PandaWiki

PandaWiki 是一款 AI 大模型驱动的开源知识库搭建系统，帮助你快速构建智能化的 产品文档、技术文档、FAQ、博客系统，借助大模型的力量为你提供 AI 创作、AI 问答、AI 搜索等能力。

⭐ 9,505 stars on GitHub · 🍴 922 forks · 📜 License: agpl-3.0 · 💻 Language: TypeScript

What is PandaWiki?

If you want a self-hosted knowledge base where AI is built into the publishing workflow rather than bolted on later, PandaWiki is worth a look. Its differentiator is the combination of wiki hosting, document ingestion, AI writing, semantic search, and chatbot-style Q&A in one open source stack.

Main components

• AI-assisted knowledge base builder for product docs, technical docs, FAQs, blogs, and internal wikis.
• Web console for creating and managing multiple knowledge bases, each published as its own wiki site.
• AI writing, AI Q&A, and AI search once you connect an LLM provider.
• Rich text editor with Markdown and HTML compatibility.
• Export support for Word, PDF, Markdown, and other document formats.
• Content import from URLs, sitemaps, RSS feeds, and offline files.
• Embeddable web widget plus integrations as chatbots for DingTalk, Feishu/Lark, and WeCom.
• Docker-based Linux deployment with a guided install script.

Clear use cases

• Build a public product documentation site with AI search and question answering for users.
• Turn scattered internal SOPs, runbooks, and technical notes into a searchable team knowledge base.
• Create a support FAQ where customers can ask natural-language questions instead of browsing categories.
• Import an existing website or sitemap and use it as the source for a structured AI-powered wiki.
• Publish developer docs while giving support, sales, or operations teams a chatbot interface over the same content.
• Maintain a lightweight company blog or documentation portal without adopting a full enterprise CMS.

The biggest strength is AI-native documentation workflows — PandaWiki does not just store pages and add a search box. It can ingest content from several sources, help create and edit articles, publish them as a wiki, and expose the same knowledge through AI Q&A and chat integrations. Compared with commercial knowledge base platforms, the unique value is control: you can self-host it, connect your own model setup, keep sensitive documentation under your infrastructure, and avoid locking your docs into a SaaS-only system.

There are a few practical caveats. The project is clearly strongest for teams comfortable running Docker on Linux and configuring LLM access; without a model configured, the headline AI features are not useful. The AGPL-3.0 license is also important for companies to review, especially if they plan to modify the software or offer it over a network.

Best for dev teams, support teams, and IT managers who want a self-hosted AI knowledge base for docs, FAQs, and internal knowledge sharing without handing everything to a proprietary SaaS platform.

Topics: the project is tagged with popular topics:

• 🏷️ ai
• 🏷️ docs
• 🏷️ document
• 🏷️ documentation
• 🏷️ kb
• 🏷️ knownledge
• 🏷️ llm
• 🏷️ self-hosted
• 🏷️ wiki

📸 Screenshots

Quick install

See the README for detailed install instructions. Most projects support Docker — if the repo has a Dockerfile, use:

git clone https://github.com/chaitin/PandaWiki.git
cd PandaWiki
docker build -t PandaWiki .
docker run -d -p 8080:8080 PandaWiki

Minimum system requirements

⚡ Deploy fast on VSIS

Use the VSIS VPS Standard 4GB RAM / 2 vCPU / 50GB SSD (~150k/tháng) plan from VSIS.NET — high-speed VN-based VPS, 24/7 support, ideal for running PandaWiki smoothly.

🎯 Benefits:

• One-command docker compose up -d deploy in 2 minutes
• Dedicated IPv4, root access, unmetered domestic bandwidth
• Daily snapshot backup
• Free install assistance from the VSIS team

👉 See matching VPS plans at vsis.net

Resources

• 🔗 GitHub: chaitin/PandaWiki
• 🌐 Homepage: https://pandawiki.docs.baizhi.cloud/
• 📚 Official docs: see README in the repo
• 💬 Community: GitHub Issues + Discussions

Article compiled from GitHub data on 05/05/2026. Star/fork counts may have changed — see live numbers via the GitHub link.