This project addresses the significant token redundancy in LLM application outputs, particularly from tools and agent intermediate steps. It offers substantial cost and efficiency benefits for developers building LLM-powered applications by compressing context before it reaches LLM providers.

How It Works

Headroom acts as a transparent proxy, intercepting and optimizing LLM context without altering application logic. It employs a pipeline featuring a "Cache Aligner" to stabilize dynamic tokens, a "Smart Crusher" to remove redundant data, and a "Context Manager" to fit token budgets. The "Compress-Cache-Retrieve" (CCR) mechanism preserves original data separately, retrieving it only when the LLM explicitly requests it, thereby enabling effective provider caching.

Quick Start & Requirements

• Primary Install: pip install headroom-ai (SDK), pip install "headroom-ai[proxy]" (Proxy), pip install "headroom-ai[langchain]" (LangChain), pip install "headroom-ai[agno]" (Agno).
• Prerequisites: Python 3.10+.
• Documentation: Links to Architecture Documentation, LangChain Integration Guide, Agno Integration Guide, Proxy Guide, Memory Guide, Compression Guide, CCR Guide, Metrics, and Troubleshooting are available.

Highlighted Details

• Demonstrates 47-92% token savings across various scenarios like code search, SRE debugging, and GitHub issue triage.
• Offers zero code changes when deployed as a proxy.
• Features reversible compression via CCR, ensuring original data is retrievable.
• Provides framework-native integrations for LangChain, Agno, and MCP, alongside proxy support for any OpenAI-compatible client.
• Includes content-aware compression for code, logs, and JSON, and optimizes for provider caching.

Maintenance & Community

Community links such as Discord or Slack are not explicitly provided. A CONTRIBUTING.md file is referenced for those interested in contributing. The project invites users to add their projects to a "Who's Using Headroom?" list.

Licensing & Compatibility

The project is licensed under the Apache License 2.0. No specific restrictions for commercial use or closed-source linking are mentioned.

Limitations & Caveats

The system introduces a minor overhead of approximately 1-5ms for compression latency. Savings are most pronounced in tool-heavy workloads and less significant in conversation-heavy applications with minimal tool interaction. Automatic model support relies on naming pattern detection, which may not cover all future or non-standard models.