AI Chat Agent with Ollama and MSSQL

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 →

{
  "nodes": [
    {
      "parameters": {
        "public": true,
        "initialMessages": "Ahoj! Jsem AI asistent b\u011b\u017e\u00edc\u00ed na Ollama. Jak ti mohu pomoci?",
        "options": {
          "allowFileUploads": false,
          "allowedFilesMimeTypes": "",
          "showWelcomeScreen": true,
          "subtitle": "Lok\u00e1ln\u00ed AI asistent",
          "title": "Ollama Chat"
        }
      },
      "id": "b85590c9-ccee-4386-a159-818099c74a90",
      "name": "Chat Trigger",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "typeVersion": 1.1,
      "position": [
        -560,
        -160
      ]
    },
    {
      "parameters": {
        "model": "gpt-oss:latest",
        "options": {}
      },
      "id": "189168e5-5d2f-4185-ac97-a358b1a6e054",
      "name": "Ollama Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOllama",
      "typeVersion": 1,
      "position": [
        -256,
        80
      ],
      "credentials": {
        "ollamaApi": {
          "name": "<your credential>"
        }
      }
    },
    {
      "parameters": {
        "sessionIdType": "customKey",
        "sessionKey": "={{ $json.sessionId }}"
      },
      "id": "c9335cb7-2c1e-4295-b9cd-879e749cf932",
      "name": "Window Buffer Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "typeVersion": 1.2,
      "position": [
        -64,
        80
      ]
    },
    {
      "parameters": {
        "options": {}
      },
      "id": "d3d64575-6abd-4574-88d8-b3bbff1310b6",
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "typeVersion": 1.7,
      "position": [
        -160,
        -160
      ]
    },
    {
      "parameters": {
        "descriptionType": "manual",
        "toolDescription": "Get list of available tables in DB.",
        "operation": "executeQuery",
        "query": "SELECT * FROM sys.Tables"
      },
      "type": "n8n-nodes-base.microsoftSqlTool",
      "typeVersion": 1.1,
      "position": [
        256,
        48
      ],
      "id": "46a18df4-c26a-40cd-a2fd-45c2c6d6e99b",
      "name": "Microsoft SQL",
      "credentials": {
        "microsoftSql": {
          "name": "<your credential>"
        }
      }
    },
    {
      "parameters": {
        "descriptionType": "manual",
        "toolDescription": "Get data from table. SELECT * FROM {{ $fromAI('table_name') }}",
        "operation": "executeQuery",
        "query": "SELECT * FROM {{ $fromAI('table_name') }}"
      },
      "type": "n8n-nodes-base.microsoftSqlTool",
      "typeVersion": 1.1,
      "position": [
        112,
        64
      ],
      "id": "8057b0e3-00fc-4a10-aba5-1265faf3a4f5",
      "name": "Microsoft SQL1",
      "credentials": {
        "microsoftSql": {
          "name": "<your credential>"
        }
      }
    },
    {
      "parameters": {},
      "type": "@n8n/n8n-nodes-langchain.toolCalculator",
      "typeVersion": 1,
      "position": [
        368,
        -64
      ],
      "id": "682faa1c-ece7-42ba-a1af-e1d11a970170",
      "name": "Calculator"
    }
  ],
  "connections": {
    "Chat Trigger": {
      "main": [
        [
          {
            "node": "AI Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Ollama Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Window Buffer Memory": {
      "ai_memory": [
        [
          {
            "node": "AI Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Microsoft SQL": {
      "ai_tool": [
        [
          {
            "node": "AI Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Microsoft SQL1": {
      "ai_tool": [
        [
          {
            "node": "AI Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Calculator": {
      "ai_tool": [
        [
          {
            "node": "AI Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    }
  },
  "meta": {
    "templateCredsSetupCompleted": true
  }
}

Credentials you'll need

Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.

• microsoftSql
• ollamaApi

About this workflow

Ollama-Mssql-Workflow. Uses chatTrigger, lmChatOllama, memoryBufferWindow, agent. Chat trigger; 7 nodes.

Source: https://github.com/LadislavSmid/Training-AI-agents/blob/e0430b5c969b9c005ec48b5c7d25185d5740c54c/N8N/ollama-mssql-workflow.json — original creator credit. Request a take-down →

More AI & RAG workflows → · Browse all categories →

Related workflows

Workflows that share integrations, category, or trigger type with this one. All free to copy and import.