Enables LLMs to search open-source AI models by semantic meaning using directional coordinates and anchors, returning scored, ranked results in under 100ms.
Enables agents to perform semantic search, text comparison, clustering, and zero-shot classification locally without API costs, allowing repeated in-loop use.
Local deterministic BM25 memory for AI agents — offline-first, no API key, SHA-256 content-addressed shards, stdio MCP transport. Same query always returns the same ranked result.
Provides 11 MCP tools for deterministic, local semantic search over your documents, including indexing, retrieval, exact-match facets, temporal truth, semantic diff, and agent-first JSON output. Enables LLMs and agents to search, retrieve, and analyze documents without cloud dependencies or per-query costs.
Enables fast, low-token code search for AI coding agents via a local BM25 engine built on SQLite FTS5, with support for camelCase, snake_case, and Japanese text. Provides a stateless MCP stdio server and a Hermes adapter for multi-agent environments.