--- title: "ragflow vs quivr" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/infiniflow-ragflow-vs-quivrhq-quivr" tools: ["infiniflow-ragflow", "quivrhq-quivr"] --- # ragflow vs quivr Neutral, constraint-first comparison with live GitHub stats. | | [ragflow](/tools/infiniflow-ragflow.md) | [quivr](/tools/quivrhq-quivr.md) | | --- | --- | --- | | Tagline | Retrieval-Augmented Generation (RAG) engine fusing Agent capabilities with LLM context management | Opiniated RAG for integrating GenAI in your apps | | Stars | 84,561 | 39,190 | | Forks | 9,862 | 3,719 | | Open issues | 2,325 | 29 | | Language | Go | Python | | Adopt for | Decide whether to use RAGFlow based on its unique integration of retrieval and AI agent capabilities for generating enhanced context layers with LLMs, while considering its language choice (Go) and Apache-2.0 license. | Quivr is an opinionated RAG framework for integrating Generative AI into apps, emphasizing customizability and compatibility with multiple LLMs and vectorstores. It allows for quick setup and customization to meet varied | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | AI Agents, Data & Retrieval | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ragflow](/tools/infiniflow-ragflow.md) | [quivr](/tools/quivrhq-quivr.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 363d | | Open issues (now) | 2.3k | 29 | | Security scan | 4 low (4 low) | No lockfile | | Full report | [trust report](/tools/infiniflow-ragflow/trust.md) | [trust report](/tools/quivrhq-quivr/trust.md) | **Typed relationship:** ragflow _(alternative)_ quivr Both Quivr and ragflow are RAG engines, offering retrieval-augmented generation capabilities but with their own specific approaches and features. ## Decision facts: ragflow - **Pricing:** freemium - RAGFlow is offered under an Apache-2.0 license, making the core functionality free and open-source. However, there may be additional costs associated with hosting, infrastructure maintenance, and any云 - **Adopt for:** Decide whether to use RAGFlow based on its unique integration of retrieval and AI agent capabilities for generating enhanced context layers with LLMs, while considering its language choice (Go) and Apache-2.0 license. ## Decision facts: quivr - **Adopt for:** Quivr is an opinionated RAG framework for integrating Generative AI into apps, emphasizing customizability and compatibility with multiple LLMs and vectorstores. It allows for quick setup and customization to meet varied ## Choose when ### Choose ragflow if… - ragflow is primarily Go; quivr is Python. - License: ragflow is Apache-2.0, quivr is Other. - Pricing: RAGFlow is offered under an Apache-2.0 license, making the core functionality free and open-source. However, there may be additional costs associated with hosting, infrastructure maintenance, and any云. - Both Quivr and ragflow are RAG engines, offering retrieval-augmented generation capabilities but with their own specific approaches and features. - Tags unique to ragflow: context-management, llm-context-layer, agentic-ai, retrieval-augmented-generation. - Also covers AI Agents. - ragflow ships Docker support for self-hosted deployment. - When you need a tool that integrates both retrieval-augmented generation and AI agent functionalities to enhance the contextual layer for any use case involving large language models. ### Choose quivr if… - quivr is primarily Python; ragflow is Go. - License: quivr is Other, ragflow is Apache-2.0. - Both Quivr and ragflow are RAG engines, offering retrieval-augmented generation capabilities but with their own specific approaches and features. - Tags unique to quivr: llm, ai, vector, api. - Also covers LLM Frameworks. - You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ## When NOT to use ragflow - If your project strictly requires a Python environment as RAGFlow is written in Go, transitioning or integrating might pose technical challenges. - In situations where you need real-time processing capabilities superior to what's currently offered by RAGFlow’s architecture without significant customization efforts. - When looking for specialized RAG platforms that offer more mature features like extensive pre-trained models or advanced data handling specific to niche industries. ## When NOT to use quivr - If your application strictly demands a non-opinionated approach to RAG where every detail must be manually configured from scratch. - When you require proprietary or highly restricted licensing terms, as Quivr has a 'Other' license that may not align with these needs. - Your project is limited to only specific LLMs not compatible with Quivr's broad support, such as certain bespoke models not covered by its wide umbrella. ## Common questions ### What is the difference between ragflow and quivr? ragflow: Retrieval-Augmented Generation (RAG) engine fusing Agent capabilities with LLM context management. quivr: Opiniated RAG for integrating GenAI in your apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose ragflow over quivr? Choose ragflow over quivr when ragflow is primarily Go; quivr is Python; License: ragflow is Apache-2.0, quivr is Other; Pricing: RAGFlow is offered under an Apache-2.0 license, making the core functionality free and open-source. However, there may be additional costs associated with hosting, infrastructure maintenance, and any云; Both Quivr and ragflow are RAG engines, offering retrieval-augmented generation capabilities but with their own specific approaches and features; Tags unique to ragflow: context-management, llm-context-layer, agentic-ai, retrieval-augmented-generation; Also covers AI Agents; ragflow ships Docker support for self-hosted deployment; When you need a tool that integrates both retrieval-augmented generation and AI agent functionalities to enhance the contextual layer for any use case involving large language models. ### When should I choose quivr over ragflow? Choose quivr over ragflow when quivr is primarily Python; ragflow is Go; License: quivr is Other, ragflow is Apache-2.0; Both Quivr and ragflow are RAG engines, offering retrieval-augmented generation capabilities but with their own specific approaches and features; Tags unique to quivr: llm, ai, vector, api; Also covers LLM Frameworks; You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ### When should I avoid ragflow? If your project strictly requires a Python environment as RAGFlow is written in Go, transitioning or integrating might pose technical challenges. In situations where you need real-time processing capabilities superior to what's currently offered by RAGFlow’s architecture without significant customization efforts. When looking for specialized RAG platforms that offer more mature features like extensive pre-trained models or advanced data handling specific to niche industries. ### When should I avoid quivr? If your application strictly demands a non-opinionated approach to RAG where every detail must be manually configured from scratch. When you require proprietary or highly restricted licensing terms, as Quivr has a 'Other' license that may not align with these needs. Your project is limited to only specific LLMs not compatible with Quivr's broad support, such as certain bespoke models not covered by its wide umbrella. ### Is ragflow or quivr more popular on GitHub? ragflow has more GitHub stars (84,561 vs 39,190). Stars measure visibility, not whether either tool fits your constraints. ### Are ragflow and quivr open source? Yes - both are open-source projects on GitHub (ragflow: Apache-2.0, quivr: Other). ### Where can I find alternatives to ragflow or quivr? GraphCanon lists graph-backed alternatives at /tools/infiniflow-ragflow/alternatives and /tools/quivrhq-quivr/alternatives (/tools/infiniflow-ragflow/alternatives.md, /tools/quivrhq-quivr/alternatives.md), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at /compare/infiniflow-ragflow-vs-quivrhq-quivr.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ragflow or quivr? ragflow: Very active. quivr: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for ragflow and quivr? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ragflow: /tools/infiniflow-ragflow/trust; quivr: /tools/quivrhq-quivr/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=infiniflow-ragflow`](/api/graphcanon/graph?tool=infiniflow-ragflow) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_