engram-core 0.3.0

Memory that sticks. Universal memory layer for AI agents.

ENGRAM

Memory that sticks. For every AI agent.

Universal memory layer for AI agents — persistent, searchable, zero-config.

from engram import Memory

mem = Memory()
mem.store("User prefers Python", type="preference", importance=8)
results = mem.search("programming language")
context = mem.recall(limit=10)

Features

• Zero config — pip install engram-core and go. SQLite out of the box, no external services.
• 5-line API — Store, search, recall. That's it.
• MCP Server — First-class Model Context Protocol integration for Claude Code and other MCP clients.
• REST API — FastAPI server with WebSocket real-time updates.
• Multi-agent — Namespace isolation + cross-namespace search for agent teams.
• Privacy-first — Runs 100% locally. Your data never leaves your machine.
• Smart dedup — SHA-256 content hashing prevents duplicate memories.
• Full-text search — SQLite FTS5 for fast, typo-tolerant search.
• Optional embeddings — Plug in sentence-transformers for semantic search.
• Memory decay — Automatic forgetting curve for stale memories.

Installation

pip install engram-core

With optional extras:

pip install engram-core[server]      # REST API + WebSocket
pip install engram-core[mcp]         # MCP server
pip install engram-core[embeddings]  # Semantic search
pip install engram-core[all]         # Everything

Quick Start

Python SDK

from engram import Memory

mem = Memory()

# Store
mem.store("User prefers dark mode", type="preference", importance=8)
mem.store("Fixed bug by adding null check", type="error_fix", importance=7)

# Search
results = mem.search("dark mode")
for r in results:
    print(f"{r.memory.content} (score: {r.score:.2f})")

# Recall priority context
for entry in mem.recall(min_importance=7):
    print(f"[{entry.memory_type.value}] {entry.content}")

# Multi-agent namespaces
agent1 = Memory(namespace="researcher")
agent2 = Memory(namespace="coder")

REST API

# Start server
engram-server

# Store
curl -X POST http://localhost:8100/v1/memories \
  -H "Content-Type: application/json" \
  -d '{"content": "Important fact", "importance": 8}'

# Search
curl -X POST http://localhost:8100/v1/search \
  -H "Content-Type: application/json" \
  -d '{"query": "fact"}'

MCP Server (Claude Code)

Add to your MCP config:

{
  "mcpServers": {
    "engram": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "cwd": "/path/to/engram"
    }
  }
}

Want automatic memory recall? Check out the Claude Code Hook example — every new session starts with your important memories pre-loaded, zero manual steps.

Docker

docker compose -f docker/docker-compose.yml up

Comparison

Feature	Engram	Mem0	Letta	Zep
Self-hosted (Zero Infra)	SQLite out-of-box	Cloud-only	Complex setup	Graph DB needed
pip install + 5 lines	Yes	~10 lines + API key	~20 lines setup	SDK + config
MCP Server	First-class	No	No	No
Open Source	MIT	Partial	Apache 2.0	BSL
Multi-Agent Namespaces	Native	User/Session/Agent	Single Agent	User-level
Privacy-First	Local by default	Cloud by default	Local possible	Cloud-focused
Cost	Free (self-hosted)	$0.01+/memory	Free	Enterprise pricing

License

MIT