🧠 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.

<a href="https://glama.ai/mcp/servers/@tstockham96/engram"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@tstockham96/engram/badge" alt="Engram MCP server" /> </a>

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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.

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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.

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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

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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

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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();

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API Reference

Full REST API and MCP tool documentation: engram.fyi/docs

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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	--

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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 -- Email corpus evaluation

See EVAL.md for methodology and paper/engram-paper.md for the full research paper.

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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.

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License

Proprietary License

Engram is proprietary software. You may install and use it freely for internal purposes. See LICENSE for full terms.

For commercial licensing, contact tstockham96@gmail.com.

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Links

• 🌐 Website
• 📊 Research & Benchmarks
• 📦 npm
• 🐛 Issues