rig vs ragflow

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 and ragflow alternatives (rig markdown twin, ragflow markdown twin), 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 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; ragflow trust report.