Multi-Agent Orchestrator：亚马逊开源AI智能体自动协作黑科技！重构人机交互逻辑，1秒精准分配任务

npm install multi-agent-orchestrator

import { MultiAgentOrchestrator, BedrockLLMAgent, LexBotAgent } from "multi-agent-orchestrator"; const orchestrator = new MultiAgentOrchestrator(); // 添加一个支持 Converse API 的 Bedrock LLM 代理 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.Agent 智能体4;, streaming: true }) ); // 添加一个 Lex Bot 代理用于处理旅行相关查询 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", }) ); // 示例使用 const response = await orchestrator.routeRequest( "I want to book a flight", 'user123', 'session456' ); // 处理响应（流式或非流式） if (response.streaming == true) { console.log("\n RESPONSE STREAMING \n"); // 立即发送元数据 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: `); // 流式传输内容 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 { // 处理非流式响应（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}`); }

# 可选：设置虚拟环境 python -m venv venv source venv/bin/activate # 在 Windows 上使用 `venv\Scripts\activate` pip install multi-agent-orchestrator[aws]

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: # 在这里处理响应流 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--v1:0", callbacks=BedrockLLMAgentCallbacks() )) orchestrator.add_agent(tech_agent) # 添加一个 Lex Bot 代理用于处理旅行相关查询 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(): # 示例使用 response = await orchestrator.route_request( "I want to book a flight", 'user123', 'session456' ) # 处理响应（流式或非流式） if response.streaming: print("\n RESPONSE STREAMING \n") # 立即发送元数据 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: ") # 流式传输内容 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: # 处理非流式响应（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())