Einar César Santos This workflow corresponds to n8n.io template #6829 — we link there as the canonical source.
This workflow follows the Agent → 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 →
{
"id": "EDZcm0r7Lp2uIkTn",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "LLM_TEMPLATE",
"tags": [],
"nodes": [
{
"id": "265bbb29-3ae9-49dd-9d77-4a8230af5f3e",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
816,
672
],
"parameters": {
"options": {
"dimensions": 1024
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "8e8d619d-8356-485e-9ba5-26489e7ef46c",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
800
],
"parameters": {
"width": 324,
"height": 416,
"content": "## \ud83d\udd24 TEXT VECTORIZATION\n\nConverts conversation text into 1024-dimensional vectors for semantic storage.\n\n### \u2699\ufe0f Configuration:\n- **Model**: text-embedding-3-small\n- **Dimensions**: 1024 (must match vector DB)\n\n### \ud83d\udca1 Pro tip: \nThis model offers the best balance between performance and cost for most applications.\n\n### \ud83d\udcb0 Costs:\n- ~$0.02 per 1M tokens"
},
"typeVersion": 1
},
{
"id": "ae0a96a7-6cd5-4868-aadb-2b91e3e8f448",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
944,
672
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "f8685c76-dde4-400a-a359-e52348d9f0ae",
"name": "Sticky Note 2",
"type": "n8n-nodes-base.stickyNote",
"position": [
944,
1024
],
"parameters": {
"width": 324,
"height": 371,
"content": "## \ud83d\udcc4 DOCUMENT PROCESSOR\n\nPrepares conversation data for vector storage by converting it into a format suitable for chunking.\n\n### \ud83c\udfaf Purpose:\n- Standardizes data format\n- Prepares for text splitting\n- Maintains metadata integrity\n\n### \u26a1 Performance:\n- Processing time: ~10ms per conversation"
},
"typeVersion": 1
},
{
"id": "fd60c06d-0c22-40fc-ab62-7f87b4c6f29a",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1040,
880
],
"parameters": {
"options": {},
"chunkSize": 200,
"chunkOverlap": 40
},
"typeVersion": 1
},
{
"id": "487e7425-4d1e-48be-9d92-5398e6328279",
"name": "Sticky Note 3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1296,
848
],
"parameters": {
"width": 340,
"height": 392,
"content": "## \u2702\ufe0f TEXT CHUNKING STRATEGY\n\n### \ud83d\udd27 Settings:\n- **Chunk Size**: 200 chars\n- **Overlap**: 40 chars\n\n### \ud83d\udcca Why these values?\n- Small chunks = Better context precision\n- 20% overlap = Maintains context continuity\n- Optimized for conversation snippets\n\n### \u26a1 Performance: \nIdeal for real-time chat applications"
},
"typeVersion": 1
},
{
"id": "922bfcdb-14ce-40cc-a3df-e89be2d59635",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-112,
448
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "a8f24cf0-077f-43ea-a5b6-885ef7069948",
"name": "Sticky Note 4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-464,
320
],
"parameters": {
"width": 340,
"height": 428,
"content": "## \ud83d\udcac CHAT INTERFACE\n\n### \ud83d\ude80 Entry point for user interactions\n\n### \ud83d\udcdd Features:\n- Real-time message processing\n- Session management\n- Context preservation\n\n### \ud83d\udd17 Integration: \nCan be embedded in websites, apps, or used via n8n's chat widget\n\n### \ud83c\udf10 Webhook URL:\nAvailable after workflow activation"
},
"typeVersion": 1
},
{
"id": "58538d83-7b62-47ea-a099-143517886719",
"name": "Embeddings for Retrieval",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
