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docs: update README.md
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README.md

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@@ -18,6 +18,10 @@ AgentMesh uses a modular layered design for flexible and extensible multi-agent
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protocol support coming soon for even more tool extensions.
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- **Multi-Platform**: Run via CLI, Docker, or SDK. WebUI and integration with common software coming soon.
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## Demo
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https://github.com/user-attachments/assets/a0e565c4-94ef-4ddf-843d-a0c5aab640c6
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## Quick Start
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Choose one of these three ways to build and run your agent team:
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Add your model `api_key` - AgentMesh supports `openai`, `claude`, `deepseek`, `qwen`, and others.
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The template includes two examples for quick testing:
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- `general_team`: A single general-purpose agent skilled in search, research, and information organization. Recommended to configure with google_search and browser tools.
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- `software_team`: A development team with three roles (product manager, engineer, and tester) that can collaborate on web application development and testing tasks, delivering complete project documentation and code.
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You can modify the template to customize different models, tools, system prompts, and other configurations for each agent.
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> The template includes two examples:
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> - `general_team`: A general-purpose agent for search and research tasks.
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> - `software_team`: A development team with three roles that collaborates on web applications.
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>
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> You can customize models, tools, and system prompts for each agent.
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#### 1.3 Execution
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docker-compose run --rm agentmesh bash
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```
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Once the container starts, you'll enter the command line. The usage is the same as in section 1.3 - specify a team to start the interactive mode:
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Once the container starts, you'll enter the command line. The usage is the same as in section 1.3 - specify a team to
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start the interactive mode:
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```bash
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python main.py -l # List available agent teams

docs/README-CN.md

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@@ -14,6 +14,10 @@ AgentMesh 采用模块化分层设计,提供灵活且可扩展的多智能体
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- **可扩展工具**:内置搜索引擎、浏览器、文件系统、终端等工具,并将通过支持 MCP 协议获得更多工具扩展
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- **多端运行**:支持命令行、Docker、SDK 等多种运行方式,即将支持 WebUI 及多种常用软件的集成
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## Demo
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https://github.com/user-attachments/assets/e2e553c9-bc4a-448d-a5d4-61be21765277
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## 快速开始
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提供三种使用方式快速构建并运行你的 Agent Team:
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填写需要用到的模型 `api_key`,支持 `openai``claude``deepseek``qwen` 等模型。
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配置模板中预置了两个示例供快速体验:
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- `general_team`:包含一个通用Agent,擅长进行搜索、研究、信息整理。建议配置 google_search 和 browser 工具
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- `software_team`:Agent开发团队,包含产品经理、工程师、测试人员三种角色,可以协作完成web应用的开发测试任务,可交付完整的项目文档和代码。
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你可以基于配置模板进行修改,为每个Agent设置不同的模型、工具、系统提示词。
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> 配置模板中预置了两个示例:
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> - `general_team`:通用智能体,适用于搜索和研究任务。
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> - `software_team`:开发团队,包含产品、工程和测试三个角色,可通过协作开发web网站,交付完整的项目代码和文档
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>
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> 你可以基于配置模板进行修改,为每个智能体设置不同的模型、工具、系统提示词。
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#### 1.3 运行
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同时也可以进入命令行交互模式,通过输入问题进行多轮对话:
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```bash
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python main.py -l # 查看可用agent team
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python main.py -t general_team # 指定一个team
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python main.py -l # 查看可用agent team
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python main.py -t general_team # 指定一个team后开始多轮对话
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```
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### 2. Docker运行
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容器启动后将进入命令行,与 1.3 中的使用方式相同,指定team后进入交互模式后即可开始对话:
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```bash
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python main.py -l # 查看可用agent team
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python main.py -t general_team # 指定一个team后开始多轮对话
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python main.py -l # 查看可用agent team
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python main.py -t general_team # 指定一个team后开始多轮对话
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```
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### 3. SDK集成

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