Learn how to secure your Weaviate vector database with API keys, OIDC, and role-based access control (RBAC). Includes practical examples and setup steps.
Memory isn't just a feature for AI applications—it's infrastructure. As agents scale, the limited loop of stateless interactions breaks down, and continuity becomes a systems problem that requires active maintenance.
2025 was a defining year for us at Weaviate. Instead of chasing shiny features, we focused on an overarching goal - upgrading our infrastructure and technology in order to better support AI systems.
Why vector databases are here to stay.
The Weaviate C# client is now generally available! This release brings a modern and intuitive API for .NET developers, making it easier than ever to build AI-powered applications.
This release introduces Object Time-to-Live (TTL), zstd compression support, flat index RQ quantization, multimodal support with Weaviate Embeddings, runtime configurable OIDC certificates and much more.
Context engineering is how AI agents manage LLM memory—selecting, retrieving, and organizing context from short-term and long-term memory to improve reliability in production.
Weaviate recognized by AWS Partners in Benelux as leaders in helping customers drive innovation
The Weaviate Java client v6 is now generally available! This release brings a completely redesigned API that embraces modern Java patterns, simplifies common operations, and makes working with vector databases more intuitive than ever.
Learn how to use the Dify and Weaviate integration to build RAG applications.
1.34 introduces flat index support with RQ quantization, server-side batching improvements, new client libraries, Contextual AI integration and much more.
Weaviate announces two CVEs that are fixed in updated versions of our product.
