n8n vs LangChain vs CrewAI Enterprise Guide

Yes. n8n's own documentation says its AI nodes use LangChain's JavaScript framework. The AI Agent node, the chain nodes, the vector store nodes and the memory sub-nodes are LangChain ideas packaged as visual cluster nodes. When n8n has no node for something, the LangChain Code node lets you write LangChain JavaScript inside the workflow. So a LangChain n8n setup is normal: n8n gives you the canvas and app connectors, and LangChain does the model work underneath.

n8n is a workflow tool. You connect apps on a canvas, trigger runs from a schedule or webhook, and add AI steps where needed. LangChain is a code library in Python or JavaScript that developers import into their own software. Pick n8n when the job is moving data between apps with one or two AI decisions in the middle. Pick LangChain with LangGraph when the agent is the product and you need tests, code review and custom logic.

They are three products from the same company. LangChain is the framework for building agents and LLM apps from ready-made parts and integrations. LangGraph is the low-level runtime for long-running agents that keep state, survive failures and pause for a human. LangSmith is the paid platform for tracing, testing and deploying those agents. LangChain and LangGraph are free under the MIT licence; LangSmith charges per seat and per trace above a free allowance.

Use LangGraph when the agent has to loop, branch, remember, or wait. Plain LangChain is fine for a straight line of steps, such as fetch a document, summarise it, return the answer. LangGraph adds persistence, so an agent can stop for days waiting for approval and resume where it left off, plus interrupts for human review. A refund dispute or an order change with a manager sign-off are typical LangGraph jobs.

LangGraph saves graph state through its persistence layer, so a run can fail or pause and pick up from the same step later. In n8n, the Wait node offloads the execution data to the database while paused and reloads it when the resume condition, such as a webhook call, is met. In CrewAI, Flows give event-driven control around Crews; we add an external store when a run must survive a restart.

Usually n8n, because it only calls a model when the workflow reaches an AI node; every other step is plain code or an API call. LangGraph sits close behind, since you decide which graph nodes use a model and which are hand-coded. CrewAI Crews let several agents talk to each other, which tends to mean more calls and slower runs for the same task. Measure it on your own workflow before you commit.

n8n is better when the agent mostly calls business apps, such as reading a HubSpot deal, checking stock in a spreadsheet and posting to Slack. Its AI Agent node handles tool calls and the rest of the canvas handles the apps. CrewAI is better when several agents with different roles need to research, draft and review together, written in Python. Many teams run a CrewAI crew behind an n8n webhook to get both.

Yes. The n8n Code node runs JavaScript or Python as a step in a workflow. The old Python mode, built on Pyodide (Python compiled to WebAssembly), was removed in n8n 2.0, and n8n now documents a native Python option. On n8n cloud you cannot run bash scripts or install custom nodes; the pricing page lists both as self-hosted only. For heavy Python agent logic, a separate LangGraph or CrewAI service is usually cleaner.

Start with n8n when the ERP has a standard REST API and the job is moving records, because the visual run log makes failures easy to trace. Move to code in LangGraph when you hit SOAP endpoints, odd XML, strict field validation, or logic that needs unit tests. Our ERP integration work covers Odoo, NetSuite, SAP Business One and ERPNext.

MCP is an open standard for exposing tools and data to AI clients in one format, so a connector built once can serve many agents. All three now touch it. n8n lists "Agents, MCP and AI nodes" on every cloud plan. CrewAI lists "Export as MCP server" on its free and Enterprise plans. LangChain and LangGraph agents can call MCP servers from code. Build the MCP server around your system of record, not around one framework.

Yes, and it is the pattern we recommend most often. n8n receives webhooks, queues work, cleans the data and writes results back to apps. It hands the reasoning step to a LangGraph service over a private HTTP endpoint and waits for the answer. Each tool does what it is good at: n8n for connectors and visible run logs, LangGraph for state, retries and human approval.

In LangGraph, the graph hits an interrupt, saves state and waits until your app or a Slack button sends the decision. In n8n, the Wait node pauses the run and resumes on a webhook call, so an approve link in an email works. In CrewAI, you put the approval step in a Flow around the Crew. Whichever you use, log who approved what and when.

Yes, on self-hosted n8n. The n8n pricing page lists LangSmith tracing and OpenTelemetry tracing (preview) as available in self-hosted editions. That lets you see each model call, prompt and tool result from n8n AI nodes in the same LangSmith project as your LangGraph services. On n8n cloud you rely on n8n execution logs and insights instead.

