twilio-agent-connect

name	twilio-agent-connect
description	Use when building or integrating Twilio Agent Connect (TAC) to connect third-party LLM agent runtimes with Twilio Voice, Messaging, ConversationRelay, Conversation Memory, Conversation Orchestrator, or Enterprise Knowledge.

Twilio Agent Connect

Overview

Twilio Agent Connect (TAC) is a Python and TypeScript SDK that integrates third-party LLM agentic applications with Twilio's communication technologies. TAC provides middleware for identity resolution, memory/context management (via Conversation Memory), conversation orchestration (via Conversation Orchestrator), and multi-channel handling (Voice, SMS, RCS, WhatsApp, Chat).

Key Architecture Principle: TAC is not an agent runtime itself—it's middleware that enables existing LLM applications (OpenAI Agents SDK, Bedrock, LangChain, Microsoft Foundry, etc.) to leverage Twilio Conversations services.

Product Context

Core Twilio Conversations Services

TAC integrates with three core Twilio Conversations services:

1. Conversation Memory (Memory Store) - Persistent user context and memory management

   • Profile storage with traits and attributes
   • Observation and summary storage
   • Session history with full conversation context
   • Identity resolution (profile lookup by phone/email)

2. Conversation Orchestrator - Multi-channel conversation lifecycle management

   • Unified conversation API across all channels
   • Participant management
   • Communication routing
   • Conversation grouping and configuration

3. Enterprise Knowledge - Knowledge base integration

   • Semantic search across knowledge bases
   • RAG (Retrieval-Augmented Generation) support
   • Knowledge chunk retrieval with relevance scoring

Supported Channels

TAC provides built-in support for:

• Voice - ConversationRelay (WebSocket-based real-time voice)
• SMS - Text messaging
• RCS - Rich Communication Services
• WhatsApp - WhatsApp Business messaging
• Chat - Web chat integrations

All channels support both inbound (customer-initiated) and outbound (agent-initiated) conversations.

ConversationRelay-Only Mode

TAC supports a simplified "ConversationRelay-only" mode for getting started with voice conversations without requiring Conversation Orchestrator or Conversation Memory setup. This mode provides:

• TwiML generation
• WebSocket protocol handling
• Voice conversation lifecycle management
• Callback-based message processing

Installation

Python SDK

Requirements: Python 3.10+

# Using uv (recommended)
uv add git+https://github.com/twilio/twilio-agent-connect-python.git

# With server support (includes FastAPI and uvicorn for TACFastAPIServer)
uv add git+https://github.com/twilio/twilio-agent-connect-python.git --extra server

# Using pip
pip install git+https://github.com/twilio/twilio-agent-connect-python.git
pip install "git+https://github.com/twilio/twilio-agent-connect-python.git[server]"

TypeScript SDK

Requirements: Node.js 22.13+

# Clone and build (not yet published to npm)
git clone https://github.com/twilio/twilio-agent-connect-typescript.git
cd twilio-agent-connect-typescript
npm install
npm run build

Quick Start

Multi-Channel Agent with OpenAI (Python)

from dotenv import load_dotenv
from openai import AsyncOpenAI
from tac import TAC, TACConfig
from tac.adapters.openai import with_tac_memory
from tac.channels.sms import SMSChannel
from tac.channels.voice import VoiceChannel
from tac.server import TACFastAPIServer

load_dotenv()

tac = TAC(config=TACConfig.from_env())
voice_channel = VoiceChannel(tac)
sms_channel = SMSChannel(tac)
openai_client = AsyncOpenAI()

conversation_history = {}
SYSTEM_INSTRUCTIONS = (
    "You are a customer service agent speaking with a user over voice or SMS. "
    "Keep responses short and conversational — a sentence or two. "
    "Do not use markdown, asterisks, bullets, or emojis; your words will be "
    "spoken aloud or sent as plain text."
)

async def handle_message_ready(user_message, context, memory_response):
    conv_id = context.conversation_id

    if conv_id not in conversation_history:
        conversation_history[conv_id] = []
    conversation_history[conv_id].append({"role": "user", "content": user_message})

