--- title: "RAGLight vs ragflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bessouat40-raglight-vs-infiniflow-ragflow" tools: ["bessouat40-raglight", "infiniflow-ragflow"] --- # RAGLight vs ragflow *GraphCanon updated Aug 22, 2026* ## Verdict Pick RAGLight if rAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP; pick ragflow if rAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. [RAGLight](https://raglight.mintlify.app/) reports 670 GitHub stars, 101 forks, and 12 open issues, last pushed Jun 25, 2026. [ragflow](https://ragflow.io) has 87k stars, 10k forks, and 2.0k open issues, last pushed Jul 31, 2026. Figures are from public GitHub metadata via [RAGLight's repository](https://github.com/Bessouat40/RAGLight) and [ragflow's repository](https://github.com/infiniflow/ragflow). | | [RAGLight](/tools/bessouat40-raglight.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Tagline | A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools. | Retrieval-Augmented Generation engine with agent capabilities | | Stars | 670 | 86,541 | | Forks | 101 | 10,167 | | Open issues | 12 | 1,993 | | Language | Python | Go | | Adopt for | RAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP. | RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RAGLight](/tools/bessouat40-raglight.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 57d | 0d | | Open issues (now) | 12 | 2.0k | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/bessouat40-raglight/trust.md) | [trust report](/tools/infiniflow-ragflow/trust.md) | ## Decision facts: RAGLight - **Adopt for:** RAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP. ## Decision facts: ragflow - **Requirements:** Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services. - **Adopt for:** RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. - **License detail:** Apache-2.0 License ## Choose when ### Choose RAGLight if… - RAGLight is primarily Python; ragflow is Go. - License: RAGLight is MIT, ragflow is Apache-2.0. - Tags unique to RAGLight: data-science, framework, huggingface, mcp-tools. - When you require seamless integration with various Language Models (LLMs) like Hugging Face or OpenAI models, making RAGLight a suitable choice for diverse model environments. ### Choose ragflow if… - ragflow is primarily Go; RAGLight is Python. - License: ragflow is Apache-2.0, RAGLight is MIT. - Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.. - Tags unique to ragflow: context management, rag. - ragflow ships Docker support for self-hosted deployment. - - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ## When NOT to use RAGLight - Avoid using RAGLight if your workflow strictly demands proprietary integration methods that are not supported by its modular framework structure. - If the project focuses on a specific LLM without the need for flexibility or interchangeability, the overhead of configuring diverse integrations in RAGLight might be unnecessary. ## When NOT to use ragflow - - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function. ## Common questions ### What is the difference between RAGLight and ragflow? RAGLight: A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.. ragflow: Retrieval-Augmented Generation engine with agent capabilities. See the comparison table for live GitHub stats and shared categories. ### When should I choose RAGLight over ragflow? Choose RAGLight over ragflow when RAGLight is primarily Python; ragflow is Go; License: RAGLight is MIT, ragflow is Apache-2.0; Tags unique to RAGLight: data-science, framework, huggingface, mcp-tools; When you require seamless integration with various Language Models (LLMs) like Hugging Face or OpenAI models, making RAGLight a suitable choice for diverse model environments. ### When should I choose ragflow over RAGLight? Choose ragflow over RAGLight when ragflow is primarily Go; RAGLight is Python; License: ragflow is Apache-2.0, RAGLight is MIT; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Tags unique to ragflow: context management, rag; ragflow ships Docker support for self-hosted deployment; - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ### When should I avoid RAGLight? Avoid using RAGLight if your workflow strictly demands proprietary integration methods that are not supported by its modular framework structure. If the project focuses on a specific LLM without the need for flexibility or interchangeability, the overhead of configuring diverse integrations in RAGLight might be unnecessary. ### When should I avoid ragflow? - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function. ### Is RAGLight or ragflow more popular on GitHub? ragflow has more GitHub stars (86,541 vs 670). Stars measure visibility, not whether either tool fits your constraints. ### Are RAGLight and ragflow open source? Yes - both are open-source projects on GitHub (RAGLight: MIT, ragflow: Apache-2.0). ### Where can I find alternatives to RAGLight or ragflow? GraphCanon lists graph-backed alternatives at [RAGLight alternatives](/tools/bessouat40-raglight/alternatives) and [ragflow alternatives](/tools/infiniflow-ragflow/alternatives) ([RAGLight markdown twin](/tools/bessouat40-raglight/alternatives.md), [ragflow markdown twin](/tools/infiniflow-ragflow/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 [this comparison](/compare/bessouat40-raglight-vs-infiniflow-ragflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RAGLight or ragflow? RAGLight: Steady. ragflow: Very active. 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 RAGLight and ragflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RAGLight trust report](/tools/bessouat40-raglight/trust); [ragflow trust report](/tools/infiniflow-ragflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bessouat40-raglight`](/api/graphcanon/graph?tool=bessouat40-raglight) - 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/_