📰 Weaviate Blog

HFresh is Weaviate's disk-based vector index for memory-efficient vector search, combining low heap usage with incremental background maintenance.

Part 3: We turn a messy creative archive into a searchable library using a manifest, Weaviate, and hybrid search.

Why AI won’t replace creatives, and how it can remove friction from messy workflows, lost files, and creative processes.

When a Weaviate query is slow, the first question is where the time went. Query profiling returns a per-stage, per-shard timing breakdown, making query performa

This release brings the HFresh disk-based vector index and the built-in MCP Server to general availability, rebuilds cluster-wide async replication to run from

Server-side batching, retries, the blobHash data type, and multimodal ingestion — what to use when, with code.

Weaviate Cloud is now free to start across the entire product suite.

Engram, Weaviate's managed memory and context service for agentic applications, is now generally available.

Weaviate Cloud now supports more granular role-based access control with new Editor and Viewer roles for improved security and organizational management.

Use Weaviate's built-in MCP server to give Claude Code, Cursor, and VS Code hybrid search over your codebase and docs. No glue code.

Tokenization makes or breaks hybrid search. See how Weaviate's accent folding, custom stopwords, and /v1/tokenize endpoint power multilingual BM25.

A Researcher's Perspective on Retrieval Quality in RAG Systems

This release introduces the built-in MCP Server, Extensible Tokenizers, Diversity Search (MMR), and Query Profiling as previews, along with Incremental Backups,

A deep dive into Engram, our managed memory service for agents which is simple to get started but adaptable to any use case.

Weaviate Shared Cloud is now generally available on AWS in US East and Europe, giving teams a fully managed, AI-native database on the provider and region that

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 spe

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 b

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 B

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.

Build production-ready agent workflows with a single prompt in Claude Code, Cursor, and GitHub Copilot.

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 bec