> Markdown version of [/events/world-congress-2026-north-america/sessions/1419-headroom-a-context](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1419-headroom-a-context). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Headroom: A Context Optimization Layer for LLM Applications


- **Event:** World Congress 2026 North America

## Description

Large Language Models are expensive. With context windows expanding to 200K+ tokens, a single API call can cost several dollars—and in production systems handling thousands of requests, these costs compound quickly. Most optimization efforts focus on model selection or prompt engineering, but there's an overlooked dimension: the context itself often contains massive redundancy.
                                                                                                                                    
Headroom is an open-source Python library 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.

What makes Headroom different?

Traditional compression destroys information irreversibly. Headroom introduces CCR (Compress-Cache-Retrieve), a reversible compression architecture.
The compression itself is content-aware. Code gets AST-parsed to preserve signatures while compressing function bodies. JSON arrays undergo statistical analysis—we identify outliers, errors, change points, and representative samples rather than blindly truncating. Markdown preserves headers and structure. Each content type gets specialized handling.

Real-world results:                                         
  - 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                                                                                                                     

## Speaker

### [Tejas Chopra](https://www.wearedevelopers.com/@tejas-chopra)

Senior Software Engineer at Netflix


## Related talks at this congress

- [Context Engineering Kung Fu](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1410-context-engineering) — Carl Lapierre
- [Understanding LLM Architectures: Inside the Design of Modern Models](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1669-understanding-llm) — Jofia Jose Prakash
- [No Single Model to Rule Them All: Building Resilient AI Agents Across Open & Closed LLMs](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1671-no-single-model-to) — Emmanuel Acheampong
- [Fast, Cheap, and Accurate: Optimizing LLM Inference with vLLM and Quantization](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1415-fast-cheap-and) — Legare Kerrison, Cedric Clyburn

## Watch remotely

Can’t make it to San José? Watch this session live with Pro. You also get:

- All full videos, bookmarks, and playlists
- World Congress livestreams

[See pricing](https://www.wearedevelopers.com/pricing)

## Links

- [Get tickets](https://www.wearedevelopers.com/world-congress-north-america/tickets)