n8n + Memanto

Add persistent memory to your n8n workflows using Memanto’s REST API. n8n workflows are stateless by default — each execution starts fresh. With Memanto, you can store context from one workflow run and recall it in future runs, giving your automations a memory that grows over time.

How It Works

n8n Workflow -> HTTP Request nodes -> Memanto Server -> Moorcheh.ai

Memanto exposes a simple REST API. In n8n, you call it using the built-in HTTP Request node — no custom code or plugins required.

Memanto authenticates with Moorcheh on the server using MOORCHEH_API_KEY. n8n nodes do not send an Authorization header. The only header you pass from n8n is X-Session-Token for memory operations.

Prerequisites

n8n (self-hosted or cloud)
Memanto server accessible from your n8n instance, with MOORCHEH_API_KEY configured in its environment

Step 1: Start Memanto Server

On your server or locally:

export MOORCHEH_API_KEY=your_moorcheh_key
pip install memanto
memanto serve

If n8n is running in the cloud or Docker, expose Memanto via a public URL or use a tunnel like ngrok.

Core Workflow Patterns

Pattern 1: Activate Session + Remember

Use this at the start of a workflow to open a session and store context. Node 1 — Activate Session (HTTP Request)

Field	Value
Method	POST
URL	`http://your-memanto-server:8000/api/v2/agents/n8n-agent/activate`
Authentication	None

This returns a session_token. Reference it in later nodes via:

{{ $node["Activate Session"].json.session_token }}

Node 2 — Remember (HTTP Request)

Field	Value
Method	POST
URL	`http://your-memanto-server:8000/api/v2/agents/n8n-agent/remember`
Headers	`X-Session-Token`: `{{ $node["Activate Session"].json.session_token }}`
JSON Body	`{ "content": "{{ $json.message }}", "type": "fact" }`

Pattern 2: Recall Context

Retrieve relevant memories before making an LLM call or sending a response. Node — Recall (HTTP Request)

Field	Value
Method	POST
URL	`http://your-memanto-server:8000/api/v2/agents/n8n-agent/recall`
Headers	`X-Session-Token`: `{{ $node["Activate Session"].json.session_token }}`
JSON Body	`{ "query": "{{ $json.userMessage }}", "limit": 5 }`

The response contains a memories array. Access the first result with:

{{ $json.memories[0].content }}

Or join all results into a single string using a Code node:

const memories = $input.first().json.memories || [];
return [{ json: { context: memories.map(m => `- ${m.content}`).join("\n") } }];

Pattern 3: AI-Powered Answer from Memory

Let Memanto answer a question directly using its built-in RAG: Node — Answer (HTTP Request)

Field	Value
Method	POST
URL	`http://your-memanto-server:8000/api/v2/agents/n8n-agent/answer`
Headers	`X-Session-Token`: `{{ $node["Activate Session"].json.session_token }}`
JSON Body	`{ "question": "{{ $json.question }}" }`

Returns answer — a grounded response based on stored memories, no external LLM call needed.

Example: Customer Support Workflow

This workflow receives a customer message via webhook, recalls past context, and sends a personalized reply.

[Webhook] → [Activate Session] → [Recall Context] → [Format Prompt] → [OpenAI] → [Remember Exchange] → [Respond]

Webhook Node — receives:

{ "customer_id": "cust_123", "message": "I need help with my order" }

Activate Session — POST /api/v2/agents/{{ $json.customer_id }}/activate Using the customer ID as the agent ID gives each customer their own isolated memory. Recall Context — POST /api/v2/agents/{{ $json.customer_id }}/recall with body { "query": "{{ $json.message }}", "limit": 5 } Code Node — Format Prompt

const memories = $input.first().json.memories || [];
const context = memories.length
  ? memories.map(m => `- ${m.content}`).join("\n")
  : "No prior context.";

return [{
  json: {
    prompt: `Customer history:\n${context}\n\nCustomer: ${$("Webhook").item.json.message}\nAgent:`
  }
}];

OpenAI Node — use the formatted prompt to generate a reply. Remember Exchange — POST /api/v2/agents/.../remember with body { "content": "<message + reply>", "type": "fact" }

Memory Types in n8n

Set the type field in the Remember body to categorize what you store:

Type	When to use
`fact`	Objective information about the user or entity
`preference`	User likes, dislikes, or settings
`decision`	Choices made during the workflow
`commitment`	Promises or follow-up actions
`event`	Things that happened (order placed, ticket opened)
`error`	Issues or failures to avoid repeating

Sessions in Long-Running Workflows

Memanto auto-renews sessions that are near expiry on memory requests, so most workflows can simply reuse the token from the activation step. There is no separate /session/extend endpoint. For scheduled or long-lived workflows, the simplest pattern is to activate a fresh session at the start of each run rather than persisting the token between executions:

[Trigger] → [Activate Session] → ...remaining nodes use the new token...

If a request returns 401 Unauthorized, re-run the Activate Session node and continue with the new token.

Integrations

n8n

n8n + Memanto

How It Works

Prerequisites

Step 1: Start Memanto Server

Core Workflow Patterns

Pattern 1: Activate Session + Remember

Pattern 2: Recall Context

Pattern 3: AI-Powered Answer from Memory

Example: Customer Support Workflow

Memory Types in n8n

Sessions in Long-Running Workflows

Next Steps

​n8n + Memanto

​How It Works

​Prerequisites

​Step 1: Start Memanto Server

​Core Workflow Patterns

​Pattern 1: Activate Session + Remember

​Pattern 2: Recall Context

​Pattern 3: AI-Powered Answer from Memory

​Example: Customer Support Workflow

​Memory Types in n8n

​Sessions in Long-Running Workflows

​Next Steps

n8n + Memanto

How It Works

Prerequisites

Step 1: Start Memanto Server

Core Workflow Patterns

Pattern 1: Activate Session + Remember

Pattern 2: Recall Context

Pattern 3: AI-Powered Answer from Memory

Example: Customer Support Workflow

Memory Types in n8n

Sessions in Long-Running Workflows

Next Steps