AWS Agent Squad: The Open Source Framework That's Changing Multi-Agent Development

Multi-agent AI is powerful—but hard to scale. AWS Agent Squad makes it simple to deploy robust agent teams. Open source, production-ready, and built for flexibility.

I've been building AI systems for the past year, and I keep running into the same problem: orchestrating multiple AI agents is surprisingly complex. You need routing logic, context management, conversation history, fallback mechanisms—the list goes on.

Then I discovered AWS Agent Squad (formerly Multi-Agent Orchestrator). In just a few hours, I had a working multi-agent prototype that would’ve taken me weeks to build from scratch.

The Problem with DIY Agent Orchestration

Most developers building multi-agent systems end up reinventing the same components over and over:

• Intent Classification: Which agent should handle this query?

• Context Management: How do agents share conversation history?

• Routing Logic: What happens when an agent can't handle a request?

• Response Handling: Streaming vs. non-streaming responses from different agents

• Storage Management: Where do conversations get saved and retrieved?

I spent months building these components for different projects. The code was never quite right, always had edge cases, and took forever to debug.

Enter AWS Agent Squad

Agent Squad is AWS Labs' open-source framework that solves agent orchestration once and for all. It's flexible, lightweight, and handles all the complex orchestration logic you don't want to write yourself.

The core value proposition: You focus on building great agents. Agent Squad handles everything else.

Here's what impressed me most about the architecture:

Intelligent Classification

The framework includes built-in classifiers that use LLMs to analyze user requests, agent descriptions, and conversation history to route queries to the most appropriate agent. The classifier has a global view of all agent conversations while individual agents only see their own context.

Universal Agent Support

Agent Squad works with any type of agent:

• Bedrock LLM Agents (with Converse API support)

• Amazon Lex Bots

• Amazon Bedrock Agents

• Lambda Functions

• OpenAI Agents

• Custom Agents (extend the base Agent class)

Production-Ready Features

• Dual language support: Fully implemented in both Python and TypeScript

• Streaming support: Handle both streaming and non-streaming responses

• Context persistence: Automatic conversation saving and retrieval

• Flexible storage: Works with in-memory, DynamoDB, or custom storage solutions

• Universal deployment: Run on AWS Lambda, local environment, or any cloud platform

The SupervisorAgent Game-Changer

The newest addition to Agent Squad is the SupervisorAgent—a sophisticated coordinator that implements "agent-as-tools" architecture. This is where things get really interesting.

Instead of just routing individual queries, the SupervisorAgent can:

• Coordinate multiple specialized agents working together on complex tasks

• Execute subtasks in sequence or in parallel, depending on your setup

• Maintain conversation history across all team members

• Dynamically delegate subtasks to appropriate team members

Think of it like having a project manager that knows exactly which specialists to bring in for different parts of a complex job.

Real-World Implementation Examples

AWS has published several compelling use cases and demos that show Agent Squad in practice:

Multilingual Flight Reservations

A travel system using Agent Squad with an Amazon Lex bot and two additional agents to handle flight bookings in multiple languages with just a few lines of code.

E-commerce Support System

An AI-driven multi-agent system for automated customer email support, featuring:

• Automated response generation for common queries

• Intelligent routing of complex issues to human support

• Human-in-the-loop interactions for edge cases

• Real-time chat and email-style communication

Enterprise Bedrock Integration

A system that scales Amazon Bedrock Agents beyond knowledge base limitations using Agent Squad and the InvokeInlineAgent API, enabling dynamic agent creation and knowledge base selection for enterprise-scale applications.

The Developer Experience

Setting up Agent Squad takes minutes, not days. Here's the basic flow:

import { AgentSquad, BedrockLLMAgent, LexBotAgent } from "agent-squad";

const orchestrator = new AgentSquad();

// Add specialized agents
orchestrator.addAgent(new BedrockLLMAgent({
    name: "Tech Agent",
    description: "Handles all technology-related queries",
    streaming: true
}));

orchestrator.addAgent(new LexBotAgent({
    name: "Travel Agent", 
    description: "Manages flight bookings and travel requests",
    botId: process.env.LEX_BOT_ID,
    botAliasId: process.env.LEX_BOT_ALIAS_ID
}));

// Route requests automatically
const response = await orchestrator.routeRequest(
    "Book me a flight to Tokyo",
    'user123', 
    'session456'
);

That's it. No complex routing logic, no conversation management, no context switching—it just works.

