Agent Squad – An open-source multi-agent conversation orchestration framework

What is Agent Squad?

Agent Squad is a lightweight, open-source multi-agent framework designed to coordinate multiple AI agents in handling complex conversations. Supporting both Python and TypeScript, Agent Squad intelligently routes user input to the most suitable agent and maintains shared context to ensure coherent interactions. It offers features such as smart intent classification, flexible agent response modes (streaming and non-streaming), and modular architecture for easy customization and integration. Agent Squad is ideal for scenarios such as intelligent customer service, planning assistants, and enterprise copilots, empowering developers to build efficient multi-agent systems with ease.

Key Features of Agent Squad

• Smart Intent Classification
  Dynamically routes user input to the most appropriate agent based on the input content, context, and agent descriptions, ensuring efficient request handling.

• Dual Language Support
  Available in both Python and TypeScript to meet the preferences of different developer communities.

• Flexible Agent Responses
  Supports both streaming (real-time) and non-streaming (batch) response formats, catering to various types of agents.

• Context Management
  Maintains and shares conversational context across agents to ensure continuity and consistency in interactions.

• Extensible Architecture
  Modular design allows for seamless integration of new agents or customization of existing features.

• Universal Deployment
  Can be deployed on AWS Lambda, local environments, or other cloud platforms, offering great flexibility.

• Team Coordination via SupervisorAgent
  Includes a special “SupervisorAgent” that enables coordinated task execution by managing multiple specialized agents in parallel while preserving context for consistent responses.

How Agent Squad Works (Technical Overview)

1. User Input Analysis
   The user’s request is first processed by a classifier that analyzes the input along with historical context.

2. Agent Selection
   Based on the agent’s specialization and capability descriptions, the classifier selects the most suitable agent to handle the current request.

3. Agent Processing
   The chosen agent receives the input and generates a response.

4. Context Update and Response Delivery
   The orchestrator updates the conversation history and agent context before returning the response to the user.

Project Links

• Official Website: https://awslabs.github.io/agent-squad/

• GitHub Repository: https://github.com/awslabs/agent-squad

Application Scenarios for Agent Squad

• Intelligent Customer Service
  Coordinates multiple domain-specific agents to handle customer inquiries more effectively.

• Travel Planning
  Integrates agents for flights, hotels, itineraries, and more to deliver end-to-end travel services.

• Enterprise Copilot
  Serves as an internal productivity tool to help employees complete work-related tasks.

• Multi-Role Dialogues
  Enables multi-character interactions, making it ideal for virtual roleplay or educational tutoring.

• E-commerce Support
  Automates customer support tasks in e-commerce while allowing human intervention when necessary to enhance user experience.