multi-agent-orchestrator

multi-agent-orchestrator

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

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Multi-Agent Orchestrator is a flexible and powerful framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries to the most suitable agent based on context and content, supports dual language implementation in Python and TypeScript, offers flexible agent responses, context management across agents, extensible architecture for customization, universal deployment options, and pre-built agents and classifiers. It is suitable for various applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently.

README:

Multi-Agent Orchestrator 

Flexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations.

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📚 Explore Full Documentation

🔖 Features

  • 🧠 Intelligent intent classification — Dynamically route queries to the most suitable agent based on context and content.
  • 🔤 Dual language support — Fully implemented in both Python and TypeScript.
  • 🌊 Flexible agent responses — Support for both streaming and non-streaming responses from different agents.
  • 📚 Context management — Maintain and utilize conversation context across multiple agents for coherent interactions.
  • 🔧 Extensible architecture — Easily integrate new agents or customize existing ones to fit your specific needs.
  • 🌐 Universal deployment — Run anywhere - from AWS Lambda to your local environment or any cloud platform.
  • 📦 Pre-built agents and classifiers — A variety of ready-to-use agents and multiple classifier implementations available.

What's the Multi-Agent Orchestrator ❓

The Multi-Agent Orchestrator is a flexible framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries and maintains context across interactions.

The system offers pre-built components for quick deployment, while also allowing easy integration of custom agents and conversation messages storage solutions.

This adaptability makes it suitable for a wide range of applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently.

🤖 Looking for details on Amazon Bedrock's multi-agent collaboration capability announced during Matt Garman's keynote at re:Invent 2024?

🚀 Visit the Amazon Bedrock Agents page to explore how multi-agent collaboration enables developers to build, deploy, and manage specialized agents designed for tackling complex workflows efficiently and accurately. ⚡

🏗️ High-level architecture flow diagram



High-level architecture flow diagram



  1. The process begins with user input, which is analyzed by a Classifier.
  2. The Classifier leverages both Agents' Characteristics and Agents' Conversation history to select the most appropriate agent for the task.
  3. Once an agent is selected, it processes the user input.
  4. The orchestrator then saves the conversation, updating the Agents' Conversation history, before delivering the response back to the user.

Introducing SupervisorAgent: Agents Coordination

The Multi-Agent Orchestrator now includes a powerful new SupervisorAgent that enables sophisticated team coordination between multiple specialized agents. This new component implements a "agent-as-tools" architecture, allowing a lead agent to coordinate a team of specialized agents in parallel, maintaining context and delivering coherent responses.

SupervisorAgent flow diagram

Key capabilities:

  • 🤝 Team Coordination - Coordonate multiple specialized agents working together on complex tasks
  • ⚡ Parallel Processing - Execute multiple agent queries simultaneously
  • 🧠 Smart Context Management - Maintain conversation history across all team members
  • 🔄 Dynamic Delegation - Intelligently distribute subtasks to appropriate team members
  • 🤖 Agent Compatibility - Works with all agent types (Bedrock, Anthropic, Lex, etc.)

The SupervisorAgent can be used in two powerful ways:

  1. Direct Usage - Call it directly when you need dedicated team coordination for specific tasks
  2. Classifier Integration - Add it as an agent within the classifier to build complex hierarchical systems with multiple specialized teams

Here are just a few examples where this agent can be used:

  • Customer Support Teams with specialized sub-teams
  • AI Movie Production Studios
  • Travel Planning Services
  • Product Development Teams
  • Healthcare Coordination Systems

Learn more about SupervisorAgent →

💬 Demo App

In the screen recording below, we demonstrate an extended version of the demo app that uses 6 specialized agents:

  • Travel Agent: Powered by an Amazon Lex Bot
  • Weather Agent: Utilizes a Bedrock LLM Agent with a tool to query the open-meteo API
  • Restaurant Agent: Implemented as an Amazon Bedrock Agent
  • Math Agent: Utilizes a Bedrock LLM Agent with two tools for executing mathematical operations
  • Tech Agent: A Bedrock LLM Agent designed to answer questions on technical topics
  • Health Agent: A Bedrock LLM Agent focused on addressing health-related queries

Watch as the system seamlessly switches context between diverse topics, from booking flights to checking weather, solving math problems, and providing health information. Notice how the appropriate agent is selected for each query, maintaining coherence even with brief follow-up inputs.

