Provides AI coding agents with durable architecture memory for repositories by generating structured project maps of responsibilities, relationships, and risks.
Deterministic, local-first repository context for coding agents. Maps an issue, prompt, or git diff to ranked files to read first, likely test commands, and review-risk notes—no API key required.
Generates optimized repository maps and enables identifier search with graph-ranking, designed for efficient LLM context caching and codebase navigation.
Provides token-efficient code retrieval for coding agents by indexing repositories and enabling ranked snippet search, symbol outlines, and surgical line reads.
Enables token-efficient semantic search and analysis over any directory of files through hybrid search, directory overview, structural analysis, and dependency graphs.
Provides codebase indexing and retrieval tools that give AI agents token-efficient, query-relevant context packages (symbols, imports, and dependencies) instead of scanning entire repositories.