Reduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.
Deterministic repository context packing for AI coding agents: selects, compresses, and budgets only the files a task needs. Measured 83% fewer input tokens at the same task coverage, fully local, no LLM in the loop.
Persistent, compact project memory for AI coding agents, enabling them to read a small digest instead of re-scanning the codebase every session, saving tokens and costs.
Token-optimized semantic code search with automatic context expansion for AI coding assistants, enabling efficient discovery of code relationships and reducing token usage.