LLM tokens are expensive. With context windows expanding to 1M+ tokens, a single API call can cost several dollars & in production systems handling thousands of requests, these costs compound quickly.
Most optimization efforts focus on model selection or prompt engineering, but the context itself often contains massive redundancy.

Headroom is an open-source Python library (https://github.com/chopratejas/headroom) that sits between your application and your LLM provider, transparently optimizing context before it reaches the model.

The core insight is simple: LLM contexts—especially in agentic workflows—are filled with repetitive tool outputs, verbose JSON arrays, and boilerplate that consumes tokens without adding proportional value. In this session, we will cover what these bloated tokens are, how to measure them, and some real world implications.

We will also mention some novel concepts such as reversible compression, cache aligners, compression routers, and even persistent memory

Real-world results from using token compression:
- 50-90% token reduction on typical agentic workloads
- Drop-in integrations for LangChain, OpenAI, Anthropic, and any OpenAI-compatible provider
- Zero code changes required when using the proxy server

Headroom, with 20k Github stars, is the premier token compression platform used by Developers.