Elite Voice Agents

Tag: Twilio

Tutorial for LiveKit Cloud & Twilio (Step by Step Guide)

The “Tutorial for LiveKit Cloud & Twilio (Step by Step Guide)” helps you deploy a LiveKit cloud agent to your mobile device from scratch. It walks you through setting up Twilio, Deepgram, Cartesia, and OpenAI keys, configuring SIP trunks, and using the command line to deploy a voice agent that can handle real inbound calls.

The guide follows a clear sequence—SOP, Part 1 and Part 2, local testing, cloud deployment, Twilio setup, and live testing—with timestamps so you can jump to what you need. You’ll also learn how to run the stack cost-effectively using free credits and service tiers, ending with a voice agent capable of handling high-concurrency sessions and free minutes on LiveKit.

Prerequisites and system requirements

Before you begin, make sure you have a developer machine or cloud environment where you can run command-line tools, install SDKs, and deploy services. You’ll need basic familiarity with terminal commands, Git, and editing environment files. Expect to spend time configuring accounts and verifying network access for SIP and real-time media. Plan for both local testing and eventual cloud deployment so you can iterate quickly and then scale.

Supported operating systems and command-line tools required

You can run the agent and tooling on Linux, macOS, or Windows (Windows Subsystem for Linux recommended). You’ll need a shell (bash, zsh, or PowerShell), Git, and a package/runtime manager for your chosen language (Node.js with npm or pnpm, Python with pip, or Go). Install CLIs for LiveKit, Twilio, and any SDKs you choose to use. Common tools include curl or HTTPie for API testing, and a code editor like VS Code. Make sure your OS network settings allow RTP/UDP traffic for media testing and that you can adjust firewall rules if needed.

Accounts to create beforehand: LiveKit Cloud, Twilio, Deepgram, Cartesia, OpenAI

Create accounts before you start so you can obtain API keys and configure services. You’ll need a LiveKit Cloud project for the media plane and agent hosting, a Twilio account for phone numbers and SIP trunks, a Deepgram account for real-time speech-to-text, a Cartesia account if you plan to use their tooling or analytics, and an OpenAI account for language model responses. Having these accounts ready prevents interruptions as you wire services together during the tutorial.

Recommended quota and free tiers available including LiveKit free minutes and Deepgram credit

Take advantage of free tiers to test without immediate cost. LiveKit typically provides developer free minutes and a “Mini” tier you can use to run small agents and test media; in practice you can get around 1,000 free minutes and support for dozens to a hundred concurrent sessions depending on the plan. Deepgram usually provides promotional credits (commonly $200) for new users to test transcription. Cartesia often includes free minutes or trial analytics credits, and OpenAI has usage-based billing and may include initial credits depending on promotions. For production readiness, plan a budget for additional minutes, transcription usage, and model tokens.

Hardware and network considerations for running a mobile agent locally and in cloud

When running a mobile agent locally, a modern laptop or small server with at least 4 CPU cores and 8 GB RAM is fine for development; more CPU and memory will help if you run multiple concurrent sessions. For cloud deployment, choose an instance sized for your expected concurrency and CPU-bound model inference tasks. Network-wise, ensure low-latency uplinks (preferably under 100 ms to your Twilio region) and an upload bandwidth that supports multiple simultaneous audio streams (each call may require 64–256 kbps depending on codec and signaling). Verify NAT traversal with STUN/TURN if you expect clients behind restrictive firewalls.

Permissions and billing settings to verify in cloud and Twilio accounts

Before testing live calls, confirm billing is enabled on Twilio and LiveKit accounts so phone number purchases and outbound connection attempts aren’t blocked. Ensure your Twilio account is out of trial limitations if you need unrestricted calling or PSTN access. Configure IAM roles or API key scopes in LiveKit and any cloud provider so the agent can create rooms, manage participants, and upload logs. For Deepgram and OpenAI, monitor quotas and set usage limits or alerts so you don’t incur unexpected charges during testing.

Architecture overview and data flow

Understanding how components connect will help you debug and optimize. At a high level, your architecture will include Twilio handling PSTN phone numbers and SIP trunks, LiveKit as the SIP endpoint or media broker, a voice agent that processes audio and integrates with Deepgram for transcription, OpenAI for AI responses, and Cartesia optionally providing analytics or tooling. The voice agent sits at the center, routing media and events between these services while maintaining session state.

High-level diagram describing LiveKit, Twilio SIP trunk, voice agent, and transcription services

Imagine a diagram where PSTN callers connect to Twilio phone numbers. Twilio forwards media via a SIP trunk to LiveKit or directly to your SIP agent. LiveKit hosts the media room and can route audio to your voice agent, which may run as a worker inside LiveKit Cloud or a separate service connected through the SIP interface. The voice agent streams audio to Deepgram for real-time transcription and uses OpenAI to generate contextual replies. Cartesia can tap into logs and transcripts for analytics and monitoring. Each arrow in the diagram represents a media stream or API call with clear directionality.

How inbound phone calls flow through Twilio into SIP/LiveKit and reach the voice agent

When a PSTN caller dials your Twilio number, Twilio applies your configured voice webhook or SIP trunk mapping. If using a SIP trunk, Twilio takes the call media and SIP-signals it to the SIP URI you defined (which can point to LiveKit’s SIP endpoint or your SIP proxy). LiveKit receives the SIP INVITE, creates or joins a room, and either bridges the call to the voice agent participant or forwards media to your agent service. The voice agent then receives RTP audio, processes that audio for transcription and intent detection, and sends audio responses back into the room so the caller hears the agent.

Where Deepgram and OpenAI fit in for speech-to-text and AI responses

Deepgram is responsible for converting the live audio streams into text in real time. Your voice agent will stream audio to Deepgram and receive partial and final transcripts. The agent feeds these transcripts, along with session context and possibly prior conversation state, into OpenAI models to produce natural responses. OpenAI returns text that the agent converts back into audio (via a TTS service or an audio generation pipeline) and plays back to the caller. Deepgram can also handle diarization or confidence scores that help decide whether to reprompt or escalate to a human.

