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catfish-1234/sessionmem

by Various

Local-first MCP memory layer for AI coding assistants — persistent cross-session memory, stored entirely on your machine

C

MCP

catfish-1234/sessionmem

Added 18 June 2026

#ai #claude-code #cline #codex #cursor #developer-tools #local-first #mcp

Overview

Local-first MCP memory layer for AI coding assistants. Stores persistent cross-session memory entirely on the user's machine. Built in TypeScript.

Best for

Best for
Developers who want persistent, private memory for AI coding assistants without cloud storage

Use cases

  • Maintain context across AI coding sessions without cloud storage
  • Store project-specific knowledge for repeated use
  • Enable offline memory persistence for AI coding assistants

How to use

Install

npm install -g sessionmem

Tools exposed

  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Antigravity
  • QCoder

Tested with

Claude Code, Cursor, Cline, Codex, Windsurf

Notes

Local-first MCP memory layer for AI coding assistants. Stores persistent cross-session memory entirely on the user’s machine. Built in TypeScript.

3 stars on GitHub. Last updated 2026-06-16. Licensed MIT.

Use cases

  • Maintain context across AI coding sessions without cloud storage
  • Store project-specific knowledge for repeated use
  • Enable offline memory persistence for AI coding assistants

Pros

  • Local storage ensures privacy and no cloud dependency
  • Cross-session memory reduces repeated instructions
  • Open source and self-hosted with a TypeScript codebase

Cons

  • Limited adoption and community support (3 stars)
  • Requires manual setup with MCP-compatible assistants
  • Potential memory overhead for large projects

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

Pros

  • Local storage ensures privacy and no cloud dependency
  • Cross-session memory reduces repeated instructions
  • Open source and self-hosted with a TypeScript codebase

Cons

  • Limited adoption and community support (3 stars)
  • Requires manual setup with MCP-compatible assistants
  • Potential memory overhead for large projects
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