March 2025 Summaries

The Agora Conversational AI Extension is now available on the Dify Marketplace as a plugin, bringing powerful real-time voice AI capabilities to developers within the Dify ecosystem. This partnership combines Agora's expertise in low-latency, interactive AI solutions with Dify's open, community-driven plugin marketplace, enabling developers to create smarter, more engaging AI agents with ease. The extension is powered by the Ten Framework, an Agora-supported open-source framework and community project for building conversational AI, and offers ultra-low-latency software-defined real-time network capabilities, making it ideal for applications like personal assistance, customer service, or sales support. With seamless integration and high-quality voice interactions, this partnership paves the way for smarter, more engaging AI agents that can deliver real-time responses even in poor network conditions.

Conversational AI is revolutionizing how people interact with artificial intelligence by enabling natural, real-time voice conversations between users and AI agents. Agora's Conversational AI Engine allows developers to connect their existing workflows to an Agora channel, facilitating real-time voice conversations without abandoning their current AI infrastructure. This guide builds a Python backend server using FastAPI and Uvicorn that handles the connection between users and Agora's Conversational AI Engine, powering voice-based AI conversations for applications. The server is configured with environment variables such as Agora app ID, customer secret, and LLM URL, which are set in a `.env` file. The server includes routes for inviting and removing agents from conversations, generating tokens, and handling health checks.

Agora's Conversational AI Engine enables fast, natural voice interaction with large language models (LLMs) by solving common issues of latency, network instability, and background noise interference. The Engine uses advanced real-time processing, intelligent acoustic algorithms, and a global network infrastructure to deliver ultra-low latency, interruption handling, background noise suppression, and crystal-clear audio processing for lifelike voice AI experiences. It empowers developers to integrate responsive, intelligent AI voice interactions into their applications, overcoming major roadblocks and enabling seamless, natural conversations in any environment and on any device.

Conversational AI is transforming user interactions with artificial intelligence by enabling natural, real-time voice conversations with AI agents, providing more intuitive and efficient interactions. Developers who have built custom large language model (LLM) workflows for text-based agents can now connect these workflows to an Agora channel using Agora's Conversational AI Engine, allowing for real-time voice conversations without needing to overhaul existing AI infrastructures. The guide details the process of creating a Fastify backend server to manage voice-based AI interactions, including setting up the environment, creating necessary directories and files, implementing agent routes, and integrating token generation for seamless operation with Agora's Conversational AI Engine. The server uses TypeScript and Fastify, incorporates environment validation and request validation middleware, and is designed to support dynamic token generation and conversational agent management, providing a robust base for integrating voice AI capabilities into applications.

This Express server handles real-time voice conversations with AI agents using Agora's Conversational AI Engine. It provides a basic structure for connecting to an existing LLM workflow and enables users to invite, remove, and manage AI agents within the conversation. The server includes validation middleware to ensure proper request handling and uses environment variables to configure various settings such as TTS vendors, LLM configurations, and Agora credentials. The development server is set up using Nodemon for automatic restarts when files change, allowing developers to quickly test and iterate on their API endpoints.

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