Roles of Cartesia if it is used for additional tooling or analytics

Cartesia can provide observability, session analytics, or attached tooling for your voice conversations. If you integrate Cartesia, it can consume transcripts, call metadata, sentiment scores, and event logs to visualize agent performance, highlight keywords, and produce call summaries. You might use Cartesia for post-call analytics, searching across transcripts, or building dashboards that track concurrency, latency, and conversion metrics.

Latency, concurrency, and session limits to be aware of

Measure end-to-end latency from caller audio to AI response. Transcription and model inference add delay: Deepgram streaming is low-latency (tens to hundreds of milliseconds) but OpenAI response time depends on model and prompt size (hundreds of milliseconds to seconds). Factor in network round trips and audio encoding/decoding overhead. Concurrency limits come from LiveKit project quotas, Deepgram connection limits, and OpenAI rate limits; ensure you’ve provisioned capacity for peak sessions. Monitor session caps and use backpressure or queueing in your agent to protect system stability.

Create and manage API keys

Properly creating and storing keys is essential for secure, stable operation. You’ll collect keys from LiveKit, Twilio, Deepgram, OpenAI, and Cartesia and use them in configuration files or secret stores. Limit scope when possible and rotate keys periodically.

Generate LiveKit Cloud API keys and configure project settings

In LiveKit Cloud, create a project and generate API keys (API key and secret). Configure project-level settings such as allowed origins, room defaults, and any quota or retention policies. If you plan to deploy agents in the cloud, create a service key or role with permissions to create rooms and manage participants. Note the project ID and any region settings that affect media latency.

Obtain Twilio account SID, auth token, and configure programmable voice resources

From Twilio, copy your Account SID and Auth Token to a secure location (treat them like passwords). In Twilio Console, enable Programmable Voice, purchase a phone number for inbound calls, and set up a SIP trunk or voice webhook. Create any required credential lists or IP access control if you use credential-based SIP authentication. Ensure that your Twilio settings (voice URLs or SIP mappings) point to your LiveKit or SIP endpoint.

Create Deepgram API key and verify $200 free credit availability

Sign into Deepgram and generate an API key for real-time streaming. Confirm your account shows the promotional credit balance (commonly $200 for new users) and understand how transcription billing is calculated (per minute or per second). Restrict the key so it is used only by your voice agent services or set per-key quotas if Deepgram supports that.

Create OpenAI API key and configure usage limits and models

Generate an OpenAI API key and decide which models you’ll use for agent responses. Configure rate limits or usage caps in your account to avoid unexpected spend. Choose faster, lower-cost models for short interactive responses and larger models only where more complex reasoning is needed. Store the key securely.

Store keys securely using environment variables or a secret manager

Never hard-code keys in source. Use environment variables for local development (.env files that are .gitignored), and use a secret manager (cloud provider secrets, HashiCorp Vault, or similar) in production. Reference secret names in deployment manifests or CI/CD pipelines and grant minimum permissions to services that need them.

Install CLI tools and SDKs

You’ll install the command-line tools and SDKs required to interact with LiveKit, Twilio, Deepgram, Cartesia, and your chosen runtime. This keeps local development consistent and allows you to script tests and deployments.

Install LiveKit CLI or any required LiveKit developer tooling

Install the LiveKit CLI to create projects, manage rooms, and inspect media sessions. The CLI also helps with deploying or debugging LiveKit Cloud agents. After installing, verify by running the version command and authenticate the CLI against your LiveKit account using your API key.

Install Twilio CLI and optionally Twilio helper libraries for your language

Install the Twilio CLI to manage phone numbers, SIP trunks, and test calls from your terminal. For application code, install Twilio helper libraries in your language (Node, Python, Go) to make API calls for phone number configuration, calls, and SIP trunk management.

Install Deepgram CLI or SDK and any Cartesia client libraries if needed

Install Deepgram’s SDK for streaming audio to the transcription service from your agent. If Cartesia offers an SDK for analytics or instrumentation, add that to your dependencies so you can submit transcripts and metrics. Verify installation with a simple transcript test against a sample audio file.

Install Node/Python/Go runtime and dependencies for the voice agent project

Install the runtime for the sample voice agent (Node.js with npm or yarn, Python with virtualenv and pip, or Go). Install project dependencies, and run package manager diagnostics to confirm everything is resolved. For Node projects, run npm ci or install; for Python, create a venv and pip install -r requirements.txt.

Verify installations with version checks and test commands

Run version checks for each CLI and runtime to ensure compatibility. Execute small test commands: list LiveKit rooms, fetch Twilio phone numbers, send a sample audio to Deepgram, and run a unit test from the repository. These checks prevent surprises when you start wiring services together.

Clone, configure, and inspect the voice agent repository

You’ll work from an example repository or template that integrates SIP, media handling, and AI hooks. Inspecting the structure helps you find where to place keys and tune audio parameters.

Clone the example repository used in the tutorial or a template voice agent

Use Git to clone the provided voice agent template. Choose the branch that matches your runtime and read the README for runtime-specific setup. Having the template locally lets you modify prompts, adjust retry behavior, and instrument logging.

Review project structure to locate SIP, media, and AI integration files

Open the repository and find directories for SIP handling, media codecs, Deepgram integration, and OpenAI prompts. Typical files include the SIP session handler, RTP adapter, transcription pipeline, and an AI controller that constructs prompts and handles TTS. Understanding this layout lets you quickly change behavior or add logging.

Update configuration files with LiveKit and third-party API keys

Edit the configuration or .env file to include LiveKit project ID and secret, Twilio credentials, Deepgram key, OpenAI key, and Cartesia token if applicable. Keep example .env.sample files for reference and never commit secrets. Some repos include a config.json or YAML file for codec and session settings—update those too.

Set environment variables and example .env file entries for local testing

Create a .env file with entries like LIVEKIT_API_KEY, LIVEKIT_API_SECRET, TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, DEEPGRAM_API_KEY, OPENAI_API_KEY, and CARTESIA_API_KEY. For local testing, you may also set DEBUG flags, local port numbers, and TURN/STUN endpoints. Document any optional flags for tracing or mock mode.

