vectorize-io/hindsight

Modern, secure MCP server for accessing ZIM format knowledge bases offline. Enables AI models to search and navigate Wikipedia, educational content, and other compressed knowledge archives with smart retrieval, caching, and comprehensive API.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context

Local FAISS vector database for RAG with document ingestion (PDF/TXT/MD/DOCX), semantic search, re-ranking, and CLI tools for indexing and querying

Enable AI assistants to conduct structured, persona-driven sessions including interview preparation, personal reflection, and coaching conversations. Built-in timer management and performance evaluation tools.

Five-level AI collaboration system with persistent memory and anticipatory capabilities. MCP-native integration for Claude and other LLMs with local-first architecture via MemDocs.

Production-ready RAG platform combining Graph RAG, vector search, and full-text search. Best choice for building your own Knowledge Graph and for Context Engineering