Perform semantic searches across indexed codebases using RAG (Retrieval-Augmented Generation) to find relevant code snippets based on meaning and context.
Analyzes entire code projects and automatically prepares and injects data for RAG systems by processing source files and integrating with knowledge graphs.
Search the web and extract content using intelligent RAG techniques to retrieve, process, and summarize information from web pages for research and analysis.
Enables storing and searching personal notes, documents, and snippets using semantic search and RAG capabilities across Claude Desktop, VS Code, and Open WebUI.
Provides a project memory bank and RAG context provider for enhanced code understanding and management through vector embeddings, integrated with RooCode and Cline.
Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.