chopratejas
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175746cc26
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Prepare for OSS release v0.2.0
This commit prepares Headroom for public open source release with
comprehensive documentation, licensing, and community infrastructure.
License & Legal:
- Add Apache 2.0 LICENSE file
- Add NOTICE file with third-party attributions
- Add SECURITY.md for vulnerability reporting
Community:
- Add CONTRIBUTING.md with contribution guidelines
- Add CODE_OF_CONDUCT.md (Contributor Covenant)
- Add GitHub issue templates (bug report, feature request)
- Add pull request template
Documentation:
- Update README.md with compelling value proposition
- Add docs/getting-started.md
- Add docs/proxy.md for proxy server documentation
- Add docs/transforms.md for transform reference
- Add docs/api.md for API reference
- Add examples/README.md
Package Infrastructure:
- Add headroom/py.typed for PEP 561 compliance
- Add headroom/cli.py for CLI entry point
- Add .github/workflows/ci.yml for CI pipeline
- Add .github/workflows/publish.yml for PyPI publishing
- Update pyproject.toml with proper metadata
New Features:
- Add multi-provider support (Google, Cohere, LiteLLM, OpenAI-compatible)
- Add universal tokenizer registry with multiple backends
- Add model registry with pricing and context limits
- Add production proxy server with caching and rate limiting
Code Quality:
- Fix 83 lint issues via ruff auto-fix
- Fix version consistency (benchmarks 0.1.0 → 0.2.0)
- Add skip decorators for optional dependency tests
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2026-01-07 11:36:44 -08:00 |
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chopratejas
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9c7d4512d6
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Initial commit: Headroom SDK - LLM context optimization toolkit
A comprehensive SDK for optimizing LLM context windows, reducing token
usage while preserving critical information for AI agents.
Core Features:
- SmartCrusher: Statistical compression of tool outputs (70-85% reduction)
- CacheAligner: Prefix optimization for prompt cache hits
- RollingWindow: Intelligent context window management
- BM25/Hybrid relevance scoring for smart item selection
Integrations:
- OpenAI and Anthropic provider support
- LangChain integration (ChatModel, Callbacks, Runnable)
- MCP (Model Context Protocol) integration for tool compression
Test Coverage:
- 372 tests passing across all modules
- 35 performance benchmarks
- Real-world agent evaluations with 88% token savings
Key Components:
- headroom/transforms/: Core compression transforms
- headroom/providers/: OpenAI and Anthropic support
- headroom/integrations/: LangChain and MCP integrations
- headroom/relevance/: BM25 and hybrid scoring
- headroom/pricing/: Model pricing registry
- benchmarks/: Performance benchmark suite
- examples/: Usage examples and demos
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2026-01-06 23:16:58 -08:00 |
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