RAGFlow

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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems.

Is RAGFlow free to self-host?

Yes — it is open source and runs on your own hardware.

Does RAGFlow need a lot of resources?

A server with 1–2GB RAM is enough for most workloads.

How do I deploy RAGFlow?

Easiest via Docker — see the installation guide for commands.

Where does my data go?

Nowhere — everything is processed locally on your server.

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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems.

Alternatives to RAGFlow

If RAGFlow isn't quite right, these are common self-hosted alternatives in the same category:

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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems.

Strengths

• Full self-hosted control over your data
• Straightforward Docker-based deployment
• Open-source license

Weaknesses

• Initial setup requires Docker familiarity
• You are responsible for maintenance and updates
• Resource needs can grow under heavy load

Verdict

RAGFlow is a solid self-hosted choice — its strengths outweigh the usual maintenance overhead.

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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems.

Prerequisites

• Docker installed (version 24.0+)
• Docker Compose (version 2.20+)
• At least 1GB RAM (2GB recommended)

Quick start with Docker

# Pull the image
docker pull infiniflow/ragflow:latest

# Run the container
docker run -d --name ragflow -p 8080:8080 infiniflow/ragflow:latest

Key features

• Self-hosted and open source
• Docker-based deployment
• License: Apache-2.0
• Repository: https://github.com/infiniflow/ragflow
• Docker image: infiniflow/ragflow:latest

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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems.

Key features

• Self-hosted and open source
• Docker-based deployment
• License: Apache-2.0
• Repository: https://github.com/infiniflow/ragflow
• Docker image: infiniflow/ragflow:latest

What it's good for

RAGFlow runs entirely on your own infrastructure — your data never leaves your server.