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.
Code graph context engine that parses codebases with tree-sitter (170+ languages), builds structural dependency graphs, and provides 24 MCP tools for code intelligence. One prepare_context call gives your AI agent the right files for any task. Includes focus, blast radius, hotspots, dead code detection, and hybrid search.
Provides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.
Deterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.
Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.
Cross-repository code knowledge graph MCP server for Java, Kotlin, JavaScript, and TypeScript. Indexes source code into embedded KuzuDB via tree-sitter and exposes 30+ tools for call-flow tracing, multi-hop taint analysis (OWASP/CWE/PCI/STIG), entry-point reachability filtering, performance hotspot detection, and license compliance — without reading source files. 95% fewer tokens vs source-read