The Production Architecture

What makes Agent Squad production-ready is its thoughtful architecture:

Request Flow

User Input → Classification → Agent Selection → Processing → Context Storage

1. User Input: Query enters the system

2. Classification: Built-in classifier analyzes request and conversation history

3. Agent Selection: Most appropriate agent is chosen based on capabilities

4. Processing: Selected agent handles the request with full context

5. Storage: Conversation automatically updated for future context

Context Management

• Global classifier view: Sees conversation history across all agents

• Agent isolation: Each agent only accesses its own conversation thread

• Automatic persistence: Conversations saved and retrieved transparently

• Session management: User ID and session ID tracking built-in

Failure Handling

• Graceful degradation: If an agent fails, system continues operating

• Routing fallbacks: Backup agent selection for edge cases

• Error boundaries: Isolated agent failures don't crash the system

Deployment Flexibility

Agent Squad runs anywhere:

• AWS Lambda: Serverless deployment with minimal cold start overhead

• Local Development: Full feature parity for testing and debugging

• Container Deployment: Docker-ready for any orchestration platform

• Edge Computing: Lightweight enough for edge deployment scenarios

The modular installation means you only install what you need:

pip install "agent-squad[aws]"     # Core + AWS integrations
pip install "agent-squad[openai]"  # Core + OpenAI integration  
pip install "agent-squad[all]"     # Everything included

Performance Characteristics

Based on the demos and examples, Agent Squad shows impressive performance:

• Fast routing and classification (typically sub-second)

• Parallel processing: SupervisorAgent can coordinate multiple agents simultaneously

• Context switching: Seamless handoffs between specialized agents

• Memory efficiency: Intelligent context management reduces overhead

The framework includes a demo app showcasing 6 specialized agents (travel, weather, restaurant, math, tech, health) that seamlessly switch context between diverse topics while maintaining conversation coherence.

The Open Source Advantage

Being open source and backed by AWS Labs gives Agent Squad significant advantages:

• Community-driven: Active development with community contributions

• AWS Integration: Native support for all AWS AI services

• Production Tested: Used internally by AWS teams

• No Vendor Lock-in: Works with any LLM provider or cloud platform

• Extensible: Easy to add custom agents and classifiers

When to Use Agent Squad

Agent Squad is perfect when you need:

• Multiple specialized agents working together

• Production-grade orchestration without building from scratch

• AWS ecosystem integration with flexibility for other providers

• Rapid prototyping to production deployment pipeline

• Context-aware routing based on conversation history

Skip Agent Squad if you have a single agent use case or need highly specialized routing logic that doesn't fit the classification model.

Getting Started

The quickstart takes about 10 minutes:

1. Install: pip install "agent-squad[aws]" or npm install agent-squad

2. Configure AWS: Set up credentials and model access

3. Create agents: Add your specialized agents to the orchestrator

4. Test locally: Run conversations through the system

5. Deploy: Ship to Lambda, containers, or your preferred platform

The Future of Agent Orchestration

Agent Squad represents a mature approach to multi-agent systems. Instead of everyone building custom orchestration logic, we get a production-tested framework that handles the hard parts.

The SupervisorAgent capability points toward more sophisticated agent coordination patterns. As AI agents become more capable, orchestrating teams of specialists will become the standard approach for complex tasks.

The bottom line: Agent Squad eliminates months of infrastructure work and lets you focus on what matters—building great agents that solve real problems.

For AI builders, this is the framework that finally makes multi-agent systems accessible and reliable.

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Sources and Notes

Official GitHub Repository
https://github.com/aws/agent-squad

Key Architecture Details

• Built-in classifier for intelligent routing

• Agent isolation with shared classifier context

• Python and TypeScript SDKs

• Persistent memory via in-memory or DynamoDB

• Flexible deployment: Lambda, local, containers

Use Case Demos

• Flight Booking Example

• Customer Support Routing

• Bedrock Knowledge Base Scaling

SupervisorAgent

• Introduced as a higher-order agent

• Supports orchestration, subtask delegation, and multi-agent coordination

• Execution parallelism depends on runtime implementation and agent type