CrewAI vs LangGraph in 2026: which agent framework fits your team

CrewAI and LangGraph are the two Python frameworks most teams shortlist when they move past a single prompt-and-tool loop. Both are open source under the MIT licence. Both now aim at long-running agents that keep state and pause for people.

They start from different ideas, though. CrewAI asks you to describe a team. LangGraph asks you to draw a graph. That choice shapes everything after it.

The short version

• CrewAI is quickest when the work maps onto roles: a researcher, a writer, a reviewer. Crews of agents collaborate inside Flows, which hold the structure and state.
• LangGraph is the lower-level option: nodes, edges and shared state, with precise control over every step and durable execution built in.

Side by side

CrewAI: describe the team

CrewAI's docs recommend using both of its halves. Flows are "the backbone of your AI application": structured, event-driven workflows that manage state and control execution. Crews are "the units of work within your Flow": teams of autonomous agents that collaborate on a task the Flow hands them.

For human review, the @human_feedback decorator pauses a Flow, shows the output to a person and collects feedback. By default it waits for console input, which suits development. For production, a feedback provider raises HumanFeedbackPending, the Flow's state is saved automatically, using SQLite by default, and you call flow.resume(feedback) when the answer comes in.

CrewAI's platform has a free Basic plan with 50 workflow executions a month, a visual editor and an AI copilot. Enterprise is priced by contact and adds SSO, RBAC, PII redaction and deployment on CrewAI's cloud, a VPC or your own infrastructure.

Fits teams that think in roles and want a working multi-agent prototype fast.

LangGraph: draw the graph

LangGraph lets you mix deterministic, hand-written steps with steps an LLM drives, and its headline features are durable execution, streaming and human-in-the-loop. Persistence comes from checkpointers, which save a thread's graph state for conversation continuity, human review, time travel and fault tolerance.

Human review works through interrupt(). The graph pauses, surfaces a value to the caller, and continues when you send Command(resume=...). A checkpointer and a thread ID are required. One behaviour to design around: when resumed, the node restarts from its beginning, so code before the interrupt() runs again. LangGraph's docs advise making side effects before an interrupt idempotent, or moving them after it.

LangSmith, LangChain's platform, has a free Developer plan with up to 5,000 base traces a month and a Plus plan at $39 per seat a month with 10,000 base traces and one free small serverless deployment. Deployments are billed on compute in LangSmith units, at $1 each, for example 0.0675 per vCPU-hour of runtime.

Fits engineers who want explicit control of every transition and are comfortable designing for replay.

What both leave to you

Whichever framework you choose, the production questions are yours to answer: where state lives and for how long, how a question reaches the right person and how their answer comes back, what happens when a model provider fails mid-run, which model each step uses, and how you show afterwards exactly what the agent did and why.

The hosted platforms help with deployment and tracing. The design is still your code.

Where Momor fits

Momor Workflows is not a framework. It is a hosted engine that runs workflow definitions, for teams that would rather declare the process than build the runtime.

Declared, not coded. A workflow is YAML, or built with a C# builder, and edited in the Designer as a flow and as YAML side by side. Steps cover asking a model, calling a tool, branching, looping over a list, asking a person, calling another workflow and responding.

Resume without re-running. Before the engine does anything outside itself, such as asking a model or calling a tool, it records that it is about to, and it records the answer when it arrives. A resumed run continues from the first step that did not finish, and a finished step is never run again because a later one was interrupted.

Waits that cost nothing. A Wait step puts a question to a person and the run keeps its place, holding no worker, for as long as the answer takes. Questions are answered in Momor's Chat, in the Workflows app's list of open questions, through the API, or in an MCP client that can show a form.

Limits with a policy. A workflow declares a budget of calls, model requests and working time. When one runs out, its policy decides: keep the best result so far, ask a person whether to continue, or return a value you set.

Models per step, through one gateway. Every model call goes through Momor Gateway. A step can name its models, and a call moves to another provider when one fails.

Versions that hold. Each saved version is named by its content hash, and a run finishes on the version it started with.

What to weigh: you trade a general-purpose language for a fixed set of step kinds, so logic that does not fit them belongs in a tool your workflow calls. Runs start from an API call, Momor's Chat or an MCP client, with no schedule, email or webhook trigger yet. And Workflows is priced under an agreement rather than per seat.

If you are choosing a framework to own the runtime, pick between CrewAI and LangGraph by how you think about the work. If you would rather not own the runtime, read the Workflows quickstart or the core ideas.