Explain key configuration options such as audio codecs, sample rates, and session limits

Key options include the audio codec (PCMU/PCMA for telephony compatibility, or Opus for higher fidelity), sample rates (8 kHz for classic telephony, 16 kHz or 48 kHz for better ASR), and audio channels. Session limits in config govern max concurrent calls, buffer sizes for streaming to Deepgram, and timeouts for AI responses. Tune these to balance latency, transcription accuracy, and cost.

Local testing: run the voice agent on your machine

Testing locally allows rapid iteration before opening to PSTN traffic. You’ll verify media flows, transcription accuracy, and AI prompts with simulated calls.

Start LiveKit server or use LiveKit Cloud dev mode for local testing

If you prefer a local LiveKit server, run it on your machine and point the agent to localhost. Alternatively, use LiveKit Cloud’s dev mode to avoid local server setup. Ensure the agent’s connection parameters (API keys and region) match the LiveKit instance you use.

Run the voice agent locally and confirm it registers with LiveKit

Start your agent process and observe logs verifying it connects to LiveKit, registers as a participant or service, and is ready to accept media. Confirm the agent appears in the LiveKit room list or via the CLI.

Simulate inbound calls locally by using Twilio test credentials or SIP tools

Use Twilio test credentials or SIP softphone tools to generate SIP INVITE messages to your configured SIP endpoint. You can also replay pre-recorded audio into the agent using RTP injectors or SIP clients to simulate caller audio. Verify the agent accepts the call and audio flows are established.

Test Deepgram transcription and OpenAI response flows from a sample audio file

Feed a sample audio file through the pipeline to Deepgram and ensure you receive partial and final transcripts. Pass those transcripts into your OpenAI prompt logic and verify you get sensible replies. Check that TTS or audio playback works and that the synthesized response is played back into the simulated call.

Common local troubleshooting steps including port, firewall, and codec mismatches

If things fail, check that required ports (SIP signaling and RTP ports) are open, that NAT or firewall rules aren’t blocking traffic, and that sample rates and codecs match across components. Look at logs for SIP negotiation failures, codec negotiation errors, or transcription timeouts. Enabling debug logging often reveals mismatched payload types or dropped packets.

Setting up Twilio for SIP and phone number handling

Twilio will be your gateway to the PSTN, so set up trunks, numbers, and secure mappings carefully.

Create a Twilio SIP trunk or configure Programmable Voice depending on architecture

Decide whether to use a SIP trunk (recommended for direct SIP integration with LiveKit or a SIP proxy) or Programmable Voice webhooks if you want TwiML-based control. Create a SIP trunk in Twilio, and add an Origination URI that points to your SIP endpoint. Configure the trunk settings to handle codecs and session timers.

Purchase and configure a Twilio phone number to receive inbound calls

Purchase an inbound-capable phone number in the Twilio console and assign it to route calls to your SIP trunk or voice webhook. Set the voice configuration to either forward calls to the SIP trunk or call a webhook that uses TwiML to instruct call forwarding. Ensure the number’s voice capabilities match your needs (PSTN inbound/outbound).

Configure SIP domain, authentication methods, and credential lists for secure SIP

Create credential lists and attach them to your trunk to use username/password authentication if needed. Alternatively, use IP access control to restrict which IPs can originate calls into your SIP trunk. Configure SIP domains and enforce TLS for signaling to protect call setup metadata.

Set up voice webhook or SIP URI mapping to forward incoming calls to LiveKit/SIP endpoint

If you use a webhook, configure the TwiML to dial your SIP URI that points to LiveKit or your SIP proxy. If using a trunk, set the trunk’s origination and termination URIs appropriately. Make sure the SIP URI includes the correct transport parameter (e.g., transport=tls) if required.

Verify Twilio console settings and TwiML configuration for proper media negotiation

Use Twilio’s debugging tools and logs to confirm SIP INVITEs are sent and that Twilio receives 200 OK responses. Check media codec negotiation to ensure Twilio and LiveKit agree on a codec like PCMU or Opus. Use Twilio’s diagnostics to inspect signaling and media problems and iterate.

Connecting Twilio and LiveKit: SIP trunk configuration details

Connecting both systems requires attention to SIP URI formats, transport, and authentication.

Define the exact SIP URI and transport protocol (UDP/TCP/TLS) used by LiveKit

Decide on the SIP URI format your LiveKit or proxy expects (for example, sip:user@host:port) and whether to use UDP, TCP, or TLS. TLS is preferred for signaling security. Ensure the URI is reachable and resolves to the LiveKit ingress or proxy that accepts SIP calls.

Configure Twilio trunk origination URI to point to LiveKit Cloud agent or proxy

In the Twilio trunk settings, add the LiveKit SIP URI as an Origination URI. Specify transport and port, and if using TLS you may need to provide or trust certificates. Confirm the URI’s hostname matches the certificate subject when using TLS.

Set up authentication mechanism such as IP access control or credential-based auth

For security, prefer IP access control lists that only permit Twilio’s egress IPs, or set up credential lists with scoped usernames and strong passwords. Store credentials in Twilio’s credential store and bind them to the trunk. Audit these credentials regularly.

Testing SIP registration and call flow using Twilio’s SIP diagnostics and logs

Place test calls and consult Twilio logs to trace SIP messaging. Twilio provides detailed SIP traces that show INVITEs, 200 OKs, and RTP negotiation. Use these traces to pinpoint header mismatches, authentication failures, or codec negotiation issues.

Handle NAT, STUN/TURN, and TLS certificate considerations for reliable media

RTP may fail across NAT boundaries if STUN/TURN aren’t configured. Ensure your LiveKit or proxy has proper STUN/TURN servers and that TURN credentials are available if needed. Maintain valid TLS certificates on your SIP endpoint and rotate them before expiration to avoid signaling errors.

Integrating Deepgram for real-time transcription

Deepgram provides the speech-to-text layer; integrate it carefully to handle partials, punctuation, and robustness.

