Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.
Provides persistent knowledge graph memory for AI agents with local semantic search using Neo4j and ONNX embeddings, enabling offline operation with zero API costs.
Enables AI agents to search, read, and traverse a local knowledge base of Markdown files using full-text search and relationship graph, reducing token usage.
Enables AI agents to build, populate, and search knowledge graphs by providing tools for entity extraction, relationship mapping, and graph traversal. It manages the underlying database infrastructure so users can create searchable knowledge bases from text through natural language commands.
A local-first knowledge base server that enables AI clients to store, retrieve, and manage documents using semantic search. Provides privacy-focused, offline-capable memory for AI assistants with tools for ingesting, querying, updating, and deleting knowledge.
A local OKF-compatible knowledge engine for AI agents. Enables capturing agent conversations, hybrid semantic+keyword search, MCP serving to agents, interactive graph visualization, and OKF bundle export.