Agent Squad
VerifiedOrchestrate multiple AI agents to manage complex, context-aware conversations.
What is Agent Squad?
Agent Squad coordinates several specialized AI agents within a single conversation flow. A built-in classifier examines each incoming message along with agent characteristics and prior context to select the most appropriate agent. Responses are returned to the user while the orchestrator updates shared conversation history for continuity.
Developers can add custom agents or swap in different classifier implementations. A SupervisorAgent option lets one lead agent delegate subtasks to a team of agents running in parallel. Pre-built agents and storage options reduce initial setup time for common scenarios.
The framework suits teams building chatbots, customer-support systems, or multi-step AI workflows that need reliable routing and context retention without heavy infrastructure.
Capabilities
What you can build with Agent Squad
Multi-agent customer support
Route billing questions to one agent and technical troubleshooting to another while keeping the full conversation thread coherent.
Research assistant teams
Let a supervisor agent split complex queries across specialized research agents that work in parallel and combine results.
Internal workflow automation
Connect agents that handle scheduling, document lookup, and reporting within a single conversational interface.
Install Agent Squad
pip install agent-squadnpm install agent-squad- 1Install the package with pip or npm depending on your language preference.
- 2Import the AgentSquad class and create an instance with your chosen classifier.
- 3Register the agents you want to use, including any pre-built or custom ones.
- 4Add a message handler that passes user input to the orchestrator and returns the response.
- 5Run the application locally or deploy the same code to AWS Lambda or another cloud service.
Agent Squad: pros & cons
Pros
- +Works in both Python and TypeScript with the same core concepts.
- +Maintains shared context across multiple agents automatically.
- +Supports streaming responses and flexible deployment targets.
- +Includes ready-made agents and a SupervisorAgent for team coordination.
Cons
- –Requires manual registration of agents and classifiers before use.
- –Performance depends on the underlying LLM providers chosen by the developer.
- –Documentation and examples live in a separate repository after the project move.
Frequently asked questions
It provides full implementations in both Python and TypeScript.
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