DeepCode Agent工作流 是 AI Skill Hub 本期精选Agent工作流之一。在 GitHub 上收获超过 15.6k 颗 Star,综合评分 8.2 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
DeepCode Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
DeepCode Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:pip 安装(推荐)
pip install deepcode
# 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 deepcode
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/HKUDS/DeepCode
cd DeepCode
pip install -e .
# 验证安装
python -c "import deepcode; print('安装成功')"
# 命令行使用
deepcode --help
# 基本用法
deepcode input_file -o output_file
# Python 代码中调用
import deepcode
# 示例
result = deepcode.process("input")
print(result)
# deepcode 配置文件示例(config.yml) app: name: "deepcode" debug: false log_level: "INFO" # 运行时指定配置文件 deepcode --config config.yml # 或通过环境变量配置 export DEEPCODE_API_KEY="your-key" export DEEPCODE_OUTPUT_DIR="./output"
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🎯 Watch our complete introduction - See how DeepCode transforms research papers and natural language into production-ready code
<p> <a href="https://youtu.be/PRgmP8pOI08" target="_blank"> <img src="https://img.shields.io/badge/▶️_Watch_Video-FF0000?style=for-the-badge&logo=youtube&logoColor=white" alt="Watch Video"/> </a> </p>
</div>
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"Where AI Agents Transform Ideas into Production-Ready Code"
</div>
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DeepCode provides a complete local Coding Agent workflow. TUI, Desktop, and Web use the same Agent, Sessions, models, Skills, permissions, and task state through the shared local service.
<p align="center"> <img src="assets/readme/verification-loop.png" alt="DeepCode Agent Harness and verification loop" width="1080" /> </p>
With uv installed, run:
uv tool install --python 3.12 deepcode-hku
deepcode init
On Windows, you can install uv with winget install --id astral-sh.uv --exact, then open a new terminal. DeepCode requires Python 3.12 or later.
To use the native app, also install DeepCode Desktop from GitHub Releases. To try changes that have not been released yet, install from source below.
<details> <summary>Install from source</summary>
You will need Git, uv, and Node.js 22 or later. Run these commands in your terminal:
git clone https://github.com/HKUDS/DeepCode.git
cd DeepCode
npm --prefix desktop ci
npm --prefix desktop run build:web
uv tool install --python 3.12 --force .
deepcode init
If you already have the repository, start in its root directory and skip cloning. You can now run deepcode from any directory. For source Desktop, also install Rust and the platform dependencies described in the Desktop guide.
</details>
If your terminal cannot find deepcode, run uv tool update-shell and open a new terminal.
Use DeepCode to explore a repository, make changes, and run tests. Choose the terminal, desktop app, or browser—your projects and conversations are available in all three.
DeepCode is not tied to one model provider. Connect OpenRouter, OpenAI, Anthropic, DeepSeek, Gemini, an OpenAI-compatible gateway, Ollama, vLLM, or another compatible endpoint with your own API Key.
Before use, a connection can check credentials, the model catalog, and a real inference request. Each Session can choose a model and Thinking Level. Changing models mid-Session affects future Turns only; it does not delete history or confuse where earlier work came from. When supported, DeepCode can also show a reasoning summary returned by the Provider.
Choose either the graphical setup or the terminal commands below.
In Desktop or Web: open Settings → AI providers, select Add provider, and enter your provider's API key. Click Save and check to load its models. Under Agent model, choose a model and click Save and verify model to try a short request. Then choose the connection and model in your conversation's message box.
In your terminal: this example connects to OpenRouter. Enter your API key when prompted; it will not be displayed as you type.
deepcode provider set my-openrouter --template openrouter --api-key
deepcode provider models my-openrouter --refresh
Choose a model from the list. Replace MODEL_ID below with its exact ID and send a short test request:
deepcode provider test my-openrouter --model MODEL_ID
You can use the same connection in all three interfaces. For other providers, local models, and tool-calling checks, see Models and providers.
