# agent-squad **Repository Path**: joey2022/agent-squad ## Basic Information - **Project Name**: agent-squad - **Description**: 多智能体框架 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: bedrock-classifier-profile - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2025-07-30 - **Last Updated**: 2025-07-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Multi-Agent Orchestrator Static Badge

Flexible and powerful framework for managing multiple AI agents and handling complex conversations.

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灵活、轻量级的开源框架，用于编排多个 AI 代理以处理复杂的对话。 ## 🔖 特性 - 🧠 智能意图分类 — 根据上下文和内容将查询动态路由到最合适的代理。 - 🔤 双语言支持 — 在 Python 和 TypeScript 中完全实现。 - 🌊 灵活的代理响应 — 支持来自不同代理的流式处理和非流式处理响应。 - 📚 上下文管理 — 维护和利用多个座席之间的对话上下文，以实现连贯的交互。 - 🔧 可扩展的架构 — 轻松集成新代理或自定义现有代理以满足您的特定需求。 - 🌐 通用部署 — 在任何地方运行 – 从 AWS Lambda 到您的本地环境或任何云平台。 - 📦 预构建的代理和分类器 — 提供各种即用型代理和多个分类器实现。 ## 什么是 Agent Squad ❓ Agent Squad 是一个灵活的框架，用于管理多个 AI 代理和处理复杂的对话。它智能地路由查询并跨交互维护上下文。 该系统提供用于快速部署的预构建组件，同时还允许轻松集成自定义代理和对话消息存储解决方案。 这种适应性使其适用于广泛的应用程序，从简单的聊天机器人到复杂的 AI 系统，适应不同的需求并有效扩展。 ## 🏗️ 高级架构流程图

![High-level architecture flow diagram](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/img/flow.jpg)