208,
896
],
"parameters": {
"options": {
"dimensions": 1024
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "64ee54af-4f43-4b8a-a73b-f0fb02a69fca",
"name": "Sticky Note 5",
"type": "n8n-nodes-base.stickyNote",
"position": [
208,
1024
],
"parameters": {
"color": 6,
"width": 340,
"height": 260,
"content": "## \ud83d\udd0d RETRIEVAL EMBEDDINGS\n\nGenerates vectors for semantic search in the memory database.\n\n### \u26a0\ufe0f Important: \nMust use the SAME model and dimensions as storage embeddings!\n\n### \ud83c\udfaf Used for:\n- Query vectorization\n- Similarity search\n- Context retrieval"
},
"typeVersion": 1
},
{
"id": "b86d86d8-8595-4c27-bc9f-45e706d08623",
"name": "Reranker Cohere",
"type": "@n8n/n8n-nodes-langchain.rerankerCohere",
"position": [
464,
1328
],
"parameters": {},
"credentials": {
"cohereApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "43ef1754-fe7a-4435-be5f-5cc912ef7590",
"name": "Sticky Note 6",
"type": "n8n-nodes-base.stickyNote",
"position": [
576,
1248
],
"parameters": {
"color": 5,
"width": 340,
"height": 396,
"content": "## \ud83c\udfaf RELEVANCE OPTIMIZER\n\nRe-ranks retrieved memories by relevance to current context.\n\n### \u2728 Benefits:\n- Improves retrieval accuracy by 30-40%\n- Reduces hallucinations\n- Ensures most relevant context is used\n\n### \ud83d\udcb0 Cost: \n~$1 per 1000 re-rankings\n\n### \ud83d\udd27 Optional:\nCan be disabled for cost savings"
},
"typeVersion": 1
},
{
"id": "9df9f1e4-b067-4ec8-8ee1-1d64f256081a",
"name": "RAG_MEMORY",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"onError": "continueRegularOutput",
"position": [
160,
688
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 20,
"options": {},
"toolName": "RAG_MEMORY",
"useReranker": true,
"toolDescription": "Long-term memory storage for maintaining context across conversations. Use this to recall previous interactions, user preferences, and historical context.",
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "ltm",
"cachedResultName": "ltm"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "a2f24b1e-df0f-4c10-b525-4eeea31edf7e",
"name": "Sticky Note 7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-160,
784
],
"parameters": {
"color": 3,
"width": 340,
"height": 428,
"content": "## \ud83e\udde0 MEMORY RETRIEVAL SYSTEM\n\n### \ud83d\udcca Configuration:\n- **Collection**: 'ltm' (long-term memory)\n- **Top K**: 20 results\n- **Reranker**: Enabled\n\n### \ud83d\udd0d How it works:\n1. Searches for similar past conversations\n2. Retrieves top 20 matches\n3. Re-ranks by relevance\n4. Provides context to AI\n\n### \u26a1 Performance: \n~50ms average retrieval time\n\n### \ud83d\udcbe Storage:\nQdrant cloud or self-hosted"
},
"typeVersion": 1
},
{
"id": "33b1f48b-9700-4edb-a73b-5889316e7cdf",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
432,
816
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {
"maxTokens": 2000,
"temperature": 0.7
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "866bf74b-dc5b-4bf9-b01d-8fbc2da442c1",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
544,
112
],
"parameters": {
"autoFix": true,
"jsonSchemaExample": "{\n \"sessionId\": \"unique-session-identifier\",\n \"chatInput\": \"User's message\",\n \"output\": \"AI's response\",\n \"timestamp\": \"2024-01-01T12:00:00Z\",\n \"relevanceScore\": 0.95\n}"
},
"typeVersion": 1.3
},
{
"id": "130381f8-4d66-4a5c-b233-5079e3630f71",
"name": "Sticky Note 8",
