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

⭐ 38618 · 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 capabilities into existing applications without the complexity of building retrieval-augmented generation (RAG) systems from scratch. It provides a practical, production ready solution that supports multiple large language models and vector stores, making it adaptable to a variety of tech stacks. This open source tool for engineering teams is particularly suited to machine learning and AI engineering roles focused on deploying AI features in real products. Quivr is mature enough for production use, offering customisation and privacy controls essential for secure environments. However, it may not be the best choice for teams seeking a lightweight or fully managed cloud service, as it requires setup and maintenance of self hosted components and databases.

When to use this project

Quivr is a strong choice when your team needs a flexible, self hosted option for integrating GenAI with control over data and model selection. Teams should consider alternatives if they prefer turnkey cloud solutions or have minimal AI infrastructure experience.

Team fit and typical use cases

Machine learning and AI engineers benefit most from Quivr by using it to embed RAG capabilities into chatbots, knowledge bases, or AI-driven applications. It fits well in products requiring customisable AI workflows and secure data handling, often involving backend and frontend collaboration. This open source tool for engineering teams supports production environments where control and adaptability are priorities.

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.