Headroom: Open‑Source AI Agent Context Compression Cuts Token Usage by 60‑95%

Token consumption in AI coding assistants

When using agents such as Claude Code, Cursor or Codex, token usage spikes because tool outputs, logs, issue data and RAG snippets are added to the conversation, and the model must read all inputs and generate outputs, each counting toward cost.

Headroom overview

Headroom is an open‑source AI‑agent context compression layer (≈40 000 GitHub stars). It compresses background noise—tool results, logs, RAG fragments, file contents and conversation history—before they reach the model, achieving 60 %–95 % token reduction.

Compression pipeline

Content type identification – JSON is processed with a JSON compressor, code with AST analysis, plain text with a small model.

Smart compression – duplicate, redundant and low‑value information is removed while preserving essential content.

Original backup – the compressed payload includes a hash ID; the original text is cached locally and can be retrieved on demand.

The CacheAligner component stabilises request prefixes to improve KV‑cache hit rates in cloud providers, further lowering token costs.

Integration modes

Proxy mode : run headroom proxy --port 8787 and point any AI client to the local port; no code changes required.

One‑line wrapper : headroom wrap claude (or codex, cursor, aider, copilot) launches the corresponding agent with compression.

Library/SDK embedding : import compress from the Python package or from the TypeScript module headroom-ai. Example (Python):

from headroom import compress
compressed = compress(messages, model="claude-sonnet-4-20250514")

Example (TypeScript):

import { compress } from "headroom-ai";
const compressed = await compress(messages, { model: "gpt-4o" });

Frameworks such as LangChain, Agno, Vercel AI SDK or LiteLLM have corresponding adapters. MCP clients can install with headroom mcp install and use the CLI utilities headroom_compress and headroom_retrieve.

Compress‑Cache‑Retrieve (CCR) mechanism

Traditional compression trades aggressiveness against information loss. CCR performs aggressive compression while storing the original locally; if the model determines that the compressed result is insufficient, it can retrieve the full text, saving up to 90 % of tokens for short results and allowing full recovery for longer ones.

Measured token savings

Official tests on real agent workloads report the following reductions:

Code search (100 results): 17 765 → 1 408 tokens (92 % saved)

SRE incident triage: 65 694 → 5 118 tokens (92 % saved)

GitHub issue classification: 54 174 → 14 761 tokens (73 % saved)

Codebase exploration: 78 502 → 41 254 tokens (47 % saved)

On benchmark suites GSM8K, TruthfulQA and SQuAD v2, model scores before and after compression are essentially unchanged, with occasional slight improvements due to noise reduction.

Output compression

Setting the environment variable HEADROOM_OUTPUT_SHAPER=1 trims repetitive phrasing and redundant code in model‑generated output, which is especially beneficial for high‑cost models such as Opus.

Installation and quick start

Python (requires 3.10+):

pip install "headroom-ai[all]"

Node:

npm install headroom-ai

Three‑step demo:

# 1. Install (see above)
# 2. Choose a mode
headroom wrap claude          # wrap a coding agent
# or
headroom proxy --port 8787   # pure proxy mode
# 3. Observe savings
headroom perf

The first run downloads a ~500 MB compression model (Kompress‑base) which is cached locally for subsequent runs. Project repository: https://github.com/chopratejas/headroom