OpenAI Releases an Open-Sourced Version of a Customer Service Agent Demo with the Agents SDK

OpenAI has open-sourced a new multi-agent customer service demo on GitHub, showcasing how to build domain-specialized AI agents using its Agents SDK. This project, titled openai-cs-agents-demo, models an airline customer service chatbot capable of handling a range of travel-related queries by dynamically routing requests to specialized agents.

Technical Overview

The system is built with a Python backend and a Next.js frontend, providing both a functional conversational interface and a visual trace of agent handoffs and guardrail activations. The architecture is divided into two main components:

• Python backend for agent orchestration using the Agents SDK
• Next.js frontend for chat interface and interactive visualization of agent transitions

Agent Functionality

The demo operates with several focused agents:

• Triage Agent
• Seat Booking Agent
• Flight Status Agent
• Cancellation Agent
• FAQ Agent

Each agent is configured with specialized instructions and tools to fulfill specific sub-tasks. For example, when a user requests to change their seat, the Triage Agent processes the input and dispatches it to the appropriate agent for action.

Guardrails and Safety Features

A key strength of the system lies in its integration of guardrails for safety and relevance. The demo features:

• Relevance Guardrail: Filters out off-topic queries.
• Jailbreak Guardrail: Blocks attempts to manipulate agent behavior.

When either guardrail is triggered, the system highlights it in the trace and sends a structured error message to the user.

Developers and Customization

The Agents SDK serves as the orchestration backbone, allowing developers to run the demo locally by starting the Python backend server with Uvicorn and launching the frontend with a single command. The entire system is configurable, enabling developers to:

• Plug in new agents
• Define custom task routing strategies
• Implement custom guardrails

This framework offers transparency into prompts, decisions, and trace logs, providing a practical foundation for real-world conversational AI systems in customer support or other enterprise domains.

Conclusion

By releasing this reference implementation, OpenAI provides a tangible example of how multi-agent coordination, tool use, and safety checks can be combined into a robust service experience. This is particularly valuable for developers seeking to understand the anatomy of agentic systems and how to build modular, controllable AI workflows that are both transparent and production-ready.

Check out the GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and subscribe to our Newsletter.