An in-memory knowledge graph MCP server that gives coding agents structural and semantic recall over codebases by indexing Python source, ADR documents, and project configuration, exposing 7 tools for search, traversal, context retrieval, and natural-language Q&A.
Local-first codebase context engine that parses code into a ranked dependency graph and serves it to AI tools via MCP for deep structural understanding.
Local-first Knowledge Management System that exposes markdown-based durable memory as MCP tools for Codex and ChatGPT, enabling search, read, write, and promotion workflows with project and global scopes.
Embeds your codebase into a local vector and graph database and exposes it as an MCP tool, enabling AI assistants to perform fast semantic search over your code using Graph RAG.
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.
