Cache stats:
- hit_rate is now token-level (cache_read / total_input) not request-level
- Track uncached_input_tokens per provider in metrics
- Preserve request_hit_rate as secondary metric
Compression-vs-cache:
- Detect when compression busts the prefix cache (expected_cached - actual_read)
- Two simple session-level numbers: tokens_saved vs cache_bust_tokens
- Log CACHE-BUST per request, aggregate in /stats and telemetry beacon
- Single new column in proxy_telemetry_v2: cache_bust_tokens
Dashboard infra:
- SQL for dashboard_summary table + pg_cron hourly refresh
- Hourly + daily aggregation from proxy_telemetry_v2
- Upgrade scripts for adding hourly_stats and cache bust columns
07-context-compression.ipynb: Context Compression technique using Headroom.
Same RAG setup as notebooks 01-06 (Lilian Weng blog posts, Claude Sonnet,
OpenAI embeddings). Replaces GPT-4o-mini pruning/summarization with local
Headroom compress() — zero extra LLM calls, zero cost.
Ready to fork and PR to https://github.com/langchain-ai/how_to_fix_your_context
Previous chunks were too dense (400 chars, no filler). Real blog post
retrieval returns verbose explanatory text (~1000 chars per chunk).
Kompress now compresses 31-47% within each item.
Demo results: 4424 → 2756 tokens (38% savings), all 12 items kept,
6/6 key concepts preserved, zero extra LLM calls.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ROOT CAUSE: compress() did not extract the user's question from messages.
The pipeline received empty context, so SmartCrusher selected items by
statistics only (position, anomaly, boundary) — keeping irrelevant chunks
and dropping relevant ones.
FIX: _extract_user_query() in compress.py finds the most recent user
message and passes it as `context` kwarg through the pipeline. SmartCrusher's
RelevanceScorer now receives the actual query and scores items by relevance.
Before: 12 RAG chunks → kept hallucination/video (0/6 key terms)
After: 12 RAG chunks → kept reward hacking content (3/4 key terms)
Also adds:
- examples/context_compression_demo.py — real compression demo for OSS PR
- examples/test_ccr.py — content preservation verification
- OSS_PR_STRATEGY.md — PR target list for LangChain ecosystem
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Updates across multiple docs to reflect the new message-level compression
with TOIN + CCR integration:
- docs/ccr.md: Add CCR-enabled components table, message-level CCR section
- docs/ARCHITECTURE.md: Expand Transform 6 with TOIN + CCR integration details
- docs/configuration.md: Add CCR integration config and marker format
- docs/proxy.md: Add CCR integration note for context management
- docs/README.md: Update to reference IntelligentContextManager as default
Also adds examples/test_intelligent_context_toin_ccr.py for scale testing
the TOIN + CCR integration with real API calls.
Implement comprehensive memory system supporting:
- Local backend (SQLite + FTS5 + HNSW) for zero-dependency operation
- Mem0 backends (Neo4j + Qdrant) for production graph memory
- DirectMem0Adapter for optimized pre-extracted data (bypasses LLM)
- Memory extraction with facts, entities, and relationships
- Proxy integration with --memory flag for automatic memory injection
Key components:
- headroom/memory/backends/: LocalBackend, Mem0Backend, DirectMem0Adapter
- headroom/memory/system.py: MemorySystem with tool-based interface
- headroom/memory/extraction.py: Entity and relationship extraction
- headroom/proxy/memory_handler.py: Proxy integration layer
- headroom/prediction/feature_extractor.py: Content analysis features
Testing:
- 217 new memory system tests covering all backends
- LoCoMo evaluation framework for memory quality assessment
- Integration tests for proxy memory functionality
Also removes deprecated example files in favor of focused test coverage.
