Find code by meaning using semantic search with natural language queries. Combines AI understanding with text matching to locate relevant snippets, handling typos and variations.
Analyze and trace code execution paths with semantic understanding, identifying data flows and dependencies to debug complex distributed systems efficiently.
Create a vector index for semantic code search by generating embeddings with tree-sitter and Jina AI, enabling efficient and accurate querying of source code files.
Find code by meaning using semantic understanding combined with exact text matching. Search with natural language queries like 'authentication logic' or 'database queries' to locate relevant snippets with file locations and line numbers.
Search and retrieve stored memories using semantic understanding, filter by source type or bucket, and sort results by date or relevance for precise information retrieval.
A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
CodeGuard MCP is a real-time AI code security scanning tool used to detect vulnerabilities, keys, and compliance issues in AI-generated code, and is suitable for code security reviews in development environments
Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.