What problem it solves

AI agents often send large tool outputs, logs, files, RAG chunks, and conversation history to language models, increasing the input context that must be processed. Developers need to reduce that input while preserving important content, keeping compression local, and retaining access to original material when necessary.

Key features

• Content-aware compression — A ContentRouter detects the type of incoming content and selects a compressor for JSON, source code, or prose. The project also provides dedicated handling for volatile content and repeated data.

  Source: README

• Flexible integration modes — Use the compression library directly in an application, route traffic through a local proxy, or wrap supported coding agents without code changes. An MCP server is also available for MCP clients.

  Source: README

• Reversible original retrieval — CCR stores original content locally so compressed content can be expanded when the full text is needed. The retrieval interface allows an agent to recover material on demand.

  Source: README

• Shared context across agents — A shared memory store supports multiple coding agents and includes automatic deduplication. The MCP interface exposes compression, retrieval, and statistics capabilities to clients.

  Source: README, server.json

Quick start

Copied verbatim from the project's README.

pip install headroom-ai

from headroom import compress

result = compress(messages, model="gpt-4o")  # OpenAI or Anthropic message dicts
client.chat.completions.create(model="gpt-4o", messages=result.messages)

Who it may suit

Inference: this may suit developers building LLM applications or operating coding agents whose workflows produce large tool outputs, logs, files, and retrieval results.

Topics to discuss

• Which agent and proxy workflows would benefit most from local context compression?

• How should compressed content balance token reduction with reliable retrieval of original details?

• What evaluation methodology should the community use to compare answers before and after compression?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/headroomlabs-ai/headroom#readme

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