Provides code intelligence for AI coding agents by indexing repositories into a hybrid knowledge graph, enabling agents to query dependencies, impact, and context through 28 MCP tools.
Indexes any TypeScript / React / Next.js repo into a queryable code graph and exposes 13 MCP tools — who-renders, who-calls, find-references, blast-radius, find-cycles, dead-code orphans, and local semantic search — so agents query structure instead of reading whole files. Built on ts-morph, so edges are resolved, not grepped.
Knowledge graph for token-efficient code reviews. Builds a structural map of your codebase with Tree-sitter, tracks changes incrementally, and gives AI agents precise context via MCP tools. Features fixed multi-word search, qualified call resolution, dual-mode embedding (ONNX local + LiteLLM cloud), and output pagination.
Turn any codebase into an AI-readable neural map — with proof. Every claim linked to code anchors (line + SHA-256 hash), every context window optimized with greedy token budgeting, every session protected by drift detection. Tree-sitter indexing across 11 languages, cross-session learning, AI enrichment, and 28 MCP tools. Zero config — just connect and your AI agent remembers everything.
Provides a dependency graph of any local repository with tools for change impact, transitive dependents, health audits, and more, enabling AI coding agents to see structure and refactor safely.
AI-powered codebase intelligence tool that builds a dependency graph of a git repo for structural Q&A and pre-PR code review. It exposes MCP tools for blast radius, review sessions, and static analysis, running locally over stdio with no LLM or network dependencies.