Accelerator
RAGLite
A lightweight, high-performance Python toolkit to streamline RAG workflows.
Build smarter RAG applications.
Faster.
RAGLite is a lightweight, high-performance Python toolkit that simplifies Retrieval-Augmented Generation (RAG) workflows. It helps teams deliver scalable, efficient, and accurate RAG applications, like AI assistants on your knowledge base…, without the complexity of traditional stacks.
Why choose RAGLite
Simplify your RAG stack
Deploy in one command
No more managing frontends, backends, databases, or glue code. With one CLI command, RAGLite handles document ingestion, evaluation, benchmarking, and integrates with Chainlit and Claude MCP, powered by a single, portable database file.
State-of-the-art retrieval quality
RAGLite is engineered for retrieval accuracy, not just speed. It uses semantic chunking, contextual headings, multi-vector embeddings, hybrid search, late chunking, and reranking to deliver relevant, high-quality results out of the box.
Compatibility & evaluation-ready
Works with any LLM available from LiteLLM, embedding model, or reranker (supported by AnswerDotAI/rerankers), including open-weight models like Qwen3. And it includes built-in tools to test and evaluate RAG performance with just two lines of code.
Built for engineers. Ready for production.
No proprietary formats or third-party orchestration.
Lightweight & fast
Designed for performance without overhead.
Use it end-to-end or integrate into existing workflows.
Actively maintained on GitHub with transparent development and community contributions, giving you full visibility and control.
RAGLite features
RAGLite insights
Ready to get started?
Start building RAG today with RAGLite
Because your time is better spent innovating, not configuring!
Whether you're an engineer building your first RAG pipeline or an executive scaling enterprise AI, RAGLite gives you the tools to move faster, with less friction.
