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Agent Squad (Formerly Multi-Agent Orchestrator)
A framework for routing requests among multiple AI agents and coordinating conversations.
https://github.com/2FastLabs/agent-squadAbout This Resource
A framework for routing requests among multiple AI agents and coordinating conversations. Originally published as AWS Multi-Agent Orchestrator, it provides components for agent selection and shared conversational context.
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Multi-Agent Orchestrator
Flexible and powerful framework for managing multiple AI agents and handling complex conversations.
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.
🔖 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.
🏗️ High-level architecture flow diagram
- The process begins with user input, which is analyzed by a Classifier.
- The Classifier leverages both Agents' Characteristics and Agents' Conversation history to select the most appropriate agent for the task.
- Once an agent is selected, it processes the user input.
- 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 among multiple specialized agents. This new component implements an "agent-as-tools" architecture, letting a lead agent coordinate specialized team members in parallel while maintaining context and delivering coherent responses.
Key capabilities:
- 🤝 Team Coordination - Coordinate 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:
- Direct Usage - Call it directly when you need dedicated team coordination for specific tasks
- 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