🗨️Ollama Chat
Configured with Chain LLM, Chat Trigger, LM Ollama. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
- Transform
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 |
| Basic LLM Chain | AI & models | Chain LLM | See node parameters |
| Ollama Model | AI & models | LM Ollama | See node parameters |
| Structured Response | Transform | Edit Fields | See node parameters |
| Error Response | Transform | Edit Fields | See node parameters |
| JSON to Object | Transform | Edit Fields | 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
🦙 Ollama Chat Workflow
🦙 Ollama Chat Workflow
A simple N8N workflow that integrates Ollama LLM for chat message processing and returns a structured JSON object.
Overview
This workflow creates a chat interface that processes messages using the Llama 3.2 model through Ollama. When a chat message is received, it gets processed through a basic LLM chain and returns a response.
Components
- Trigger Node
- Processing Node
- Model Node
- JSON to Object Node
- Structured Response Node
- Error Response Node
Workflow Structure
- The chat trigger node receives incoming messages
- Messages are passed to the Basic LLM Chain
- The Ollama Model processes the input using Llama 3.2
- Responses are returned through the chain
Prerequisites
- N8N installation
- Ollama setup with Llama 3.2 model
- Valid Ollama API credentials
Configuration
- Set up the Ollama API credentials in N8N
- Ensure the Llama 3.2 model is available in your Ollama installation
Model Node
Model Node
- Name: Ollama Model
- Type: LangChain Ollama Integration
- Model: llama3.2:latest
- Purpose: Provides the language model capabilities
Trigger Node
Trigger Node
- Name: When chat message received
- Type: Chat Trigger
- Purpose: Initiates the workflow when a new chat message arrives
Processing Node
Processing Node
- Name: Basic LLM Chain
- Type: LangChain LLM Chain
- Purpose: Handles the processing of messages through the language model and returns a structured JSON object.
Prompt (Change this for your use case)
Prompt (Change this for your use case)
Provide the users prompt and response as a JSON object with two fields:
- Prompt
- Response
Avoid any preample or further explanation. This is the question: {{ $json.chatInput }}
JSON to Object Node
JSON to Object Node
- Type: Set Node
- Purpose: A node designed to transform and structure response data in a specific format before sending it through the workflow. It operates in manual mapping mode to allow precise control over the response format.
Key Features
- Manual field mapping capabilities
- Object transformation and restructuring
- Support for JSON data formatting
- Field-to-field value mapping
- Includes option to add additional input fields
Structured Response Node
Structured Response Node
- Type: Set Node
- Purpose: Controls how the workflow responds to users chat prompt.
Response Mode
- Manual Mapping: Allows custom formatting of response data
- Fields to Set: Specify which data fields to include in response
Error Response Node
Error Response Node
- Type: Set Node
- Purpose: Handles error cases when the Basic LLM Chain fails to process the chat message properly. It provides a fallback response mechanism to ensure the workflow remains robust.
Key Features
- Provides default error messaging
- Maintains consistent response structure
- Connects to the error output branch of the LLM Chain
- Ensures graceful failure handling
The Error Response node activates when the main processing chain encounters issues, ensuring users always receive feedback even when errors occur in the language model processing.
🗨️Ollama Chat
🗨️Ollama Chat
Purpose and structure
This folder contains the 🗨️Ollama Chat workflow. Its executable nodes use chainLlm, chatTrigger, lmOllama, set. 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/1691_Stickynote_Automation_Triggered.json MIT; preserve source notices.