Weaviate Agent Skills
Build and query AI applications backed by Weaviate.
The official pack supplies onboarding, search, and agent-development guidance with scripts and resources for Weaviate workflows.
Use cases
- Set up a Weaviate-backed application
- Search collections with source context
How to use
Install the pack, configure the Weaviate endpoint and credentials, and supply required external model-provider keys. Use the quickstart to verify access.
Skills
engram_memory
Add persistent, long-term memory to LLM agents and chatbots using Engram, Weaviate's managed memory server. Use when an app needs to remember things across sessions — user preferences, profiles, past interactions, or lessons an agent learns over time (continual learning). Covers storing memories (string, conversation, pre-extracted input), semantic/keyword/hybrid search, scoping by user and topic, async run tracking, and chatbot/agent integration patterns.
weaviate/agent-skillsweaviate-cookbooks
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
weaviate/agent-skillsweaviate
Search, query, and manage Weaviate vector database collections. Use for semantic search, hybrid search, keyword search, natural language queries with AI-generated answers, collection management, data exploration, filtered fetching, data imports from PDF/CSV/JSON/JSONL files, create example data and collection creation.
weaviate/agent-skills