    # Inject conversation memory and profile into OpenAI client
    client = with_tac_memory(openai_client, memory_response, context)

    response = await client.responses.create(
        model="gpt-5.4-mini",
        instructions=SYSTEM_INSTRUCTIONS,
        input=conversation_history[conv_id]
    )

    llm_response = response.output_text
    conversation_history[conv_id].append({"role": "assistant", "content": llm_response})

    return llm_response

tac.on_message_ready(handle_message_ready)
TACFastAPIServer(tac=tac, voice_channel=voice_channel, messaging_channels=[sms_channel]).start()

Multi-Channel Agent with OpenAI (TypeScript)

import { config } from 'dotenv';
import OpenAI from 'openai';
import {
  TAC,
  TACConfig,
  VoiceChannel,
  SMSChannel,
  TACServer,
  MemoryPromptBuilder,
} from 'twilio-agent-connect';

config();

const openai = new OpenAI();
const tac = await TAC.create({ config: TACConfig.fromEnv() });
const voiceChannel = new VoiceChannel(tac);
const smsChannel = new SMSChannel(tac);

tac.registerChannel(voiceChannel);
tac.registerChannel(smsChannel);

const conversationHistory: Record<string, OpenAI.Chat.ChatCompletionMessageParam[]> = {};

const SYSTEM_INSTRUCTIONS =
  'You are a customer service agent speaking with a user over voice or SMS. ' +
  'Keep responses short and conversational — a sentence or two. ' +
  'Do not use markdown, asterisks, bullets, or emojis; your words will be ' +
  'spoken aloud or sent as plain text.';

tac.onMessageReady(async ({ conversationId, message, memory, session }) => {
  const convId = conversationId as string;

  if (!conversationHistory[convId]) {
    conversationHistory[convId] = [];
  }

  const memoryContext = MemoryPromptBuilder.build(memory, session);
  const systemPrompt = SYSTEM_INSTRUCTIONS + (memoryContext ? `\n\n${memoryContext}` : '');

  conversationHistory[convId].push({ role: 'user', content: message });

  const response = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      { role: 'system', content: systemPrompt },
      ...conversationHistory[convId],
    ],
  });

  const llmResponse = response.choices[0]?.message?.content ?? '';
  conversationHistory[convId].push({ role: 'assistant', content: llmResponse });

  return llmResponse;
});

const server = new TACServer(tac);
await server.start();

Configuration

Required Environment Variables

# Twilio Account Credentials
TWILIO_ACCOUNT_SID=ACxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
TWILIO_AUTH_TOKEN=your_auth_token
TWILIO_API_KEY=SKxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
TWILIO_API_SECRET=your_api_key_secret

# Conversation Configuration
TWILIO_CONVERSATION_CONFIGURATION_ID=conv_configuration_xxxx

# Phone Number
TWILIO_PHONE_NUMBER=[phone]

# Server Configuration (for Voice)
TWILIO_VOICE_PUBLIC_DOMAIN=your-domain.ngrok.io

Optional Memory Configuration

# Conversation Memory (optional)
TWILIO_MEMORY_STORE_ID=mem_service_xxxx
TWILIO_TRAIT_GROUPS=Contact,Preferences

Cloud Platform Integrations

AWS Integration

Package: twilio-agent-connect-aws

Connect AWS agent services to Twilio channels:

# With Strands SDK
pip install twilio-agent-connect-aws[strands,server]

# With Bedrock Agents
pip install twilio-agent-connect-aws[bedrock,server]

# With Bedrock AgentCore
pip install twilio-agent-connect-aws[agentcore,server]

Features:

• StrandsConnector - AWS Strands SDK integration with per-conversation agent isolation
• BedrockConnector - AWS Bedrock Agents (console-created agents)
• BedrockAgentCoreConnector - AWS Bedrock AgentCore (custom agent code deployment)

