Update readme of alias
Update readme of alias
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README.md
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README.md
@@ -102,7 +102,7 @@ It includes **agent deployment** and **secure sandboxed tool execution**, and ca
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| | conversational_agents/multiagent_conversation | ✅ | ❌ | Multi-agent dialogue scenario |
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| | conversational_agents/multiagent_conversation | ✅ | ❌ | Multi-agent dialogue scenario |
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| | conversational_agents/multiagent_debate | ✅ | ❌ | Agents engaging in debates |
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| | conversational_agents/multiagent_debate | ✅ | ❌ | Agents engaging in debates |
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| **Evaluation** | evaluation/ace_bench | ✅ | ❌ | Benchmarks with ACE Bench |
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| **Evaluation** | evaluation/ace_bench | ✅ | ❌ | Benchmarks with ACE Bench |
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| **Alias** | alias/ | ✅ | ✅ | Agent application running in sandbox to solve diverse real-world problems |
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| **General AI Agent** | alias/ | ✅ | ✅ | Agent application running in sandbox to solve diverse real-world problems |
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------
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------
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@@ -120,6 +120,18 @@ A powerful multi-agent data processing system that leverages Data-Juicer's 200+
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📖 **Documentation**: [English](data_juicer_agent/README.md) | [中文](data_juicer_agent/README_ZH.md)
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📖 **Documentation**: [English](data_juicer_agent/README.md) | [中文](data_juicer_agent/README_ZH.md)
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### Alias-Agent
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*Alias-Agent* (short for *Alias*) is designed to serve as an intelligent assistant for tackle diverse and complicated real-world tasks, providing three operational modes for flexible task execution:
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- **Simple React**: Employs vanilla reasoning-acting loops to iteratively solve problems and execute tool calls.
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- **Planner-Worker**: Uses intelligent planning to decompose complex tasks into manageable subtasks, with dedicated worker agents handling each subtask independently.
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- **Built-in Agents**: Leverages specialized agents tailored for specific domains, including *Deep Research Agent* for comprehensive analysis and *Browser-use Agent* for web-based interactions.
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Beyond being a ready-to-use agent, we envision Alias as a foundational template that can be adapted to different scenarios.
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📖 **Documentation**: [English](alias/README.md) | [中文](alias/README_ZH.md)
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------
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------
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## ℹ️ Getting Help
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## ℹ️ Getting Help
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README_zh.md
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README_zh.md
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| | conversational_agents/multiagent_conversation | ✅ | ❌ | 多 Agent 对话场景 |
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| | conversational_agents/multiagent_conversation | ✅ | ❌ | 多 Agent 对话场景 |
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| | conversational_agents/multiagent_debate | ✅ | ❌ | Agent 辩论 |
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| | conversational_agents/multiagent_debate | ✅ | ❌ | Agent 辩论 |
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| **评估** | evaluation/ace_bench | ✅ | ❌ | ACE Bench 基准测试 |
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| **评估** | evaluation/ace_bench | ✅ | ❌ | ACE Bench 基准测试 |
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| **Alias** | alias/ | ✅ | ✅ | 在沙盒中运行的可以解决真实问题的智能体程序 |
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| **通用智能体** | alias/ | ✅ | ✅ | 在沙盒中运行的可以解决真实问题的智能体程序 |
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---
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---
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@@ -120,6 +120,17 @@ AgentScope Runtime 是一个**全面的运行时框架**,主要解决部署和
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📖 **文档**:[English](data_juicer_agent/README.md) | [中文](data_juicer_agent/README_ZH.md)
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📖 **文档**:[English](data_juicer_agent/README.md) | [中文](data_juicer_agent/README_ZH.md)
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### Alias 智能体
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*Alias-Agent*(简称 *Alias*)旨在作为一个智能助手来处理多样且复杂的真实世界任务,提供三种操作模式以实现灵活的任务执行:
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- **Simple React**:采用经典的推理-行动循环来迭代解决问题并执行工具调用。
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- **Planner-Worker**:使用智能规划将复杂任务分解为可管理的子任务,并由专门的执行智能体独立处理每个子任务。
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- **Built-in Agents**:利用针对特定领域定制的专用智能体,包括用于全面分析的Deep Research Agent和用于基于 Web 交互的Browser-use Agent。
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除了作为一个即用型智能体,我们也希望 Alias 能够成为一个基础模板,可以迁移到不同的场景。
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📖 **文档**:[English](alias/README.md) | [中文](alias/README_ZH.md)
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---
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---
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## ℹ️ 获取帮助
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## ℹ️ 获取帮助
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</div>
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</div>
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## Project introduction
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## Project introduction
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*Alias-Agent* (short for *Alias*) is an LLM-empowered agent built on [AgentScope](https://github.com/agentscope-ai/agentscope) and [AgentScope-runtime](https://github.com/agentscope-ai/agentscope-runtime/), designed to solve diverse real-world problems. It provides three operational modes for flexible task execution:
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*Alias-Agent* (short for *Alias*) is an LLM-empowered agent built on [AgentScope](https://github.com/agentscope-ai/agentscope) and [AgentScope-runtime](https://github.com/agentscope-ai/agentscope-runtime/), designed to serve as an intelligent assistant for responding to user queries. Alias excels at decomposing complicated problems, constructing roadmaps, and applying appropriate strategies to tackle diverse real-world tasks.
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Specifically, Alias provides three operational modes for flexible task execution:
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- **Simple React**: Employs vanilla reasoning-acting loops to iteratively solve problems and execute tool calls.
