Comparisons / CrewAI vs n8n AI
CrewAI vs n8n AI: Which Agent Framework to Use?
CrewAI crewai organizes work into agents, tasks, and crews. n8n AI n8n is a workflow automation platform that added ai agent capabilities with native langchain integration. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
n8n AI
182.4k
56.5k
TypeScript
Sustainable Use License
2019-06-22
Jan Oberhauser
71.8k
n8n Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | CrewAI | n8n AI | Plain Python |
|---|---|---|---|
| Agent | Agent(role, goal, backstory, tools, llm) | AI Agent node with model, tools, and memory connected via canvas wires | A function with a system prompt and a tools dict |
| Tools | Tool registration with @tool decorator, custom Tool classes | Tool nodes (HTTP Request, Code, database) wired into the agent node | A dict: tools[name](**args) |
| Agent Loop | Internal to Agent execution, hidden from user | Agent node internally loops: call LLM → detect tool use → run tool → repeat | A while loop over messages with tool_calls check |
| Task Delegation | Crew(agents, tasks, process=sequential/hierarchical) | — | A task queue processed in a while loop with a budget cap |
| Memory | ShortTermMemory, LongTermMemory, EntityMemory | Memory node (window buffer, vector store) connected to agent node | A dict injected into the system prompt |
| State | Task output passed between agents via Crew orchestration | — | A dict tracking tool calls and results |
| Integrations | — | 500+ pre-built nodes for Slack, Gmail, Notion, databases, APIs | HTTP requests to each service's API with auth headers from environment variables |
| Orchestration | — | Visual workflow canvas with triggers, conditionals, and parallel branches | A Python script with if/else, for loops, and asyncio.gather for parallel calls |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both CrewAI and n8n AI wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use CrewAI
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.
What CrewAI does
CrewAI models multi-agent systems as a crew of specialists. Each Agent has a role ("Senior Researcher"), a goal ("Find the best data sources"), a backstory that shapes its behavior, and a set of tools it can use. Tasks define discrete units of work with expected outputs. The Crew orchestrates execution — sequentially, hierarchically, or with a custom process. CrewAI also provides memory systems (short-term, long-term, entity) and delegation, where one agent can hand off subtasks to another. The mental model is a team of people collaborating on a project. For prototyping multi-agent workflows where you want to reason about roles and responsibilities, it provides a clean vocabulary.
The plain Python equivalent
An Agent in CrewAI is a function with a system prompt that includes the role, goal, and backstory. The tools dict maps names to callables. Task delegation is a list of tasks processed in order — each task calls the assigned agent function with the task description appended to the messages. Hierarchical execution is a manager agent that decides which sub-agent to call next (just another tool choice). Memory is a dict injected into the system prompt. The entire crew pattern — multiple agents, task queue, delegation — is a for-loop over tasks, where each iteration calls the right agent function. No Crew class, no process kwarg. Just functions calling functions with a shared state dict passed between them.
When to use n8n AI
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.
What n8n AI does
n8n is a workflow automation platform — think Zapier, but self-hostable and open-source. In 2025-2026, it added native AI capabilities: an AI Agent node that runs a tool-calling loop, LLM nodes for any provider, tool nodes that let the agent call external services, and memory nodes for conversation persistence. You build agents by dragging nodes onto a canvas and connecting them with wires. The agent node internally runs the same LLM-tool-call loop every agent framework uses, but you configure it visually instead of writing code. With 500+ integration nodes — Slack, Gmail, Notion, PostgreSQL, HTTP — the agent can interact with any service without writing API code. You can inspect every execution step in the UI.
The plain Python equivalent
Every n8n node maps to a function call. The AI Agent node is a while loop that calls the LLM, checks for tool_calls, executes the matching function, and repeats. A Slack tool node is an HTTP POST to Slack's API with a bot token. A database tool node is a SQL query with a connection string. Memory is a messages list saved to a file or database. The visual canvas with conditional branches becomes if/else statements. Parallel execution becomes asyncio.gather. The entire agent with three integrations is about 60 lines of Python. What you lose is the visual builder, the pre-built auth handling for 500+ services, and the execution inspection UI.
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 →