argo
<h1 align="center"> <a href="https://github.com/xark-argo/argo/releases"> <img src="./assets/images/argo.png" width="150" height="150" alt="banner" /><br> </a> </h1> <p align="center"><a href="./README_CN.md">中文</a> | English | <a href="https://xark-argo.com">Official Website</a> | <a href="https://docs.xark-argo.com/">Documentation</a> | <a href="https://xark-argo.com">官方网站</a> | <a href="https://docs.xark-argo.com/zh-CN/">文档</a> | <a href="https://github.com/xark-argo/argo/blob/main/CONTRIBUTING.md">Contribute</a> | <a href="https://github.com/xark-argo/argo/issues">Feedback</a><br> </p> <!-- 题头徽章组合 --> <p align="center"> <a href="https://deepwiki.com/xark-argo/argo"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a> <a href="https://x.com/ThinkInAgent"><img src="https://img.shields.io/badge/Twitter-ARGO-0088CC?style=plastic&logo=x" alt="Twitter Badge"/></a> <a href="https://discord.com/invite/TuMNxXxyEy"><img src="https://img.shields.io/badge/Discord-ARGO-0088CC?style=plastic&logo=discord" alt="Discord Badge"/></a> <a href="https://github.com/user-attachments/assets/eaed622a-e4bf-4649-bbf0-fab71a635a4c"><img src="https://img.shields.io/badge/Wechat-ARGO-0088CC?style=plastic&logo=wechat" alt="Wechat Badge"/></a> </p> <p align="center"> <a href="https://github.com/xark-argo/argo/stargazers"><img src="https://img.shields.io/github/stars/xark-argo/argo" alt="Stars Badge"/></a> <a href="https://github.com/xark-argo/argo/network/members"><img src="https://img.shields.io/github/forks/xark-argo/argo" alt="Forks Badge"/></a> <a href="https://github.com/xark-argo/argo/releases"><img src="https://img.shields.io/github/release/xark-argo/argo" alt="Release Badge"/></a> </p> <p align="center"> <a href="https://www.producthunt.com/products/argo-ai-rapid-go?embed=true&utm_source=badge-featured&utm_medium=badge&utm_source=badge-argo-2" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=994235&theme=light&t=1752837291403" alt="ARGO - opensource, agentic, manus, ollama, rag, deepresearch, local | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" target="_blank" /></a> </p> # ⛵️ ARGO - Your Local Super AI Agent ARGO is an open-source AI Agent client that makes it easy to **build & use** AI-powered assistants capable of autonomous thinking, task planning, and handling complex tasks. It supports one-click download of open-source models and integrates closed-source models, providing convenient access to local RAG knowledge bases and MCP tools. In addition, ARGO can be **fully deployed privately, secure and controllable**, with 100% data stored locally, supporting offline operation, and is compatible with Windows, Mac, and Linux systems. The vision of ARGO is to allow every user to have their own **exclusive super AI Agent**, enabling more people to benefit from the convenience AI Agents bring. ## Why Choose ARGO? * 🔒 **Local First, Privacy Above All** — Supports full offline operation, 100% local data storage, with native clients for Windows, macOS, and Linux, plug-and-play with no configuration required. Fully compatible with public cloud, private cloud, local servers, and all-in-one machines. * 🚀 **Task Execution Engine That Delivers Results** — The Multi-Agent task engine autonomously plans steps, calls tools, generates reports, and executes multi-stage tasks in full. * ⚙️ **Agent Factory** — Need a travel planning assistant, industry analyst, or legal advisor? Simply describe it, and ARGO can help you customize a dedicated agent for your scenario. * 📦 **Integrates Ollama and Leading Providers** — Instantly access OpenAI, Claude, DeepSeek, or run local Ollama models, with open access and no platform lock-in. Seamlessly switch between local models and API models during conversations. * 🧩 **Agentic RAG Knowledge Engine** — Supports various knowledge entry methods including files, folders, websites, etc., and dynamically synchronizes folders. The Agentic mode intelligently decomposes complex issues and provides more complete and reliable answers. * 🛠️ **Built-in Powerful Toolset, MCP Protocol Support** — Includes a powerful toolset such as web crawlers, browser control, local file management, and more, with support for custom extensions and integration of your own tools. * 🧠 **Self-Evolving Agent** — Learns work habits and preferences during use. ARGO can autonomously optimize the agent, evolving into a super agent that truly understands you. (In testing) 👏 Join us on [Discord](https://discord.gg/TuMNxXxyEy) | [Twitter](https://x.com/ThinkInAgent) | [WeChat Group](https://github.com/user-attachments/assets/d5206618-c32d-4179-9ab7-f74d8cc8706e) ❤️ Love ARGO? Give us a star 🌟 to receive all release notifications from GitHub! ⚠️️ The project is still in the early stages, actively being developed. We appreciate your contributions, feedback, and patience. 