mcp-memory-keeper

MCP Memory Keeper solves context loss in Claude AI coding sessions by providing a persistent memory layer that preserves your work history, architectural decisions, and progress. The server stores context in SQLite and organizes information through channels, categories, and checkpoints, allowing you to save snapshots before context limits are reached. It supports full-text search, git integration with automatic branch correlation, and change tracking to monitor what's been added or modified. Developers working on complex refactoring, multi-file changes, or long debugging sessions can restore context across sessions or share memory between parallel Claude instances. Installation is straightforward via NPX, and the system uses environment variables for storage customization and token limit configuration.

["Install and configure the MCP Memory Keeper server. Ensure it’s running locally or accessible via your AI assistant’s MCP client.","Load the context for your project by referencing the relevant files or modules. For example, use `Retrieve context for [FILE_PATH]` in your prompt.","Review the retrieved context to understand past decisions, dependencies, or unresolved issues. Ask the AI to summarize key points if the context is verbose.","Use the context to guide your current task. For example, ask the AI to suggest improvements, debug issues, or generate documentation based on the stored decisions.","Store the updated context after making changes. Include a concise note summarizing the changes or next steps. For example: `Store this context with the note: 'Fixed bug in token validation and added logging.'`"]