Two weeks of dogfooding Engram, Weaviate’s memory product, in daily Claude Code sessions. This surfaced where a dedicated memory product adds value, and the specific mechanics that prevent integration with coding assistants from working well.
Multimodal embeddings allow AI systems to search and reason across text, images, audio, and video in their native formats. This blog covers the key intuitions behind how this all works and walks through three practical implementations using Weaviate and Gemini.
Use semantic search and RAG in C# with the Weaviate Managed .NET client — attribute-driven schema, type-safe queries, and safe migrations, all in idiomatic .NET.
A complete guide on how to secure Weaviate enterprise deployments with OIDC, RBAC, and multi-tenant isolation.
This release introduces HFresh vector index (Preview), and brings Server-side Batching, Object TTL, Async Replication Improvements, Drop Inverted Indices, and Backup Restoration Cancellation to general availability.
Learn how we built a production-ready, end-to-end RAG application in just 36 hours using the Query Agent and the new Weaviate Agent Skills library.
Learn how to secure your Weaviate vector database with API keys, OIDC, and role-based access control (RBAC). Includes practical examples and setup steps.
Build production-ready agent workflows with a single prompt in Claude Code, Cursor, and GitHub Copilot.
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.