Vector Databases
Vector databases purpose-built for AI applications — store, index, and search high-dimensional embeddings at scale. These are the retrieval backbone of RAG pipelines, semantic search, and long-term agent memory. Available as managed cloud services or open-source self-hosted deployments.
Algolia
Hosted search and discovery API with typo tolerance, faceting, and vector search, callable directly for agent retrieval…
ChromaDB
Open-source embedding database for local and cloud AI apps. Simple API for storing, querying, and filtering embeddings.…
Codebase Memory MCP
Indexes repositories into a persistent tree-sitter knowledge graph of functions, classes, call chains, and HTTP routes,…
Cognee
Builds queryable memory for agents by turning documents and conversations into a knowledge graph plus vector store, sel…
Elasticsearch MCP Server
Official Elastic MCP server letting agents query Elasticsearch indices, run search and aggregations, and inspect mappin…
Milvus
High-performance open-source vector database with REST API, gRPC, and an official MCP server. Stores billions of vector…
PageIndex
Indexes long documents as a reasoning-based hierarchical tree so agents retrieve by navigating structure instead of emb…
Pinecone
Fully managed vector database for AI applications. Store and search billions of high-dimensional embeddings with low la…
Qdrant
High-performance open-source vector database. Supports dense, sparse, and multi-vector search. On-premise or cloud. Rus…
Supabase MCP
Official Supabase MCP server. Lets AI agents manage Postgres databases, run queries, handle migrations, store pgvector…
Weaviate
Stores objects and vectors together, combining semantic search with structured filtering for scalable, cloud-native AI…