Quivr vs RAGFlow

Quivr

RAG

RAGFlow

RAG

Tagline

Quivr

Open-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.

RAGFlow

Open-source RAG engine with deep document parsing, hybrid search, and visual agent orchestration.

Pricing

Quivr

Free· Open source (pip install quivr-core); pay only for LLM/vector-store usage

RAGFlow

Freemium· Free tier; Starter $29/mo; Pro $129/mo; Enterprise custom

Free trial

Quivr

Yes

RAGFlow

Yes

API

Quivr

Yes

RAGFlow

Yes

Platforms

Quivr

api

RAGFlow

api

Open source

Quivr

Yes · Apache-2.0

RAGFlow

Yes · Apache-2.0

GitHub stars

Quivr

7,414

checked 2026-09-29

RAGFlow

91,511

checked 2026-09-29

Last GitHub push

Quivr

2025-02-21

RAGFlow

2026-09-29

First commit

Quivr

2024-05

RAGFlow

2023-12

Model used

Quivr

Multi-model (OpenAI, Anthropic, Mistral, Gemma)

RAGFlow

Multi-model

Best for

Quivr

Pick Quivr if you are a Python developer who wants a lightweight, model-agnostic RAG library you can extend rather than a hosted chat-your-docs SaaS.

RAGFlow

Pick RAGFlow if you need a self-hostable, citation-grounded RAG stack that can actually digest gnarly enterprise documents and feed agents.

Not for

Quivr

Skip it if you want a turnkey no-code product with a polished UI, hosted vector store, and a sales team to call.

RAGFlow

Skip it if you just want a hosted chat-with-PDF widget or you're allergic to running your own infrastructure.

Editorial score

Quivr

8.4 / 10

RAGFlow

8.1 / 10

Use cases

Quivr

document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf

RAGFlow

document-qaenterprise-searchagent-orchestrationknowledge-basehybrid-retrieval

Pros

Quivr

• Genuinely open source and pip-installable, no vendor lock-in
• Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
• Minimal boilerplate to get a working RAG assistant running
• Pairs with Megaparse for tougher PDF and document ingestion
• Customizable pipeline with tools and web search when you need more

RAGFlow

• Strong deep-document parsing for messy PDFs, tables, and scans
• Hybrid vector + BM25 retrieval with citation-grounded answers
• Fully open-source with active GitHub repo and self-host option
• Visual agent builder plus MCP integration for tool-calling clients
• Model-agnostic; works with most major LLM providers

Cons

Quivr

• Python library, not a hosted product or UI
• You manage infra, vector store, and evals yourself
• Documentation site is sparse compared to larger RAG frameworks
• LLM and embedding costs are on you

RAGFlow

• Free tier blocks API access, pushing real use to paid plans
• Self-hosting is non-trivial and resource-hungry
• Documentation and UI lag behind the engine's capabilities

Website

Quivr

core.quivr.com

RAGFlow

ragflow.io

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.