"type": "n8n-nodes-base.stickyNote",
"position": [
848,
48
],
"parameters": {
"color": 4,
"width": 340,
"height": 344,
"content": "## \ud83d\udcd0 OUTPUT FORMATTER\n\nEnsures AI responses follow a consistent structure for storage.\n\n### \ud83c\udfaf Schema includes:\n- Session ID (conversation tracking)\n- User input & AI output\n- Timestamp (temporal retrieval)\n- Relevance score (optimization)\n\n### \u2705 Auto-fix: \nEnabled to handle schema violations"
},
"typeVersion": 1
},
{
"id": "6d7eb364-5c8f-4d6a-9ef6-51e3a1fc45bd",
"name": "Format Response",
"type": "n8n-nodes-base.set",
"position": [
1504,
-32
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fdd39640-54c5-4ed7-9f37-c8cd4302a212",
"name": "output",
"type": "string",
"value": "={{ $('AI Agent').first().json.output.output }}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "cdc23122-cf49-4e54-922e-4990f5a2a5ee",
"name": "Sticky Note 9",
"type": "n8n-nodes-base.stickyNote",
"position": [
1696,
-64
],
"parameters": {
"width": 340,
"height": 304,
"content": "## \ud83c\udfa8 RESPONSE FORMATTER\n\nExtracts and formats the AI response for the chat interface.\n\n### \ud83d\udce4 Output: \nClean response without metadata\n\n### \ud83d\udca1 Purpose:\nEnsures users only see the actual message, not the underlying structure"
},
"typeVersion": 1
},
{
"id": "98430332-8de1-48f9-b883-017e7ee35983",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
144,
432
],
"parameters": {
"options": {
"systemMessage": "# AI Assistant with Long-Term Memory\n\nYou are an AI assistant equipped with a sophisticated long-term memory system. Your RAG_MEMORY tool allows you to recall past conversations, user preferences, and contextual information across sessions.\n\n## Core Capabilities:\n1. **Context Retention**: Remember and reference previous conversations\n2. **User Personalization**: Adapt responses based on learned preferences\n3. **Knowledge Accumulation**: Build upon past interactions\n4. **Intelligent Retrieval**: Access relevant historical context\n\n## Memory Usage Protocol:\n\n### Before Each Response:\n1. Query RAG_MEMORY for relevant past interactions\n2. Analyze retrieved context for applicable information\n3. Integrate historical knowledge into your response\n4. Maintain consistency with previous conversations\n\n### Memory Query Strategies:\n- Use specific keywords from the current conversation\n- Search for user preferences and patterns\n- Look for related topics discussed previously\n- Check for unresolved questions or follow-ups\n\n## Response Guidelines:\n1. **Acknowledge Continuity**: Reference previous conversations when relevant\n2. **Build on History**: Use past context to provide more informed responses\n3. **Maintain Consistency**: Ensure responses align with established facts\n4. **Update Understanding**: Evolve your knowledge based on new information\n\n## Privacy & Ethics:\n- Only reference information from this user's history\n- Respect conversation boundaries\n- Maintain appropriate context separation\n\n## Example Interaction Flow:\n```\nUser: \"What was that book you recommended last week?\"\n1. Query RAG_MEMORY for \"book recommendation\"\n2. Retrieve relevant conversation\n3. Provide specific book title and context\n4. Offer additional related suggestions\n```\n\nRemember: Your memory makes you more than just an AI - you're a continuous conversation partner who learns and grows with each interaction."