Repository: https://github.com/twilio/twilio-agent-connect-aws

Microsoft/Azure Integration

Package: twilio-agent-connect-microsoft (formerly tac-azure)

Connect Microsoft Foundry agents to Twilio channels:

# With Agent Framework
pip install twilio-agent-connect-microsoft[agent-framework,server]

# With Voice Live
pip install twilio-agent-connect-microsoft[voice-live,server]

Features:

• AgentFrameworkConnector - Microsoft Agent Framework integration
  • Supports Foundry Hosted Agents, Foundry Prompt Agents, Azure OpenAI (Responses API, Chat Completions)
  • Pluggable session persistence (in-memory, file, Cosmos DB)
  • Memory context injection and lifecycle hooks
• VoiceLiveConnector - Voice Live API integration
  • Text-in / text-streaming-out over WebSocket
  • STT and TTS handled by Twilio ConversationRelay
  • Native interrupt handling via Voice Live response.cancel
  • Server-side conversation state management
  • Tool execution with async handlers

Repository: https://github.com/twilio/twilio-agent-connect-microsoft

Key Features

Memory Management

Automatic integration with Twilio Conversation Memory for persistent user context:

• Profile retrieval with traits
• Observation and summary storage
• Session history with full message context
• Automatic profile lookup by phone/email

Conversation Lifecycle

Automatic tracking of conversation sessions and state:

• Multi-channel conversation initialization
• Participant management
• Conversation status tracking
• Graceful cleanup on conversation end

Message Flow

1. Webhook/Connection Received - Twilio sends webhook (messaging) or WebSocket connection (voice)
2. Channel Processing - Channel validates and processes the incoming event
3. Memory Retrieval - TAC optionally retrieves user memories and profile from Conversation Memory
4. Callback Invoked - Your on_message_ready callback receives user message, context, and optional memory response
5. Response Handling - Your callback returns a response string that TAC routes to the appropriate channel

Outbound Conversations

TAC supports agent-initiated conversations across all channels:

• Programmatic conversation creation
• Participant addition
• Message sending
• Full conversation lifecycle management

Voice-Specific Features

ConversationRelay Protocol

TAC handles the full ConversationRelay WebSocket protocol:

• TwiML generation for inbound calls
• WebSocket connection management
• Message parsing and validation
• Automatic conversation initialization
• Status callback handling

Voice Live API (Microsoft Integration)

The Voice Live connector provides:

• Text-in / text-streaming-out interface
• STT (Speech-to-Text) handled by Twilio
• TTS (Text-to-Speech) handled by Twilio
• Server-side interrupt handling
• No local session management required

Messaging-Specific Features

SMS Channel

• Idempotency-based deduplication using Twilio's i-twilio-idempotency-token header
• Fire-and-forget webhook processing with immediate 200 response
• Automatic conversation initialization
• Profile retrieval per message

Multi-Channel Support

TAC provides unified handling across SMS, RCS, WhatsApp, and Chat:

• Single on_message_ready callback for all channels
• Automatic channel detection and routing
• Per-channel response formatting

Advanced Features

Conversation Intelligence Integration

Process Conversation Intelligence operator results to create observations and summaries:

from tac.core.config import ConversationIntelligenceConfig

config = TACConfig.from_env()
config.conversation_intelligence_config = ConversationIntelligenceConfig(
    configuration_id="your_ci_configuration_id",
    observation_operator_sid="LY...",
    summary_operator_sid="LY...",
)

@app.post("/ci-webhook")
async def ci_webhook_handler(request: Request):
    payload = await request.json()
    result = await tac.process_conversation_intelligence_event(payload)
    return result.model_dump()

Custom Tools

TAC provides built-in tools for common operations:

• Memory recall
• Knowledge base search
• Studio Flow handoff (human escalation)
• Message sending

You can also create custom tools using the @function_tool decorator:

from tac.tools import function_tool

@function_tool()
def send_email(recipient: str, subject: str, body: str) -> bool:
    """
    Sends an email to a recipient.

