タグ：vector-database

Plugin for building LLM apps: LangGraph agents with memory and tool use, hybrid-search RAG, vector index tuning, prompt optimization and LLM evaluation. Includes 3 subagents and 8 skills.

From Zilliz: indexes your codebase as vectors in Milvus or Zilliz Cloud so coding agents find relevant code by semantic search, cutting context costs in large repos. Needs an embedding model (OpenAI, VoyageAI, Ollama and others) and a vector database.

Uses the Qdrant vector database as a semantic memory layer for agents: store information and retrieve it by meaning, connecting to a remote instance or a local path, with FastEmbed generating embeddings by default. Maintained by Qdrant.

Redis's official MCP server: read and write strings, hashes, lists, streams, JSON and other data structures in natural language, with vector indexing and search and pub/sub. Bring your own Redis instance; runs via uvx.

Chroma's official MCP: lets agents create and manage collections, add documents, and retrieve them with vector search, full-text search and metadata filters. Connects to in-memory or local persistent stores, self-hosted instances or Chroma Cloud.

MongoDB's official skill library covering document model design, driver connection setup, query optimization, natural-language query generation, Atlas full-text and vector search, and stream processing. Works alongside the MongoDB MCP Server.

Pinecone's official plugin: bundles the Pinecone MCP server and /pinecone skill commands to create vector indexes, upsert data, search in plain language, or build a document Q&A assistant with citations from Claude Code. Needs a Pinecone API key (free sign-up); Assistant features also need uv.