Deterministic repository context packing for AI coding agents: selects, compresses, and budgets only the files a task needs. Measured 83% fewer input tokens at the same task coverage, fully local, no LLM in the loop.
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.
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.
A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.
Token-efficient MCP reimplementation with progressive tool discovery, result handling, and compact wire encoding, reducing token usage by up to 89% on tool definitions.