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- **Simple React**: Employs vanilla reasoning-acting loops to iteratively solve problems and execute tool calls.
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- **Planner-Worker**: Uses intelligent planning to decompose complex tasks into manageable subtasks, with dedicated worker agents handling each subtask independently.
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- **Planner-Worker**: Uses intelligent planning to decompose complex tasks into manageable subtasks, with dedicated worker agents handling each subtask independently.
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- **Built-in Agents**: Leverages specialized agents tailored for specific domains, including *Deep Research Agent* for comprehensive analysis and *Browser-use Agent* for web-based interactions. More details can refer to the following "Basic Usage" section.
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- **Built-in Agents**: Leverages specialized agents tailored for specific domains, including *Deep Research Agent* for comprehensive analysis and *Browser-use Agent* for web-based interactions. More details can refer to the following "Basic Usage" section.
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<div align="center">
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<img src="assets/alias.png" alt="Alias-Agent Logo" width="500" height="250" style="vertical-align: middle; margin-right: 20px;">
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<h1 style="text-decoration: none; border-bottom: none; display: inline; vertical-align: middle; margin: 0;">Alias-Agent: 即开即用,自由扩展,轻松部署</h1>
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</div>
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## 项目介绍
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*Alias-Agent*(简称 *Alias*)是一个基于 [AgentScope](https://github.com/agentscope-ai/agentscope) 和 [AgentScope-runtime](https://github.com/agentscope-ai/agentscope-runtime/) 构建的LLM驱动的智能体,旨在作为响应用户查询的智能助手。Alias擅长分解复杂问题、构建路线图,并应用适当的策略来处理各种现实世界的任务。
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具体而言,Alias提供了三种操作模式,以实现灵活的任务执行:
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- **Simple React(简单React模式)**:采用经典的推理-行动循环来迭代解决问题和执行工具调用。
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- **Planner-Worker(规划者-工作者模式)**:使用智能规划将复杂任务分解为可管理的子任务,由专门的工作者智能体独立处理每个子任务。
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- **Built-in Agents(内置智能体)**:利用针对特定领域定制的专业智能体,包括用于全面分析的*深度研究智能体*和用于基于网络交互的*浏览器使用智能体*。更多详细信息可参考下面的"基本使用"部分。
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我们的目标是让Alias成为一个开箱即用的解决方案,用户可以随时部署以处理各种任务。
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## 即将推出
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除了作为现成的智能体,我们还设想Alias作为一个基础模板,可以迁移适配到不同的场景。开发者可以在工具、提示词和智能体级别扩展和自定义Alias,以满足他们的特定需求。
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我们正在积极开发以下领域的专业增强和适配:
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- **商业智能(BI)** 场景
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- **金融**分析应用
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- **问答(QA)** 系统
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敬请期待即将发布的版本!
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## 安装
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以开发模式安装Alias包:
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```bash
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pip install -e .
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# 设置沙箱环境
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# 如果您使用的是colima,则需要运行以下命令
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# export DOCKER_HOST=unix://$HOME/.colima/default/docker.sock
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# 更多详细信息可参考 https://runtime.agentscope.io/en/sandbox.html
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# 方式1:从registry拉取
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export RUNTIME_SANDBOX_REGISTRY=agentscope-registry.ap-southeast-1.cr.aliyuncs.com
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docker pull agentscope-registry.ap-southeast-1.cr.aliyuncs.com/agentscope/runtime-sandbox-alias:latest
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# 方式2:从docker hub拉取
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docker pull agentscope/runtime-sandbox-alias:latest
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```
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这将安装 `alias` 命令行工具。
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## 基本使用
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`alias` CLI提供了一个终端界面来运行AI智能体以处理各种任务。
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### 运行命令
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首先,设置API密钥:
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```bash
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# 模型API密钥
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export DASHSCOPE_API_KEY=your_dashscope_api_key_here
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# 使用其他模型:前往 src/alias/agent/run.py,将您的模型添加到 MODEL_FORMATTER_MAPPING,然后运行bash来设置您的模型和API密钥。例如:
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#export MODEL=gpt-5
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#export OPENAI_API_KEY=your_openai_api_key_here
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# 搜索API密钥(深度研究所需)
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export TAVILY_API_KEY=your_tavily_api_key_here
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```
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执行智能体任务:
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```bash
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alias_agent run --task "Your task description here"
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```
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### 示例
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#### 使用所有智能体运行(元规划者与工作者):
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```bash
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alias_agent run --task "Analyze Meta stock performance in Q1 2025"
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```
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#### 仅使用Browser-use Agent运行:
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```bash
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alias_agent run --mode browser --task "Search five latest research papers about browser-use agent"
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```
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#### 上传文件到沙箱工作区:
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```bash
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# 上传单个文件
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alias_agent run --task "Analyze this data" --files data.csv
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# 上传多个文件
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alias_agent run --task "Process these files and create a summary report" --files report.txt data.csv notes.md
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# 使用简短形式 (-f)
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alias_agent run --task "Review the documents" -f document1.pdf document2.txt
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# 与其他选项结合使用
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alias_agent run --mode all --task "Analyze the data and generate insights" --files dataset.csv --verbose
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```
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**注意**:使用 `--files` 上传的文件会自动复制到沙箱中的 `/workspace` 目录,并保留其原始文件名,使其立即可供智能体访问。
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### 获取智能体生成的文件
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在您运行 `alias_agent` 的目录中,您应该会找到一个 `sessions_mount_dir` 目录,其中包含子目录,每个子目录都包含来自沙箱挂载文件系统的 `/workspace` 的内容。所有生成的文件都应位于该处。
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