Chat with local LLMs using n8n and Ollama
Configured with Chain LLM, Chat Trigger, LM Chat Ollama. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.
Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.
How it works
The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.
| Node | Category | Type | Operation |
|---|---|---|---|
| When chat message received | Trigger | Chat Trigger | See node parameters |
| Ollama Chat Model | AI & models | LM Chat Ollama | See node parameters |
| Chat LLM Chain | AI & models | Chain LLM | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.
Requirements
An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.
Credential types declared in the source
ollamaApi
Workflow notes
Chat with local LLMs using n8n and Ollama
Chat with local LLMs using n8n and Ollama
This n8n workflow allows you to seamlessly interact with your self-hosted Large Language Models (LLMs) through a user-friendly chat interface. By connecting to Ollama, a powerful tool for managing local LLMs, you can send prompts and receive AI-generated responses directly within n8n.
How it works
- When chat message received: Captures the user's input from the chat interface.
- Chat LLM Chain: Sends the input to the Ollama server and receives the AI-generated response.
- Delivers the LLM's response back to the chat interface.
Set up steps
- Make sure Ollama is installed and running on your machine before executing this workflow.
- Edit the Ollama address if different from the default.
Ollama setup
Ollama setup
- Connect to your local Ollama, usually on http://localhost:11434
- If running in Docker, make sure that the n8n container has access to the host's network in order to connect to Ollama. You can do this by passing
--net=hostoption when starting the n8n Docker container
Chat with local LLMs using n8n and Ollama
Chat with local LLMs using n8n and Ollama
Purpose and structure
This folder contains the Chat with local LLMs using n8n and Ollama workflow. Its executable nodes use chainLlm, chatTrigger, lmChatOllama. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.
Setup
Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.
Entry points
When chat message received
Verification
Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.
Attribution
https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Stickynote/1379_Stickynote_Automation_Triggered.json MIT; preserve source notices.