AI Frameworks · headroomlabs-ai

headroom

Headroom is a Python/TypeScript compression layer that reduces LLM token consumption by 60–95% on structured data and 15–20% on code by intelligently compressing tool outputs, logs, RAG chunks, and files before they reach the model. It deploys as a library, proxy, or MCP server with no code changes required, and stores originals locally for retrieval if needed.

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Source: GitHub — github.com/headroomlabs-ai/headroom

57.6k

GitHub stars

4.2k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	headroomlabs-ai/headroom
Owner	headroomlabs-ai
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	57.6k
Forks	4.2k
Open issues	535
Latest release	v0.30.0 (2026-07-03)
Last updated	2026-07-08
Source	https://github.com/headroomlabs-ai/headroom

What headroom is

Headroom routes content through specialized compressors (SmartCrusher for JSON, CodeCompressor for AST, Kompress-v2-base for prose) and uses CacheAligner to stabilize prefixes for KV cache hits. It integrates with Claude, OpenAI, Bedrock, and supports agent wrapping (Claude Code, Cursor, Copilot, etc.), cross-agent memory deduplication, and reversible compression (CCR) with optional output token trimming via verbosity steering and effort routing.

Quickstart

Get the headroom source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/headroomlabs-ai/headroom.git2cd headroom3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

RAG and search-heavy workflows

Compress large tool results (code search, API responses, logs) before embedding them in prompts. Savings of 73–92% token reduction while maintaining accuracy on retrieval tasks.

Cost-sensitive coding agents

Drop-in proxy for Claude Code, Cursor, Codex, and similar agents to reduce both input and output token spend without modifying agent code. Particularly effective on Opus-class models where output costs 5× input.

Long-context AI applications

Library integration in LangChain, Agno, Strands, or custom agents to maximize context window usage. Reverses compression on-demand when the model requests original data.

Implementation considerations

Python 3.10+ required for CLI and full feature set; TypeScript SDK available separately via npm but without CLI tools.
Optional ML model (Kompress-v2-base) runs locally; no cloud dependency, but requires sufficient local compute and optional C++ toolchain for vector store (`[vector]` extra).
Proxy mode is stateless and language-agnostic; library mode requires code integration and explicit `compress()` calls.
Agent wrapping via `headroom wrap <agent>` modifies tool invocations; `headroom unwrap` reverses changes. Cross-agent memory deduplication requires explicit configuration.
CCR (reversible compression) stores originals locally; ensure persistent storage and consider retention/cleanup policies for long-running agents.

When to avoid it — and what to weigh

Requires exact format preservation — If your application depends on lossless round-trip of all input data (e.g., byte-for-byte logging compliance), reversible compression may not meet requirements without careful vetting of CCR retrieval semantics.
Minimal infrastructure tolerance — Headroom adds a local service (proxy or library) and optional ML model (Kompress-v2-base). Environments that cannot run Python 3.10+ or add a compression service should use alternatives.
High-frequency, low-latency constraints — Compression introduces latency (model inference, routing logic). If sub-millisecond round-trip is critical and token savings are secondary, overhead may outweigh benefits.
Proprietary data locked to specific platforms — If your LLM provider forbids local preprocessing or requires data to flow directly unmodified, check terms of service before deploying compression.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI license allowing commercial use, modification, and distribution under same terms.

Apache 2.0 is permissive and explicitly allows commercial use without restrictions. However, review the project's terms for any additional expectations (e.g., attribution, support) and confirm compatibility with your LLM provider's terms before deploying compression in production.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Runs compression locally (data does not leave your system unless sent to LLM). No persistent network calls for compression logic. CCR stores originals on disk; ensure appropriate file permissions and retention policies. Proxy mode intercepts all LLM traffic; audit proxy code for MITM or credential leakage risks. No mention of signed releases, SBOM, or security audit in excerpt; requires review.

Alternatives to consider

LLM provider native compression (e.g., Anthropic prompt caching)

Anthropic's KV cache and OpenAI's prompt caching reduce token reuse costs at inference time. No local processing required, but less aggressive compression (typically 10–20%) and limited control over what is cached.

Custom summarization or chunking (LangChain, LlamaIndex)

Framework-native tools let you build custom compress logic inline without a separate service. Less out-of-box savings, more control, integrates tightly with your app logic.

LLM-assisted compression (e.g., Claude API summarization)

Use an LLM to summarize logs or results before sending to the main model. Simple to implement, but adds latency and requires two LLM calls, offsetting token savings.

Software development agency

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Cut token costs and latency without sacrificing accuracy. Install Headroom in 60 seconds as a library, proxy, or agent wrapper.

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headroom FAQ

Will compression lose important information?

Benchmarks (GSM8K, TruthfulQA, SQuAD) show accuracy preserved or improved. CCR stores originals locally; the LLM can retrieve them via `headroom_retrieve` if needed. Test on your specific use case.

Does Headroom send my data to a cloud service?

No. Compression runs locally in your Python process or proxy. Originals are cached locally. Only the compressed output is sent to your LLM provider (Anthropic, OpenAI, etc.).

Which LLM providers are supported?

Tested with Anthropic (Claude), OpenAI, AWS Bedrock. Proxy mode works with any HTTP-compatible LLM API. Library mode supports any provider via standard `messages` API.

Can I use Headroom with existing agents (Cursor, Claude Code, Copilot)?

Yes. Use `headroom wrap <agent>` to inject compression into the agent's tool chain, or run the proxy and point the agent's API endpoint to `localhost:8787`. No code changes required.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If headroom is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Compress Your LLM Workloads Now

Cut token costs and latency without sacrificing accuracy. Install Headroom in 60 seconds as a library, proxy, or agent wrapper.

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