Yes. n8n self-hosts with Docker, npm or Docker Compose, and without a licence key it runs as the free Community edition. LangChain and LangGraph are MIT-licensed libraries, so they run wherever your Python or JavaScript runs, such as AWS ECS, Google Cloud Run or Azure Kubernetes. CrewAI's framework is MIT-licensed too, and CrewAI Enterprise can deploy in your own VPC or infrastructure.

Limit what the tools can do, not what the prompt says. We give each tool the narrowest API scope it needs, put caps on amounts and record counts, point read tasks at read-only replicas, and require a human approval step for anything that moves money or deletes data. Then a prompt injection can only reach actions you already decided were safe.

Pick the model route before you build. Options include enterprise API terms from model vendors that exclude your data from training, a private cloud deployment of a hosted model, or an open-weights model on your own servers. n8n, LangChain and CrewAI all let you swap the model provider, so the framework choice does not lock this decision.

n8n shows a visual execution log with the input and output of every node, and its Enterprise plan adds log streaming and 365 days of insights. LangGraph pairs with LangSmith or OpenTelemetry for step-by-step traces of every model and tool call. CrewAI Enterprise adds governance features such as SSO, RBAC and PII redaction on its platform. For compliance, ship all of it to one log store you control.

Check the licence first. n8n uses the Sustainable Use License, which lets you use or modify it "only for your own internal business purposes or for non-commercial or personal use". Running n8n inside your company is fine. Reselling n8n itself as a hosted product to others is not, and files marked ".ee" need an n8n Enterprise licence. LangChain, LangGraph and CrewAI are MIT, which has no such limits.

The self-hosted Community edition is free and on GitHub. The hosted cloud plans charge by monthly workflow executions, and every plan includes unlimited users, unlimited workflows and every integration. On 22 Oct 2026 the n8n pricing page showed US visitors Starter at $20 a month billed annually for 2,500 executions and Pro at $50 a month for 10,000. European visitors see euro prices. Enterprise is priced by sales.

The jump comes when you need team controls. On the n8n pricing page, SSO and LDAP, multiple environments, queue mode for scaling and Git version control sit in the Business plan, which is self-hosted only and was listed at $800 a month billed annually for 40,000 executions on 22 Oct 2026. Smaller companies can apply for the Start-up Plan, which n8n says gives 50% off Business for teams under 20 employees.

Yes. The CrewAI framework is open source on GitHub under the MIT licence, with about 60,000 stars when we checked on 22 Oct 2026. The hosted platform has a free Basic plan with a visual editor, GitHub integration and 50 workflow executions a month. The Enterprise plan has custom pricing and adds SSO, RBAC, PII redaction and deployment in your own VPC.

On 22 Oct 2026 the LangSmith pricing page listed a Developer plan at $0 for 1 seat with up to 5,000 base traces a month, and a Plus plan at $39 per seat per month with up to 10,000 base traces and access to Deployment. Usage above the allowance is pay as you go. Enterprise is custom priced and adds self-hosted or hybrid deployment, custom SSO and a support SLA.

No sign of it. The n8n Community edition repository showed 208,269 GitHub stars on the n8n pricing page on 22 Oct 2026, and n8n has shipped version 2.0, with its Code node now documenting native Python. What is changing is its role. More teams use n8n as the connector layer around code-based agents in LangGraph or CrewAI, instead of building the whole agent on the canvas.

Inside the same company, LangChain's deepagents package targets long-running agents for complex tasks. Outside it, common choices are the OpenAI Agents SDK, Microsoft AutoGen and LlamaIndex agents, plus n8n or Make for visual builds. Judge each on four things: how it stores state, how it pauses for a human, how you trace runs, and what licence it uses.

For support inside Zendesk or Gorgias, a LangGraph agent wired to your Shopify GraphQL API and a search index over your help articles works well. It can look up the order, draft the reply, and hand off to a person when the refund is above a set amount. n8n can carry the ticket in and out of the helpdesk around it.

Use both. n8n picks up the email, pulls the PDF attachment, runs OCR (text extraction) and creates the draft record in the ERP. LangGraph then matches line items against the purchase order, flags mismatches, and routes anything over tolerance to a person for approval. The finance team reads the n8n run log; developers own the LangGraph code.

CrewAI fits open-ended work where several viewpoints help: market research, competitor scans, first drafts reviewed by a separate critic agent, or scoring a proposal from a buyer, legal and finance angle. It is a weaker fit for strict, repeatable back-office steps, where a fixed n8n workflow or a LangGraph graph is easier to test.

Book a 30-minute call with founder Bhavesh Barot. We review the workflow, the systems it touches and your security rules, then recommend n8n, LangGraph, CrewAI or a mix. You see a working demo on your own data before you sign. The build is a fixed price per phase, agreed in writing, and you own the code.