What problem does it solve?
Connecting an existing LLM agent (OpenAI, Bedrock, LangChain, Microsoft Foundry) to real communication channels like voice calls, SMS, RCS, and WhatsApp requires building webhook handling, WebSocket protocols, identity resolution, and memory management from scratch. This Skill provides guidance for using Twilio Agent Connect (TAC), the middleware SDK that bridges agent runtimes with Twilio Conversations services.
Core Features & Use Cases
- Multi-Channel Agent Integration: Wire one
on_message_ready callback to Voice (ConversationRelay), SMS, RCS, WhatsApp, and Chat channels in Python or TypeScript.
- Conversation Memory & Orchestration: Inject persistent user profiles, traits, observations, and session history into LLM prompts via adapters like
with_tac_memory and MemoryPromptBuilder.
- Cloud Connectors: Connect AWS Strands/Bedrock/AgentCore or Microsoft Agent Framework and Voice Live agents to Twilio channels with dedicated packages.
- Use Case: Build a customer support agent that answers inbound phone calls and SMS messages, remembers each customer's preferences across sessions, searches an enterprise knowledge base, and hands off to a human via Studio Flow when needed.
Quick Start
Ask the agent to scaffold a Python TAC server with a VoiceChannel and SMSChannel that routes incoming messages to an OpenAI model using the quickstart pattern from this Skill.