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Integrate Retell AI and Gemini 2.5 Flash for Voice Input

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Linked challenge: Real-time Voice Assistant for Market Intelligence

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Linked challenge

Real-time Voice Assistant for Market Intelligence

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Modify your AI SDK setup to integrate Retell AI for real-time bidirectional voice communication. Configure the AI SDK to use Gemini 2.5 Flash as the underlying language model, enabling it to process transcribed voice input and generate spoken responses.

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Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

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Hold the task contract and output shape stable so generated implementations remain comparable.

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Update libraries, interfaces, and environment assumptions to match the stack you actually run.

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Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Real-time Voice Assistant for Market Intelligence

Develop a sophisticated real-time voice assistant capable of performing competitive market intelligence and product analysis. This challenge focuses on building a responsive, multi-modal agent system that leverages the AI SDK for seamless streaming interactions and sophisticated tool orchestration. The core reasoning will be powered by Google's Gemini 3 Flash, providing rapid and accurate insights based on voice input. The system will integrate Microsoft AutoGen to spin up specialized, scriptable agents that handle deep-dive research tasks, collaborating with the primary voice agent. Security and data integrity are paramount, so LatticeFlow AI will be utilized to implement robust model safety policies and secure data pipelines. For comprehensive monitoring and evaluation, LangFuse will be integrated to trace agent interactions and performance metrics, ensuring the system operates efficiently and reliably. Retell AI will provide the real-time voice-to-text and text-to-speech capabilities, enabling natural language interactions for the end-user. This challenge emphasizes cutting-edge multi-agent orchestration, real-time voice processing, and advanced AI engineering practices to deliver a high-performance, secure, and observable AI application.

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