catfish-1234/sessionmem
🔌 MCP Servercatfish-1234
A local-first, privacy-focused memory layer for AI coding assistants using SQLite to persist session context.
Sessionmem addresses the common limitation of AI coding assistants: the loss of context between sessions. By implementing the Model Context Protocol (MCP), it acts as a bridge that watches your active coding environment and synthesizes session summaries. These summaries are then injected into the prompt context at the start of new sessions, allowing the AI to recall previous decisions, architectural choices, and ongoing tasks.
Technically, Sessionmem is built for privacy and performance. It avoids cloud-based vector databases or external SaaS dependencies, opting instead for a lightweight SQLite backend stored locally on your machine. This ensures that your codebase context never leaves your local environment. The server is designed to be plug-and-play with any MCP-compliant host, providing a standardized way to manage long-term memory for LLMs. Its architecture is highly efficient, focusing on compact, high-signal summaries to minimize token usage while maximizing the relevance of the injected context.
💡Highlights
- ├─Local-first SQLite storage
- ├─MCP-compliant architecture
- └─Zero cloud dependency
🎯For
- ├─AI Software Engineers
- └─Full-stack Developers