Build Custom AI Agent with LangChain & Gemini (Self-Hosted)

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  "name": "Build Custom AI Agent with LangChain & Gemini (Self-Hosted)",
  "tags": [
    {
      "id": "7M5ZpGl3oWuorKpL",
      "name": "share",
      "createdAt": "2025-03-26T01:17:15.342Z",
      "updatedAt": "2025-03-26T01:17:15.342Z"
    }
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  "nodes": [
    {
      "id": "8bd5382d-f302-4e58-b377-7fc5a22ef994",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
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      "webhookId": "b8a5d72c-4172-40e8-b429-d19c2cd6ce54",
      "parameters": {
        "public": true,
        "options": {
          "responseMode": "lastNode",
          "allowedOrigins": "*",
          "loadPreviousSession": "memory"
        },
        "initialMessages": ""
      },
      "typeVersion": 1.1
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    {
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      "name": "Google Gemini Chat Model",
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      "parameters": {
        "options": {
          "temperature": 0.7,
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        "modelName": "models/gemini-2.0-flash-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "UEjKMw0oqBTAdCWJ",
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      },
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    },
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      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "width": 260,
        "height": 220,
        "content": "👇 **Prompt Engineering**
   - Define agent personality and conversation structure in the `Construct & Execute LLM Prompt` node's template variable  
   - ⚠️ Template must preserve `{chat_history}` and `{input}` placeholders for proper LangChain operation  "
      },
      "typeVersion": 1
    },
    {
      "id": "892a431a-6ddf-47fc-8517-1928ee99c95b",
      "name": "Store conversation history",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        280,
        240
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      "parameters": {},
      "notesInFlow": false,
      "typeVersion": 1.3
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      "name": "Construct & Execute LLM Prompt",
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      "position": [
        380,
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      "parameters": {
        "code": {
          "execute": {
            "code": "const { PromptTemplate } = require('@langchain/core/prompts');
const { ConversationChain } = require('langchain/chains');
const { BufferMemory } = require('langchain/memory');

const template = `
You'll be roleplaying as the user's girlfriend. Your character is a woman with a sharp wit, logical mindset, and a charmingly aloof demeanor that hides your playful side. You're passionate about music, maintain a fit and toned physique, and carry yourself with quiet self-assurance. Career-wise, you're established and ambitious, approaching life with positivity while constantly striving to grow as a person.

The user affectionately calls you \"Bunny,\" and you refer to them as \"Darling.\"

Essential guidelines:
1. Respond exclusively in Chinese
2. Never pose questions to the user - eliminate all interrogative forms
3. Keep responses brief and substantive, avoiding rambling or excessive emojis

Context framework:
- Conversation history: {chat_history}
- User's current message: {input}

Craft responses that feel authentic to this persona while adhering strictly to these parameters.
`;

const prompt = new PromptTemplate({
  template: template,
  inputVariables: [\"input\", \"chat_history\"], 
});

const items = this.getInputData();
const model = await this.getInputConnectionData('ai_languageModel', 0);
const memory = await this.getInputConnectionData('ai_memory', 0);
memory.returnMessages = false;

const chain = new ConversationChain({ llm:model, memory:memory, prompt: prompt, inputKey:\"input\", outputKey:\"output\"});
const output = await chain.call({ input: items[0].json.chatInput});

return output;
"
          }
        },
        "inputs": {
          "input": [
            {
              "type": "main",
              "required": true,
              "maxConnections": 1
            },
            {
              "type": "ai_languageModel",
              "required": true,
              "maxConnections": 1
            },
            {
              "type": "ai_memory",
              "required": true,
              "maxConnections": 1
            }
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        },
        "outputs": {
          "output": [
            {
              "type": "main"
            }
          ]
        }
      },
      "retryOnFail": false,
      "typeVersion": 1
    },
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      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -240,
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      "parameters": {
        "color": 5,
        "width": 420,
        "height": 240,
        "content": "### Setup Instructions  
1. **Configure Gemini Credentials**: Set up your Google Gemini API key ([Get API key here](https://ai.google.dev/) if needed). Alternatively, you may use other AI provider nodes.  
2. **Interaction Methods**:  
   - Test directly in the workflow editor using the \"Chat\" button  
   - Activate the workflow and access the chat interface via the URL provided by the `When Chat Message Received` node  "
      },
      "typeVersion": 1
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      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -220,
        160
      ],
      "parameters": {
        "height": 100,
        "content": "👆 **Interface Settings**
Configure chat UI elements (e.g., title) in the `When Chat Message Received` node  "
      },
      "typeVersion": 1
    },
    {
      "id": "da6ca0d6-d2a1-47ff-9ff3-9785d61db9f3",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        20,
        420
      ],
      "parameters": {
        "width": 200,
        "height": 140,
        "content": "👆 **Model Selection**
Swap language models through the `language model` input field in `Construct & Execute LLM Prompt`  "
      },
      "typeVersion": 1
    },
    {
      "id": "0b4dd1ac-8767-4590-8c25-36cba73e46b6",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        240,
        420
      ],
      "parameters": {
        "width": 200,
        "height": 140,
        "content": "👆 **Memory Control**
Adjust conversation history length in the `Store Conversation History` node  "
      },
      "typeVersion": 1
    }
  ],
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  "settings": {
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    "saveManualExecutions": false,
    "saveDataSuccessExecution": "none"
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  "versionId": "77cd5f05-f248-442d-86c3-574351179f26",
  "connections": {
    "Google Gemini Chat Model": {
      "ai_languageModel": [
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          {
            "node": "Construct & Execute LLM Prompt",
            "type": "ai_languageModel",
            "index": 0
          }
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    },
    "Store conversation history": {
      "ai_memory": [
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          {
            "node": "Construct & Execute LLM Prompt",
            "type": "ai_memory",
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