RAGStack is a curated stack of the best open-source software for easing implementation of the RAG pattern in production-ready applications.

Instead of managing forks and installing dozens of open-source packages while wondering if you chose the right vector database solution, a single command (brew install datastax/ragstack/ragstack) unlocks all the open source packages required to build production-ready RAG applications.


DataStax has been busy helping our customers through the pains of RAG implementation, so the RAGStack components we’ve selected have withstood production workloads and stringent testing by our engineering teams for performance, compatibility, and security.

  • RAGStack leverages the LangChain↗ ecosystem and is fully compatible with LangSmith↗ (for monitoring) and LangServe↗ (for hosting).

  • The AstraDB vector database provides the best performance and scalability for RAG applications, in addition to being particularly well-suited to RAG workloads like question answering, semantic search, and semantic caching.

  • The LangStream↗ package combines the best of event-based architectures with the latest Gen AI technologies. Develop robust Gen AI pipelines with just YAML files. Leverage Kafka for data flow, and Kubernetes for deployment and scaling.

Why RAGStack?

RAGStack offers solutions for challenges facing developers building RAG applications.

  • Productivity - abstract over the RAG pattern’s complexities to keep developers focused on business logic.

  • Performance, scalability, cost - cache a large percentage of AI calls and leverage the inherent parallelism built into AstraDB

  • Event-driven architectures - fresher data faster

  • Advanced RAG techniques - use advanced patterns like Chain of Thought and Multi-Query RAG

  • Future-proof - as new techniques are discovered, RAGStack offers enterprise users an upgrade path to always be on the cutting edge of AI.

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