vector-data
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Related contents:
The most powerful vector database for building AI applications. Open-source PostgreSQL database extension for vector data and vector search operations.
Lantern is an open-source PostgreSQL database extension to store vector data, generate embeddings, and handle vector search operations.
Qdrant (read: quadrant ) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Related contents:
A vector indexing library to bring fast, fresh and filtered search to your database.
DiskANN3 is a composable library for bringing scalable, accurate and cost-effective vector indexing to multiple databases. It draws on research from the DiskANN project.
Related contents:
The vector database to build knowledgeable AI.
The vector database for machine learning applications. Build vector-based personalization, ranking, and search systems that are accurate, fast, and scalable.
Related contents:
Memory Management Kit for Agents - Remember Me, Refine Me.
🧠 ReMe is a memory management framework built for AI agents, offering both file-based and vector-based memory systems.
It addresses two core problems of agent memory: limited context windows (early information gets truncated or lost during long conversations) and stateless sessions (new conversations cannot inherit history and always start from scratch).
ReMe gives agents real memory — old conversations are automatically condensed, important information is persisted, and the next conversation can recall it automatically.