Open WebUI 本地AI聊天界面
open-webui 是一款MCP 工具，可直接接入 Claude 等 AI 客户端（GitHub 137k⭐）。开源MCP工具：User-friendly AI Interface (Supports Ollama, OpenAI API, ...)。⭐136.8k · Python。配置方法：将 MCP 服务器条目添加至 claude_desktop_config.json，重启 Claude Desktop 即可使用。AI Skill Hub 编辑推荐，适合希望提升 AI 工作流效率的开发者和运营者。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
Open WebUI 本地AI聊天界面 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 Open WebUI 本地AI聊天界面 的版本更新，及时通知重要功能变化。
Open WebUI 本地AI聊天界面 是一款基于 Python 开发的开源工具，专注于 Web UI、大模型、本地部署 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
Open WebUI 本地AI聊天界面 是一款基于 Python 开发的开源工具，专注于 Web UI、大模型、本地部署 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 方式一：pip 安装（推荐）
pip install open-webui
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install open-webui
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/open-webui/open-webui
cd open-webui
pip install -e .
# 验证安装
python -c "import open_webui; print('安装成功')"
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 命令行使用
open-webui --help
# 基本用法
open-webui input_file -o output_file
# Python 代码中调用
import open_webui
# 示例
result = open_webui.process("input")
print(result)
# open-webui 配置文件示例（config.yml） app: name: "open-webui" debug: false log_level: "INFO" # 运行时指定配置文件 open-webui --config config.yml # 或通过环境变量配置 export OPEN_WEBUI_API_KEY="your-key" export OPEN_WEBUI_OUTPUT_DIR="./output"
Open WebUI 👋
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution.
Passionate about open-source AI? Join our team →
[!TIP] Looking for an Enterprise Plan? – Speak with Our Sales Team Today! Get enhanced capabilities, including custom theming and branding, Service Level Agreement (SLA) support, Long-Term Support (LTS) versions, and more!
For more information, be sure to check out our Open WebUI Documentation.
Key Features of Open WebUI ⭐
- 🚀 Effortless Setup: Install seamlessly using Docker or Kubernetes (kubectl, kustomize or helm) for a hassle-free experience with support for both
:ollamaand:cudatagged images.
- 🤝 Ollama/OpenAI API Integration: Effortlessly integrate OpenAI-compatible APIs for versatile conversations alongside Ollama models. Customize the OpenAI API URL to link with LMStudio, GroqCloud, Mistral, OpenRouter, and more.
- 🛡️ Granular Permissions and User Groups: By allowing administrators to create detailed user roles and permissions, we ensure a secure user environment. This granularity not only enhances security but also allows for customized user experiences, fostering a sense of ownership and responsibility amongst users.
- 📱 Responsive Design: Enjoy a seamless experience across Desktop PC, Laptop, and Mobile devices.
- 📱 Progressive Web App (PWA) for Mobile: Enjoy a native app-like experience on your mobile device with our PWA, providing offline access on localhost and a seamless user interface.
- ✒️🔢 Full Markdown and LaTeX Support: Elevate your LLM experience with comprehensive Markdown and LaTeX capabilities for enriched interaction.
- 🎤📹 Hands-Free Voice/Video Call: Experience seamless communication with integrated hands-free voice and video call features using multiple Speech-to-Text providers (Local Whisper, OpenAI, Deepgram, Azure) and Text-to-Speech engines (Azure, ElevenLabs, OpenAI, Transformers, WebAPI), allowing for dynamic and interactive chat environments.
- 🛠️ Model Builder: Easily create Ollama models via the Web UI. Create and add custom characters/agents, customize chat elements, and import models effortlessly through Open WebUI Community integration.
- 🐍 Native Python Function Calling Tool: Enhance your LLMs with built-in code editor support in the tools workspace. Bring Your Own Function (BYOF) by simply adding your pure Python functions, enabling seamless integration with LLMs.
- 💾 Persistent Artifact Storage: Built-in key-value storage API for artifacts, enabling features like journals, trackers, leaderboards, and collaborative tools with both personal and shared data scopes across sessions.
