🧠 Engram

Universal memory layer for AI agents

Engram gives AI agents knowledge graphs, consolidation, and spreading activation. Not storage. Understanding.

Quick Start

MCP Setup (recommended — Claude Code / Cursor)

npm install -g engram-sdk
engram init

That's it. 10 memory tools available via MCP.

REST API (non-Node environments)

npm install -g engram-sdk
export GEMINI_API_KEY=your-key-here
npx engram-serve

Server starts on http://127.0.0.1:3800.

Why Engram

Benchmarks (LOCOMO):

• 80.0% accuracy (vs 66.9% Mem0, 74.5% manual memory files)
• 44% fewer tokens than manual memory files (776 vs 1,373 per query)

MCP Tools Reference

REST API Reference

All endpoints return JSON. Base URL: http://127.0.0.1:3800

POST /v1/memories — Store a memory

curl -X POST http://localhost:3800/v1/memories \
  -H "Content-Type: application/json" \
  -d '{"content": "User prefers TypeScript over JavaScript", "type": "semantic"}'

{
  "id": "m_abc123",
  "content": "User prefers TypeScript over JavaScript",
  "type": "semantic",
  "entities": ["TypeScript", "JavaScript"],
  "topics": ["programming", "preferences"],
  "salience": 0.7,
  "createdAt": "2025-01-15T10:30:00.000Z"
}

GET /v1/memories/recall — Recall memories

curl "http://localhost:3800/v1/memories/recall?context=language+preferences&limit=5"

Query parameters: context (required), entities, topics, types, limit, spread, spreadHops, spreadDecay, spreadEntityHops

{
  "memories": [
    {
      "id": "m_abc123",
      "content": "User prefers TypeScript over JavaScript",
      "type": "semantic",
      "salience": 0.7
    }
  ],
  "count": 1
}

POST /v1/memories/recall — Recall (complex query)

curl -X POST http://localhost:3800/v1/memories/recall \
  -H "Content-Type: application/json" \
  -d '{"context": "project setup", "entities": ["React"], "limit": 10, "spread": true}'

Response: same shape as GET recall.

DELETE /v1/memories/:id — Forget a memory

curl -X DELETE "http://localhost:3800/v1/memories/m_abc123?hard=true"

{ "deleted": "m_abc123", "hard": true }

GET /v1/memories/:id/neighbors — Graph neighbors

curl "http://localhost:3800/v1/memories/m_abc123/neighbors?depth=2"

{
  "memories": [ ... ],
  "count": 3
}

POST /v1/consolidate — Run consolidation

curl -X POST http://localhost:3800/v1/consolidate

{
  "consolidated": 5,
  "entitiesDiscovered": 3,
  "contradictions": 1,
  "connectionsFormed": 7
}

GET /v1/briefing — Session briefing

curl "http://localhost:3800/v1/briefing?context=morning+standup&limit=10"

{
  "summary": "...",
  "keyFacts": [{ "content": "...", "salience": 0.9 }],
  "activeCommitments": [{ "content": "...", "status": "pending" }],
  "recentActivity": [{ "content": "..." }]
}

Also available as POST /v1/briefing with JSON body.

GET /v1/stats — Vault statistics

curl http://localhost:3800/v1/stats

{
  "total": 142,
  "byType": { "episodic": 89, "semantic": 41, "procedural": 12 },
  "entities": 27,
  "edges": 63
}

GET /v1/entities — List entities

curl http://localhost:3800/v1/entities

{
  "entities": [
    { "name": "TypeScript", "count": 12 },
    { "name": "React", "count": 8 }
  ],
  "count": 27
}

GET /health — Health check

curl http://localhost:3800/health

{ "status": "ok", "version": "0.1.0", "timestamp": "2025-01-15T10:30:00.000Z" }

TypeScript SDK

import { Vault } from 'engram-sdk';

const vault = new Vault({ owner: 'my-agent' });

await vault.remember('User prefers TypeScript');
const memories = await vault.recall('language preferences');
await vault.consolidate();

CLI Reference

engram init                        Set up Engram for Claude Code / Cursor / MCP clients
engram mcp                         Start the MCP server (stdio transport)
engram remember <text>             Store a memory
engram recall <context>            Retrieve relevant memories
engram consolidate                 Run memory consolidation
engram stats                       Show vault statistics
engram entities                    List known entities
engram forget <id> [--hard]        Forget a memory (soft or hard delete)
engram search <query>              Full-text search
engram export                      Export entire vault as JSON
engram eval                        Health report & value assessment
engram repl                        Interactive REPL mode
engram shadow start                Start shadow mode (server + watcher, background)
engram shadow stop                 Stop shadow mode
engram shadow status               Check shadow mode status
engram shadow results              Compare Engram vs your CLAUDE.md

Options:

--db <path>         Database file path (default: ~/.engram/default.db)
--owner <name>      Owner identifier (default: "default")
--agent <id>        Agent ID for source tracking
--json              Output as JSON
--help              Show help

Configuration

Gemini API Key

Required for embeddings, consolidation, and LLM-powered extraction:

export GEMINI_API_KEY=your-key-here

Database Location

Engram stores data in ~/.engram/ by default. Override with:

export ENGRAM_DB_PATH=/path/to/engram.db

Environment Variables

Benchmarks

Full context (dumping entire conversation history) scores highest but uses 30x more tokens and can't scale past context window limits. Engram closes most of the gap while using 96.6% fewer tokens. For comparison, Mem0 (the most popular agent memory system) scores 66.9% on the same benchmark.

Full research methodology →

Rate Limits & Free Tier

Engram works with Gemini's free API tier, but be aware of its limits:

• Free tier: ~20 requests/minute for gemini-2.5-flash, ~1,500 requests/day
• Embedding calls also count toward the limit

Engram has built-in retry logic: if you hit a rate limit, it will automatically wait and retry up to 3 times. You'll see a log message like:

[engram] Gemini embedContent rate limited. Retrying in 33s (attempt 1/3)...

If you're making heavy use of Engram (frequent remembers + recalls in quick succession), consider upgrading to a paid Gemini API key for higher limits.

Badge

Using Engram in your project? Add the badge to your README:

[![Made with Engram](https://img.shields.io/badge/memory-Engram-8B5CF6?style=flat)](https://github.com/tstockham96/engram)

License

Business Source License 1.1

You can use, modify, and self-host Engram freely. The one restriction: you cannot offer Engram as a hosted or managed service to third parties. After 4 years, each version converts to Apache 2.0.

For commercial licensing, contact tstockham96@gmail.com.

Links

• 🌐 Website
• 📊 Research
• 📦 npm
• 💻 GitHub