Enable Deepgram real-time streaming and link it to the voice agent

Enable streaming in your Deepgram account and use the SDK to create WebSocket or gRPC streams from your agent. Stream microphone or RTP-decoded audio with the correct sample rate and encoding type. Authenticate the stream using your Deepgram API key.

Configure audio format and sample rates to match Deepgram requirements

Choose audio formats Deepgram supports (16-bit PCM, Opus, etc.) and match the sample rate (8 kHz for telephony or 16 kHz/48 kHz for higher fidelity). Ensure your agent resamples audio if necessary before sending to Deepgram to avoid transcription degradation.

Process Deepgram transcription results and feed them into OpenAI for contextual responses

Handle partial transcripts by buffering partials and only sending final transcripts or intelligently using partials for low-latency responses. Add conversation context, metadata, and recent turns to the prompt when calling OpenAI so the model can produce coherent replies. Sanitize transcripts for PII if required.

Handle partial transcripts, punctuation, and speaker diarization considerations

Decide whether to wait for final transcripts or act on partials to minimize response latency. Use Deepgram’s auto-punctuation features to improve prompt quality. If multiple speakers are present, use diarization to attribute speech segments properly; this helps your agent understand who asked what and whether to hand off.

Retry and error handling strategies for transcription failures

Implement exponential backoff and retry strategies for Deepgram stream interruptions. On repeated failures, fallback to a different transcription mode or place a prompt to inform the caller there’s a temporary issue. Log failures and surface metrics to Cartesia or your monitoring to detect systemic problems.

Conclusion

You’ve seen the end-to-end components and steps required to build a voice AI agent that connects PSTN callers to LiveKit, uses Deepgram for speech-to-text, and OpenAI for responses. With careful account setup, key management, codec tuning, and testing, you can get a functioning agent that handles real phone calls.

Recap of steps to get a voice AI agent running with LiveKit Cloud and Twilio

Start by creating LiveKit, Twilio, Deepgram, Cartesia, and OpenAI accounts and collecting API keys. Install CLIs and SDKs, clone the voice agent template, configure keys and audio settings, and run locally. Test Deepgram transcription and OpenAI responses with sample audio, then configure Twilio phone numbers and SIP trunks to route live calls to LiveKit. Verify and iterate until the flow is robust.

Key tips to prioritize during development, testing, and production rollout

Prioritize secure key storage and least-privilege permissions, instrument end-to-end latency and error metrics, and test with realistic audio and concurrency. Use STUN/TURN to solve NAT issues and prefer TLS for signaling. Configure usage limits or alerts for Deepgram and OpenAI to control costs.

Resources and links to docs, example repos, and community channels

Look for provider documentation and community channels for sample code, troubleshooting tips, and architecture patterns. Example repositories and official SDKs accelerate integration and show best practices for encoding, retry, and security.

Next steps for advanced features such as analytics, multi-language support, and agent handoff

After basic functionality works, add analytics via Cartesia, support additional languages by configuring Deepgram and model prompts, and implement intelligent handoff to human agents when needed. Consider session recording, sentiment analysis, and compliance logging for regulated environments.

Encouragement to iterate, measure, and optimize based on real call data

Treat the first deployment as an experiment: gather real call data, measure transcription accuracy, latency, and business outcomes, then iterate on prompts, resourcing, and infrastructure. With continuous measurement and tuning, you’ll improve the agent’s usefulness and reliability as it handles more live calls. Good luck — enjoy building your voice AI agent!

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January 1, 2026
How to Set Up Voice AI Agents Using LiveKit + Twilio (Step by Step Guide)

In “How to Set Up Voice AI Agents Using LiveKit + Twilio (Step by Step Guide)” you’ll learn how to connect LiveKit and Twilio to build an inbound AI voice agent that you can call from your phone. The guide walks you through real code with Cursor and shows practical setup so you finish with an agent that answers calls and holds natural conversations.

You’ll move through concise sections covering account setup, Cursor and Notion guidance, initial project setup and ENV configuration, inbound agent testing, Twilio and LiveKit configuration, agent code, and final testing with timestamps for each step. Follow the examples and timestamps to reproduce the build and test the agent directly from your phone.

Overview and goals

Explain the objective: create an inbound voice AI agent reachable by phone using LiveKit + Twilio

You want to build an inbound voice AI agent that people can call from a regular phone number and have a real-time, conversational interaction. The objective is to bridge the PSTN (public telephone network) to a real-time audio routing layer (LiveKit) while injecting an AI agent (Cursor or another runtime) that can listen, maintain context, and reply with synthesized speech. The whole system needs to accept calls, stream audio into an AI pipeline, and return generated audio back into the call.

Define success criteria: answer calls, maintain conversational context, connect audio through WebRTC/SIP

Success means your system answers an incoming phone call, maintains conversation context across turns, and reliably routes audio in both directions. Practically, that includes: the call is answered by your service, audio is sent from Twilio into LiveKit (or directly to your AI runtime), the AI receives and transcribes the caller’s speech, your model produces a contextual reply, the reply is synthesized to audio and played back into the call, and context is persisted or retrievable so follow-up utterances are coherent.

High-level summary of components: Twilio for PSTN, LiveKit for real-time audio routing, Cursor or VAPI for AI

You’ll use Twilio to receive PSTN calls and act as the front door with phone numbers and webhooks. LiveKit will handle real-time audio routing and session management so your agent and any monitoring clients can join a room and exchange audio via WebRTC or SIP. Cursor (or another AI runtime like VAPI) will be responsible for speech-to-text, model inference for conversational responses, and text-to-speech. A lightweight server mediates webhooks, token generation, and integration between Twilio, LiveKit, and the AI runtime.

Expected outcomes from the guide: working local demo, deployed service, testing steps

By following this guide you should be able to run a local demo where a phone call hits your local server (exposed via ngrok), joins a LiveKit room, and the AI participates in the call. You’ll also have steps for deploying the service to a cloud provider, instructions to test end-to-end behavior, and a checklist for monitoring and scaling. The guide will leave you with a reproducible repo structure, environment variable strategy, and testing tips.

