--- title: "rig vs ragflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/0xplaygrounds-rig-vs-infiniflow-ragflow" tools: ["0xplaygrounds-rig", "infiniflow-ragflow"] --- # rig vs ragflow *GraphCanon updated Aug 20, 2026* ## Verdict Pick rig if rig is a Rust library designed to create modular and scalable LLM applications with extensive support for agentic workflows, multi-turn streaming, full compatibility with GenAI conventions, and integration capabilities; 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. [rig](https://rig.rs) reports 8.3k GitHub stars, 937 forks, and 113 open issues, last pushed Aug 20, 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 [rig's repository](https://github.com/0xPlaygrounds/rig) and [ragflow's repository](https://github.com/infiniflow/ragflow). | | [rig](/tools/0xplaygrounds-rig.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Tagline | Build modular and scalable LLM Applications in Rust | Retrieval-Augmented Generation engine with agent capabilities | | Stars | 8,328 | 86,541 | | Forks | 937 | 10,167 | | Open issues | 113 | 1,993 | | Language | Rust | Go | | Adopt for | Rig is a Rust library designed to create modular and scalable LLM applications with extensive support for agentic workflows, multi-turn streaming, full compatibility with GenAI conventions, and integration capabilities. | 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, LLM Frameworks | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rig](/tools/0xplaygrounds-rig.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Open issues (now) | 113 | 2.0k | | Stars delta | +333 (30d) | Unknown | | Open issues delta | +17 (30d) | Unknown | | Full report | [trust report](/tools/0xplaygrounds-rig/trust.md) | [trust report](/tools/infiniflow-ragflow/trust.md) | **Typed relationship:** rig _(related)_ ragflow Rig is related to RAGFlow as both are part of the broader LLM application development ecosystem, focusing on scalable deployment and management but serve different functionalities. ## Decision facts: rig - **Hosting:** self hosted - Self-hosted as a Rust library. - **Pricing:** freemium - Free to use under MIT license with potential premium support options. - **Adopt for:** Rig is a Rust library designed to create modular and scalable LLM applications with extensive support for agentic workflows, multi-turn streaming, full compatibility with GenAI conventions, and integration capabilities. - **License detail:** MIT ## 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 rig if… - rig is primarily Rust; ragflow is Go. - License: rig is MIT, ragflow is Apache-2.0. - Self-hosted as a Rust library. - Pricing: Free to use under MIT license with potential premium support options.. - Rig is related to RAGFlow as both are part of the broader LLM application development ecosystem, focusing on scalable deployment and management but serve different functionalities. - Tags unique to rig: agent, ai, artificial-intelligence, automation. - Also covers LLM Frameworks. - You should use Rig when you need to work with LLM applications in Rust and want full WASM (core library) compatibility. ### Choose ragflow if… - ragflow is primarily Go; rig is Rust. - License: ragflow is Apache-2.0, rig is MIT. - Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.. - Rig is related to RAGFlow as both are part of the broader LLM application development ecosystem, focusing on scalable deployment and management but serve different functionalities. - Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation. - Also covers Data & Retrieval. - 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 rig - Avoid using Rig if you are working on applications that do not require or support Rust as it is specifically built to facilitate LLM operations within a Rust environment. - Rig may not be suitable if your project cannot handle potential breaking changes, which are expected due to its rapidly evolving nature and upcoming feature updates. ## 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 rig and ragflow? rig: Build modular and scalable LLM Applications in Rust. ragflow: Retrieval-Augmented Generation engine with agent capabilities. See the comparison table for live GitHub stats and shared categories. ### When should I choose rig over ragflow? Choose rig over ragflow when rig is primarily Rust; ragflow is Go; License: rig is MIT, ragflow is Apache-2.0; Self-hosted as a Rust library; Pricing: Free to use under MIT license with potential premium support options.; Rig is related to RAGFlow as both are part of the broader LLM application development ecosystem, focusing on scalable deployment and management but serve different functionalities; Tags unique to rig: agent, ai, artificial-intelligence, automation; Also covers LLM Frameworks; You should use Rig when you need to work with LLM applications in Rust and want full WASM (core library) compatibility. ### When should I choose ragflow over rig? Choose ragflow over rig when ragflow is primarily Go; rig is Rust; License: ragflow is Apache-2.0, rig is MIT; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Rig is related to RAGFlow as both are part of the broader LLM application development ecosystem, focusing on scalable deployment and management but serve different functionalities; Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation; Also covers Data & Retrieval; 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 rig? Avoid using Rig if you are working on applications that do not require or support Rust as it is specifically built to facilitate LLM operations within a Rust environment. Rig may not be suitable if your project cannot handle potential breaking changes, which are expected due to its rapidly evolving nature and upcoming feature updates. ### 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 rig or ragflow more popular on GitHub? ragflow has more GitHub stars (86,541 vs 8,328). Stars measure visibility, not whether either tool fits your constraints. ### Are rig and ragflow open source? Yes - both are open-source projects on GitHub (rig: MIT, ragflow: Apache-2.0). ### Where can I find alternatives to rig or ragflow? GraphCanon lists graph-backed alternatives at [rig alternatives](/tools/0xplaygrounds-rig/alternatives) and [ragflow alternatives](/tools/infiniflow-ragflow/alternatives) ([rig markdown twin](/tools/0xplaygrounds-rig/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/0xplaygrounds-rig-vs-infiniflow-ragflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rig or ragflow? rig: Very active. 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 rig and ragflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rig trust report](/tools/0xplaygrounds-rig/trust); [ragflow trust report](/tools/infiniflow-ragflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=0xplaygrounds-rig`](/api/graphcanon/graph?tool=0xplaygrounds-rig) - 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/_