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3 commits

Author SHA1 Message Date
chopratejas
c850ccc3b2 Replace legacy memory system with HierarchicalMemory 
Major refactor of the memory module:

- Add hierarchical scoping (user → session → agent → turn)
- Add temporal versioning with supersession support
- Add pluggable adapters (SQLite store, HNSW vectors, FTS5 text search)
- Add protocol interfaces (ports) for all memory components
- Update LRUMemoryCache to implement async MemoryCache protocol
- Update wrapper.py to use HierarchicalMemory backend
- Preserve with_memory() one-liner API with zero-latency inline extraction

New files:
- adapters/: sqlite.py, hnsw.py, fts5.py, cache.py, embedders.py
- core.py: HierarchicalMemory orchestrator
- models.py: Memory, MemoryCategory, ScopeLevel
- ports.py: Protocol interfaces (MemoryStore, VectorIndex, etc.)
- config.py: MemoryConfig with backend selection
- factory.py: Component creation from config

Removed legacy files:
- store.py, fast_store.py, extractor.py, worker.py, fast_wrapper.py

Breaking change: Removes legacy memory API (pre-0.3.0)
 2026-01-22 23:28:21 -08:00
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
chopratejas
9c7d4512d6 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
 2026-01-06 23:16:58 -08:00