Prerequisites and tools

Accounts required: Twilio account with phone number, LiveKit account/cluster, Cursor or chosen AI runtime

Before you start, create accounts for the main services. You’ll need a Twilio account and at least one phone number capable of voice. You’ll need a LiveKit project or cluster with API credentials and a server URL. Finally, sign up for Cursor or your chosen AI runtime and obtain API keys for speech-to-text and text-to-speech. Having these accounts ready prevents interruptions while wiring everything together.

Developer tools: Node.js or Python runtime, Git, npm/yarn or pip, ngrok or equivalent tunneling tool

Set up a development environment: Node.js (or Python) depending on your stack, Git for version control, and a package manager like npm/yarn or pip. Install ngrok or an equivalent tunneling tool so Twilio can reach your local machine during development. You’ll also need a basic editor and terminal workflow.

Optional tools and docs: Notion guide for notes, Postman for webhook testing, logs viewer

Optional but useful: a Notion page or README to track config values and test cases, Postman for testing webhook payloads, and a logs viewer (or the provider’s dashboard) to inspect request traces and errors. These help with debugging complex call flows.

Permissions and limits to check: Twilio trial restrictions, LiveKit plan limits, API rate caps

Verify any account restrictions: Twilio trial accounts often limit outbound calls, require verified numbers, and prepend messages. LiveKit plans may cap participant count, concurrent rooms, or bandwidth. Your AI runtime can also have rate limits and cost implications. Check these in advance to avoid hitting hard limits during testing.

Account setup and initial configuration

Create and verify Twilio account, buy or port a phone number, review Twilio console basics

Create and verify your Twilio account and complete identity verification steps. Buy a phone number that supports voice in the region you expect callers. Familiarize yourself with the Twilio console so you can see incoming call logs, configure webhooks, and inspect error codes.

Create LiveKit project/cluster, note API keys and server URL, set room policies and permissions

Create a LiveKit cluster or project and note down the API key, secret, and the server URL you’ll use for token generation and client connections. Decide region or cluster based on your expected caller locations so you minimize latency. Think about room policies such as maximum participants and whether rooms are audio-only.

Sign up for Cursor (or alternative) and provision API keys for AI agent runtime

Sign up for Cursor or your AI runtime and provision API keys. Make sure you can access endpoints for speech-to-text, text-generation, and text-to-speech as needed. Test a minimal request from the command line to ensure your keys work.

Organize a Notion guide or README to track configuration values and test cases

Create a central README or Notion page to record all configuration values, webhook URLs, test phone numbers, and expected behavior for each test case. This will speed up troubleshooting and make onboarding team members easier.

Architecture and call flow design

Diagram verbal description: PSTN call -> Twilio number -> webhook -> signal LiveKit session -> agent AI handles audio -> Twilio bridges audio

Visually imagine the flow: a caller dials your Twilio phone number and Twilio sends an HTTP webhook to your server. Your server responds by instructing Twilio to send media into a WebRTC or SIP endpoint that connects to LiveKit. Your agent (or a worker) joins the corresponding LiveKit room, receives the inbound audio, and passes audio frames to the AI runtime for transcription and response generation. The AI’s synthesized audio is routed back through LiveKit and bridged to the Twilio call so the caller hears it.

Decide media path: Twilio Programmable Voice via TwiML to WebRTC gateway or SIP interface to LiveKit

You must choose how audio moves: you can use TwiML and a Twilio WebRTC gateway to directly link Twilio calls to a browser-like endpoint, or use Twilio’s SIP Interface to connect to a SIP endpoint that LiveKit can bridge. Media Streams (Twilio Media Streams) can also stream raw audio to your webhook in real time for transcription workloads. Each approach has tradeoffs in latency, complexity, and compatibility.

Describe signaling and media transport: Webhooks, WebRTC data channels, RTP, audio codecs

Signaling will be handled by Twilio webhooks and your server endpoints for LiveKit token generation. Media will flow over RTP within WebRTC or SIP sessions. You’ll need to ensure compatible audio codecs (commonly PCMU/PCMA for PSTN but Opus for WebRTC) and implement sample rate conversion where necessary. WebRTC data channels may be used for control messages or to transmit small metadata, but primary audio uses media channels.

State management and conversation context: short-term memory, external DB, or Notion/knowledge base integration

Preserving context is essential. Use short-term memory in-process for quick turn-by-turn context and an external database for longer-term state—Redis for ephemeral context, PostgreSQL for transcripts and history. You can optionally integrate Notion or another knowledge base to store conversation summaries, user profiles, or reference documents the agent should consult during inference.

Initial project setup and repository structure

Clone starter repo or create new project layout with server, client, and ai-agent directories

Start a repository with a clear layout: a server folder for webhook endpoints and token generation, a client folder for a simple web client to monitor LiveKit rooms and audio, and an ai-agent folder for the worker that interacts with the AI runtime. This separation keeps responsibilities clear and lets you scale components independently.

Set up package.json or pyproject with dependencies: livekit-client, twilio, express/fastify or Flask/FastAPI, ngrok

Initialize your project’s dependency manifest and include core libraries: the LiveKit client library for token generation and connectivity, the Twilio SDK for request verification and helper functions, an HTTP framework like Express or Fastify (Node) or Flask/FastAPI (Python), and ngrok for local tunneling. Add audio processing libs if needed for resampling and format conversion.

Create basic server endpoints for health, Twilio webhooks, and LiveKit token generation

Implement a health endpoint for uptime checks, a Twilio webhook endpoint that responds to incoming calls and can initiate a Dial or Media Stream, and a token generation endpoint to issue LiveKit tokens to the agent and any monitoring clients. Keep the server code minimal initially so you can iterate quickly.

Prepare simple client to join LiveKit room for testing and monitoring audio streams

Build a lightweight client (web or headless) that can join LiveKit rooms with an access token. Use this client to confirm that audio tracks are published, that you can mute/unmute, and to monitor raw audio streams during debugging. This client is invaluable for verifying whether issues are on the Twilio side or inside your AI pipeline.

