Top 11 Open Source ChatGPT Alternatives (Ranked by Stars) 11 款开源 ChatGPT 替代品排行（按 Star 数）
All tools below are open-source, actively maintained, and have been tested by the AI Nav team. Star counts are updated weekly via GitHub API. 以下所有工具均为开源项目，持续维护，并经 AI Nav 团队实测验证。Star 数每周通过 GitHub API 自动更新。
| # | Tool | GitHub Stars | Self-Host | Privacy | Best For |
|---|---|---|---|---|---|
| 1 | Open WebUI | ⭐ 140,564 | ✅ Docker/Local | 100% Local | Ollama frontend, most popular UI |
| 2 | ChatGPT Next Web | ⭐ 88,190 | ✅ One-click Vercel | API-based | Lightweight, deploy in 1 minute |
| 3 | LobeChat | ⭐ 78,348 | ✅ Docker | API-based | Modern UI with plugins |
| 4 | AnythingLLM | ⭐ 61,227 | ✅ Desktop/Docker | 100% Local | Document Q&A + multi-model |
| 5 | PrivateGPT | ⭐ 57,206 | ✅ Local | 100% Local | Ask questions to your documents |
| 6 | LibreChat | ⭐ 38,599 | ✅ Docker | API-based | Multi-model: GPT + Claude + Gemini |
| 7 | Jan | ⭐ 42,935 | ✅ Desktop App | 100% Local | Offline, no internet required |
| 8 | SillyTavern | ⭐ 29,040 | ✅ Local | 100% Local | AI roleplay and creative writing |
| 9 | h2oGPT | ⭐ 11,988 | ✅ Docker | 100% Local | Enterprise document Q&A |
| 10 | big-AGI | ⭐ 6,988 | ✅ Docker | API-based | Professional personas and tools |
| 11 | Enchanted | ⭐ 5,961 | ✅ iOS App | 100% Local | iPhone/iPad Ollama client |
Which Alternative Should You Choose? 应该选择哪款替代品？
Your choice depends on what you value most: speed of setup, privacy, document Q&A, or multi-model support. Here are the clearest recommendations by scenario: 你的选择取决于最优先考虑的因素：快速上手、隐私保护、文档问答或多模型支持。以下是各场景最明确的推荐：
How to Set Up Open WebUI in 5 Minutes 5 分钟搭建 Open WebUI 完整指南
Open WebUI with Ollama is the most recommended setup for a private ChatGPT alternative. Here's the complete process: Open WebUI 配合 Ollama 是最推荐的私有 ChatGPT 替代方案。以下是完整部署流程：
Install Docker Desktop 安装 Docker Desktop
Download from docker.com for Mac, Windows, or Linux. Docker is required to run Open WebUI in an isolated container. 前往 docker.com 下载适用于 Mac、Windows 或 Linux 的版本。Docker 用于在隔离容器中运行 Open WebUI。
Install Ollama (the local model runner) 安装 Ollama（本地模型运行器）
Run the one-line installer below. Ollama handles downloading and running open-source LLMs like Llama 3.2, Mistral, and Gemma locally. 运行以下一行安装命令。Ollama 负责下载并在本地运行 Llama 3.2、Mistral、Gemma 等开源大模型。
Pull a language model 拉取语言模型
Llama 3.2 is recommended for first-time users — it runs on 8GB RAM and provides excellent general chat quality. The download is ~2GB. 推荐新用户使用 Llama 3.2——8GB 内存即可运行，通用聊天质量优秀。下载大小约 2GB。
Run Open WebUI with Docker 用 Docker 运行 Open WebUI
The command below starts Open WebUI on port 3000. The --add-host flag allows the container to reach Ollama running on your host machine.
以下命令在 3000 端口启动 Open WebUI。--add-host 参数让容器能访问宿主机上的 Ollama 服务。
Open your browser and start chatting 打开浏览器，开始对话
Navigate to http://localhost:3000. Create an admin account on first launch, select your model from the dropdown, and start chatting privately.
访问 http://localhost:3000，首次启动时创建管理员账号，从下拉菜单选择模型，即可开始私密对话。
# Step 2: Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Step 3: Pull a model (Llama 3.2 – recommended for beginners)
ollama pull llama3.2
# Step 4: Run Open WebUI
docker run -d -p 3000:8080 \
--add-host=host.docker.internal:host-gateway \
-v open-webui:/app/backend/data \
--name open-webui \
--restart always \
ghcr.io/open-webui/open-webui:main
# Step 5: Open browser
# Visit: http://localhost:3000"Open WebUI has become the de facto standard UI for local LLMs. In our testing, the installation-to-first-chat time is genuinely under 5 minutes if you already have Docker. The interface is polished enough that non-technical users can navigate it comfortably — something most open-source tools sacrifice for power-user features."
— AI Nav Editorial Team, tested June 2026Why Choose Open Source Over ChatGPT? 为什么选择开源而非 ChatGPT？
Open Source Advantages
- Zero monthly cost after setup
- Data never leaves your machine
- Works fully offline
- Customizable and extensible
- No rate limits or usage caps
- Run on your own hardware
Trade-offs vs ChatGPT
- Requires initial setup time
- Model quality varies
- GPU needed for best performance
- No DALL-E image generation
- Plugins ecosystem smaller
- Mobile app less polished
Related Guides 相关指南
Frequently Asked Questions 常见问题
ollama pull llama3.2) and all inference happens on your machine. The only requirement is sufficient RAM: 8GB for 7B models, 16GB+ for 13B+ models.