ragflow vs llm-app

Common questions

What is the difference between ragflow and llm-app?

ragflow: Retrieval-Augmented Generation (RAG) engine with agent capabilities. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. See the comparison table for live GitHub stats and shared categories.

When should I choose ragflow over llm-app?

Choose ragflow over llm-app when ragflow is primarily Go; llm-app is Jupyter Notebook; License: ragflow is Apache-2.0, llm-app is MIT; RAGFlow can be integrated with these ready-to-deploy templates to enhance its retrieval mechanisms and augment the generation phase from RAG systems; Tags unique to ragflow: agent-harness, agentic-ai, agentic-retrieval, agentic-search; Also covers AI Agents; ragflow ships Docker support for self-hosted deployment; When you need an open-source RAG engine that can integrate with external LLM and embedding services to enhance context management.

When should I choose llm-app over ragflow?

Choose llm-app over ragflow when llm-app is primarily Jupyter Notebook; ragflow is Go; License: llm-app is MIT, ragflow is Apache-2.0; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; RAGFlow can be integrated with these ready-to-deploy templates to enhance its retrieval mechanisms and augment the generation phase from RAG systems; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Evaluation & Observability, Inference & Serving, Model Training; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti.

When should I avoid ragflow?

If your project does not require integration with external LLM and embedding services, as RAGFlow relies on these services. If you are looking for a lightweight solution, as RAGFlow's Docker image is approximately 2 GB in size. When you prefer tools that do not require building a Docker image, as RAGFlow's setup involves creating a Docker image.

When should I avoid llm-app?

Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities.

Is ragflow or llm-app more popular on GitHub?

ragflow has more GitHub stars (90,923 vs 58,920). Stars measure visibility, not whether either tool fits your constraints.

Are ragflow and llm-app open source?

Yes - both are open-source projects on GitHub (ragflow: Apache-2.0, llm-app: MIT).

Where can I find alternatives to ragflow or llm-app?

GraphCanon lists graph-backed alternatives at ragflow alternatives and llm-app alternatives (ragflow markdown twin, llm-app 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, ragflow or llm-app?

ragflow: Very active. llm-app: Steady. 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 llm-app?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ragflow trust report; llm-app trust report.