Engram MCP Server

🧠 Engram

The intelligence layer for AI agents

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:

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.

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

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 evaluation
• eval-codebase-v2.ts -- Enterprise codebase navigation benchmark
• eval-enron.ts