- 📚 Local RAG Integration: Dive into the future of chat interactions with groundbreaking Retrieval Augmented Generation (RAG) support using your choice of 9 vector databases and multiple content extraction engines (Tika, Docling, Document Intelligence, Mistral OCR, PaddleOCR-vl, External loaders). Load documents directly into chat or add files to your document library, effortlessly accessing them using the
#command before a query.
- 🔍 Web Search for RAG: Perform web searches using 15+ providers including
SearXNG,Google PSE,Brave Search,Kagi,Mojeek,Tavily,Perplexity,serpstack,serper,Serply,DuckDuckGo,SearchApi,SerpApi,Bing,Jina,Exa,Sougou,Azure AI Search, andOllama Cloud, injecting results directly into your chat experience.
- 🌐 Web Browsing Capability: Seamlessly integrate websites into your chat experience using the
#command followed by a URL. This feature allows you to incorporate web content directly into your conversations, enhancing the richness and depth of your interactions.
- 🎨 Image Generation & Editing Integration: Create and edit images using multiple engines including OpenAI's DALL-E, Gemini, ComfyUI (local), and AUTOMATIC1111 (local), with support for both generation and prompt-based editing workflows.
- ⚙️ Many Models Conversations: Effortlessly engage with various models simultaneously, harnessing their unique strengths for optimal responses. Enhance your experience by leveraging a diverse set of models in parallel.
- 🔐 Role-Based Access Control (RBAC): Ensure secure access with restricted permissions; only authorized individuals can access your Ollama, and exclusive model creation/pulling rights are reserved for administrators.
- 🗄️ Flexible Database & Storage Options: Choose from SQLite (with optional encryption), PostgreSQL, or configure cloud storage backends (S3, Google Cloud Storage, Azure Blob Storage) for scalable deployments.
- 🔍 Advanced Vector Database Support: Select from 9 vector database options including ChromaDB, PGVector, Qdrant, Milvus, Elasticsearch, OpenSearch, Pinecone, S3Vector, and Oracle 23ai for optimal RAG performance.
- 🔐 Enterprise Authentication: Full support for LDAP/Active Directory integration, SCIM 2.0 automated provisioning, and SSO via trusted headers alongside OAuth providers. Enterprise-grade user and group provisioning through SCIM 2.0 protocol, enabling seamless integration with identity providers like Okta, Azure AD, and Google Workspace for automated user lifecycle management.
- ☁️ Cloud-Native Integration: Native support for Google Drive and OneDrive/SharePoint file picking, enabling seamless document import from enterprise cloud storage.
- 📊 Production Observability: Built-in OpenTelemetry support for traces, metrics, and logs, enabling comprehensive monitoring with your existing observability stack.
- ⚖️ Horizontal Scalability: Redis-backed session management and WebSocket support for multi-worker and multi-node deployments behind load balancers.
- 🌐🌍 Multilingual Support: Experience Open WebUI in your preferred language with our internationalization (i18n) support. Join us in expanding our supported languages! We're actively seeking contributors!
- 🧩 Pipelines, Open WebUI Plugin Support: Seamlessly integrate custom logic and Python libraries into Open WebUI using Pipelines Plugin Framework. Launch your Pipelines instance, set the OpenAI URL to the Pipelines URL, and explore endless possibilities. Examples include Function Calling, User Rate Limiting to control access, Usage Monitoring with tools like Langfuse, Live Translation with LibreTranslate for multilingual support, Toxic Message Filtering and much more.
- 🌟 Continuous Updates: We are committed to improving Open WebUI with regular updates, fixes, and new features.
Want to learn more about Open WebUI's features? Check out our Open WebUI documentation for a comprehensive overview!
---
We are incredibly grateful for the generous support of our sponsors. Their contributions help us to maintain and improve our project, ensuring we can continue to deliver quality work to our community. Thank you!
What's Next? 🌟
Discover upcoming features on our roadmap in the Open WebUI Documentation.
