Most AI agents waste tokens on uncompressed tool outputs, logs, and RAG
chunks. On real workloads: 17,765 tokens → 1,408 — same answer,
fraction of the cost.
Headroom sits between your code and the LLM and compresses everything
automatically. Runs locally, your data never leaves your machine.
What’s covered:
→ How Headroom’s ContentRouter picks the right compressor per content type
→ Architecture: SmartCrusher (JSON), CodeCompressor (AST), Kompress-base (text)
→ Live Python demo: before vs. after on real agent output
🎥 <a href="https://youtu.be/xBHL40UIVlk" target="_blank" rel="noopener nofollow">https://youtu.be/xBHL40UIVlk</a>
🔗 <a href="https://github.com/headroomlabs-ai/headroom" target="_blank" rel="noopener nofollow">https://github.com/headroomlabs-ai/headroom</a>
🔗 <a href="https://github.com/XamHans/headroom-demo" target="_blank" rel="noopener nofollow">https://github.com/XamHans/headroom-demo</a>
Are you compressing context before it hits the LLM — or paying full token price?
#llm #python #ai #devtools #tokenoptimization #agents #headroom

For developers transitioning to AI Engineering. 
RAG, Agents, MCP, Evals — practical content, no fluff.
New video every week.

J-HAYER - Builders gonna build

Headroom is an open-source tool that sits between your application code and the LLM, automatically compressing tool outputs, logs, and RAG chunks before they consume tokens. It uses a ContentRouter to select the appropriate compressor per content type: SmartCrusher for JSON, CodeCompressor for AST-based code, and Kompress-base for general text. A real-world demo shows compression from 17,765 tokens down to 1,408 — a 92% reduction — with no code changes required. Data stays local and never leaves the machine.

Cut AI Token Costs by 92% — No Code Changes Required