OPENAI Open Source A new multi-agent customer service demonstration on Github, showing how to create AI agents specializing in the field using its agent SDK. This project – was openai-cs-agents-demo—EMODES OF A Air Customer Chat capable of managing a range of travel -related queries by dynamically buying the requests for specialized agents. Built with a Backend Python and a following front. JS, the system provides both a functional conversational interface and a visual trace of agent transfers and caretaker activations.
The architecture is divided into two main components. The Backend Python manages agent orchestration using the agent's SDKs, while the following front offers a cat interface and an interactive visualization of agent's transitions. This configuration provides transparency in the decision -making and delegation process as agents' sorting, responds or rejecting user requests. The demo works with several targeted agents: a sorting agent, a seats reservation agent, a flight agent, a cancellation agent and an FAQ agent. Each of them is configured with instructions and specialized tools to carry out their specific subtaches.
When a user enters a request – such as “modify my seat” or “cancel my flight” – the sorting agent deals with entry to determine the intention and sends the request to the appropriate downstream agent. For example, a booking request for reservation will be sent to the headquarters' booking agent, which can check the confirmation numbers, offer choices of siege cards and finalize the seats. If a cancellation is requested, the system retains the cancellation agent, which follows a structured flow to confirm and execute the cancellation. The demo also includes a flight agent for real -time flight requests and a FAQ agent who answers general questions about luggage policies or types of aircraft.
A key strength of the system lies in its integration of railings for safety and relevance. The demo has two: a relevant railings and a jailbreak railings. The keeper of relevance filters off -topic requests – for example, rejecting guests like “write to me a poem on strawberries”. The Jailbreak goalkeeper blocks tents to bypass the system limits or manipulate the behavior of agents, such as asking the model to reveal his internal instructions. When one or the other guard is triggered, the system highlights it in the trace and sends a structured error message to the user.
The agents' SDK itself serves as a orchestration skeleton. Each agent is defined as a composable unit with fast models, access to the tool, transfer logic and output diagrams. The SDK manages chaining agents via “transfers”, supports real-time tracing and allows developers to apply input / output stresses with railing. This framework is the same as the internal experiences of OpenAi with agents of use of tools and reasoning, but now exposed in an educational and extensible format.
The developers can execute the demo locally by starting the Backend Python server with Uvicorn and launching the front with one npm run dev order. The entire system is configurable – developers can connect new agents, define their own tasks routing strategies and implement personalized tutors. With complete transparency in prompts, decisions and trace newspapers, the demo offers a practical base for conversational AI systems in the real world in customer support or other business areas.
By publishing this reference implementation, Openai provides a tangible example of how multi-agent coordination, the use of tools and safety checks can be combined in a robust service experience. It is particularly precious for developers who seek to understand the anatomy of agent systems – and how to build modular and controllable workflows which are both transparent and ready for production.
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