Environment variables and secure secrets management

List required env vars: TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_PHONE_NUMBER, LIVEKIT_API_KEY, LIVEKIT_API_SECRET, CURSOR_KEY or VAPI_KEY

Define environment variables clearly: TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_PHONE_NUMBER, LIVEKIT_API_KEY, LIVEKIT_API_SECRET, and your AI runtime key (CURSOR_KEY or VAPI_KEY). Also include PORT, NGROK_AUTH_TOKEN, DATABASE_URL, and any other service-specific secrets you need.

Create an .env file example and .env.local for local testing; never commit secrets to git

Provide an example .env.example file with placeholder values and create a .env.local for your actual local secrets. Make sure .gitignore includes .env and other secrets so you never commit keys to your repo.

Use secret storage for production: environment variables in cloud, HashiCorp Vault, or cloud secret manager

For production, switch from local .env files to secure secret managers provided by your cloud provider, or a dedicated secret manager like HashiCorp Vault. Configure role-based access control so only the services that need keys can retrieve them.

Rotate keys and manage access control for team members

Implement key rotation policies and audit access. When team members join or leave, update access control in your secret manager. Rotate keys periodically and after any suspected compromise.

LiveKit configuration and room setup

Provision LiveKit API keys and select region/cluster for latency considerations

When provisioning LiveKit keys, pick the cluster region closest to your expected callers and agent runtime to minimize latency. Note both the public server URL for clients and any internal server parameters for token signing.

Configure room defaults: max participants, audio-only room, track publishing permissions

Set room defaults to match your use case: audio-only rooms reduce bandwidth and simplify processing. Limit max participants if the room is dedicated to a single caller and a single agent, and configure publishing permissions so only authorized agents and monitoring clients can publish audio.

Generate access tokens server-side for participants and agents with appropriate grants

Always generate LiveKit access tokens server-side with appropriate grants: grant only the capabilities a participant needs, such as join, publish, or subscribe. Short-lived tokens reduce risk if a token is intercepted.

Test LiveKit connect flow using a lightweight client to confirm audio join and mute/unmute work

Validate the LiveKit integration with your lightweight client. Confirm you can join a room, publish and subscribe to audio tracks, and perform mute/unmute. This testing ensures the basic real-time plumbing is correct before adding AI processing.

Twilio configuration and webhook wiring

Buy Twilio phone number and configure Voice webhook to point to your server endpoint

In the Twilio console, buy a phone number that supports voice and configure its Voice webhook to point to your server’s Twilio endpoint. During development, point it to your ngrok URL. Make sure your server can respond quickly to Twilio requests or handle asynchronous flows.

Decide webhook response strategy: TwiML to Dial to a WebRTC/SIP gateway or REST-based media stream

Decide whether you’ll respond with TwiML that instructs Twilio to Dial to a WebRTC or SIP gateway, or whether you’ll use Twilio Media Streams to stream audio to a WebSocket endpoint for transcription. The TwiML Dial approach bridges the call into a media-capable endpoint, whereas Media Streams is better when you need raw audio frames for low-latency transcription.

If using Twilio Media Streams or SIP Interface, set up proper JSON webhook handlers and Twilio console settings

If you use Media Streams, implement WebSocket handlers or webhook endpoints that accept the stream events and audio payloads. For SIP Interface, configure SIP domains and authentication so Twilio can connect to LiveKit or your SIP endpoint. Ensure event and status callbacks are handled so you can react to call lifecycle events.

Use ngrok to expose local endpoints for Twilio testing; update Twilio webhook URL during development

Run ngrok (or an equivalent) to expose your local server and update Twilio’s webhook URL during development. Keep ngrok running while testing and update the URL if it changes. Use ngrok logs to debug incoming requests and responses.

Building the inbound AI agent: code walkthrough

Outline agent responsibilities: accept audio, transcribe, run model inference, generate audio response, send audio back

Your AI agent must accept streamed audio, transcribe it to text, feed sequential context into a conversational model, decide on a reply, synthesize the reply to audio, and inject the audio back into the LiveKit room or Twilio call. It also should log transcripts and optionally manage conversation state and fallback behaviors.

Integrate Cursor or chosen AI runtime: auth, session management, text-to-speech and speech-to-text endpoints

Integrate the AI runtime by authenticating with your API key and creating persistent sessions as appropriate. Use their speech-to-text endpoint to transcribe chunks and their text-generation endpoint for inference. Use text-to-speech for audio output and cache voices or settings to reduce setup overhead between turns.

Implement audio handling: capture RTP/WebRTC audio frames, manage buffering, convert sample rates and codecs

You’ll need to capture audio frames from LiveKit (or Twilio Media Streams) and buffer them into sensible chunks for transcription. Convert sample rates and codecs as necessary—common conversions include PCM16 mono at 16k or 16k with Opus decoding. Ensure you handle jitter, packet reordering, and silence frames, and implement VAD (voice activity detection) if you want to avoid transcribing silence.

Show sample pseudocode for main loops: receive audio -> transcribe -> generate reply -> synthesize -> send audio

Here’s a concise pseudocode main loop to illustrate the flow:

while call_active: audio_chunk = receive_audio_from_livekit() if is_silence(audio_chunk): continue transcript = ai_runtime.stt(audio_chunk, context_id) update_conversation_history(context_id, “user”, transcript) prompt = build_prompt(conversation_history[context_id]) model_reply = ai_runtime.generate_text(prompt) update_conversation_history(context_id, “agent”, model_reply) tts_audio = ai_runtime.text_to_speech(model_reply, voice=”friendly”) send_audio_to_livekit(tts_audio, target_participant=twilio_bridge)

This loop assumes you manage context_id and conversation history, and that you have helper functions for STT and TTS.

Conclusion

Recap the end-to-end process: accounts, config, code, testing, deployment, and monitoring

You’ve walked through creating an inbound voice AI agent: create accounts (Twilio, LiveKit, AI runtime), wire up configuration and secrets, implement a server to handle Twilio webhooks and LiveKit token generation, build or join a LiveKit room to route audio, process audio with an AI runtime to transcribe and respond, and test locally with ngrok before deploying to production. Each step needs validation and monitoring.

