How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory in Python

A Twilio tutorial demonstrates how to build a Python FastAPI voice agent that preserves conversational context across separate calls by combining Twilio Conversation Relay, Conversation Memory, and OpenAI’s gpt-4o-mini model. The application identifies callers by phone number, creates or retrieves a Twilio Memory Store profile, recalls prior summaries and observations, and adds this history to the LLM’s system prompt so the agent can continue previous discussions naturally. During each call, a WebSocket handler receives transcribed speech, streams model responses back to the caller, maintains an in-call message history, and handles interruptions by removing incomplete assistant responses. When a call ends, the app asks the LLM to create a concise transcript summary containing relevant customer, vehicle, issue, estimate, and next-step details, then saves it to Twilio for later retrieval. The guide covers environment configuration, API credentials, Memory Store and Conversation Orchestrator setup, FastAPI routes and TwiML configuration, local testing with Uvicorn and ngrok, and verification through two calls from the same number, while noting that Conversation Memory should not be used for sensitive information.

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