Headroom is a context optimization layer for LLM applications that reduces token usage by 50-90% through intelligent compression, caching, and memory systems. It provides a transparent proxy between LLM clients (like Claude Code, Cursor, etc.) and LLM providers (Anthropic, OpenAI, etc.), implementing CCR (Compress-Cache-Retrieve) for lossless compression with retrieval capability. The system features a hybrid polyglot architecture with a Rust-based high-performance proxy core, Python ecosystem for extensibility, and TypeScript SDK. Key capabilities include agent wrapping, cross-agent memory using Qdrant/Neo4j, observability through metrics and dashboard, and a learning system that analyzes interaction patterns to generate optimization rules. Deployment options include Docker, system services, and direct execution.
VeritasGraph is a comprehensive, governed GraphRAG and agent framework for building enterprise-grade knowledge graphs with verifiable attribution. The repository contains a core Python library (VeritasReason) for knowledge graph construction, semantic extraction, reasoning, provenance tracking, and export, along with multiple domain-specific applications: a HIPAA-safe clinical knowledge graph pipeline (clinical-kg), a floorplan extraction and validation web app (floorplan-kg), a municipality incident reporting chatbot, and a Model Context Protocol (MCP) server for IDE integration. The architecture is modular, with a plugin-based method registry pattern, unified configuration management, and a pipeline orchestration engine. While the primary character is an application framework, it also includes research-oriented components such as clinical benchmarks (MedKG-Signal) and evaluation suites, making it a hybrid project with a strong application focus.