Highlight key success factors: secure env, audio handling, robust testing, and cost control

Key success factors are secure secret management, robust audio handling (codecs and resampling), effective context management, and rigorous testing across edge cases like call transfers and network jitter. Also monitor costs for trunking, hours of streaming, and AI runtime usage and optimize model calls to control spend.

Suggested next actions: run the Twilio test, iterate on prompts, and prepare for production deployment

Next, run a live Twilio test by calling your number, iterate on prompt design to improve agent responses, add telemetry and logging, prepare deployment artifacts (Docker images, cloud infra), and test failover scenarios. Consider load testing and adding rate limits or autoscaling.

Resources and references to consult: Twilio docs, LiveKit docs, Cursor/VAPI docs, and the Notion guide

Keep the Twilio and LiveKit documentation and your AI runtime docs at hand for API specifics and best practices. Maintain your Notion guide or README with configuration details, runbooks, and test scripts so you and your team can reproduce the setup or onboard others quickly.

Good luck — you’re now equipped to build an inbound voice AI agent that answers calls, maintains context, and routes audio end-to-end using LiveKit and Twilio.

If you want to implement Chat and Voice Agents into your business to reduce missed calls, book more appointments, save time, and make more revenue, book a discovery call here: https://brand.eliteaienterprises.com/widget/bookings/elite-ai-30-min-demo-call

December 27, 2025
Import Phone Numbers into Vapi from Twilio for AI Automation

You can streamline your AI automation phone setup with a clear step-by-step walkthrough for importing Twilio numbers into Vapi. This guide shows you how to manage international numbers and get reliable calling across the US, Canada, Australia, and Europe.

You’ll be guided through creating a Twilio trial account, handling authentication tokens, and importing numbers into Vapi, plus how to buy trial numbers in Vapi for outbound calls. The process also covers setting up European numbers and the documentation required for compliance, along with geographic permissions for outbound dialing.

Overview of Vapi and Twilio for AI Automation

You are looking to combine Vapi and Twilio to build conversational AI and voice automation systems; this overview gives you the high-level view so you can see why the integration matters. Twilio is a mature cloud communications platform that provides telephony APIs, SIP trunking, and global phone number inventory; Vapi is positioned as an AI orchestration and telephony-first platform that focuses on routing, AI agent integration, and simplified number management for voice-first automation. Together they let you own the telephony layer while orchestrating AI-driven conversations, routing, and analytics.

Purpose of integrating Vapi and Twilio for conversational AI and voice automation

You integrate Vapi and Twilio so you can leverage Twilio’s global phone number reach and telephony reliability while using Vapi’s AI orchestration, call logic templates, and project-level routing. This setup lets your AI agents answer inbound calls, run IVR and NLU flows, execute outbound campaigns, and hand off to humans when needed — all with centralized control over voice policies, call recording, and AI model selection.

Key capabilities each platform provides (call routing, SIP, telephony APIs, AI orchestration)

You’ll rely on Twilio for telephony primitives: phone numbers, SIP trunks, PSTN interconnects, media streams, and robust REST APIs. Twilio handles low-level telephony and regulatory relationships. Vapi complements that with AI orchestration: attaching conversational flows, managing agent models, intelligent routing rules, multi-language handling, and templates that tie phone numbers to AI behaviors. Vapi also provides project scoping, environment separation (dev/staging/prod), and easier UI-driven attachment of call flows.

Typical use cases: IVR, outbound campaigns, virtual agents, multilingual support

You will commonly use this integration for IVR systems that route by intent, AI-driven virtual agents that handle natural conversations, large-scale outbound campaigns for reminders or surveys, and multilingual support where language detection and model selection happen dynamically. It’s also useful for toll-free help lines, appointment scheduling, and hybrid human-AI handoffs where an agent escalates to a human operator.

Supported geographic regions and phone number types relevant to AI deployments

You should plan deployments around supported regions: Twilio covers a wide set of countries, and Vapi can import and manage numbers from regions Twilio supports. Important number types include local, mobile, national, and toll-free numbers. Note that EU countries and some regulated regions require documentation and different provisioning timelines; North America, Australia, and some APAC regions are generally faster to provision and test for AI voice workloads.

Prerequisites and Account Setup

You’ll need to prepare accounts, permissions, and financial arrangements before moving numbers and running production traffic.

Choosing between Twilio trial and paid account: limits and implications

If you’re experimenting, a Twilio trial account is fine initially, but you’ll face restrictions: outbound calls are limited to verified numbers, messages and calls carry trial prefixes or confirmations, and some API features are constrained. For production or full exports of number inventories, a paid Twilio account is recommended so you avoid verification restrictions and gain full telephony capabilities, higher rate limits, and the ability to port numbers.

Setting up a Vapi account and project structure for AI automation

When you create a Vapi account, define projects and environments (for example: dev, staging, prod). Each project should map to a logical product line or regional operation. Environments let you test call flows and AI agents without impacting production. Create a naming convention for projects and resources so you can easily assign numbers, AI agents, and routing policies later.

Required permissions and roles in Twilio and Vapi (admin, API access)

You need admin or billing access in both platforms to buy/port numbers and create API keys. Create least-privilege API keys: one set for listing and exporting numbers, another for provisioning within Vapi. In Twilio, ensure you can create API Keys and access the Console. In Vapi, make sure you have roles that permit number imports, routing policy changes, and webhook configuration.

Billing and payment considerations for buying and porting numbers

You must enable billing and add a payment method on both platforms if you will purchase, port, or renew numbers. Factor recurring costs for number rental, per-minute usage, and AI processing. Porting fees and local operator charges vary by country; budget for verification documents that might carry administrative fees.

Checking regional availability and regulatory restrictions before proceeding

Before you buy or port, check which countries require KYC, proof of address, or documented use cases for virtual numbers. Some countries restrict outbound robocalls or have emergency-calling requirements. Confirm that the number types you need (e.g., toll-free or mobile) are available for the destination region and that your intended use complies with local telephony rules.

