常见问题

问：我可以使用除 Gemini 之外的其他模型吗？ 答：是的，ADK 是模型无关的。虽然示例经常引用 Gemini，但该框架通过其工具生态系统和配置支持与其它模型的集成。

问：如何安装可选集成？ 答：您可以运行以下命令来安装扩展模块：pip install "google-adk[extensions]"。这允许与特定工具或服务更紧密地集成。

来源与致谢

来源：google/adk-python，commit 288a9e6575629ec97bcfb7f77e9d902260c1b43f。采用 Apache 2.0 许可证。文档改编自官方 ADK 文档。

完整上游材料译文

Agent Development Kit (ADK) 2.0

一个开源的、代码优先的 Python 框架，用于构建、评估和部署复杂的 AI agent，提供灵活性和控制力。

重要链接： 文档, 示例 & ADK Web。

其他语言的 ADK： ADK Java, ADK Kotlin, ADK Go & ADK Typescript。

Agent Development Kit (ADK) 是一个灵活且模块化的框架，将软件开发原则应用于 AI agent 的创建。它旨在简化 agent 工作流的构建、部署和编排，从简单任务到复杂系统均可适用。虽然针对 Gemini 进行了优化，但 ADK 保持模型无关性、部署无关性，并与其他框架兼容。

✨ 主要特性

• 工作流运行时：基于图的执行引擎，用于为 agent 应用组合确定性执行流，支持路由、扇出/扇入、循环、重试、状态管理、动态节点、人机协同以及嵌套工作流。

• 任务 API：结构化的 agent 间委托机制，支持多轮任务模式、单轮受控输出、混合委托模式、人机协同，并将任务 agent 作为工作流节点。

• 模块化多 Agent 系统：通过将多个专用 agent 组合成灵活的层次结构来设计可扩展的应用程序。

• 丰富的工具生态系统：利用预构建工具、自定义函数、OpenAPI 规范、MCP 工具或集成现有工具，赋予 agent 多样化的能力，所有这些都旨在与 Google 生态系统紧密集成。

• 代码优先开发：直接在 Python 中定义 agent 逻辑、工具和编排，以实现最大的灵活性、可测试性和版本控制。

• Agent 配置：无需代码即可构建 agent。查看 Agent Config 功能。

• 工具确认：工具确认流程 (HITL)，可以通过显式确认和自定义输入来保护工具执行。

• 随处部署：轻松将 agent 容器化并部署在 Cloud Run 上，或使用 Vertex AI Agent Engine 无缝扩展。

🚀 安装

稳定版（推荐）

您可以使用 pip 安装 ADK 的最新稳定版本：

pip install google-adk

要求： Python 3.11+。

为了保护传递性依赖，我们建议使用配套的约束文件进行安装（适用于 Python 3.11 至 3.14）。

请选择与您的 Python 版本匹配的约束文件：

# For example, for Python 3.11
curl -o constraints-3.11.txt https://raw.githubusercontent.com/google/adk-python/main/constraints-3.11.txt
pip install google-adk -c constraints-3.11.txt
rm constraints-3.11.txt

要安装可选集成，您可以使用以下命令：

pip install "google-adk[extensions]"

发布周期大致为每两周一次。

开发版本

错误修复和新功能首先合并到 GitHub 上的 main 分支。如果您需要访问尚未包含在官方 PyPI 发布中的更改，可以直接从 main 分支安装：

pip install git+https://github.com/google/adk-python.git@main

注意：开发版本直接基于最新的代码提交构建。虽然它包含了最新的修复和功能，但也可能包含稳定版中不存在的实验性更改或错误。主要用于测试即将发布的更改或在官方发布之前获取关键修复。

快速入门

初学者提示： ADK 应用程序使用两个主要类构建： Agent（定义 AI 的指令、工具和行为）和 Workflow（在基于图的流程中编排 agent 和任务）。

Agent

from google.adk import Agent

root_agent = Agent(
    name="greeting_agent",
    model="gemini-3.5-flash",
    instruction="You are a helpful assistant. Greet the user warmly.",
)

工作流

from google.adk import Agent, Workflow

generate_fruit_agent = Agent(
    name="generate_fruit_agent",
    instruction="Return the name of a random fruit. Return only the name.",
)

generate_benefit_agent = Agent(
    name="generate_benefit_agent",
    instruction="Tell me a health benefit about the specified fruit.",
)

root_agent = Workflow(
    name="root_agent",
    edges=[("START", generate_fruit_agent, generate_benefit_agent)],
)

本地运行

# Interactive CLI
adk run path/to/my_agent

# Web UI (supports multi-agent directories or pointing directly to a single agent folder)
adk web path/to/agents_dir

开发 UI

内置的开发 UI，帮助您测试、评估、调试和展示您的 agent。

评估 Agent

从仓库根目录运行捆绑的评估示例：

adk eval \
    contributing/samples/evaluation/home_automation_agent \
    contributing/samples/evaluation/basic_criteria/home_automation.evalset.json \
    --config_file_path contributing/samples/evaluation/basic_criteria/eval_config.json

🚀 部署选项

使用 Docker 容器在本地部署 Agent：

adk deploy docker --with_ui <agent-folder>

在 Google Cloud (Cloud Run) 中部署 Agent

adk deploy cloud_run --with_ui <agent-folder>

您可以在 adk 命令中设置以下环境变量，或者在 .env 文件中设置。

adk deploy cloud_run --with_ui --env GOOGLE_GENAI_USE_ENTERPRISE=1 <agent-folder>

📚 文档

• 入门指南: https://adk.dev/
• 指南: 参见 docs/guides/ 以获取关于 agent、工具、事件、插件和工作流的面向任务的逐步讲解。
• 示例: 参见 contributing/samples/ 以获取可运行的示例 agent。

🤝 贡献

我们欢迎社区的贡献！无论是错误报告、功能请求、文档改进还是代码贡献，请参阅我们的：

• 通用贡献指南和流程。
• 代码贡献指南 以开始参与。

社区仓库

我们拥有 adk-python-community 仓库，这里汇集了一个不断增长的生态系统，包含社区贡献的工具、第三方服务集成以及扩展 ADK 核心功能的部署脚本。

Vibe Coding（氛围编码）

如果您希望通过 vibe coding 开发 agent，ADK 文档网站上发布的 llms.txt 和 llms-full.txt 可用作 LLM 的上下文。前者是摘要版，后者包含完整信息，适用于您的 LLM 拥有足够大的上下文窗口的情况。

社区活动

• [已完成] ADK 第一次社区会议于 2025 年 10 月 15 日（星期三）举行。请记得加入我们的群组以获取会议录像和演示文稿的访问权限。

📄 许可证

本项目采用 Apache 2.0 许可证授权 — 详见 LICENSE 文件。

祝您 Agent 构建愉快！

来源与许可证

Source · License · 288a9e6575629ec97bcfb7f77e9d902260c1b43f

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