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Overview

Add long-term memory to any n8n AI workflow. Your agent remembers users across sessions — preferences, past conversations, resolved issues. No custom code needed, just HTTP Request nodes.

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Quick start

  1. Download the workflow from GitHub
  2. In n8n, go to Workflows → Import from File
  3. Add your Mengram API key as a Header Auth credential
  4. Activate and test

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How it works

Webhook → Search Memories → Build Prompt → AI Response → Save to Memory → Respond
The workflow adds 3 HTTP Request nodes to any AI agent:
  1. Search memories — POST to /v1/search with the user’s message to find relevant past context
  2. AI Agent responds — system prompt includes retrieved memories, agent responds with full context
  3. Save new memories — POST to /v1/add to store the conversation. Mengram auto-extracts facts and deduplicates

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Credential setup

Create a Header Auth credential in n8n: 
Name: Mengram API Key
Header Name: Authorization
Header Value: Bearer om-your-api-key

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API endpoints used

NodeMethodURLBody
Search MemoriesPOSThttps://mengram.io/v1/search{"query": "...", "user_id": "...", "limit": 5}
Save to MemoryPOSThttps://mengram.io/v1/add{"messages": [...], "user_id": "..."}

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Swap the LLM

The workflow uses OpenAI gpt-4o-mini by default. To use a different LLM, change the URL and body in the AI Response node:
  • Anthropic: https://api.anthropic.com/v1/messages
  • Ollama (local): http://localhost:11434/api/chat
  • Any OpenAI-compatible API: just change the URL and model name

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Example

curl -X POST http://localhost:5678/webhook/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "I prefer Python and use Railway for hosting", "user_id": "user-123"}'

# Later...
curl -X POST http://localhost:5678/webhook/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "What hosting should I deploy to?", "user_id": "user-123"}'

# Agent remembers Railway preference and responds accordingly