RAGFlow: review, pricing, security and alternatives

What RAGFlow does

RAGFlow belongs to the data and rag family: agents that query databases and company documents to answer questions or produce analyses. On our grid it ranks #1 of the 15 projects of this family listed in the directory.

You use it from: web app, API, and developer SDK. Usable models: GPT (OpenAI), Claude (Anthropic), Gemini (Google), DeepSeek, Qwen (Alibaba), and local models (Ollama, llama.cpp, vLLM…). It can run on your own machine or server.

Who is RAGFlow for?

It is for teams that want an AI to answer from their own documents or databases. Typical tasks people give it:

• query internal documentation in plain language
• ask a database questions without SQL
• index and summarise large file collections

It is a good candidate if you prefer doing everything in a browser, with no install, your data must stay on your machines, and you want the option of a local model, with no API fees.

Access and human control

Its documentation does not detail the access granted to the agent: check the settings before connecting it to your accounts.

We could not establish whether it asks for human approval before sensitive actions: test it first on an unimportant account or folder.

RAGFlow pricing

The code is free, under the Apache-2.0 licence.

Getting started with RAGFlow

1. Install it: the project ships as a single Go binary, downloadable from the repository releases. The exact command is in the repository README.
2. Pick the model: an API key from a provider, or a local model if your machine has enough graphics memory (the VRAM calculator tells you).
3. Give it a first playground with nothing at stake: a test folder, a secondary account or a throwaway project. Only open your real accounts once you have seen how it behaves.
4. Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.

Security: risk level

Give it read-only access and check generated queries before any data change.

Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.

Project activity and releases

Created on December 12, 2023, the infiniflow/ragflow repository has 92,807 stars, 11,100 forks and 931 open issues, with 10,385 commits in total. It received changes this month.

Over the last 52 weeks: 7,866 commits, an average of 151.3 per week.

58 releases in total; 23 in the last twelve months. Latest release: v0.27.3, on October 23, 2026.

RAGFlow vs other data and rag

GitHub stars of the best-rated open-source projects in the data and rag family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.

RAGFlow and other data and rag at a glance

The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.

How we rated it

Score: 91/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 36/40: 92,807 stars on a log scale (40 points from about 200,000).
• Activity 30/30: last commit less than a month ago, 17 releases in six months, 7,866 commits in a year.
• Framework 25/30: licence, default human approval, self-hosting, model choice and documentation site.

The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.