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CrewAI vs LangGraph: Which One Fits Your Agent?

2026-11-14

CrewAI is built around a team of role-based agents, while LangGraph is a low-level framework where you define the graph of a stateful agent yourself. Both are MIT-licensed Python projects, and both can pause for a human. We build POLYROB, an agent you install rather than program, where the closest thing to a CrewAI role is a named worker you create with polyrob workers and hand a task with delegate_task(profile=...) (cli/commands/[email protected]).

This page compares the two on what their own READMEs say, then shows where a ready-made self-hosted agent fits when neither library is what you need. Facts about CrewAI and LangGraph were read from their GitHub READMEs on 2026-11-14.

What is the difference between CrewAI and LangGraph?

The short version: CrewAI starts from the team, LangGraph starts from the graph.

Sources: CrewAI README and LangGraph README, both fetched 2026-11-14.

How does CrewAI work?

CrewAI’s README calls it “an open-source Python framework with high-level abstractions and low-level APIs for building production-ready multi-agent workflows”. It has two halves.

Crews are teams of agents with a role, a goal and a backstory. In the README’s example a researcher agent and a reporting analyst agent each get a JSON file, and a crew.jsonc lists the tasks, the agents that own them and a process, either sequential or hierarchical. The hierarchical process assigns a manager that coordinates the crew by delegating and checking results.

Flows are the second half: event-driven workflows with state, branching and routing, for the parts of a system where you want precise control rather than agent autonomy. The README says the two are meant to be used together.

Pick CrewAI when the job reads like a small team: a researcher hands notes to a writer, who hands a draft to an editor, and you want that written as roles and tasks.

How does LangGraph work?

LangGraph’s README lists five reasons to use it:

• Durable execution: agents persist through failures and resume from where they left off.
• Human-in-the-loop: inspect and modify agent state at any point in a run.
• Memory: short-term working memory and long-term memory across sessions.
• Debugging with LangSmith: traces of execution paths and state transitions.
• Deployment: infrastructure for long-running, stateful workflows.

The README also says LangGraph “can be used without LangChain” and points to Deep Agents, a higher-level package built on LangGraph, for readers who want to build agents quickly.

Pick LangGraph when you need to own the control flow: an agent that must pause for approval, resume after a crash, or take a different branch depending on state, and you are happy to write that graph.

Which one handles state and human input better?

On paper, both do. This is where a quick comparison article usually picks a winner, and the READMEs do not support one.

CrewAI’s README lists “tools, memory, knowledge, checkpointing, async execution, and MCP/A2A support” among its capabilities, and says it supports human input and structured outputs. Its Flows give “secure, consistent state management between tasks”. LangGraph’s README puts durable execution and interrupts at the front of its pitch.

The difference is the level you work at. In LangGraph you describe how state moves through the graph and where it can stop. In CrewAI you describe who does which task, and reach for Flows when you need explicit control. Neither README publishes a benchmark, so we do not claim one is faster or more reliable.

Which should you choose?

A rule of thumb built from the two READMEs:

• Your problem is naturally a team with roles and handoffs: CrewAI.
• Your problem is a long-running process with branches, pauses and resumes that you want to control exactly: LangGraph.
• You want autonomy in some steps and strict control in others: either works, CrewAI with Flows or LangGraph with a node that calls a model.
• You want an agent you can talk to today, not a framework to program: neither, read on.

When is neither the right answer?

Both are libraries. You write the code that defines the agent, then decide where it runs, how people reach it, what it remembers and who may talk to it. That is the right trade when the agent is a component of your own product.

If you want the agent itself, the work is different. POLYROB is an open-source, self-hosted agent that you install, talk to in a terminal, a web console or a chat app, and give goals. Its README says the agent loop and LLM layer are native code with “no third-party agent framework”, with memory and state in local SQLite files ([email protected]). Some of what the two READMEs list as features is already built in:

• A crew-style helper is a worker. A worker is a named agent configuration with a description of when to use it, a standing brief, an optional tool list that is never wider than its parent’s, and an optional pinned model (cli/commands/[email protected]).
• Delegated children get fewer tools. SUBAGENT_LEAST_PRIVILEGE is on by default, so a sub-agent runs with a narrowed toolset (docs/[email protected], agents/task/agent/[email protected]).
• Goals and jobs survive a restart. A fresh install starts with an empty board and stays that way until you add work (docs/guide/[email protected]).
• Autonomous runs stop at 150 steps and $2.00 by default. The step cap is AUTONOMOUS_MAX_STEPS and the spend cap is AUTONOMOUS_RUN_BUDGET_USD; both can be changed, and the spend check fails open if the cost probe errors (docs/[email protected]).
• It reaches your chats. Telegram, WhatsApp, email, Discord, Slack, Signal, Feishu and DingTalk each have a directory under surfaces/ (surfaces/@v1.7.9).
• It is an MCP client, so tools from MCP servers are available without glue code (tools/mcp/[email protected]). See how the MCP client works.

Data handling differs too. CrewAI’s README says it collects anonymous telemetry and that you can turn it off with the environment variable OTEL_SDK_DISABLED set to true. POLYROB’s README says there is no hosted version and that only the LLM calls, and the tools you turn on, leave your machine ([email protected]).

Installing it is one command, covered on the install page:

curl -fsSL https://polyrob.dev/install.sh | bash

The README also shows how to download the script and read it before running it. For the longer story of running one yourself, see our guide to a self-hosted AI agent, and for how it compares with the library route more broadly, see the LangGraph alternatives page.

Where does POLYROB fall short of CrewAI and LangGraph?

We would rather say it here than have you find it later.

• It is not a library for custom graphs or hand-built crews. If your product needs a specific branching workflow written in code, LangGraph gives you that control and POLYROB does not.
• Its extension ecosystem is small: seven first-party packs, and a third-party pack loads only when you name it (docs/[email protected]).
• It is a young project with a small community, so fewer tutorials and answered questions exist. CrewAI and LangGraph both have courses and documentation sites.
• Sub-agent delegations are session-scoped and in memory (cli/commands/[email protected]), so do not expect a delegated child to survive a restart the way a goal does.
• It runs on Linux, macOS and WSL2 only ([email protected]).

What should you do next?

If you are choosing between the two libraries, write down whether the work is a team of roles or a graph with pauses. Roles point to CrewAI, a graph points to LangGraph, and a prototype of either is a short afternoon.

If what you wanted was an agent that already works, read the overview of an open-source AI agent, run the install command and give it a first goal. You can add a library later, since POLYROB speaks MCP and exposes a REST API for your own code to call.

FAQ: CrewAI, LangGraph and POLYROB

Does CrewAI need LangChain?

No. CrewAI’s README says it is a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration.

Can LangGraph run without LangChain?

Yes. The LangGraph README says it can be used without LangChain, though it works with LangChain products. For how the two relate, see LangChain vs LangGraph.

Can CrewAI use local models?

Yes. The CrewAI README says agents use the OpenAI API by default and can be pointed at other models, with a local model through Ollama given as an example.

Is POLYROB a replacement for either?

No. They are libraries for writing an agent, and POLYROB is an agent you run. Code that uses either library could call a POLYROB agent, since POLYROB exposes a REST API and an A2A surface (docs/[email protected]).

Sources

Fetched 2026-11-14: the READMEs of CrewAI and LangGraph. POLYROB facts are cited as [email protected] from the public repository.

POLYROB is an open-source AI agent you run and own. Install it or read the docs.