TypeScript Local Demo

Prerequisites

• Node.js and npm installed
• AWS account with appropriate permissions
• Basic familiarity with TypeScript and async/await patterns

Quick Setup

1. Create a new project:

mkdir test_multi_agent_orchestrator
cd test_multi_agent_orchestrator
npm init

2. Install dependencies:

npm install multi-agent-orchestrator

Implementation

1. Create a new file named quickstart.ts:

2. Initialize the orchestrator:

import { MultiAgentOrchestrator } from "multi-agent-orchestrator";

const orchestrator = new MultiAgentOrchestrator({
  config: {
    LOG_AGENT_CHAT: true,
    LOG_CLASSIFIER_CHAT: true,
    LOG_CLASSIFIER_RAW_OUTPUT: false,
    LOG_CLASSIFIER_OUTPUT: true,
    LOG_EXECUTION_TIMES: true,
  }
});

3. Add specialized agents:

import { BedrockLLMAgent } from "multi-agent-orchestrator";

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.",
  })
);

orchestrator.addAgent(
  new BedrockLLMAgent({
    name: "Health Agent",
    description: "Focuses on health and medical topics such as general wellness, nutrition, diseases, treatments, mental health, fitness, healthcare systems, and medical terminology or concepts.",
  })
);

4. Implement the main logic:

const userId = "quickstart-user";
const sessionId = "quickstart-session";
const query = "What are the latest trends in AI?";
console.log(`\nUser Query: ${query}`);

async function main() {
  try {
    const response = await orchestrator.routeRequest(query, userId, sessionId);
    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}`);
  } catch (error) {
    console.error("An error occurred:", error);
  }
}

main();

5. Run the application:

npx ts-node quickstart.ts

Implementation Notes

• Uses default Bedrock Classifier with anthropic.claude-3-5-sonnet-20240620-v1:0
• Utilizes Bedrock LLM Agent with anthropic.claude-3-haiku-20240307-v1:0
• Implements in-memory storage by default

Next Steps

• Add additional specialized agents
• Implement persistent storage with DynamoDB
• Add custom error handling
• Implement streaming responses

Ready to build your own multi-agent chat application? Check out the complete source code in our GitHub repository.