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RAGFlow

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs.

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Rag Document Apache 2.0 Medium setup 80,655 stars

Overview

Plain English

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs.

Technical

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs.

Technical scorecard

License Apache 2.0
Commercial use Yes
OpenAI-compatible API No
REST API No
Fine-tuning support No
Quantization support No
Docker available No
GUI / no-code available No
Telemetry None
Offline after setup Yes

Data & Privacy

Does it send data online?

After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.

Does it store history?

Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.

License checks?

Commercial use is marked as allowed or likely allowed by the listed license.

Telemetry?

None

Last verified: May 17, 2026. Maintainer verification should be treated as directory guidance, not legal advice.

Setup & Installation

Medium

A developer can usually get this running with standard docs.

Prerequisites

Python, Docker, Bare Metal

# Start with the official project documentation
# https://ragflow.io/

Hardware Requirements

RAM8 GB minimum / 16 GB recommended
Hardware tagsCPU Only, NVIDIA GPU (CUDA)
Model formatsNot specified
Primary languagePython

Works Well With