Measure token savings per AI coding agent, optimize context, and share a live local knowledge graph across 16 CLI clients.

Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.

Enables AI agents to run private web searches against a self-hosted SearXNG instance, rerank results with a local model, and fetch or extract pages locally into markdown, including PDFs, with optional caching, crawling, and cited local-LLM answers.

It enables AI agents to access live web data through page fetching, search, prebuilt scrapers, datasets, and remote browsers, using free direct requests and local Chrome rendering with Bright Data only as a fallback for bot walls. It also adds budget controls, caching, pagination, cancellation, retries, SSRF protection, and both stdio and HTTP transports.