quivr
Opiniated RAG framework for integrating GenAI in apps 🧠
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is quivr?
Quivr is an opinionated Retrieval-Augmented Generation (RAG) framework that simplifies the integration of Generative AI into applications. It supports various LLMs and vectorstores, allowing developers to focus on their product rather than RAG.
Key differentiator
“Quivr stands out by offering a flexible and customizable RAG framework that supports multiple LLMs and vectorstores, enabling developers to focus on their product rather than the intricacies of RAG.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Core concepts and detailed configuration options are not well-documented
GitHub issues have slow response times from maintainers, few contributors
Fit analysis
Who is it for?
✓ Best for
Developers needing a customizable RAG framework for integrating GenAI in their apps
Teams that require flexibility in choosing both the LLM and vectorstore for their projects
✕ Not a fit for
Projects requiring real-time streaming capabilities as quivr is designed for batch processing
Applications with strict budget constraints, as setting up self-hosted solutions can be resource-intensive
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Integrations
Next step
Get Started with quivr
Step-by-step setup guide with code examples and common gotchas.