Headroom — Context Compression + MCP for Agents

Main

• Start with wrap mode: headroom wrap claude|codex|cursor gives quick wins without rewriting your app stack.

• Use proxy mode for language-agnostic pipelines: point any OpenAI-compatible client at the proxy and keep data local (per README).

• Treat CCR as reversible: README emphasizes originals are retrievable, so you can compress aggressively without losing auditability.

• Measure savings: capture before/after token counts (README demo shows 10,144 → 1,260) and tune only where it matters.

Source-backed notes

• README lists three usage modes: library, proxy (headroom proxy), and agent wrap (headroom wrap ...).
• README states it provides an MCP server with tools like headroom_compress/headroom_retrieve/headroom_stats.
• README demo includes a concrete token reduction example (10,144 → 1,260) and describes CCR as reversible.

FAQ

• Do I need to change my app?: Not necessarily — start with headroom wrap ... or run headroom proxy as a drop-in endpoint.
• Is compression reversible?: README says CCR keeps originals; the agent can retrieve raw content on demand.
• How do MCP clients use it?: Install/enable the Headroom MCP server (README mentions MCP-native entrypoints) and call compress/retrieve tools.