akadmin/headroom
1
0
Fork 0
mirror of https://github.com/headroomlabs-ai/headroom.git synced 2026-08-27 14:17:10 -04:00
Code Issues Projects Releases Packages Wiki Activity Actions
headroom/docs
History
chopratejas e4a41faa33 Fix all ruff lint and format errors for CI 
- Fix E402: Move module-level imports to top of file
- Fix F401: Add noqa for availability check imports
- Fix F402: Rename loop variables shadowing imports
- Fix E722: Replace bare except with except Exception
- Fix B904: Add exception chaining (from e)
- Fix F811: Remove duplicate imports
- Fix B027: Add noqa for empty close() method
- Fix E741: Rename ambiguous variable l -> label
- Fix I001: Import sorting issues
- Apply ruff format to all 106 files

All 902 tests pass.
2026-01-10 15:33:44 -08:00
..
api.md Prepare for OSS release v0.2.0 2026-01-07 11:36:44 -08:00
ARCHITECTURE.md feat: Add CCR architecture, TOIN telemetry, and DevEx improvements 2026-01-10 10:12:13 -08:00
getting-started.md Prepare for OSS release v0.2.0 2026-01-07 11:36:44 -08:00
HEADROOM_DEEP_ANALYSIS.md Fix all ruff lint and format errors for CI 2026-01-10 15:33:44 -08:00
HEADROOM_FEATURES.md Fix all ruff lint and format errors for CI 2026-01-10 15:33:44 -08:00
PATH_TO_10_OUT_OF_10.md Fix all ruff lint and format errors for CI 2026-01-10 15:33:44 -08:00
proxy.md Prepare for OSS release v0.2.0 2026-01-07 11:36:44 -08:00
quickstart.md Publish headroom-ai v0.2.0 to PyPI with DevEx fixes 2026-01-10 14:51:08 -08:00
README.md Prepare for OSS release v0.2.0 2026-01-07 11:36:44 -08:00
transforms.md Prepare for OSS release v0.2.0 2026-01-07 11:36:44 -08:00
troubleshooting.md Publish headroom-ai v0.2.0 to PyPI with DevEx fixes 2026-01-10 14:51:08 -08:00

README.md

Headroom Documentation

Welcome to the Headroom documentation.

Quick Links

Overview

Headroom is the Context Optimization Layer for LLM applications. It reduces your LLM costs by 50-90% through intelligent context compression.

Core Concepts

  1. Transforms: Stateless functions that modify message arrays to reduce tokens
  2. Providers: Adapters for different LLM providers (OpenAI, Anthropic, etc.)
  3. Pipeline: Chains multiple transforms together
  4. Proxy: HTTP server that applies transforms transparently

Getting Help