> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mengram.io/llms.txt
> Use this file to discover all available pages before exploring further.

# n8n Integration

> Add persistent memory to any n8n AI agent workflow with a ready-to-import template.

## 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.

## Quick start

1. [Download the workflow](https://github.com/alibaizhanov/mengram/tree/main/examples/n8n) 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

## 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

## Credential setup

Create a **Header Auth** credential in n8n:

```
Name: Mengram API Key
Header Name: Authorization
Header Value: Bearer om-your-api-key
```

## API endpoints used

| Node            | Method | URL                            | Body                                             |
| --------------- | ------ | ------------------------------ | ------------------------------------------------ |
| Search Memories | POST   | `https://mengram.io/v1/search` | `{"query": "...", "user_id": "...", "limit": 5}` |
| Save to Memory  | POST   | `https://mengram.io/v1/add`    | `{"messages": [...], "user_id": "..."}`          |

## 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

## Example

```bash theme={null}
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
```

## Links

* [Workflow on GitHub](https://github.com/alibaizhanov/mengram/tree/main/examples/n8n)
* [API Reference](/api-reference)