ContextHeadroom

Context compression and token-savings demo by Sharad Khare.

ContextHeadroom is a practical, developer-first project that demonstrates how to reduce LLM API costs in tool-heavy workflows by keeping what matters (errors, anomalies, recent context) and removing repetitive boilerplate.

Why this project

Agent pipelines often send huge logs, tool responses, and JSON blobs into model context windows. Much of that payload is repetitive. This project shows a simple strategy to shrink context while preserving answer quality.

What you get

• Synthetic production-style log generation
• Anomaly-aware compression simulation
• Before/after token estimation
• Easy path to production via headroom-ai

Quick start

cd contextheadroom
python -m venv .venv
# Windows
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
python headroom_insight_demo.py

Example result

• Baseline payload: full 120 log entries
• Compressed payload: first + anomalies + latest entries
• Typical reduction: large double-digit token savings while retaining the critical outage signal

Production usage with Headroom

Install:

pip install "headroom-ai[all]"

Run proxy mode (zero code changes):

headroom proxy --port 8787

Then route clients:

ANTHROPIC_BASE_URL=http://localhost:8787 claude
OPENAI_BASE_URL=http://localhost:8787/v1 cursor

Who this is for

• AI teams troubleshooting oversized context windows
• Developers building tool-calling and agent workflows
• Prompt engineers improving context efficiency
• Cost-focused builders scaling LLM applications

Use cases

• Compress logs and tool output before model calls
• Preserve anomalies while trimming repetitive context
• Benchmark context headroom strategies for production
• Improve response quality per token spent

License

MIT © Sharad Khare