📰 Weaviate Blog

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

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 bec

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

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-po

This release introduces Object Time-to-Live (TTL), zstd compression support, flat index RQ quantization, multimodal support with Weaviate Embeddings, runtime co

Context engineering is how AI agents manage LLM memory—selecting, retrieving, and organizing context from short-term and long-term memory to improve reliability

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.

What building AI apps with Weaviate and cooking have in common? Let’s find out!

Learn how Confluent’s Streaming Agents and Weaviate combine real-time context with semantic understanding for agentic AI.

A guide to seamlessly transform data sitting in lakes and warehouses for GenAI capable applications

A strategic guide to the costs, risks and rewards of upgrading embedding models in production AI

1.33 brings compression by default for optimal resource utilization, powerful 1-bit rotational quantization (RQ), streamlined server-side batch imports, enhance

Hands-on patterns: LLM-as-Judge with LangChain and W&B

Learn how chunking strategies improve LLM RAG pipelines, retrieval quality, and agent memory performance across production AI systems.

Elysia is an open-source, decision tree-based agentic RAG framework that dynamically displays data, learns from user feedback, and chunks documents on-demand. B

Key consideration for customizing an embedding model through fine-tuning it on company- or domain-specific data to improve the downstream retrieval performance

LangChain and Weaviate v3 power scalable AI with fast, type-safe RAG workflows, practical code, and future-ready features like LLM eval and event-driven design.

1.32 adds collection aliases for no-downtime collection migrations, efficient & powerful rotational quantization (RQ), GA replica movement, memory reduction wit

How Weaviate's day zero, day one, day two mindset helps developers build AI-native applications.

How we built cross-platform vector search and syncing for a thinking tool using Weaviate, RAG, and multi-tenant architecture.

Announcing Weaviate Enterprise Cloud new HIPAA compliance on AWS, enabling secure PHI storage, search, and vector-powered AI for healthcare workloads.

Learn how Weaviate and NVIDIA enable fast, scalable vector search for agentic AI.