Been running n8n with Ollama for a few months now for work automation. Wanted to share what I’ve learned since it’s not super well-documented.
The setup is just Docker Compose with n8n + Ollama + Postgres. n8n’s HTTP Request node talks directly to Ollama’s REST API — no custom nodes needed.
What I’m running:
- Email digest every morning (IMAP → Ollama → Slack)
- Document summarization (PDF watcher → Ollama → notes)
- Lead scoring from form webhooks
Zero API costs, everything stays on my server. If anyone wants the workflow templates I have a pack: https://workflows.neatbites.com/
Happy to answer questions about the setup.
Happy to answer questions about the setup.
Tell me about the hardware, please and thank you.
I do something similar with the base model m4 Mac mini. It’s my inference box right now, it handles Immich ML, photo prism AI, and runs Ollama talking to a small web app I call to summarize things. It’s summaries are shit. The bigger the model, the more it hallucinates. So I settle for 1B and 4th grade responses
I really like n8n. It appeals to my visual sense which makes up for a lot of hard programming experience. I don’t run it full with the AI aspect. Not because I have some agenda against AI, but that my equipment is not good enough to run AI efficiently. I use it for a lot of automation around the lab.
What model do you mostly use for those tasks
I’ll piggyback onto this question: With the models you use, how do they compare to current models from the big players?
Has anyone tried ActivePieces? How does it compare?
Briefly. I didn’t like it as much as I like n8n. Perhaps it was not suitable to my use case. I hear a lot of good things about ActivePieces tho. You know, give it a spin and see if it gehaws with your flow. From what I understand, both can acomplish about the same. I think ActivePieces is geared more towards cloud deployments whereas n8n keeps things local.