1.该过程从用户输入开始，由 Classifier 进行分析。 2.分类器利用 Agents' Characteristics 和 Agents' Conversation history 来为任务选择最合适的代理。 3.选择代理后，它将处理用户输入。 4.然后，编排器保存对话，更新代理的对话历史记录，然后将响应传回给用户。 ## 💬 Demo App To quickly get a feel for the Multi-Agent Orchestrator, we've provided a Demo App with a few basic agents. This interactive demo showcases the orchestrator's capabilities in a user-friendly interface. To learn more about setting up and running the demo app, please refer to our [Demo App](https://awslabs.github.io/multi-agent-orchestrator/deployment/demo-web-app/) section.
在下面的屏幕录像中，我们演示了使用 6 个专用代理的演示应用程序的扩展版本： - Travel Agent：由 Amazon Lex 机器人提供支持 - Weather Agent：利用带有查询 open-meteo API 的工具的 Bedrock LLM Agent - Restaurant Agent：作为 Amazon Bedrock Agent 实施 - Math Agent：利用带有两个工具的 Bedrock LLM Agent 来执行数学运算 - 技术代理：一个 Bedrock LLM 代理，旨在回答有关技术主题的问题 - Health Agent：专注于解决与健康相关的查询的 Bedrock LLM Agent 观看系统在不同主题之间无缝切换上下文，从预订航班到查看天气、解决数学问题和提供健康信息。 请注意如何为每个查询选择合适的代理，即使有简短的后续输入，也能保持连贯性。 该演示重点介绍了该系统处理复杂的多轮对话的能力，同时保留上下文并利用各个领域的专业代理。 ![](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/img/demo-app.gif?raw=true) ## 🚀 示例和快速入门 Check out our [documentation](https://awslabs.github.io/multi-agent-orchestrator/) for comprehensive guides on setting up and using the Multi-Agent Orchestrator! ## 🌟 Use cases and implementations Discover creative implementations and diverse applications of the Multi-Agent Orchestrator: - **[From 'Bonjour' to 'Boarding Pass': Multilingual AI Chatbot for Flight Reservations](https://community.aws/content/2lCi8jEKydhDm8eE8QFIQ5K23pF/from-bonjour-to-boarding-pass-multilingual-ai-chatbot-for-flight-reservations)** This article demonstrates how to build a multilingual chatbot using the Multi-Agent Orchestrator framework. The article explains how to use an **Amazon Lex** bot as an agent, along with 2 other new agents to make it work in many languages with just a few lines of code. ### TypeScript Version #### Installation ```bash npm install multi-agent-orchestrator ``` #### Usage The following example demonstrates how to use the Multi-Agent Orchestrator with two different types of agents: a Bedrock LLM Agent with Converse API support and a Lex Bot Agent. This showcases the flexibility of the system in integrating various AI services. ```typescript import { MultiAgentOrchestrator, BedrockLLMAgent, LexBotAgent } from "multi-agent-orchestrator"; const orchestrator = new MultiAgentOrchestrator(); // Add a Bedrock LLM Agent with Converse API support orchestrator.addAgent( new BedrockLLMAgent({ name: "Tech Agent", description: "Specializes in technology areas including software development, hardware, AI, cybersecurity, blockchain, cloud computing, emerging tech innovations, and pricing/costs related to technology products and services.", streaming: true }) ); // Add a Lex Bot Agent for handling travel-related queries orchestrator.addAgent( new LexBotAgent({ name: "Travel Agent", description: "Helps users book and manage their flight reservations", botId: process.env.LEX_BOT_ID, botAliasId: process.env.LEX_BOT_ALIAS_ID, localeId: "en_US", }) ); // Example usage const response = await orchestrator.routeRequest( "I want to book a flight", 'user123', 'session456' ); // Handle the response (streaming or non-streaming) if (response.streaming == true) { console.log("\n** RESPONSE STREAMING ** \n"); // Send metadata immediately console.log(`> Agent ID: ${response.metadata.agentId}`); console.log(`> Agent Name: ${response.metadata.agentName}`); console.log(`> User Input: ${response.metadata.userInput}`); console.log(`> User ID: ${response.metadata.userId}`); console.log(`> Session ID: ${response.metadata.sessionId}`); console.log( `> Additional Parameters:`, response.metadata.additionalParams ); console.log(`\n> Response: `); // Stream the content for await (const chunk of response.output) { if (typeof chunk === "string") { process.stdout.write(chunk); } else { console.error("Received unexpected chunk type:", typeof chunk); } } } else { // Handle non-streaming response (AgentProcessingResult) console.log("\n** RESPONSE ** \n"); console.log(`> Agent ID: ${response.metadata.agentId}`); console.log(`> Agent Name: ${response.metadata.agentName}`); console.log(`> User Input: ${response.metadata.userInput}`); console.log(`> User ID: ${response.metadata.userId}`); console.log(`> Session ID: ${response.metadata.sessionId}`); console.log( `> Additional Parameters:`, response.metadata.additionalParams ); console.log(`\n> Response: ${response.output}`); } ``` ### Python Version #### Installation ```bash # Optional: Set up a virtual environment python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` pip install multi-agent-orchestrator ``` #### Usage Here's an equivalent Python example demonstrating the use of the Multi-Agent Orchestrator with a Bedrock LLM Agent and a Lex Bot Agent: ```python import os import asyncio from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator from multi_agent_orchestrator.agents import BedrockLLMAgent, LexBotAgent, BedrockLLMAgentOptions, LexBotAgentOptions, AgentCallbacks orchestrator = MultiAgentOrchestrator() class BedrockLLMAgentCallbacks(AgentCallbacks): def on_llm_new_token(self, token: str) -> None: # handle response streaming here print(token, end='', flush=True) tech_agent = BedrockLLMAgent(BedrockLLMAgentOptions( name="Tech Agent", streaming=True, description="Specializes in technology areas including software development, hardware, AI, \ cybersecurity, blockchain, cloud computing, emerging tech innovations, and pricing/costs \ related to technology products and services.", model_id="anthropic.claude-3-sonnet-20240229-v1:0", callbacks=BedrockLLMAgentCallbacks() )) orchestrator.add_agent(tech_agent) # Add a Lex Bot Agent for handling travel-related queries orchestrator.add_agent( LexBotAgent(LexBotAgentOptions( name="Travel Agent", description="Helps users book and manage their flight reservations", bot_id=os.environ.get('LEX_BOT_ID'), bot_alias_id=os.environ.get('LEX_BOT_ALIAS_ID'), locale_id="en_US", )) ) async def main(): # Example usage response = await orchestrator.route_request( "I want to book a flight", 'user123', 'session456' ) # Handle the response (streaming or non-streaming) if response.streaming: print("\n** RESPONSE STREAMING ** \n") # Send metadata immediately print(f"> Agent ID: {response.metadata.agent_id}") print(f"> Agent Name: {response.metadata.agent_name}") print(f"> User Input: {response.metadata.user_input}") print(f"> User ID: {response.metadata.user_id}") print(f"> Session ID: {response.metadata.session_id}") print(f"> Additional Parameters: {response.metadata.additional_params}") print("\n> Response: ") # Stream the content async for chunk in response.output: if isinstance(chunk, str): print(chunk, end='', flush=True) else: print(f"Received unexpected chunk type: {type(chunk)}", file=sys.stderr) else: # Handle non-streaming response (AgentProcessingResult) print("\n** RESPONSE ** \n") print(f"> Agent ID: {response.metadata.agent_id}") print(f"> Agent Name: {response.metadata.agent_name}") print(f"> User Input: {response.metadata.user_input}") print(f"> User ID: {response.metadata.user_id}") print(f"> Session ID: {response.metadata.session_id}") print(f"> Additional Parameters: {response.metadata.additional_params}") print(f"\n> Response: {response.output.content}") if __name__ == "__main__": asyncio.run(main()) ``` These examples showcase: 1. The use of a Bedrock LLM Agent with Converse API support, allowing for multi-turn conversations. 2. Integration of a Lex Bot Agent for specialized tasks (in this case, travel-related queries). 3. The orchestrator's ability to route requests to the most appropriate agent based on the input. 4. Handling of both streaming and non-streaming responses from different types of agents. 📚 Featured Articles We're excited to share contributions that showcase the power and flexibility of the Multi-Agent Orchestrator. Check out this featured article: From 'Bonjour' to 'Boarding Pass': Multilingual AI Chatbot for Flight Reservations Create a global flight reservation chatbot in minutes! This tutorial walks you through building a multilingual chatbot using the Multi-Agent Orchestrator framework. Learn to effortlessly chain AI agents for instant language processing and booking across multiple languages. Transform complex, multi-step processes into a polyglot flight reservation system. ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guide](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/CONTRIBUTING.md) for more details. ## 📄 LICENSE This project is licensed under the Apache 2.0 licence - see the [LICENSE](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/LICENSE) file for details. ## 📄 Font License This project uses the JetBrainsMono NF font, licensed under the SIL Open Font License 1.1. For full license details, see [FONT-LICENSE.md](https://github.com/JetBrains/JetBrainsMono/blob/master/OFL.txt).