Provides AI coding assistants with deep, semantic understanding of local codebases via AST-aware chunking, cross-repo symbol graphs, and architectural memory, enabling context-aware code search and dependency tracing.
Enables AI coding agents to build and query local repository memory, returning ranked files, context packs, relationship maps, semantic recall, and feedback-tuned search without uploading code.
Architectural memory layer for AI coding.
Automatically extracts decisions, detects
security gaps, and analyzes git history
from your codebase in one command.
Provides AI coding agents with durable architecture memory for repositories by generating structured project maps of responsibilities, relationships, and risks.
Turns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.