read on the vendors' own docs, pricing pages and GitHub repositories on 24 October 2026; the sources are listed below. Prices are list prices in US dollars before tax. GitHub stars change daily and measure attention, not quality. Neither vendor publishes a per-trace or per-execution overage price on its pricing page. For the tracing tools in more depth, see AI agent observability tools.
Each is the right answer for someone, and sometimes neither is.
Choose LangGraph when
FitsThe agent runs a business process with real consequences, so you want every step, branch and state change written down, approvals that pause mid-run, and checkpoints that survive a restart. Teams that need TypeScript as well as Python get both.
Watch forIt is low-level: you write the graph, the state schema and the routing yourself, which is more code up front. LangSmith is the natural tracing tool, and its deployment platform bills by compute and memory beyond the one small deployment on Plus.
Choose CrewAI when
FitsThe work splits naturally into roles (researcher, writer, reviewer) handing tasks along, and you want a working multi-agent prototype quickly. Flows give you event-driven control and persisted state around the crews when it grows up.
Watch forA hierarchical crew lets a manager model decide who does what, which is harder to predict and test than an explicit graph. Telemetry is on by default and built-in tracing goes to CrewAI's platform, so check both against your data rules.
Choose plain code when
FitsOne model call or a short, fixed sequence of calls does the job. A function that calls the model's API with tools, plus your own retries, logging and a database row for state, is easier to read, test and hand over than any framework.
Watch forYou will write your own pause-and-resume and tracing if the workflow grows. That is the point at which a framework starts paying for itself.
Choose an n8n workflow when
FitsThe steps are known and the rules are clear: move data between apps, route a form, send a reminder. A rules-based workflow is cheaper to run and easier to audit; see rules-based automation vs AI agents and n8n vs Zapier vs Make.
Watch forWorkflow tools add AI steps, but long-running agents with complex state, approvals and evaluations outgrow a canvas. Keep the workflow for the plumbing and put the agent in code.
What it costs to run each.
Prices checked on 24 October 2026, in US dollars before tax. Model usage is billed by your model provider on top of everything here. The LSU arithmetic is ours.
Free under MIT, on your own servers
Both libraries cost nothing. LangGraph and CrewAI are MIT-licensed, so you can run either inside your own service on your own cloud at no licence cost, with state in your own database: Postgres for LangGraph checkpoints, SQLite or a custom backend for CrewAI flows. What you pay for is hosting, model calls and the time to build tracing and deployment yourself.
Seats, traces and metered deployments
Developer is $0 for one seat with 5k base traces a month. Plus is $39 a seat a month with 10k base traces and one free small serverless deployment; traces beyond that are pay as you go, at a rate the pricing page does not show. Extra deployments bill in LSUs at $1 each: 0.0675 per vCPU-hour of runtime compute and 0.177 per vCPU-hour of database compute. Our arithmetic: one always-on vCPU for a 730-hour month is about $49 of runtime compute and $129 of database compute, before memory. Enterprise (hybrid or self-hosted) is quoted.
A free tier, then a quote
Basic is free with 50 workflow executions a month and community support. Enterprise is priced individually and adds deployment in your own VPC or on your own infrastructure, SSO, role-based access, PII redaction and dedicated support. CrewAI publishes no Enterprise prices or execution allowances, so budget for a sales conversation before you can compare numbers.
A production agent for a flat fee
Choosing the framework is the small part. The work is in the tools, permissions, evaluation set, human checkpoints and monitoring around it. Gigabit Agents builds and deploys a production agent for one flat fee from $8,000, on whichever framework, or none, fits the job, and Managed AI Operations keeps it monitored and evaluated afterwards at $3,000–$20,000 a month.
Where each fact came from.
All read on 24 October 2026.
Do not pick a framework from a star count or a demo. Build the smallest real version of your workflow in both, with your own tools and data, and see which one you can still read a month later, because the framework you can debug is the one you will keep. Check first whether you need an agent at all: many "agent" projects are a fixed sequence of steps that a script or an n8n workflow runs more cheaply. We are a partner of neither LangChain nor CrewAI, so we have no reason to prefer one.
LangGraph vs CrewAI
LangGraph vs CrewAI: which is better?
Neither in general. LangGraph suits production agents where you need explicit control of every step, pause-and-resume for approvals and durable state, at the cost of more code. CrewAI suits work that splits into roles handing tasks along, and gets a multi-agent prototype running faster, with Flows for control as it grows. Build a small version of your own workflow in each before committing.
Is LangGraph free?
Yes. The LangGraph library is open source under the MIT licence, checked in its GitHub repository on 24 October 2026. LangSmith, the separate platform for tracing, evaluation and deployment, has a free Developer plan (one seat, 5k base traces a month), a Plus plan at $39 a seat a month, and quoted Enterprise pricing.
Is CrewAI free?
Yes. The CrewAI framework is open source under the MIT licence, checked in its GitHub repository on 24 October 2026. CrewAI AMP, the paid platform, has a free Basic plan with 50 workflow executions a month and a quoted Enterprise plan.
Does CrewAI use LangChain?
CrewAI's README describes it as a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration. LangGraph, by contrast, comes from LangChain Inc; its README says it can be used without LangChain and integrates with LangChain products when you want them.
Can I use LangSmith with CrewAI?
Yes. LangSmith's observability docs say it works with many frameworks and list CrewAI among its integrations. CrewAI's own docs also list Langfuse, Arize Phoenix, MLflow, OpenLIT, Opik and Weave, alongside its built-in tracing to CrewAI AMP.
Which has better human-in-the-loop support?
Both can pause for a person. LangGraph's interrupt() pauses anywhere inside a node or tool and resumes with Command(resume=...), with the state held by a checkpointer, so the approval can come later. CrewAI's @human_feedback decorator pauses a Flow method for approval or review and can route on the answer; it needs CrewAI 1.8.0 or higher.
Do I need an agent framework at all?
Often not. If the job is one model call or a short fixed sequence, plain code with your own logging is simpler. If the steps and rules are known in advance, a rules-based workflow in a tool such as n8n is cheaper and easier to audit. A framework earns its place when the agent must decide its own next step, keep state across a long run, or wait for a person mid-run.
Where to go next.
What are agentic workflows?
What makes a workflow agentic, and when a fixed sequence of steps is the better design.
What are agentic workflows?Rules-based automation vs AI agents
When a workflow with clear rules beats an agent, and when it does not.
Rules-based automation vs AI agentsAI agent observability tools
LangSmith, Langfuse, Phoenix and others compared on licence, self-hosting and price.
AI agent observability toolsGigabit Agents
Production AI agents built and deployed for a flat fee, from $8,000.
Gigabit Agents
Deciding how to build an agent? Start from the workflow.
Tell us what the agent should do, which systems it touches and who has to approve what. We reply with the design we would use, framework or none, and the flat fee, from $8,000.