Comparisons / CrewAI vs n8n AI

CrewAI vs n8n AI: Which Agent Framework to Use?

CrewAI vs n8n AI, head to head

CrewAI and n8n AI both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

CrewAI organizes work into Agents, Tasks, and Crews.

n8n is a workflow automation platform that added AI agent capabilities with native LangChain integration.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick CrewAI if

Pick CrewAI if crewAI shines for multi-agent setups where you want named roles ("researcher", "writer"). But the core mechanics — tool dispatch, the agent loop, task scheduling — are the same patterns you can build in plain Python. The tradeoffs in its intro should match how your team already thinks about agents; n8n AI will feel like translation if they don't.

Full CrewAIcomparison →

Pick n8n AI if

Pick n8n AI if n8n AI is the right choice when your team builds automations visually, needs 500+ integrations out of the box, and wants to self-host. But the AI agent logic inside each node is the same loop you would write in Python — the value is in the integration catalog and visual builder, not the agent pattern. The tradeoffs in its intro should match how your team already thinks about agents; CrewAI will feel like translation if they don't.

Full n8n AIcomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. CrewAI has its own class hierarchy and tool registration conventions; n8n AI has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

CrewAI

GitHub Stars

48.0k

Forks

6.5k

Language

Python

License

MIT

Created

2023-10-27

Created by

João Moura

github.com/crewAIInc/crewAI

n8n AI

GitHub Stars

182.4k

Forks

56.5k

Language

TypeScript

License

Sustainable Use License

Created

2019-06-22

Created by

Jan Oberhauser

Weekly downloads

71.8k

Cloud/SaaS

n8n Cloud

Production ready

Yes

github.com/n8n-io/n8n

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptCrewAIn8n AI
Agent`Agent(role, goal, backstory, tools, llm)`AI Agent node with model, tools, and memory connected via canvas wires
ToolsTool registration with `@tool` decorator, custom `Tool` classesTool nodes (HTTP Request, Code, database) wired into the agent node
Agent LoopInternal to `Agent` execution, hidden from userAgent node internally loops: call LLM → detect tool use → run tool → repeat
Task Delegation`Crew(agents, tasks, process=sequential/hierarchical)`
Memory`ShortTermMemory`, `LongTermMemory`, `EntityMemory`Memory node (window buffer, vector store) connected to agent node
StateTask output passed between agents via `Crew` orchestration
Integrations500+ pre-built nodes for Slack, Gmail, Notion, databases, APIs
OrchestrationVisual workflow canvas with triggers, conditionals, and parallel branches

Or build your own in 60 lines

Both CrewAI and n8n AI implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.

No framework. No dependencies. No opinions. Just the code.

Build it from scratch →