How to Install 🚀
Installation via Python pip 🐍
Open WebUI can be installed using pip, the Python package installer. Before proceeding, ensure you're using Python 3.11 to avoid compatibility issues.
1. Install Open WebUI: Open your terminal and run the following command to install Open WebUI:
pip install open-webui
2. Running Open WebUI: After installation, you can start Open WebUI by executing:
open-webui serve
This will start the Open WebUI server, which you can access at http://localhost:8080
Quick Start with Docker 🐳
[!NOTE] Please note that for certain Docker environments, additional configurations might be needed. If you encounter any connection issues, our detailed guide on Open WebUI Documentation is ready to assist you.
[!WARNING] When using Docker to install Open WebUI, make sure to include the -v open-webui:/app/backend/data in your Docker command. This step is crucial as it ensures your database is properly mounted and prevents any loss of data.
[!TIP] If you wish to utilize Open WebUI with Ollama included or CUDA acceleration, we recommend utilizing our official images tagged with either:cudaor:ollama. To enable CUDA, you must install the Nvidia CUDA container toolkit on your Linux/WSL system.
Installation with Default Configuration
- If Ollama is on your computer, use this command:
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
- If Ollama is on a Different Server, use this command:
To connect to Ollama on another server, change the OLLAMA_BASE_URL to the server's URL:
docker run -d -p 3000:8080 -e OLLAMA_BASE_URL=https://example.com -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
- To run Open WebUI with Nvidia GPU support, use this command:
docker run -d -p 3000:8080 --gpus all --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:cuda
Installation for OpenAI API Usage Only
- If you're only using OpenAI API, use this command:
docker run -d -p 3000:8080 -e OPENAI_API_KEY=your_secret_key -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
Installing Open WebUI with Bundled Ollama Support
This installation method uses a single container image that bundles Open WebUI with Ollama, allowing for a streamlined setup via a single command. Choose the appropriate command based on your hardware setup:
- With GPU Support: Utilize GPU resources by running the following command:
docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
- For CPU Only: If you're not using a GPU, use this command instead:
docker run -d -p 3000:8080 -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
Both commands facilitate a built-in, hassle-free installation of both Open WebUI and Ollama, ensuring that you can get everything up and running swiftly.
After installation, you can access Open WebUI at http://localhost:3000. Enjoy! 😄
Other Installation Methods
We offer various installation alternatives, including non-Docker native installation methods, Docker Compose, Kustomize, and Helm. Visit our Open WebUI Documentation or join our Discord community for comprehensive guidance.
Keeping Your Docker Installation Up-to-Date
Check our Updating Guide available in our Open WebUI Documentation.
Troubleshooting
Encountering connection issues? Our Open WebUI Documentation has got you covered. For further assistance and to join our vibrant community, visit the Open WebUI Discord.
Open WebUI: Server Connection Error
If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the --network=host flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: http://localhost:8080.
Example Docker Command:
docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main成熟的开源AI Web UI项目，生态完整、社区活跃、功能丰富。星标高达13.6万，说明认可度极高。适合企业和个人自建AI助手。
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 需要从图片、PDF 提取文字的文档自动化场景
- 跨境业务、多语言内容运营团队
- 做语音类 AI 产品的开发者
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Python 依赖冲突：建议用 venv / uv 隔离环境
- Docker：open-webui 提供官方镜像，docker compose up 一键启动
- CLI：直接 npm install -g / pip install，命令行调用
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 需要从图片、PDF 提取文字的文档自动化场景
- 跨境业务、多语言内容运营团队
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
- +GitHub 136.8k Star，社区高度认可
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
AI Skill Hub 点评：Open WebUI 本地AI聊天界面 的核心功能完整，质量优秀。对于AI 技术爱好者来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | open-webui |
| 原始描述 | 开源MCP工具：User-friendly AI Interface (Supports Ollama, OpenAI API, ...)。⭐136.8k · Python |
| Topics | Web UI大模型本地部署开源 |
| GitHub | https://github.com/open-webui/open-webui |
| License | NOASSERTION |
| 语言 | Python |
收录时间：2026-05-13 · 更新时间：2026-05-26 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。