Building Voice Agents with Gemini Live API and Agora’s Conversational AI

Key Concepts

• Agora: A real-time engagement platform powering 80 billion minutes of voice and video traffic monthly.
• Gemini 3.1 Flash Live: A multimodal AI model capable of low-latency, real-time interaction across 70+ languages.
• Conversational AI: Agora’s feature that enables voice-based AI agents.
• Tool Calling: The ability for an LLM to trigger external functions (e.g., controlling robot motors or updating a shopping cart).
• Latency: The delay in communication, which is minimized in these demos through Agora’s infrastructure.

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1. Implementation Process: Integrating Gemini 3.1 Flash

To integrate Gemini 3.1 Flash into an Agora project, follow these steps:

1. Environment Setup: Clone the Agora agent quickstart repository and create an .env.local file.
2. Credential Configuration:
   • Obtain an Agora App ID and Primary Certificate from the Agora Console.
   • Enable "Conversational AI" within the Agora project settings.
   • Provide a valid Gemini API Key.
3. Code Modification:
   • Navigate to the App/API/invite-agent route.
   • Import Gemini Live.
   • Replace the standard chained pipeline (Speech-to-Text -> LLM -> Text-to-Speech) with the withML method.
   • Initialize new Gemini Live, specifying the model (gemini-3.1-flash-preview), API key, and the WebSocket server URL.
   • Configure optional parameters such as input/output modalities and custom system prompts/greetings.
4. Deployment: Run npm run dev to launch the local development server.

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2. Real-World Applications

The video demonstrates two primary use cases for low-latency AI agents:

• Hardware Integration (Robotics): The presenter uses a "Ricci" robot equipped with over 70 tool calls. The AI model processes voice input and translates it into physical motor movements, demonstrating how LLMs can control hardware in real-time.
• Software-Only (Food Ordering): A "Foodora" demo showcases a conversational agent managing a dynamic shopping cart. The agent handles complex state management, such as adding items (e.g., "Transformer" pulled pork, "Llama Bowl"), recommending desserts ("Backprop brownie"), and processing cancellations/swaps in real-time.

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3. Key Arguments and Technical Perspectives

• Efficiency of withML: The presenter emphasizes moving away from traditional, high-latency "chained" pipelines (where audio is transcribed, processed, and synthesized separately) in favor of Agora’s withML approach, which streamlines the interaction for faster response times.
• Multimodality: The model’s ability to switch languages (English, German, French, Chinese) on the fly demonstrates the versatility of Gemini 3.1 Flash in global, real-time applications.
• Tool Calling Capability: The demonstration highlights that the AI is not just a chatbot but an agent capable of executing logic—whether that is triggering physical hardware or updating a database/cart state.

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4. Notable Quotes

• "Agora powers 80,000,000,000 minutes of real-time voice and video a month." — Mason, Field Product Manager.
• "We've given it over 70 different tool calls that it can call as we speak to it... controlling the motors inside of here to determine how it behaves." — Regarding the integration of AI with physical robotics.

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5. Synthesis and Conclusion

The integration of Gemini 3.1 Flash with Agora’s real-time infrastructure provides a robust framework for building low-latency, voice-enabled AI agents. By bypassing traditional, slow processing chains and utilizing direct tool-calling capabilities, developers can create highly responsive applications that range from interactive robotics to complex e-commerce assistants. Agora’s platform serves as the backbone for these interactions, ensuring that the high volume of data required for real-time, multimodal AI is handled with minimal latency.