MemorySQL centralizes AI coding session context for local privacy
MemorySQL captures and indexes session data from multiple AI coding assistants to keep project context consistent when you switch agents, and it ships from Logic647 as a Windows utility for developers. The app runs a local Model Context Protocol server and stores data in SQLite, offering full-text and local semantic search plus automated handoff briefs. It fits developers and AI power users who need private, searchable continuity between Cursor, Claude Code, Codex, and other assistants.
What does MemorySQL capture and share?
MemorySQL records complete AI sessions from supported agents and stores them in a local SQLite knowledge base. It automatically captures sessions for more than a dozen coding assistants, indexes text for full-text and semantic lookup, and exposes that history via an integrated Model Context Protocol server so compatible agents can retrieve the prior session context without manual export.
Does it affect system performance during normal use?
Storage and search run locally using sqlite-vec and local models for semantic search, so searches happen offline and against a local database. The app is Electron-based for Windows, which implies typical Electron memory use, while heavy semantic indexing is done locally rather than over a network; the review notes CPU and RAM impact depend on local model operations and index size.
Is MemorySQL safe to use in development environments?
Data stays on the user's machine by design: session data and generated logs are stored in plaintext on local storage with no external analytics. The app's MCP server exposes context to connected agents, so safe use requires controlling which assistants connect to that server; the local-first model preserves data ownership but places responsibility for local security on the developer.
Do developers need technical knowledge to operate it?
Configuration suits technical users; the app integrates with local LLM providers like Ollama and remote APIs for summary generation, and it requires setting up agent connections to the MCP endpoint. Automated capture reduces manual steps, yet configuring local models, MCP access, and managing plaintext storage makes the tool most immediately useful to developers and AI power users rather than casual users.
MemorySQL is a practical continuity tool for developer workflows
MemorySQL delivers a focused, local-first approach to keeping AI assistants aligned with an ongoing project state; it emphasizes developer control over project context and offline search. The trade-off is reliance on local configuration and storage management, which suits technically minded users. Recommended.