The demo highlights the system's ability to handle complex, multi-turn conversations while preserving context and leveraging specialized agents across various domains.

🎯 Examples & Quick Start

Get hands-on experience with the Multi-Agent Orchestrator through our diverse set of examples:

  • Demo Applications:
    • Streamlit Global Demo: A single Streamlit application showcasing multiple demos, including:
      • AI Movie Production Studio
      • AI Travel Planner
    • Chat Demo App:
      • Explore multiple specialized agents handling various domains like travel, weather, math, and health
    • E-commerce Support Simulator: Experience AI-powered customer support with:
      • Automated response generation for common queries
      • Intelligent routing of complex issues to human support
      • Real-time chat and email-style communication
      • Human-in-the-loop interactions for complex cases
  • Sample Projects: Explore our example implementations in the examples folder:

Examples are available in both Python and TypeScript. Check out our documentation for comprehensive guides on setting up and using the Multi-Agent Orchestrator framework!

📚 Deep Dives: Stories, Blogs & Podcasts

Discover creative implementations and diverse applications of the Multi-Agent Orchestrator:

  • 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.

  • Beyond Auto-Replies: Building an AI-Powered E-commerce Support system

    This article demonstrates how to build an AI-driven multi-agent system for automated e-commerce customer email support. It covers the architecture and setup of specialized AI agents using the Multi-Agent Orchestrator framework, integrating automated processing with human-in-the-loop oversight. The guide explores email ingestion, intelligent routing, automated response generation, and human verification, providing a comprehensive approach to balancing AI efficiency with human expertise in customer support.

  • Speak Up, AI: Voicing Your Agents with Amazon Connect, Lex, and Bedrock

    This article demonstrates how to build an AI customer call center. It covers the architecture and setup of specialized AI agents using the Multi-Agent Orchestrator framework interacting with voice via Amazon Connect and Amazon Lex.

  • Unlock Bedrock InvokeInlineAgent API's Hidden Potential

    Learn how to scale Amazon Bedrock Agents beyond knowledge base limitations using the Multi-Agent Orchestrator framework and InvokeInlineAgent API. This article demonstrates dynamic agent creation and knowledge base selection for enterprise-scale AI applications.

  • Supercharging Amazon Bedrock Flows

    Learn how to enhance Amazon Bedrock Flows with conversation memory and multi-flow orchestration using the Multi-Agent Orchestrator framework. This guide shows how to overcome Bedrock Flows' limitations to build more sophisticated AI workflows with persistent memory and intelligent routing between flows.

🎙️ Podcast Discussions

  • 🇫🇷 Podcast (French): L'orchestrateur multi-agents : Un orchestrateur open source pour vos agents IA

  • 🇬🇧 Podcast (English): An Orchestrator for Your AI Agents

TypeScript Version

Installation

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.

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

# 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[aws]

Default 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:

import sys
import asyncio
from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator
from multi_agent_orchestrator.agents import BedrockLLMAgent, BedrockLLMAgentOptions, AgentStreamResponse

orchestrator = MultiAgentOrchestrator()

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",
))
orchestrator.add_agent(tech_agent)


health_agent = BedrockLLMAgent(BedrockLLMAgentOptions(
  name="Health Agent",
  streaming=True,
  description="Specializes in health and well being",
))
orchestrator.add_agent(health_agent)

async def main():
    # Example usage
    response = await orchestrator.route_request(
        "What is AWS Lambda?",
        'user123',
        'session456',
        {},
        True
    )

    # 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:
            async for chunk in response.output:
              if isinstance(chunk, AgentStreamResponse):
                  print(chunk.text, 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.

Modular Installation Options

The Multi-Agent Orchestrator is designed with a modular architecture, allowing you to install only the components you need while ensuring you always get the core functionality.

Installation Options

1. AWS Integration:

 pip install "multi-agent-orchestrator[aws]"

Includes core orchestration functionality with comprehensive AWS service integrations (BedrockLLMAgent, AmazonBedrockAgent, LambdaAgent, etc.)

