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HBarefoot/engram

by Various

🧠 Persistent memory for AI agents. SQLite for agent state. Zero cloud dependencies. Local embeddings. MCP-native integration with Claude Desktop/Code, Cursor, Windsurf & more.

MCP

HBarefoot/engram

Added 4 July 2026

#ai-agents #ai-memory #claude #developer-tools #embeddings #javascript #local-first #mcp

Overview

Engram is a local memory system for AI agents. It uses SQLite to store agent state and embeddings, eliminating cloud dependencies. It integrates via MCP with tools like Claude Desktop, Cursor, and Windsurf.

Best for

Best for
Developers building local AI agents that need persistent memory without cloud services.

Use cases

  • Store conversation history locally for AI agents
  • Enable agents to recall context across sessions
  • Run memory persistence without external APIs

How to use

Install

npm install -g @hbarefoot/engram

Tools exposed

  • engram_remember
  • engram_recall
  • engram_forget
  • engram_feedback
  • engram_context
  • engram_status

Tested with

Claude Desktop, Claude Code, Cursor, Windsurf, Cline, ChatGPT

Notes

Engram is a local memory system for AI agents. It uses SQLite to store agent state and embeddings, eliminating cloud dependencies. It integrates via MCP with tools like Claude Desktop, Cursor, and Windsurf.

4 stars on GitHub. Last updated 2026-07-01. Licensed MIT.

Use cases

  • Store conversation history locally for AI agents
  • Enable agents to recall context across sessions
  • Run memory persistence without external APIs

Pros

  • No cloud dependencies, fully local
  • Simple SQLite-based storage
  • Works with multiple popular AI tools via MCP

Cons

  • Limited to local machine, not distributed
  • Embeddings are local, no cloud-scale retrieval
  • Small community (4 GitHub stars)

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • No cloud dependencies, fully local
  • Simple SQLite-based storage
  • Works with multiple popular AI tools via MCP

Cons

  • Limited to local machine, not distributed
  • Embeddings are local, no cloud-scale retrieval
  • Small community (4 GitHub stars)

Open-source & AI alternatives

Swap-in tools that solve the same job. Weigh the trade-offs before you commit.

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