Lossless context compression for LLMs, packing text into 2x-8x fewer tokens with byte-exact reconstruction. Provides MCP tools to compress files/text and expand exact slices, verified by sha256.
MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.
Portable, auditable, local-first MCP memory for MCP-compatible AI agents and coding workflows. It keeps durable project memory outside the model runtime, compresses continuity into smaller working packs, and carries forward operational state so agents can resume with less repetition.
Give your AI persistent, structured memory and let humans see it too. It stores project knowledge as Markdown files in a hierarchical tree, accessible via MCP tools and a built-in Web UI.