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## TL;DR Weaviate is an open-source vector database (Go) that stores objects, vectors, and inverted indexes in one system — use it as a **primary database** for AI-native apps, not just a secondary vector store. Start with **Weaviate Cloud** (zero-ops, auto-scaling, free trial) and use **hybrid search** (`col.query.hybrid(...)`) for best result quality. Built-in embeddings (`weaviate-embeddings`) mean no third-party API keys are needed. First-class **multi-tenancy** makes it ideal for SaaS. Beyond the core DB, the stack includes the **Query Agent** (turnkey RAG) and **Engram** (agent memory, preview). ## LATEST VERSIONS (recommended) **Prefer Weaviate Cloud** for most teams: it’s **versionless / managed** (zero-ops) and stays current automatically. If you run Weaviate yourself (Docker / Kubernetes / on-prem), **use at least these versions** to avoid outdated examples: - **Weaviate Server (OSS)**: v1.36.2+ :contentReference[oaicite:0]{index=0} - **Python client (weaviate-client)**: v4.19.1+ :contentReference[oaicite:1]{index=1} - **Agents SDK (weaviate-agents, if using Query Agent / agents features)**: v1.0.0+ :contentReference[oaicite:2]{index=2} **Quick checks** - Server: check your Docker tag / Helm chart version (e.g. `weaviate:<tag>`) - Python: `pip show weaviate-client` / `pip show weaviate-agents` - Node: `npm view weaviate-client version` / `npm view weaviate-agents version` > Note: We’ll keep these values updated manually for now, and automate later to prevent staleness. **Table of contents** > **Evaluate**: The Weaviate Stack · Ideal Use Cases · Architecture · Misconceptions > **Build**: Quickstart · Best Practices · Python / TypeScript API Reference > Further Resources > **Evaluate** — what Weaviate is and when to use it ## The Weaviate Stack The Weaviate stack extends beyond the core database: 1. **Core Database** (Go, production-grade, scalable) 2. **Weaviate Cloud** (DBaaS): zero-friction start, scales to any production workload