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This skill provides implementation patterns for building advanced voice agents and automating phone call workflows using the Retell AI platform. It covers SDK initialization, agent listing, and includes robust error handling for common telephony issues (e.g., unauthorized access, rate limiting), ensuring reliable integration into your enterprise applications.

This skill provides deep dive implementation patterns for migrating and utilizing Retell AI. It focuses on building sophisticated voice agents and automating phone call interactions using the official SDK. Users will learn how to manage agents, handle API calls, and implement robust error handling for complex telephony applications, making it ideal for building automated call centers or interactive voice systems.

This skill provides implementation patterns for setting up and managing a multi-environment connection with Retell AI. It is essential for developers building advanced AI voice agents, integrating complex phone call automation, or utilizing robust telephony workflows. It guides users through SDK usage and handles common API error scenarios.

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

This guide provides a comprehensive production checklist and SDK pattern for integrating Retell AI into voice agents and telephony systems. It covers the necessary steps, error handling, and implementation patterns for deploying reliable, AI-powered phone call automation and voice interactions in a production environment.

Provides implementation patterns and reference architecture for building advanced AI voice agents and automating complex phone call workflows using the Retell AI SDK. This skill is designed for developers requiring robust voice interaction, real-time telephony integration, and comprehensive conversational AI capabilities in their applications.

This skill provides implementation patterns for building robust and reliable AI voice agents and telephony automation systems using the Retell AI SDK. It covers best practices for handling common communication errors (like rate limiting or invalid API keys) and ensures stable interaction flow for voice-based applications and phone call automation scenarios.

This resource provides production-ready, structured patterns for building robust voice agent applications using the Retell AI SDK. It covers best practices such as implementing singleton client management, defining typed agent configurations with integrated LLMs, adding automatic retry logic for call failures, and managing large-scale outbound campaigns. Use this when building stable, scalable, and reliable voice automation systems.

This skill provides comprehensive implementation patterns and detailed steps for migrating and upgrading Retell AI integrations. It is designed for developers building advanced AI voice agents, phone call automation, and complex telephony workflows using the Retell SDK. It covers setup procedures, best practices, and standard error handling for robust deployment.

This skill provides a comprehensive guide for setting up and managing webhook endpoints for Retell AI. It allows developers to capture and process crucial call lifecycle events—such as call start, end, and analysis—as well as handle function calls triggered during live conversations. Use this when building sophisticated voice agents or call automation systems that require real-time backend interaction with Retell AI.

Guide for orchestrating Retently workflows using the Rube MCP-driven Composio toolkit, emphasizing always searching for current tool schemas, checking active connections, and executing via RUBE_MULTI_EXECUTE_TOOL with schema-compliant arguments.

Guide to automating Rev AI tasks through Composio’s Rube MCP toolkit, covering setup, mandatory tool discovery, connection checks, and schema-driven execution best practices for reliable results.