Headroom

01 / The problem

What needs to change

AI agents can spend large portions of their context windows on verbose tool output, logs, files, retrieval results, and conversation history before the model reaches the reasoning that matters.

02 / The approach

A focused path through the complexity

The original Headroom project routes content through specialized compressors, preserves retrievable source material locally, and exposes library, proxy, agent-wrapper, and MCP integration paths.

03 / Key capabilities

What the system is designed to do

• Content-aware compression for JSON, code, prose, logs, and retrieval output
• Drop-in proxy and agent wrappers for multiple coding assistants
• MCP compression, retrieval, statistics, and cross-agent memory tools
• Reversible source caching for on-demand recovery
• Local operations, health reporting, and savings measurement

04 / Architecture

Technology and system shape

• Python library and command-line runtime with a TypeScript SDK
• OpenAI- and Anthropic-compatible proxy request paths
• Content router with format-specific compression strategies
• Local reversible cache and MCP retrieval surface
• FastAPI health, readiness, configuration, and operator controls

05 / Results

What Mark contributed

• Mark's current upstream pull request proposes configurable compression deadlines across OpenAI/Codex and Anthropic paths while preserving the project's fail-closed default behavior.
• An earlier upstream proposal explored Codex plugin packaging, Cognitive Context Manager interoperability guidance, and corrected savings reporting; it was reviewed but not merged.

06 / Lessons

What the work teaches

• Contributor work should solve a reproducible operator problem while respecting the original project's architecture and safety defaults.
• Open-source attribution should distinguish product authorship, repository stewardship, and the exact scope and status of a contribution.

07 / Screenshots

Approved visuals