AutomationFlowsAI & RAG › Local Ollama AI Chat Assistant

Local Ollama AI Chat Assistant

Original n8n title: 🔐🦙🤖 Private & Local Ollama Self-hosted AI Assistant

ByJoseph LePage @joe on n8n.io

Transform your local N8N instance into a powerful chat interface using any local & private Ollama model, with zero cloud dependencies ☁️. This workflow creates a structured chat experience that processes messages locally through a language model chain and returns formatted…

Chat trigger trigger★★★★☆ complexityAI-powered14 nodesChat TriggerChain LlmLm Ollama
AI & RAG Trigger: Chat trigger Nodes: 14 Complexity: ★★★★☆ AI nodes: yes Added:

This workflow corresponds to n8n.io template #2729 — we link there as the canonical source.

This workflow follows the Chainllm → Chat Trigger recipe pattern — see all workflows that pair these two integrations.

The workflow JSON

Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →

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{
  "id": "Telr6HU0ltH7s9f7",
  "name": "\ud83d\udde8\ufe0fOllama Chat",
  "tags": [],
  "nodes": [
    {
      "id": "9560e89b-ea08-49dc-924e-ec8b83477340",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        280,
        60
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "c7919677-233f-4c48-ba01-ae923aef511e",
      "name": "Basic LLM Chain",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "onError": "continueErrorOutput",
      "position": [
        640,
        60
      ],
      "parameters": {
        "text": "=Provide the users prompt and response as a JSON object with two fields:\n- Prompt\n- Response\n\nAvoid any preample or further explanation.\n\nThis is the question: {{ $json.chatInput }}",
        "promptType": "define"
      },
      "typeVersion": 1.5
    },
    {
      "id": "b9676a8b-f790-4661-b8b9-3056c969bdf5",
      "name": "Ollama Model",
      "type": "@n8n/n8n-nodes-langchain.lmOllama",
      "position": [
        740,
        340
      ],
      "parameters": {
        "model": "llama3.2:latest",
        "options": {}
      },
      "credentials": {
        "ollamaApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "61dfcda5-083c-43ff-8451-b2417f1e4be4",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -380,
        -380
      ],
      "parameters": {
        "color": 4,
        "width": 520,
        "height": 860,
        "content": "# \ud83e\udd99 Ollama Chat Workflow\n\nA simple N8N workflow that integrates Ollama LLM for chat message processing and returns a structured JSON object.\n\n## Overview\nThis 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.\n\n## Components\n- **Trigger Node**\n- **Processing Node**\n- **Model Node**\n- **JSON to Object Node**\n- **Structured Response Node**\n- **Error Response Node**\n\n## Workflow Structure\n1. The chat trigger node receives incoming messages\n2. Messages are passed to the Basic LLM Chain\n3. The Ollama Model processes the input using Llama 3.2\n4. Responses are returned through the chain\n\n## Prerequisites\n- N8N installation\n- Ollama setup with Llama 3.2 model\n- Valid Ollama API credentials\n\n## Configuration\n1. Set up the Ollama API credentials in N8N\n2. Ensure the Llama 3.2 model is available in your Ollama installation\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "64f60ee1-7870-461e-8fac-994c9c08b3f9",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        340,
        280
      ],
      "parameters": {
        "width": 560,
        "height": 200,
        "content": "## Model Node\n- Name: Ollama Model\n- Type: LangChain Ollama Integration\n- Model: llama3.2:latest\n- Purpose: Provides the language model capabilities"
      },
      "typeVersion": 1
    },
    {
      "id": "bb46210d-450c-405b-a451-42458b3af4ae",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        200,
        -160
      ],
      "parameters": {
        "color": 6,
        "width": 280,
        "height": 400,
        "content": "## Trigger Node\n- Name: When chat message received\n- Type: Chat Trigger\n- Purpose: Initiates the workflow when a new chat message arrives"
      },
      "typeVersion": 1
    },
    {
      "id": "7f21b9e6-6831-4117-a2e2-9c9fb6edc492",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        520,
        -380
      ],
      "parameters": {
        "color": 3,
        "width": 500,
        "height": 620,
        "content": "## Processing Node\n- Name: Basic LLM Chain\n- Type: LangChain LLM Chain\n- Purpose: Handles the processing of messages through the language model and returns a structured JSON object.\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "871bac4e-002f-4a1d-b3f9-0b7d309db709",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        560,
