MemContext
Persistent memory layer for AI coding agents — save once, retrieve forever across all sessions.
Project Overview
MemContext is a persistent memory layer for AI coding agents that solves the problem of AI assistants forgetting everything between sessions. It provides automatic memory saving and semantic retrieval via the Model Context Protocol (MCP). Features include intelligent memory with automatic save and retrieve, semantic search using 1536-dimensional vector embeddings for meaning-based retrieval, cross-tool sync across Claude, Cursor, Windsurf, Cline, Codex and more, auto-updating memories that evolve without duplicates, encrypted and private storage, and project-scoped memory organization. Built as a Turborepo monorepo with a Hono API backend, Next.js dashboard, MCP server, and marketing website — all sharing types via a packages layer.
Technical Challenges
Implementing semantic search with 1536-dim vector embeddings for meaning-based memory retrieval instead of keyword matching
Building automatic memory deduplication and relation classification (saved, updated, extended) using AI
Designing cross-tool memory sync that works across Claude, Cursor, Windsurf, Cline, and other MCP-compatible clients
Architecting a Turborepo monorepo with clean separation between API, MCP server, dashboard, and website apps
Implementing rate limiting, secure API key authentication, and encrypted storage for user privacy
Tech Stack
Status
Current Status
Completed & Live
Last Update
2026
