The MemMachine integration for n8n allows you to build sophisticated AI agents that remember users across sessions. By using the MemMachine community nodes, you can store interactions, retrieve deep context, and trace memory operations directly within your automation canvas.
Overview
This integration provides two specialized nodes:
- MemMachine Manager: Store messages and “Enrich” your agent prompts with semantic context.
- MemMachine AI Memory: A native memory node that plugs directly into n8n’s AI Agent node.
Install the Community Node
If you are using a self-hosted n8n instance:
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Navigate to Settings > Community Nodes.
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Click Install a community node.
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Enter the package name:
@memmachine/n8n-nodes-memmachine.
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Acknowledge the risk and click Install.
Configure Your Credentials
When you add a MemMachine node, you will be prompted to create credentials. You will need your API Key from the MemMachine Dashboard.
MemMachine Manager Node
The Manager node is used for manual memory operations at any point in your workflow.
Action: Store a message
Saves a specific content string to long-term memory.
| Parameter | Type | Description |
|---|
| orgId / projectId | String | Required. Your unique organization and project identifiers. |
| producer | String | Required. Who created this message (e.g., “Agent” or “User”). |
| producedFor | String | Required. Intended recipient of the message. |
| episodeContent | String | Required. The actual message text to store. |
| episodeType | Options | dialog (default), summary, or observation. |
| groupId | String | Unique group identifier (defaults to default). |
| agentId / userId | String | Required. Identifiers for the specific AI agent and user. |
Action: Enrich with context
Retrieves historical context to inject into an LLM prompt.
| Parameter | Type | Default | Description |
|---|
| query | String | - | Required. Natural language query for semantic search. |
| limit | Number | 50 | Maximum number of memory results to return. |
| filterBySession | Boolean | true | Restricts results to the current session context. |
| enableTemplate | Boolean | true | Renders a formatted context string using a template. |
| contextTemplate | String | - | Markdown template for formatting (uses system default if empty). |
MemMachine AI Memory Node
Connect this node directly to the Memory input of a native n8n AI Agent node.
| Parameter | Type | Default | Description |
|---|
| contextWindowLength | Number | 10 | Max recent messages to include in chat history. |
| enableMemoryTemplate | Boolean | false | Whether to wrap history in a formatted template. |
| historyCount | Number | 5 | Number of episodic memories to include in the template. |
| shortTermCount | Number | 10 | Number of short-term memories to include. |
Observability & Tracing
MemMachine nodes support native OTLP Tracing for debugging complex agent logic. When enabled, you can visualize memory operations in external tools like Jaeger.
| Parameter | Type | Description |
|---|
| tracingEnabled | Boolean | Set to true to enable operation tracing. |
| traceVerbosity | Options | minimal, normal, or verbose. |
| exportToJaeger | Boolean | If true, sends traces to your Jaeger instance. |
| jaegerOtlpEndpoint | String | The endpoint URL (e.g., http://jaeger:4318/v1/traces). |
Pro-Tip: When using the AI Memory Node, ensure your agentId and userId are passed consistently across the workflow to ensure the agent’s “recognition” of the user remains persistent.