2. Anthropic Integration:

pip install "multi-agent-orchestrator[anthropic]"

3. OpenAI Integration:

pip install "multi-agent-orchestrator[openai]"

Adds OpenAI's GPT models for agents and classification, along with core packages.

4. Full Installation:

pip install "multi-agent-orchestrator[all]"

Includes all optional dependencies for maximum flexibility.

🙌 We Want to Hear From You!

Have something to share, discuss, or brainstorm? We’d love to connect with you and hear about your journey with the Multi-Agent Orchestrator framework. Here’s how you can get involved:

  • 🙌 Show & Tell: Got a success story, cool project, or creative implementation? Share it with us in the Show and Tell section. Your work might inspire the entire community! 🎉

  • 💬 General Discussion: Have questions, feedback, or suggestions? Join the conversation in our General Discussions section. It’s the perfect place to connect with other users and contributors.

  • 💡 Ideas: Thinking of a new feature or improvement? Share your thoughts in the Ideas section. We’re always open to exploring innovative ways to make the orchestrator even better!

Let’s collaborate, learn from each other, and build something incredible together! 🚀

📝 Pull Request Guidelines

Issue-First Policy

This repository follows an Issue-First policy:

  • Every pull request must be linked to an existing issue
  • If there isn't an issue for the changes you want to make, please create one first
  • Use the issue to discuss proposed changes before investing time in implementation

How to Link Pull Requests to Issues

When creating a pull request, you must link it to an issue using one of these methods:

  1. Include a reference in the PR description using keywords:

    • Fixes #123
    • Resolves #123
    • Closes #123

  2. Manually link the PR to an issue through GitHub's UI:

    • On the right sidebar of your PR, click "Development" and then "Link an issue"

Automated Enforcement

We use GitHub Actions to automatically verify that each PR is linked to an issue. PRs without linked issues will not pass required checks and cannot be merged.

This policy helps us:

  • Maintain clear documentation of changes and their purposes
  • Ensure community discussion before implementation
  • Keep a structured development process
  • Make project history more traceable and understandable

🤝 Contributing

⚠️ We value your contributions! Before submitting changes, please start a discussion by opening an issue to share your proposal.

Once your proposal is approved, here are the next steps:

  1. 📚 Review our Contributing Guide
  2. 💡 Create a GitHub Issue
  3. 🔨 Submit a pull request

✅ Follow existing project structure and include documentation for new features.

🌟 Stay Updated: Star the repository to be notified about new features, improvements, and exciting developments in the Multi-Agent Orchestrator framework!

Authors

👥 Contributors

Big shout out to our awesome contributors! Thank you for making this project better! 🌟 ⭐ 🚀

contributors

Please see our contributing guide for guidelines on how to propose bugfixes and improvements.

📄 LICENSE

This project is licensed under the Apache 2.0 licence - see the 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.

For Tasks:

Click tags to check more tools for each tasks
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Motia is an AI agent framework designed for software engineers to create, test, and deploy production-ready AI agents quickly. It provides a code-first approach, allowing developers to write agent logic in familiar languages and visualize execution in real-time. With Motia, developers can focus on business logic rather than infrastructure, offering zero infrastructure headaches, multi-language support, composable steps, built-in observability, instant APIs, and full control over AI logic. Ideal for building sophisticated agents and intelligent automations, Motia's event-driven architecture and modular steps enable the creation of GenAI-powered workflows, decision-making systems, and data processing pipelines.

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wxflows Screenshot
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watsonx.ai Flows Engine is a powerful tool for building, running, and deploying AI agents. It allows users to create tools from various data sources and deploy them to the cloud. The tools built with watsonx.ai Flows Engine can be integrated into any Agentic Framework using the SDK for Python & JavaScript. The platform offers a range of tools and integrations, including exchange, wikipedia, google_books, math, and weather. Users can also build their own tools and leverage integrations like LangGraph, LangChain, watsonx.ai, and OpenAI. Examples of applications built with watsonx.ai Flows Engine include an end-to-end Agent Chat App, Text-to-SQL Agent, YouTube transcription agent, Math agent, and more. The platform provides comprehensive support through Discord for any questions or feedback.

github
: 59

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