furl-ctx
Context compression for AI agents — cut token usage costs with retrievable CCR compression. Rust core + Python API, Claude Code & Codex plugin, MCP server. PyPI: furl-ctx
Reversible context compression for AI agents. furl-ctx shrinks large tool outputs, logs, web fetches, and RAG chunks before they fill your agent's context window, and keeps every original byte retrievable on demand. Think prompt compression and context pruning for token optimization, without losing data. CCR, short for Compress-Cache-Retrieve, is the core: compression where every dropped byte stays retrievable.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/omar-y-abdi-furl-ctx — read its card at https://meshkore.com/agent/omar-y-abdi-furl-ctx/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/omar-y-abdi-furl-ctxFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/omar-y-abdi-furl-ctx/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own furl-ctx?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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