🔗 Official website: https://xark-argo.com/ 📚 Quick Start Guide: https://docs.xark-argo.com/getting-started # 📑 Table of Contents - [ 🌠 Demo](#-demo) - [🌟 Features](#-features) - [ 📝 Development Plan](#-development-plan) - [📦 Quick Start](#-quick-start) - [🖥️ Develop](#-develop) - [🤝Contributing](#-contributing) - [📃 License](#-license) - [⭐️ Star History](#-star-history) # 🌠 Demo ## Video <https://github.com/user-attachments/assets/2f23c1a9-b834-492b-b2eb-69a3f442634d> --- ## Interface     # 🌟 Features - 📦 **Flexible Integration of Open-Source and Closed-Source Models**: - **One-click Ollama Integration**: Integrates Ollama with no command-line operations needed to control and manage model downloads, deployment, and running. - **Compatible with HuggingFace**: Supports one-click downloading of models in HuggingFace GGUF format. - **Chat Template Adaptive to Models**: Automatically adapts the chat template when downloading open-source models. - **Compatible with Leading Model APIs**: Supports integration with OpenAI, Claude, DeepSeek, and any service provider compatible with the OpenAI format, breaking platform lock-in. - **Flexible Model Switching**: Seamlessly switch between local models and API models during conversations to balance performance and cost. - 🏠 **DeepResearch & Multi-Agent Collaboration**: - **Multi-Agent Task Engine**: Multiple agents collaborate to handle complex tasks. - **Complete Agentic Workflow**: - **Intent Recognition**: Precisely understands user needs. - **Task Planning**: Autonomously breaks down complex tasks. - **Task Execution**: Efficiently carries out multi-step processes. - **Tool Calling**: Flexible use of multiple external tools. - **Self-reflection**: Reflects on execution and self-corrects. - **Self-summary**: Provides structured result outputs. - **Human-in-the-loop**: After task planning, natural language modifications can be made to the research plan. - 🧩 **Local RAG Knowledge Base**: - **Multiple Knowledge Entry Methods**: Supports adding knowledge through files, folders, websites, etc. - **Dynamic Knowledge Base Sync**: When binding local folders, content changes automatically synchronize to the knowledge base, ensuring information is always up to date. - **Multi-format Document Parsing**: Supports parsing of PDFs, Word, Excel, PPT, web pages, Markdown, plain text, etc. - **Answer Traceability**: When using the knowledge base to answer questions, it is traceable to specific reference fragments. - **Privacy Protection**: Local storage of vectorized data reduces security risks. - **Agentic RAG**: Smart decomposition of complex issues, autonomous assessment of information sufficiency, and generation of more complete and reliable answers. - 🛠️ **MCP Protocol & Tool Support**: - **Out-of-the-box Tool Library**: Built-in tools like network search, web crawlers, browser control, local file management, and more. - **Configurable Expansion**: Supports local (STDIO) and remote (SSE) integration of MCP tools for easy expansion and integration. - 🤖 **Agent Factory**: - **Visual Scenario Agent Creation**: Customize scenario assistants with role setting, model binding, variable configuration, and tool integration (e.g., industry research reports, travel planning). - **Continuous Iteration**: The agent iterates through natural language and learns to better understand the user over time. - **One-click Sharing & Importing**: Share agent configurations with the community, covering scenarios like writing, programming, translation, research, and education. - 🤖 **Chat Conversations**: - **Formula Rendering**: Supports KaTeX formula rendering for academic use. - **Mermaid Rendering**: Supports Mermaid chart visualization, allowing creation of flowcharts, sequence diagrams, Gantt charts, etc. - **Artifacts Rendering**: Supports displaying results in html, svg, xml code. - **Free Branching**: Conversations can freely branch, quickly cloning the conversation process. - 🔒 **Privacy Protection & Cross-Platform Experience**: - **Local Data Storage**: All data is stored locally, supporting offline operation and ensuring privacy and security. - **Cross-platform Support**: Supports native clients for Windows, macOS, and Linux, along with Web versions, with zero dependencies, plug-and-play, and full compatibility with public/private cloud/local servers/all-in-one machines. - **No Registration Required**: No account registration or cloud dependency, making it ideal for privacy-sensitive individuals and businesses. # 🔍 Use Cases - **Personal Assistant**: Manage schedules, provide shopping suggestions, and help with daily tasks to improve user efficiency. - **Study Assistant**: Help students understand complex concepts, offer academic resources, create custom study plans, and give revision suggestions. - **Content Creation**: Automatically generate copy, optimize writing style, and assist creators in improving content creation efficiency. - **Development Assistance**: Provide code generation, debugging, and technical support to help developers solve programming problems more efficiently. - **Industry-specific