Deterministic context compression for MCP agents, reducing token usage via 11 tools for prompts, history, shell output, file deltas, and code navigation without ML or GPU.
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.
Local-first MCP and coding-agent reliability harness that captures bounded, sanitized failure evidence and generates deterministic executable regression tests. Capture is opt-in; no API key or hosted service is required.
Provides reversible context compression for AI agents, reducing token usage while preserving the ability to retrieve original content, and serves as an MCP server for integration with tools like GitHub Copilot and Claude Code.