    Args:
        recipient: Email address
        subject: Email subject
        body: Email body

    Returns:
        True on success, False on failure
    """
    # Implementation here
    return True

Adapter Pattern

TAC provides adapters for automatic memory injection into LLM runtimes:

Python OpenAI Adapter:

from tac.adapters.openai import with_tac_memory

client = with_tac_memory(openai_client, memory_response, context)
# Memory and profile automatically injected into system messages

TypeScript Memory Prompt Builder:

import { MemoryPromptBuilder } from 'twilio-agent-connect';

const memoryContext = MemoryPromptBuilder.build(memory, session);
const systemPrompt = SYSTEM_INSTRUCTIONS + `\n\n${memoryContext}`;

Documentation Links

• Quickstart Guide: https://www.twilio.com/docs/platform/tac/quickstart
• Overview Documentation: https://www.twilio.com/docs/platform/tac/overview
• Python SDK: https://github.com/twilio/twilio-agent-connect-python
• TypeScript SDK: https://github.com/twilio/twilio-agent-connect-typescript
• AWS Integration: https://github.com/twilio/twilio-agent-connect-aws
• Microsoft Integration: https://github.com/twilio/twilio-agent-connect-microsoft

Setup Wizard

TAC includes a web-based setup wizard to automatically create required Twilio services:

# Python SDK
git clone https://github.com/twilio/twilio-agent-connect-python.git
cd twilio-agent-connect-python
make setup  # Opens http://localhost:8080

The wizard creates:

• Conversation Memory store
• Conversation Configuration
• Generates .env file with all required credentials

Common Use Cases

Customer Support Agent

Build an AI-powered customer support agent with:

• Multi-channel support (voice, SMS, WhatsApp)
• Persistent customer memory and context
• Knowledge base integration
• Human handoff capability

Outbound Campaign Agent

Create an agent that initiates conversations:

• Schedule outbound calls or messages
• Personalized messaging based on customer profile
• Conversation tracking and analytics

Voice IVR Replacement

Replace traditional IVR with conversational AI:

• Natural language understanding
• Context-aware responses
• Seamless handoff to human agents

Multi-Language Support

Build globally accessible agents:

• Automatic language detection
• Multi-language conversation memory
• Localized responses

Best Practices

Error Handling

TAC provides lenient error handling:

• Profile lookup failures fall back to Conversation Orchestrator API
• Memory retrieval failures continue without exceptions
• All errors logged with appropriate severity levels

Performance Optimization

• Use immediate 200 responses for webhooks to prevent retries
• Enable conversation deduplication for high-traffic applications
• Leverage conversation grouping for related interactions

Security

• Never commit API keys or tokens to version control
• Use environment variables for all credentials
• Implement webhook signature validation (Twilio SDK provides helpers)
• Use HTTPS for all webhook endpoints

Testing

• Use ngrok for local webhook testing
• Test each channel independently before multi-channel deployment
• Implement logging for debugging webhook processing
• Use TAC's built-in logging with channel-specific logger names

Troubleshooting

Common Issues

Memory not retrieving:

• Verify TWILIO_MEMORY_STORE_ID is set
• Check profile_id is present in webhook data
• Enable DEBUG logging: TWILIO_TAC_LOG_LEVEL=DEBUG

Voice not connecting:

• Verify TWILIO_VOICE_PUBLIC_DOMAIN is accessible
• Check TwiML endpoint returns valid XML
• Ensure WebSocket endpoint is reachable
• Verify Conversation Configuration is active

Duplicate messages:

• Ensure webhook returns 200 immediately
• Verify idempotency token is passed to channel
• Check deduplication capacity is sufficient

Channel isolation issues:

• Verify each channel has distinct conversation sessions
• Check configuration_id filtering is enabled
• Ensure conversation status is properly tracked

Version Requirements

• Python SDK: Python 3.10+
• TypeScript SDK: Node.js 22.13+
• Twilio SDK: twilio>=9.8.3

License

MIT License - see repository LICENSE files for details.