Build Multi-Agent System with OpenAI Realtime API

How to Build a Multi-Agent System Integrated with OpenAI Realtime API

January 15, 2025

1. Demonstration

Before diving into the implementation details, let’s first demonstrate the final effect of the Multi-Agent System with OpenAI Realtime API. In this demonstration, multiple agents will interact based on the user’s input, retrieving and returning responses from the OpenAI API in real time. Suppose the user asks a complex question, the multiple agents will collaborate according to their respective tasks, providing a multi-dimensional answer. This collaboration not only improves processing efficiency but also better satisfies the user’s needs.

2. What is a Multi-Agent System and OpenAI Realtime Chat?

When building a multi-agent system, we first need to understand the concepts of Multi-Agent Systems (MAS) and OpenAI Real-time API, as well as how they can be combined to enhance system efficiency and user experience.

2.1 Multi-Agent System (MAS)

A Multi-Agent System (MAS) is a system composed of multiple agents. Each agent can be viewed as an autonomous individual with a certain level of perception, action, and decision-making capabilities. The agents are not isolated; they collaborate through information exchange to accomplish more complex tasks. This collaborative work allows the system to handle tasks that a single agent cannot accomplish on its own.

Characteristics of Agents:

Autonomy: Each agent can independently execute tasks and make decisions based on the environment.
Sociality: Agents can communicate and cooperate with each other, sharing information to achieve a common goal.
Adaptability: Agents can adjust to environmental changes and improve from experience.
Purposefulness: Each agent has its own goals and tasks, typically working toward those goals.

Applications of Multi-Agent Systems:

Task Scheduling: In large systems, multiple agents can schedule and allocate tasks based on different tasks and resources, such as in automated warehouse management.
Problem Solving: Multiple agents can collaboratively analyze and solve problems by exchanging information, speeding up the problem-solving process, such as in intelligent transportation systems.
Decision Support: In complex decision-making scenarios, multiple agents can simulate different decision paths, providing diversified decision support, such as in financial market analysis.

Through cooperation and coordination among agents, the overall capability of the system can be greatly enhanced, especially in tasks that require real-time responses and processing, where efficiency and flexibility far exceed traditional single-agent systems.

2.2 OpenAI Realtime API

The OpenAI Real-time API is a powerful feature provided by OpenAI that allows developers to create natural and smooth conversational systems. With this API, we can send user chat requests to the system, which will then generate real-time responses. The OpenAI API is based on large-scale language models (such as GPT-4) that can understand and generate natural language.

Core Features of OpenAI Real-time Chat API:

Natural Language Processing: OpenAI’s language models can understand complex language inputs and generate grammatically correct and semantically reasonable responses.
Real-time: The API can generate conversational responses in a very short time, making the user experience smoother and more immediate.
Flexibility: Developers can customize the input and output formats of the API to suit different conversational scenarios, such as customer support, technical help, and educational tutoring.
Context Understanding: The API maintains the coherence of conversations by handling multi-turn dialogues and generating responses based on previous interactions.

2.3 The Benefits of Combining Multi-Agent System with OpenAI Realtime API

Enhanced Agents: Each agent can leverage the OpenAI API to improve its natural language understanding and generation capabilities, making the agents smarter and more efficient.
Real-time Feedback: With the real-time API, the system can return responses in a short amount of time, enhancing the user experience. Whether it’s a simple Q&A or a complex multi-turn conversation, the system can process and respond instantly.
Multi-tasking: Multiple agents can handle different tasks in parallel, such as weather queries, news recommendations, and personalized services, improving the system’s efficiency.
Scalability: The system can easily scale by adding more agents as needed. Each agent can leverage the OpenAI API to handle tasks in different domains, without the need for retraining models or massive changes to the system architecture.

For example, if you are developing a virtual assistant system with multiple agents:

Weather Agent: Handles weather queries.
News Agent: Provides news recommendations.
Chat Agent: Handles daily conversations.

When a user asks, “What’s the weather today?”, the weather agent sends a request to the OpenAI real-time API, which generates a weather-related response and returns it to the user. Similarly, if the user asks a question about the news, the news agent handles it and returns the news content. If the user asks, “Are today’s news and weather related?”, the chat agent can analyze the context and generate a coherent response based on the inputs from the other agents.

3. Overall System Architecture

When constructing a multi-agent system combined with the OpenAI Real-time API, the system architecture design is crucial. Here’s a typical architecture for a multi-agent system:

3.1 System Components

User Interface: This is the part of the system that directly interacts with the user. It’s usually a chat window through which users input information and receive feedback from multiple agents.
Agent Manager: Responsible for managing and scheduling the tasks of various agents. It decides which agent should handle the user’s request based on the input.
Agent: Each agent handles a different task. For example, one agent might handle weather queries, another might handle news recommendations, and yet another might be responsible for personalized suggestions.
OpenAI Real-time API: The core natural language processing engine, which generates natural language responses based on the requests of each agent.
Data Storage: Stores system state, user data, and chat history to allow agents to continuously learn and adapt.

3.2 System Workflow

Initialize the system, establish a WebSocket connection, and pass real-time API parameters (such as voice type) and historical data configuration.
The user interacts with the AI model through the real-time API.
If the AI can answer the user’s question, the conversation continues through the real-time API.
If the AI cannot process the input, the system uses a function (which determines whether the AI can handle the current request) to decide whether to call the multi-agent system.
If the multi-agent system needs to be invoked, the user’s input is sent to the agent manager. The agent manager analyzes the problem and decides which agent to call.
After the agent returns the answer, the agent manager checks if any other agents need to be called. Once all agents have processed the request, the results are aggregated.
The final result is returned to the user through the real-time API.

**Figure 1** **Overall System Flowchart**

Core Real-time API Response Events:

session.created: Creates a session and sends relevant configuration.
session.updated: Session is established with configuration completed.
input_audio_buffer.speech_started: User starts speaking.
input_audio_buffer.speech_stopped: User stops speaking.
conversation.item.input_audio_transcription.completed: The full transcription of the user’s input.
response.audio_transcript.delta: AI response as streamed text.
response.audio.delta: AI response as streamed audio.
response.audio_transcript.done: Full AI response transcription.
response.audio.done: Response completed.
response.function_call_arguments.done: Function call completed.

4. Core Implementation

4.1 Tech Stack Selection

To build this system, we used the following tech stack:

Frontend: JavaScript for implementing the user interaction interface.
Backend: Java for exposing the multi-agent system API, and Python for building the multi-agent system.
OpenAI Realtime API: Used to generate conversation content, ensuring the multi-agent system can interact in real-time.
Database: Local storage is used to temporarily store chat history and other data.

4.2 Key Implementation Steps

Setting up the OpenAI Realtime API WebSocket Connection:

To prevent OpenAI API key leakage, we set up the connection to the OpenAI Real-time API on the backend. The frontend JavaScript connects to the WebSocket created by the backend, which then connects to the OpenAI Real-time API.

Building the Multi-Agent System:

We set up a WebSocket connection, then used Langgraph’s open-source multi-agent system to create the agents and manage tasks.

Java Integration:

The multi-agent system is exposed through a Java interface for communication between the backend and frontend.

5. Takeaways

By building a Multi-Agent System with OpenAI Realtime API, we achieve the following key benefits:

Efficiency: The system can handle multiple requests simultaneously, improving response speed and accuracy.
Flexibility: Each agent can work independently and be optimized, facilitating future expansion and modifications.
User Experience: Real-time generated conversations make interactions with the system more natural, providing users with instant feedback.
Scalability: The system can easily scale by adding more agents as needed to handle broader functionality.

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