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tstockham96/engram
БесплатноIntelligent agent memory with semantic recall, automatic consolidation, contradiction detection, and bi-temporal knowledge graph. 80% on LOCOMO benchmark using
автор: tstockham96
Описание
Intelligent agent memory with semantic recall, automatic consolidation, contradiction detection, and bi-temporal knowledge graph. 80% on LOCOMO benchmark using 96% fewer tokens than full-context approaches.
README
The intelligence layer for AI agents
npm version License: BSL 1.1 GitHub stars
Every AI agent is born smart but amnesiac. Engram fixes that. It doesn't just store memories -- it learns, consolidates patterns, detects contradictions, and surfaces context you didn't ask for.
Install
npm install -g engram-sdk
engram init
That's it. Works with Claude Code, Cursor, or any MCP client. Also available as a REST API and TypeScript SDK.
Why Engram
Existing memory solutions are storage layers -- they save facts and retrieve them. Engram is an intelligence layer with three tiers:
| Tier | What it does | Who has it |
|---|---|---|
| Explicit Memory | Stores facts, preferences, conversation turns | Everyone |
| Implicit Memory | Detects behavioral patterns from how users work | Engram only |
| Synthesized Memory | Consolidation produces insights nobody asked for | Engram only |
Key insight: Engram invests intelligence at read time (when the query is known), not write time (when you don't know what'll matter). This is the fundamental architectural difference from Mem0, Zep, and LangMem.
Benchmarks
Evaluated on LOCOMO -- the standard benchmark for agent memory systems. Same benchmark Mem0 used to claim state of the art.
| System | Accuracy | Tokens/Query |
|---|---|---|
| Engram | 80.0% | 1,504 |
| Full Context | 88.4% | 23,423 |
| Mem0 (published) | 66.9% | -- |
| MEMORY.md | 28.8% | -- |
10 conversations, 1,540 questions, 4 categories. 19.6% relative improvement over Mem0 with 93.6% fewer tokens than full context.
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.
Full benchmark methodology and per-category breakdown
Features
- MCP Server -- 10 memory tools for Claude Code, Cursor, and any MCP client
- REST API -- Full HTTP API for any language or framework
- TypeScript SDK -- Embedded use for Node.js agents
- CLI -- Interactive REPL, bulk operations, eval tools
- Model-agnostic -- Works with Gemini, OpenAI, Ollama, Groq, Cerebras (any OpenAI-compatible provider)
- Zero infrastructure -- SQLite, no Docker, no Neo4j, no Redis
- Consolidation -- LLM-powered memory merging, contradiction detection, pattern discovery
- Entity-aware recall -- Knows "Sarah" in the query should boost memories about Sarah
- Bi-temporal model -- Tracks when facts were true, not just when they were stored
- Spreading activation -- Graph-based context surfacing
Quick Start
MCP Setup (Claude Code / Cursor)
npm install -g engram-sdk
engram init
REST API
npm install -g engram-sdk
export GEMINI_API_KEY=your-key-here
npx engram-serve
Server starts on http://127.0.0.1:3800.
Remember and Recall
# 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"}'
# Recall relevant memories
curl "http://localhost:3800/v1/memories/recall?context=language+preferences&limit=5"
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();
API Reference
Full REST API and MCP tool documentation: engram.fyi/docs
Configuration
| Variable | Description | Default |
|---|---|---|
GEMINI_API_KEY |
Gemini API key for embeddings and consolidation | -- |
ENGRAM_LLM_BASE_URL |
Custom API base URL (Groq, Cerebras, Ollama, etc.) | provider default |
ENGRAM_LLM_MODEL |
LLM model name | provider default |
ENGRAM_DB_PATH |
SQLite database path | ~/.engram/default.db |
PORT |
Server port | 3800 |
ENGRAM_AUTH_TOKEN |
Bearer token for API auth | -- |
Benchmarks & Eval Scripts
This repo contains the evaluation scripts used to benchmark Engram:
eval-locomo.ts-- LOCOMO benchmark (the main result)eval-letta.ts-- Letta Context-Bench evaluationeval-codebase-v2.ts-- Enterprise codebase navigation benchmarkeval-enron.ts-- Email corpus evaluation
See EVAL.md for methodology and paper/engram-paper.md for the full research paper.
Pricing
| Tier | Price | Memories | Agents |
|---|---|---|---|
| Free | $0 | 1,000 | 1 |
| Developer | $29/mo | 10,000 | 1 |
| Team | $99/mo | 50,000 | 5 |
| Business | $499/mo | Unlimited | Unlimited |
| Enterprise | Custom | Custom | Custom |
Hosted API coming soon. Self-hosting is free.
License
Engram is proprietary software. You may install and use it freely for internal purposes. See LICENSE for full terms.
For commercial licensing, contact [email protected].
Links
- 🌐 Website
- 📊 Research & Benchmarks
- 📦 npm
- 🐛 Issues
Как установить
Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"tstockham96-engram": {
"command": "npx",
"args": []
}
}
}