<img src="https://github.com/Zongwei9888/Experiment_Images/raw/e389750e733ec2c1b94986cb990036899dcaec52/DeepCode_images/Area.gif" alt="DeepCode Desktop coding agent demo" width="100%" style="border-radius: 10px; box-shadow: 0 8px 20px rgba(45,55,72,0.3); margin: 15px 0;"/>
Work with DeepCode in a visual workspace for Sessions, goals, tool activity, code changes, and verification. </div>
DeepCode provides TUI, Desktop, and Web clients over one shared local service. They share Projects, Session history, models, Skills, permissions, Goals, and Automations. Use deepcode, deepcode desktop, or deepcode web; see Quick start for installation and your first task.
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Plugins optionally package Skills behind a validated local manifest; standalone project and user Skills continue to install and run without a Plugin. New packages must use the Agent Plugins 1.0.0 manifest and fixed skills/ layout. Add a trusted folder from the Desktop Plugins workspace or with `deepcode plugin add <path>`; its Skills then appear in the ordinary Skill catalog and Composer. A standards-compliant mcp.json may also contribute MCP servers; registration remains inert and those processes start only inside an Agent Session. DeepCode does not execute Plugin hooks or Apps and does not download from a Marketplace. See the local Plugin contract.
DeepCode 是一个强大的自动化开发工具,旨在将复杂的科研论文(Research Papers)直接转化为可运行的代码。通过集成先进的 AI 能力,它能够理解学术逻辑并实现高效的代码转换,帮助开发者从理论研究快速跨越到工程实现阶段。
DeepCode 具备核心的 Paper2Code 能力,能够深度解析论文逻辑。同时,它内置了 Process Intelligence 功能,通过 Adaptive Flow 技术实现动�� Agent 选择,能够根据输入任务的复杂度自动调整处理流程,确保复杂任务的执行效率与准确性。
在安装 DeepCode 之前,请确保您的系统已配置好必要的运行环境:需要 Python 3.9+ 作为核心运行时,Node.js 18+ 用于驱动全新的 UI 前端,并安装 npm 8+ 用于包管理。建议在安装前检查相关环境版本是否符合要求。
DeepCode 提供多种灵活的安装方式:推荐使用 Direct Installation 通过 `pip install deepcode-hku` 直接安装;对于开发者,可以通过 Git 源码进行开发模式安装,并推荐使用 UV 工具进行管理;此外,您也可以通过 Docker 容器化环境快速部署,实现开箱即用的体验。
项目提供了 Quick Start 快速入门指南,包含丰富的示例代码。用户可以通过 CLI 命令行模式或全新的 Web UI 界面进行交互。无论是进行简单的代码转换任务,还是复杂的流程自动化,DeepCode 都能提供直观的操作体验。
DeepCode 采用单文件配置管理模式,所有设置均统一存储在 `deepcode_config.json` 中,确保了配置的唯一性(Single Source of Truth)。用户需在此文件中配置必要的 API Keys,并支持通过 `${ENV_VAR}` 引用环境变量,同���支持 MCP 服务器的路径配置。
DeepCode 提供了多样化的交互接口。除了支持开发者习惯的 Terminal-Based CLI 命令行界面外,还提供了直观的图形化界面。通过不同的模式,用户可以在终端或 Web 端灵活切换,满足从快速调试到深度开发的各种场景需求。
DeepCode 构建了强大的模块化工作流,支持通过 MCP (Model Context Protocol) 扩展功能,例如集成 filesystem 服务器以实现文件系统操作。此外,通过 nanobot 集成,用户还可以将 DeepCode 接入 Feishu、Telegram 或 Discord 等聊天平台,实现跨平台的智能协作。
针对安装与运行过程中可能遇到的问题,我们整理了详细的 Troubleshooting 常见问题解答。例如,针对 Docker 构建时出现的 `tsc: not found` 错误或 Docker 连接失败等问题,提供了从清理缓存到重新构建的标准化解决方案。
DeepCode展现了AI工程化落地的良好实践,15.6k星表明社区认可度高。多模态代码生成能力符合当下需求,代码质量有保证,值得AI开发者重点关注。
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。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,DeepCode Agent工作流 在Agent工作流赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | DeepCode |
| 原始描述 | 开源AI工作流:"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"。⭐15.6k · Python |
| Topics | AI编码代码生成LLM智能体工作流自动化开源 |
| GitHub | https://github.com/HKUDS/DeepCode |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-14 · 更新时间:2026-05-16 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
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