Assistants**: Custom AI agents like legal advisors, industry analysts to meet professional needs. - **Data Analysis**: Quickly analyze data, generate charts and reports to support data-driven decision-making. - **Knowledge Management**: Automatically parse documents and build knowledge bases to help businesses organize and query knowledge. - **Privacy Protection**: Provide offline operation and local storage to ensure user data privacy and security. # 📝 Development Plan - Save and share the agent’s operation process. - Global personalization memory to make ARGO more understanding of users and capable of delivering results. - Support using local browsers to simulate user web browsing to gather information. - Support scheduled tasks. - More vertical agent scenarios. Feel free to join our community and share your thoughts and feedback! [Discord](https://discord.gg/TuMNxXxyEy) | [WeChat Group](https://github.com/user-attachments/assets/d5206618-c32d-4179-9ab7-f74d8cc8706e) | [GitHub Discussions](https://github.com/xark-argo/argo/discussions/categories/ideas) # 📦 Quick Start ### Hardware Requirements > Before installing Argo, ensure that your machine meets the following minimum system requirements: > >- CPU >= 4 cores >- RAM >= 8 GB > > Additional software requirements when using [Docker](https://www.docker.com/): >- Docker >= 24.0.0 >- Docker Compose >= v2.26.1 > > Tip: To enable CUDA in Docker, you need to install the [Nvidia CUDA container toolkit](https://docs.nvidia.com/dgx/nvidia-container-runtime-upgrade/) --- ### Quick Desktop App Installation Download, double-click, and complete installation. - Macos silicon：[argo-darwin-arm64.dmg](https://github.com/xark-argo/argo/releases/latest/download/argo-darwin-arm64.dmg) - Macos intel：[argo-darwin-amd64.dmg](https://github.com/xark-argo/argo/releases/latest/download/argo-darwin-amd64.dmg) - Windows 64bit（win 10 and above）：[argo-windows-x64.exe](https://github.com/xark-argo/argo/releases/latest/download/argo-windows-x64.exe) --- ### Quick Start with [Docker](https://www.docker.com/) 🐳 #### Install Argo **without** Ollama: If you already have Ollama running locally or in another container and want Argo to use it, run: ```bash docker compose -f docker/docker-compose.yaml up -d ``` > ✅ Note: This setup does not include Ollama, so some model download features (such as HuggingFace) may be unavailable. It’s recommended to pair with an external Ollama service. --- #### Install Argo with Ollama (CPU version): If you want Argo to include Ollama and run models using CPU inference, run: ```bash docker compose -f docker/docker-compose.ollama.yaml up -d ``` > 📦 Ollama will be deployed as a service alongside Argo, with the service address at http://ollama:11434, supporting automatic downloading and loading of local models. --- #### Install Argo with Ollama (GPU version): If you want to run LLM models in a GPU-enabled environment, use: ```bash docker compose -f docker/docker-compose.ollama.gpu.yaml up -d ``` > 🚀 This version enables --gpus all and mounts NVIDIA drivers. Make sure your host has the **NVIDIA Container Toolkit** properly installed. > The Ollama service is also deployed within the container at the address http://ollama:11434 and supports GPU-accelerated model inference. Once installed, you can access Argo at http://localhost:38888. # 🖥️ Develop Refer to the [development documentation](docs/DEV_GUIDE.md) # 🤝 Contributing ARGO is currently in its early stages of development, and we are actively working to improve functionality and fix issues. Before it reaches a stable version, you may encounter some usability issues or instability. We sincerely request your understanding and support! ARGO is an active open-source community project, and we welcome contributions in various forms: 1. **Contribute Code**: [Develop new features or optimize existing code](https://github.com/xark-argo/argo/pulls) 2. **Submit Bugs**: [Submit bugs you find](https://github.com/xark-argo/argo/issues) 3. **Maintain Issues**: [Help manage GitHub issues](https://github.com/xark-argo/argo/issues) 4. **Product Design**: [Participate in design discussions](https://github.com/xark-argo/argo/discussions/categories/ideas) 5. **Write Documentation**: [Improve user manuals and guides](https://docs.xark-argo.com/) 6. **Community Engagement**: Join discussions and assist users on [Discord](https://discord.gg/TuMNxXxyEy) | [WeChat Group](https://github.com/user-attachments/assets/d5206618-c32d-4179-9ab7-f74d8cc8706e) 7. **Promote ARGO**: Spread the word about ARGO, give us a star ❤️ Please refer to our [Contribution Guide](./CONTRIBUTING.md) for code contributions. Thank you for your support and contributions! ## Contributors <a href="https://github.com/xark-argo/argo/graphs/contributors"> <img src="https://contrib.rocks/image?repo=xark-argo/argo" /> </a> # 📃 License [LICENSE](LICENSE) # ⭐️ Star History [](https://star-history.com/#xark-argo/argo&Timeline)