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

⭐ 38659 · 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 need to build retrieval-augmented generation (RAG) systems from scratch. It offers a production ready solution that supports multiple large language models and vector stores, enabling engineering teams to focus on product features rather than underlying AI infrastructure. This open source tool for engineering teams is particularly suited to machine learning and AI engineers who require a flexible, self hosted option for managing AI-driven search and chat functionalities with data privacy in mind. While Quivr is mature and reliable for many production environments, it may not be the best fit for teams seeking a fully managed cloud service or those with very specific customisation needs beyond its current scope.

When to use this project

Quivr is a strong choice when teams need a self hosted option for integrating generative AI with their own data sources, especially where control over privacy and customisation is essential. Teams should consider alternatives if they prefer fully managed services or require specialised integrations not supported by Quivr's current framework.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from Quivr, using it to embed generative AI features such as chatbots and semantic search into their products. It is commonly found in applications involving secure data handling, knowledge bases, and customer support tools where a production ready solution for RAG is needed. Engineers appreciate its flexibility in supporting various LLMs and vector stores, making it a practical choice for real-world AI deployments.

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.