- Add quality_retention_eval.py for needle-in-haystack testing to verify
intelligent compression retains critical information (100% retention achieved)
- Add intelligent_context_integration_test.py for comprehensive pipeline testing
- Add test_progressive_summarizer.py with 36 tests for ProgressiveSummarizer
- Add HeadroomConfig parameter to HeadroomClient for direct config injection
- Update pipeline.py with IntelligentContextManager wiring and logging
- Fix all ruff linting issues and format for Python 3.12 compatibility
- Add comprehensive_eval.py benchmark for multi-scenario evaluation
- Add real_data_demo.py for production-scale volume testing
- Add reasoning agent test examples (groq, debug)
The plist template was using ${HEADROOM_PROXY_PORT} in ProgramArguments,
but LaunchAgent doesn't expand environment variables in that context.
Changed to use __PORT__ placeholder which install.sh replaces via sed.
This fixes the "invalid int value" error when starting the proxy service.
AI review: Clean (via pre-commit hook)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add comprehensive macOS deployment support for running headroom proxy as a
persistent background service using LaunchAgent. This enables automatic startup,
crash recovery, and proper lifecycle management for local development environments.
Files added:
- examples/deployment/macos-launchagent/com.headroom.proxy.plist.template
- examples/deployment/macos-launchagent/install.sh (shellcheck-clean)
- examples/deployment/macos-launchagent/uninstall.sh (shellcheck-clean)
- examples/deployment/macos-launchagent/shell-integration.sh (bash + zsh)
- examples/deployment/macos-launchagent/README.md
- docs/macos-deployment.md
Key features:
- Configurable port via HEADROOM_PROXY_PORT environment variable (default: 8787)
- Automated installation and uninstallation scripts
- Shell integration supporting both bash and zsh
- Comprehensive documentation with troubleshooting guide
- All shell scripts are shellcheck-clean (zero errors, warnings, or info messages)
Files modified:
- .gitignore: Added CLAUDE.md to prevent committing local config
- docs/README.md: Added Deployment & Operations section with navigation entry
AI review: Pending (will be run by pre-commit hook)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Previously, HeadroomAgnoModel called wrapped_model.response() which ran the
tool execution loop internally. This meant tool outputs (often 60k+ chars)
were never optimized - only the initial messages were compressed.
The fix delegates response() to the inherited Model.response(), which calls
self.invoke() for each API call. Our invoke() override optimizes messages
before delegating to wrapped_model.invoke(), ensuring tool outputs are
compressed on every API request.
Results from multi_tool_agent_test.py with Claude Sonnet:
- Tokens before optimization: 25,713
- Tokens after optimization: 6,100
- Tokens saved: 19,613 (76.3%)
- Both baseline and optimized found all critical information
Also adds:
- multi_tool_agent_test.py: Real function calling test with 4 tools
- multi_tool_compression_test.py: Direct compression test
- README update with multi-tool agent test results
- Replace marketing claims with actual API test results
- Add needle-in-haystack test: critical error at position 67/100
- Show real JSON compression: 100 entries → 6 entries (93.9% reduction)
- Verified with Claude Sonnet: 87.6% fewer tokens, 4/4 correct answers
- Add example scripts for reproducing the tests
Features:
- with_fast_memory(): Zero-latency inline extraction (Letta-style)
- Memory extracted as part of LLM response, no extra API calls
- Semantic retrieval with local embeddings (sub-50ms)
- with_memory(): Background extraction for non-blocking memory
- SQLite + FTS5 storage with vector similarity search
- Multi-user isolation by user_id
Memory enables temporal compression - extract key facts instead of
carrying full conversation history (4000 tokens → 50 tokens).
Includes:
- Comprehensive test suite (71 new tests)
- Documentation (docs/memory.md)
- Benchmark examples comparing approaches
- E2E test with LLM-as-judge evaluation
- Renamed package from 'headroom' to 'headroom-ai' (PyPI name conflict)
- Fixed numpy/jinja2 imports to be lazy (core install no longer crashes)
- Fixed SQLite default path (now uses temp directory)
- Fixed f-string {tool} crash in proxy server
- Updated README with correct package name and examples
- Added quickstart and troubleshooting docs
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
