🔐🦙Private & local Ollama self-hosted + dynamic LLM router
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 7 nodes and runs when a chat message arrives. Along the way it connects Ollama Chat Model.
How it runs
Trigger: When a chat message arrives.
- LLM Router (AI Agent): runs an AI agent, with model: Ollama phi; memory: Router Chat Memory
- AI Agent: runs an AI agent, with model: Ollama Dynamic LLM; memory: Agent Chat Memory
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in LLM Router (v1.7), AI Agent with Dynamic LLM (v1.7): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) LLM Router (v1.7), AI Agent with Dynamic LLM (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (4)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Ollama Chat Model
@n8n/n8n-nodes-langchain.lmChatOllama - AI Agent
@n8n/n8n-nodes-langchain.agent - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
From the author
Who is this for? This workflow template is designed for AI enthusiasts, developers, and privacy-conscious users who want to leverage the power of local large language models (LLMs) without sending data to external services. It's particularly valuable for…