        -200
      ],
      "parameters": {
        "color": 7,
        "width": 420,
        "height": 200,
        "content": "### Prompt (Change this for your use case)\nProvide the users prompt and response as a JSON object with two fields:\n- Prompt\n- Response\n\n\nAvoid any preample or further explanation.\nThis is the question: {{ $json.chatInput }}"
      },
      "typeVersion": 1
    },
    {
      "id": "c9e1b2af-059b-4330-a194-45ae0161aa1c",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1060,
        -280
      ],
      "parameters": {
        "color": 5,
        "width": 420,
        "height": 520,
        "content": "## JSON to Object Node\n- Type: Set Node\n- 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.\n\n**Key Features**\n- Manual field mapping capabilities\n- Object transformation and restructuring\n- Support for JSON data formatting\n- Field-to-field value mapping\n- Includes option to add additional input fields\n"
      },
      "typeVersion": 1
    },
    {
      "id": "3fb912b8-86ac-42f7-a19c-45e59898a62e",
      "name": "Sticky Note6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1520,
        -180
      ],
      "parameters": {
        "color": 6,
        "width": 460,
        "height": 420,
        "content": "## Structured Response Node\n- Type: Set Node\n- Purpose: Controls how the workflow responds to users chat prompt.\n\n**Response Mode**\n- Manual Mapping: Allows custom formatting of response data\n- Fields to Set: Specify which data fields to include in response\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "fdfd1a5c-e1a6-4390-9807-ce665b96b9ae",
      "name": "Structured Response",
      "type": "n8n-nodes-base.set",
      "position": [
        1700,
        60
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "13c4058d-2d50-46b7-a5a6-c788828a1764",
              "name": "text",
              "type": "string",
              "value": "=Your prompt was: {{ $json.response.Prompt }}\n\nMy response is: {{ $json.response.Response }}\n\nThis is the JSON object:\n\n{{ $('Basic LLM Chain').item.json.text }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "76baa6fc-72dd-41f9-aef9-4fd718b526df",
      "name": "Error Response",
      "type": "n8n-nodes-base.set",
      "position": [
        1460,
        660
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "13c4058d-2d50-46b7-a5a6-c788828a1764",
              "name": "text",
              "type": "string",
              "value": "=There was an error."
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "bde3b9df-af55-451b-b287-1b5038f9936c",
      "name": "Sticky Note7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1240,
        280
      ],
      "parameters": {
        "color": 2,
        "width": 540,
        "height": 560,
        "content": "## Error Response Node\n- Type: Set Node\n- 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.\n\n**Key Features**\n- Provides default error messaging\n- Maintains consistent response structure\n- Connects to the error output branch of the LLM Chain\n- Ensures graceful failure handling\n\nThe 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.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "b9b2ab8d-9bea-457a-b7bf-51c8ef0de69f",
      "name": "JSON to Object",
      "type": "n8n-nodes-base.set",
      "position": [
        1220,
        60
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "12af1a54-62a2-44c3-9001-95bb0d7c769d",
              "name": "response",
              "type": "object",
              "value": "={{ $json.text }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    }
  ],
  "active": false,
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "5175454a-91b7-4c57-890d-629bd4e8d2fd",
  "connections": {
    "Ollama Model": {
      "ai_languageModel": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "JSON to Object": {
      "main": [
        [
          {
            "node": "Structured Response",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Basic LLM Chain": {
      "main": [
        [
          {
            "node": "JSON to Object",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Error Response",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

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About this workflow

Transform your local N8N instance into a powerful chat interface using any local & private Ollama model, with zero cloud dependencies ☁️. This workflow creates a structured chat experience that processes messages locally through a language model chain and returns formatted…

Source: https://n8n.io/workflows/2729/ — original creator credit. Request a take-down →

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