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.
Reduces log files to remove noise and duplicate information, cutting tokens by 70-90% for AI agents, preserving only errors, warnings, and unique events.
Filters verbose terminal output from commands like npm install, pip install, docker build, and pytest, reducing context token consumption for AI agents by condensing logs, removing progress bars, and grouping repeated warnings.
An MCP server that intelligently filters and compresses tool outputs to reduce context window usage, saving up to 90% of tokens by removing noise such as passing tests and redundant information.
