ragflow vs llm-app

Common questions

What is the difference between ragflow and llm-app?

ragflow: Retrieval-Augmented Generation 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; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; 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: agentic-ai, context management, rag; Also covers AI Agents; 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 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; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; 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, vector-database; Also covers LLM Frameworks, Vector Databases; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.

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.

When should I avoid llm-app?

- You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.

Is ragflow or llm-app more popular on GitHub?

ragflow has more GitHub stars (86,541 vs 59,037). 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.