quivr open source analysis

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

Project overview

⭐ 38765 · Python · Last activity on GitHub: 2025-07-09

GitHub: https://github.com/QuivrHQ/quivr

Why it matters for engineering teams

Quivr addresses the challenge of integrating generative AI into applications without requiring teams to build retrieval-augmented generation (RAG) systems from scratch. It offers a practical, production ready solution that supports multiple large language models and vector stores, allowing engineering teams to focus on product development rather than backend complexity. This open source tool for engineering teams is particularly suited to machine learning and AI engineers who need a flexible and customisable framework for conversational AI and knowledge management. While Quivr is mature enough for production use, teams should consider alternatives if they require a fully managed service or have very specific compliance requirements that exceed its current privacy and security features.

When to use this project

Quivr is a strong choice when you need a self hosted option for conversational AI that integrates with various LLMs and vector databases. Teams should consider alternatives if they prioritise fully managed cloud solutions or need specialised support for less common language models or vector stores.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from Quivr, using it to build chatbots, knowledge bases, and AI-powered applications that require RAG capabilities. It is commonly used in products that demand customisable AI integration with existing data sources, offering a practical open source tool for engineering teams focused on maintaining control over their infrastructure and data privacy.

Best suited for

Topics and ecosystem

ai api chatbot chatgpt database docker framework frontend groq html javascript llm openai postgresql privacy rag react security typescript vector

Activity and freshness

Latest commit on GitHub: 2025-07-09. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.