IntelligentContext is a message-level compressor that drops low-value
messages. This change adds bidirectional TOIN integration:
- Dropped messages stored in CCR for potential retrieval
- Drops recorded to TOIN for cross-user learning
- Retrieval feedback improves future importance scoring
When messages are dropped and users retrieve them via CCR, TOIN learns
to score those patterns higher next time. This creates a feedback loop
that improves drop decisions across all users.
Changes:
- Add _create_message_signature() for TOIN pattern tracking
- Add _get_compression_store() for CCR integration
- Add _store_dropped_in_ccr() to store dropped messages
- Add _record_drops_to_toin() to record drops for learning
- Update marker to include CCR reference when available
- Update docs with TOIN + CCR integration section
- Update tests to accept both marker formats
Previously, only SmartCrusher (JSON arrays) recorded compressions to TOIN.
Now all compression strategies record to TOIN for cross-user learning:
- CODE_AWARE: AST-based code compression
- SEARCH: grep/ripgrep results
- LOG: build/test output
- LLMLINGUA: ML-based text compression
- TEXT: heuristic text compression
Changes:
- Add _create_content_signature() for non-JSON content types
- Add _record_to_toin() method to ContentRouter
- Update _apply_strategy_to_content() to record after compression
- Update docs/transforms.md with TOIN integration section
This enables the feedback loop where TOIN learns from retrieval patterns
across all content types, not just JSON arrays.
Co-Authored-By: Claude <noreply@anthropic.com>
ContentRouter now routes purely based on content analysis instead of
relying on hardcoded tool name mappings. This makes the router work
with any MCP tool regardless of naming convention.
Changes:
- Remove generate_source_hint() function and _strategy_from_hint() method
- Remove source_hint parameter from compress() method
- Remove _get_tool_source_hint() from IntelligentContextManager
- Update tests to remove source hint test cases
- Update docs to document content detection approach
CodeAwareCompressor:
- Tree-sitter based AST parsing for Python, JS, TS, Go, Rust, Java, C, C++
- Preserves imports, signatures, type annotations, error handlers
- Guarantees syntactically valid output
- Uses tree-sitter-language-pack for broad language support
ContentRouter:
- Intelligent compression orchestrator
- Auto-routes content to optimal compressor based on type detection
- Source hint support for high-confidence routing
Custom Model Configuration:
- HEADROOM_MODEL_LIMITS env var and ~/.headroom/models.json support
- Pattern-based inference for unknown models (opus/sonnet/haiku tiers)
- Support for Claude 4.5, Claude 4, o3, o3-mini
- Graceful fallback - never crashes on unknown models
Integrate Microsoft's LLMLingua-2 ML-based compression as an opt-in
feature for the proxy server, with excellent developer experience.
Features:
- New CLI flags: --llmlingua, --llmlingua-device, --llmlingua-rate
- ProxyConfig options: llmlingua_enabled, llmlingua_device, llmlingua_target_rate
- Smart startup hints when llmlingua is available but not enabled
- Helpful error messages when enabled but not installed
- LLMLinguaCompressor inserted before RollingWindow in pipeline
Why opt-in:
- Heavy dependencies (~2GB torch, transformers)
- 10-30s cold start for model loading
- ~1GB RAM when loaded
- Default proxy stays lightweight (<5ms overhead)
Tests:
- 26 new tests in test_proxy_llmlingua.py covering config, setup,
banner status, CLI args, DevEx messages, and edge cases
Documentation:
- Updated README.md with proxy integration section
- Updated docs/proxy.md with LLMLingua CLI options
- Updated docs/transforms.md with LLMLinguaCompressor reference
- Updated docs/ARCHITECTURE.md with pipeline and file structure
- Updated CHANGELOG.md with new feature