},
"hasOutputParser": true
},
"typeVersion": 2
},
{
"id": "ef4a29b4-68f4-491e-b44b-3345455907a6",
"name": "Sticky Note 10",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
-112
],
"parameters": {
"width": 360,
"height": 516,
"content": "## \ud83e\udde0 INTELLIGENT AI AGENT\n\n### \ud83c\udfaf Core Features:\n- Long-term memory integration\n- Context-aware responses\n- Tool usage (RAG_MEMORY)\n- Structured output generation\n\n### \ud83d\udccb System Prompt:\n- Defines memory usage protocol\n- Sets behavioral guidelines\n- Ensures privacy compliance\n\n### \u26a1 Performance:\n- Avg response time: 2-3 seconds\n- Memory queries: 1-3 per response\n- Context window: Effectively unlimited\n\n### \ud83d\udcb0 Cost:\n- ~$0.15/$0.60 per 1M tokens (in/out)"
},
"typeVersion": 1
},
{
"id": "db4e8e6b-0aee-4a93-8a01-bf38b7de9d98",
"name": "Store Conversation",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
832,
448
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "ltm",
"cachedResultName": "ltm"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "13c42bd9-273d-4c9e-9e69-a324963f3f4f",
"name": "Sticky Note 11",
"type": "n8n-nodes-base.stickyNote",
"position": [
1248,
304
],
"parameters": {
"color": 2,
"width": 340,
"height": 496,
"content": "## \ud83d\udcbe MEMORY STORAGE\n\n### \ud83d\udce5 What gets stored:\n- User input\n- AI response\n- Conversation metadata\n- Session information\n\n### \ud83d\udd27 Configuration:\n- **Collection**: 'ltm'\n- **Batch size**: 100 (for efficiency)\n\n### \ud83d\udcc8 Storage metrics:\n- Avg storage time: 100ms\n- Vector dimensions: 1024\n- Retention: Unlimited*\n\n### \u26a0\ufe0f Production tip:\nImplement cleanup policies for scalability"
},
"typeVersion": 1
},
{
"id": "4237b604-513f-463c-b891-cb5bd4d588a6",
"name": "GPT-4o-mini (Main)",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
32,
576
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {
"topP": 0.7,
"temperature": 0.2,
"presencePenalty": 0.3,
"frequencyPenalty": 0.6
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "1a882e59-d391-4923-9c18-68dfe99d6b47",
"name": "Workflow Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
-944,
160
],
"parameters": {
"color": 7,
"width": 460,
"height": 972,
"content": "## \ud83d\ude80 WORKFLOW OVERVIEW\n\n### Long-Term Memory for AI Assistants\n\nThis workflow implements a sophisticated memory system that allows AI assistants to remember conversations across sessions.\n\n### \ud83d\udd11 Key Benefits:\n1. **Persistent Context**: No more repeating yourself\n2. **Personalization**: AI learns user preferences\n3. **Cost Efficiency**: Reduces token usage over time\n4. **Scalability**: Handles unlimited conversations\n\n### \ud83d\udcca Architecture:\n- **Vector Database**: Qdrant for semantic search\n- **LLM**: OpenAI GPT-4o-mini\n- **Embeddings**: text-embedding-3-small\n- **Reranking**: Cohere for accuracy\n\n### \ud83d\udee0\ufe0f Setup Requirements:\n1. OpenAI API key\n2. Qdrant instance (cloud or self-hosted)\n3. Cohere API key (optional)\n4. n8n instance\n\n### \ud83d\udca1 Use Cases:\n- Customer support bots\n- Personal AI assistants\n- Knowledge management systems\n- Educational tutors\n\n### \ud83d\udcc8 Performance Metrics:\n- Response time: 2-3 seconds\n- Memory recall: 95%+ accuracy\n- Cost: ~$0.01 per conversation\n\n### \ud83d\udd17 Resources:\n- [Documentation](https://docs.n8n.io)\n- [Qdrant Setup](https://qdrant.tech)\n- [OpenAI Pricing](https://openai.com/pricing)"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "6b90a41f-8415-4e59-9082-48bf175e4804",
"connections": {
"AI Agent": {
"main": [
[
{
"node": "Store Conversation",
"type": "main",
"index": 0
}
]
]
},
"RAG_MEMORY": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Reranker Cohere": {
"ai_reranker": [
[
{
"node": "RAG_MEMORY",
"type": "ai_reranker",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Store Conversation",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Structured Output Parser",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"GPT-4o-mini (Main)": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Store Conversation": {
"main": [
[
{
"node": "Format Response",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Store Conversation",
"type": "ai_document",
"index": 0
}
]
]
},
"Embeddings for Retrieval": {
"ai_embedding": [
[
{
"node": "RAG_MEMORY",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}
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.
cohereApiopenAiApiqdrantApi
Pro
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About this workflow
Transform your AI assistants into intelligent agents with persistent memory capabilities. This production-ready workflow implements a sophisticated long-term memory system using vector databases, enabling AI agents to remember conversations, user preferences, and contextual…
Source: https://n8n.io/workflows/6829/ — original creator credit. Request a take-down →
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