n8n-nodes-engram

Knowledge graph memory for n8n AI agents
Persistent memory with entity extraction, temporal fact tracking, community detection, graph traversal, and hybrid search.

Installation · Nodes · Storage · Extraction · Search · Security · Development

Note: This is a community node. It is not officially supported by n8n. Use at your own discretion.

Overview

Engram gives your n8n AI agents a knowledge graph memory. Instead of losing context between conversations, agents build and query a persistent graph of entities (people, organizations, concepts) and the relationships between them.

Every conversation is stored as an episode. An optional LLM extraction pipeline identifies entities and facts from dialogue, building a growing knowledge graph. When the agent receives a new message, Engram searches the graph for relevant facts and injects them as context—giving the agent long-term memory that goes beyond simple chat history.

Highlights

Architecture

Installation

Via n8n Community Nodes

1. Go to Settings > Community Nodes in your n8n instance
2. Enter n8n-nodes-engram
3. Click Install

Manual

cd ~/.n8n
npm install n8n-nodes-engram

Quick Start

Minimal Setup (No LLM Required)

1. Add an Engram Memory node to your AI Agent workflow
2. Connect it to the agent's Memory input
3. Set Storage Backend to Embedded (Graphology)
4. Leave Knowledge Extraction as Disabled

Your agent now has persistent conversation history stored as episodic memory.

Full Setup (With Extraction)

1. Create an Engram Extraction LLM credential:
   • Base URL: OpenAI-compatible endpoint (e.g. https://api.openai.com/v1)
   • API Key: Your API key
2. Add an Engram Memory node, enable Knowledge Extraction
3. Select an extraction model (e.g. gpt-4o-mini)
4. Optionally enable Semantic Search (Embeddings) with an embedding model
5. Optionally enable Graph Traversal (BFS) for context enrichment

The extraction pipeline automatically identifies entities and relationships from conversations and builds a queryable knowledge graph.

Node Reference

Engram Memory

AI Memory node—connects to the AI Agent memory input.

On each conversation turn, Engram Memory:

• Loads relevant facts from the knowledge graph as system context, plus recent chat history
• Saves messages as episodes, optionally running the full extraction pipeline

Engram Explorer

Regular node—CRUD operations on the knowledge graph.

Engram Admin

Regular node—administration and analysis.

Engram Trigger

Polling trigger—fires when new entities, relationships, or episodes appear in the graph.

Storage Backends

Both backends implement the same IGraphStorage interface. All features work identically on either backend.

Knowledge Extraction

When enabled, the extraction pipeline processes each conversation turn:

User message + AI response
        |
   Entity Extractor ── identifies people, orgs, locations, concepts
        |
   Entity Deduplicator ── merges "Bob Smith" with "Bob"
        |
   Relationship Extractor ── extracts facts ("Alice works at Acme")
        |
   Contradiction Detector ── flags conflicting facts
        |
   Embedding Generator ── (optional) vectors for semantic search

Supported providers: Any OpenAI-compatible API—OpenAI, OpenRouter, Ollama, LM Studio, Together AI, and others.

Configurable entity types: person, organization, location, concept, event, or custom types.

Search

Hybrid search automatically activates when both text and vector search are available.

All search operations support optional temporal filters (valid_after, valid_before, created_after, created_before) to scope results by time.

Temporal Queries

Engram tracks when facts are valid (valid_at, invalid_at) and when they were superseded (expired_at). You can query the graph by time:

Date parameters use ISO 8601 format (e.g. 2026-01-15T00:00:00.000Z). The LLM agent can convert natural language like "last week" into concrete dates before calling these operations.

Community Detection

Clusters related entities using label propagation. Communities are computed on demand via Engram Admin > Detect Communities and include member entities, key entities ranked by connectivity, and optional LLM-generated summaries.

Graph Traversal

BFS (breadth-first search) walks the knowledge graph outward from seed entities, collecting connected entities and relationships within a configurable hop limit.

• BFS from Entity: Start from specific entity UUIDs
• BFS from Episodes: Start from entities referenced in recent episodes
• Automatic enrichment: When enabled on the Memory node, a shallow BFS (default: 1 hop) runs after each search to enrich agent context

Data Storage & Security

Engram stores conversation data and extracted knowledge. You should understand where this data lives before deploying to production.

Embedded backend:

• Data is stored as a JSON file at ~/.n8n/storage/n8n-nodes-engram/{workflowId}-engram.json by default
• You can override the default with Custom Storage Path when using embedded storage
• The file contains all entities, relationships, episodes, and embeddings in plain text
• Backups: copy the JSON file

Neo4j backend:

• Data is stored in your Neo4j database instance
• Engram does not manage Neo4j authentication, encryption, or access control—configure these in Neo4j directly
• Credentials (URI, username, password) are stored in n8n's encrypted credential store

LLM API calls:

• When extraction is enabled, conversation content is sent to the configured LLM provider for entity/relationship extraction
• Embedding generation sends entity names and facts to the configured embedding model
• No data is sent to any external service unless you explicitly enable extraction and configure an API credential

General:

• Engram does not phone home, collect telemetry, or make network calls beyond the LLM API you configure
• All data stays on your infrastructure
• Group IDs isolate data between different conversations/users

Credentials

Engram Extraction LLM

Used for entity extraction, community summaries, and embedding generation.

Engram Neo4j Connection

Required only with the Neo4j storage backend.

Development

Prerequisites

• Node.js >= 18.10
• npm

Setup

git clone https://github.com/casistack/n8n-nodes-engram.git
cd n8n-nodes-engram
npm install

Commands

Git Hooks (Husky)

CI/CD

• Pull requests: Lint, type-check, test across Node 18/20/22
• Releases: Automated npm publish on GitHub Release

Support

If you find Engram useful, consider supporting the project:

License

AGPL-3.0