LibreAI / RAG & Knowledge / QAnything
// rag · netease-youdao/QAnything

QAnything

Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.

★ 14.2k stars⑂ 1.4k forksAGPL-3.0updated 2026-09
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

QAnything is an open-source rag & knowledge project by netease-youdao (netease-youdao/QAnything on GitHub). It has 14,197 GitHub stars and 1,354 forks, is released under the AGPL-3.0 license, and was last updated on GitHub on 23 September 2026.

GitHub stars
14,197
Forks
1,354
License
AGPL-3.0
Last updated on GitHub
23 September 2026
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Prepare your machine

    Install Docker and Docker Compose and make sure you have at least 20GB of RAM; no GPU is required for the default setup.

  2. Clone the repository

    Pull the QAnything repo, which contains the per-platform Docker Compose files for one-click startup.

  3. Start the stack

    Run the Docker Compose file for your operating system and wait for the 'qanything backend service is ready' log line (about 30 seconds).

  4. Configure a model and ask

    Open the web UI at http://localhost:8777/qanything/, set your OpenAI-compatible API base and key (or a local Ollama model) in the front-end settings, upload documents and start asking.

// features

Why people love it

Two-stage retrieval

Embedding retrieval followed by reranking with BCEmbedding models keeps answer quality stable as data volume grows, instead of degrading like single-stage retrieval.

Many file formats

Supports PDF, Word, PPT, Excel, Markdown, email, TXT, images (with OCR), CSV and web links, with strong parsing of tables, columns and cross-page layouts.

Offline and CPU-friendly

Designed for data security: install and use it with the network unplugged, running by default in a pure CPU environment on Windows, Mac or Linux.

Cross-language Q&A

Ask in Chinese or English regardless of the language of the source files.

Flexible modes

Quick-start mode (upload and ask, or fileless chat), retrieval-only mode, FAQ matching, custom Bots and hybrid BM25-plus-embedding search.

Independent components

PDF parsing, OCR, embedding and rerank services are independent and replaceable, and can be called separately over HTTP.

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// run it

Run it yourself

  1. Clone the repository

    Pull the QAnything code with its Docker Compose files.

    git clone https://github.com/netease-youdao/QAnything.git
    cd QAnything
  2. Start with Docker Compose

    Launch the stack with the compose file for your platform (Linux shown).

    docker compose -f docker-compose-linux.yaml up
  3. Open the web app

    Once the log says the backend service is ready, open the front end in your browser.

    # Open http://localhost:8777/qanything/ - see README for details
// use cases

What you can build

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// faq

Questions, answered

Who maintains QAnything?

QAnything is developed by NetEase Youdao's open-source team and community contributors.

Is it free to use?

The code is AGPL-3.0 licensed; note the README says commercial use should also follow the license of the underlying QwenLM models if you use them.

Does it need a paid API key?

Version 2.0 gets its LLM through an OpenAI-compatible interface: that can be a paid hosted API with your key, or a free local model served by Ollama.

How does QAnything compare with Dify or FastGPT?

Those are broader LLM app platforms; QAnything focuses specifically on local knowledge-base Q&A with strong document parsing and two-stage retrieval, deployable fully offline on CPU.

How do I get started?

Clone the repo, run the Docker Compose file for your OS, open http://localhost:8777/qanything/, set your model endpoint, and upload your first files.

// keep exploring

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// alternatives

Alternatives in RAG & Knowledge

The most-starred other RAG & Knowledge projects in the LibreAI catalogue — the realistic shortlist if QAnything is not the right fit.

Get QAnything

Free and open — grab the source, star the repo, and start creating.