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Basic Information

Agent Squad is an open-source framework for orchestrating multiple AI agents to handle complex, multi-turn conversations. It provides a developer-focused orchestration layer that classifies user intent, selects the most appropriate agent, routes requests, and preserves conversation context across interactions. The project ships with pre-built agent types and classifier implementations, example applications, and both Python and TypeScript SDKs so teams can prototype and deploy agent-based systems quickly. It supports streaming and non-streaming responses, modular installation options for AWS, Anthropic, and OpenAI integrations, and can run in diverse environments including local setups, cloud platforms and serverless functions. The repository includes demo apps and sample projects demonstrating common use cases like travel planning, customer support, and multi-agent studios. Documentation and examples show how to add custom agents, configure classifiers, and persist conversation history for coherent multi-agent behavior.

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Features
The README documents several core features: intent classification that dynamically routes queries to suitable agents, dual-language SDKs in Python and TypeScript, and support for streaming and non-streaming agent responses. It emphasizes context management to maintain conversation history, an extensible architecture for adding custom agents or storage backends, and universal deployment across AWS Lambda, local environments, or any cloud. The project includes pre-built agents and classifier implementations, integration examples for Bedrock and Amazon Lex, and modular install options for AWS, Anthropic, and OpenAI. A prominent new component, SupervisorAgent, enables agent-as-tools team coordination with parallel processing, smart context management, dynamic delegation of subtasks, and compatibility with various agent types. The repo also provides demo applications, sample projects, and recipes showing streaming APIs, prompt routing, and text-to-structured-output patterns.
Use Cases
Agent Squad helps developers build sophisticated multi-agent conversational systems by centralizing routing, coordination, and memory management so individual agents can remain specialized and focused. It reduces engineering effort by supplying classifiers, pre-built agents, and example apps that illustrate common scenarios such as e-commerce support, travel booking, weather queries, math tools, and health advice. SupervisorAgent enables orchestration of specialist teams, which is useful for composite tasks that require parallel subtasks and aggregated responses. Modular install options let teams pick only needed integrations (for example AWS services or OpenAI models), and streaming support allows low-latency interactions for conversational UIs. The framework also shows patterns for human-in-the-loop workflows, persistent conversation storage, and scaling Bedrock agents, making it practical for production deployments and experimentation with hierarchical or tool-based agent architectures.

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