AI agent platforms compared — Dify, n8n and Lindy do different jobs

Three kinds of platform

1. Dify — answering from your own documents

Dify suits building a chatbot that reads internal manuals and policies and answers questions about them. The RAG pipeline is assembled in a GUI, so you do not implement vector search yourself, and the finished app publishes as a web page or an API.

The free Sandbox gives 200 message credits, one workspace, one member, five apps and 50 knowledge documents. Enough to evaluate, quickly exhausted in real use. Professional at $590/year moves that to 5,000 message credits a month, three members, 50 apps and 500 knowledge documents.

The open-source edition is the significant part. An organisation that cannot put internal documents in someone else's cloud can run the same capability on its own servers.

2. n8n — connecting the workflow

n8n is not an AI tool with automation attached; it is an automation platform with LLM nodes available. It fits work that crosses services: classify an inbound email, post it to the right person's Slack, record it in a spreadsheet.

In that kind of work the AI is one step and the rest is branching and data shaping. A useful rule: if the job is 70% model, use Dify; if it is 20% model, use n8n.

3. Lindy — resident, and ahead of you

Lindy does not stop at building a flow. The assistant stays resident, watching the inbox and the calendar, and handles the secretarial pattern — drafting replies, setting up meetings, updating the CRM.

There is $50 of credit to try, and the paid plan is $49.99 a month for 5,000 credits. Credit consumption varies by task, so measure what your own use costs during the free period before forecasting.

Choosing: three questions

• Do you want answers from internal documents? → Dify
• Do you want data moved between SaaS tools? → n8n
• Do you want a routine human judgement taken over? → Lindy

If two or more are yes, pick one and build something small. Forcing all three into one tool makes the configuration complicated enough that nobody can operate it. Most automation projects die because nobody but the author understands what was built.

What catches everyone out

Three things bite after launch on any of these platforms: model cost rises in proportion to runs; changing a prompt means past behaviour is no longer reproducible; and when the person who built it changes role, nobody will touch it. The third is the most common.

The remedy is unglamorous — write down the purpose and the assumptions behind the flow. A GUI shows the structure, but it never shows why a particular branch is there.

Frequently asked questions