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Basic Information

This repository is an example Next.js application that demonstrates core capabilities of Daily Bots and how to integrate real-time voice and bot functionality into a web app. It is intended as a developer-facing demo and boilerplate showing how to configure and start Daily Bots via the Daily API using server-side routes, environment variables, and a configurable voice client. The project highlights the use of RTVI and Pipecat technologies and includes configuration for a Voice Client in rtvi.config.ts. It documents required properties for the Daily Bots start endpoint and supports optional integration with external model providers by allowing a user-provided OpenAI API key. The repo also surfaces example branches for vision and function-calling demonstrations to illustrate extended capabilities.

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App Details

Features
Provides a Next.js demo app preconfigured to interact with the Daily Bots start endpoint and demonstrate voice-based agents. Includes server-side API routes (api/route.ts and optional api/dialin and api/dialout) to securely pass secrets and configuration to Daily Bots. Uses RTVI-compatible client libraries including realtime-ai, realtime-ai-react, and realtime-ai-daily to manage real-time voice interactions. Centralized Voice Client configuration in rtvi.config.ts allows editing prompts, services, and settings. Environment example and instructions for setting NEXT_PUBLIC_BASE_URL, DAILY_BOTS_URL, DAILY_API_KEY, and optional OPENAI_API_KEY are included. Example branches show vision and function-calling use cases. Basic install and run instructions are provided (yarn, yarn run dev).
Use Cases
This demo helps developers quickly prototype and learn how to add real-time voice AI agents to a Next.js application by providing a working codebase and configuration patterns. It shows secure server routes for passing required bot properties and secrets, a clear place to edit voice client behavior via rtvi.config.ts, and how to supply service maps and config arrays to the Daily Bots API. The example clarifies environment setup and optional bring-your-own-model usage for OpenAI, and illustrates how to extend functionality with vision or function-calling examples. It reduces integration friction by demonstrating the recommended libraries and a deployable workflow so teams can iterate on prompts, services, and runtime settings without building infrastructure from scratch.

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