Preparing Twilio for Number Export

To smoothly export numbers, gather metadata and create stable credentials.

Locating and listing phone numbers in the Twilio Console

Start by visiting the Twilio Console’s phone numbers section and list all numbers across your account and subaccounts. You’ll want to export the inventory to a file so you can map them into Vapi. Note friendly names and any custom voice/webhook URLs currently attached.

Understanding phone number metadata: SID, country, capabilities, type

Every Twilio number has metadata you must preserve: the Phone Number in E.164 format, the unique SID, country and region, capabilities flag (voice, SMS, MMS), the number type (local, mobile, toll-free), and any configured webhooks or SIP addresses. Capture these fields because they are essential for correct routing and capability mapping in Vapi.

Creating API credentials and keys in Twilio (Account SID, Auth Token, API Keys)

Generate API credentials: your Account SID and Auth Token for account-level access and create API Keys for scoped programmatic operations. Use API Keys for automation and rotate them periodically. Keep the master Auth Token secure and avoid embedding it in scripts without proper secret management.

Identifying trial-account restrictions: outbound destinations, verified caller IDs, usage caps

If you’re on a trial account, remember that outbound calls and messages are limited to verified recipient numbers, and messages may include trial disclaimers. Also, rate limits and spending caps may be enforced. These restrictions will affect your ability to test large-scale outbound campaigns and can prevent certain automated exports unless you upgrade.

Organizing numbers by project, subaccount, or tagging for easier export

Use Twilio subaccounts or your own tagging/naming conventions to group numbers by project, region, or environment. Subaccounts make it simpler to bulk-export a specific subset. If you can’t use subaccounts, create a CSV that includes a project tag column to map numbers into Vapi projects later.

Exporting Phone Numbers from Twilio

You can export manually via the Console or automate extraction using Twilio’s REST API.

Export methods: manual console export versus automated REST API extraction

For a one-off, you can copy numbers from the Console. For recurring or large inventories, use the REST API to programmatically list numbers and write them into CSV or JSON. Automation prevents manual errors and makes it easy to keep Vapi in sync.

REST API endpoints and parameters to list and filter phone numbers

Use Twilio’s IncomingPhoneNumbers endpoint to list numbers (for example, GET /2010-04-01/Accounts//IncomingPhoneNumbers.json). You can filter by phone number, country, type, or subaccount. For subaccounts, iterate over each subaccount SID and call the same endpoint. Include page size and pagination handling when you have many numbers.

Recommended CSV/JSON formats and the required fields for Vapi import

Prepare a standardized CSV or JSON with these recommended fields: phone_number (E.164), twilio_sid, friendly_name, country, region/state, capabilities (comma-separated: voice,sms), number_type (local,tollfree,mobile), voice_webhook (if present), sms_webhook, subaccount (if applicable), and tags/project. Vapi typically needs phone_number, country, and capabilities at minimum.

Filtering by capability (voice/SMS), region, or number type to limit exports

When exporting, filter to only the numbers you plan to import to Vapi: voice-capable numbers for voice AI, SMS-capable for messaging AI. Also filter by region if you’re deploying regionally segmented AI agents to reduce import noise and simplify verification.

Handling Twilio subaccounts and aggregating exports into a single import file

If you use Twilio subaccounts, call the listing endpoint for each subaccount and consolidate results into a single file. Include a subaccount column to preserve ownership context. Deduplicate numbers after aggregation and ensure the import file has consistent schemas for Vapi ingestion.

Securing Credentials and Compliance Considerations

Protect keys, respect privacy laws, and follow best practices for secure handling.

Secure storage best practices for Account SID, Auth Token, and API keys

You should store Account SIDs, Auth Tokens, and API keys in a secure secret store or vault. Avoid checking them into source control or sending them in email. Use environment variables in production containers with restricted access and audit logging.

Credential rotation and least-privilege API key usage

Rotate your credentials regularly and create API keys with the minimum permissions required. For example, generate a read-only key for listing numbers and a constrained provisioning key for imports. Revoke any unused keys immediately.

GDPR, CCPA and data residency implications when moving numbers and metadata

When exporting number metadata, be mindful that phone numbers can be personal data under GDPR and CCPA. Keep exports minimal, store them in regions compliant with your data residency obligations, and obtain consent where required. Use pseudonymization or redaction for any associated subscriber information you don’t need.

KYC and documentation requirements for certain countries (especially EU)

Several jurisdictions require Know Your Customer (KYC) verification to activate numbers or services. For EU countries, you may need business registration, proof of address, and designated legal contact information. Start KYC processes early to avoid provisioning delays.

Redaction and minimization of personally identifiable information in exports

Only export fields needed by Vapi. Remove or redact any extra PII such as account holder names, email addresses, or records linked to user profiles unless strictly required for regulatory compliance or porting.

Setting Up Vapi for Number Import

Configure Vapi so imports attach correctly to projects and AI flows.

Creating a Vapi project and environment for telephony/AI workloads

Within Vapi, create projects that match your Twilio grouping and create environments for testing and production. This structure helps you assign numbers to the correct AI agents and routing policies without mixing test traffic with live customers.

Obtaining and configuring Vapi API keys and webhook endpoints

Generate API keys in Vapi with permissions to perform number imports and routing configuration. Set up webhook endpoints that Vapi will call for voice events and AI callbacks, and ensure those webhooks are reachable and secured (validate signatures or use mutual TLS where supported).

Configuring inbound and outbound routing policies in Vapi

Define default inbound routing (which AI agent or flow answers a call), fallback behaviors, call recording preferences, and outbound dial policies like caller ID and rate limits. These defaults will be attached to numbers during import unless you override them per-number.

Understanding Vapi number model and required import fields

Review Vapi’s number model so your import file matches required fields. Typical required fields include the phone number (E.164), country, capabilities, and the project/environment assignment. Optionally include desired call flow templates and tags.

Preparing default call flows or templates to attach to imported numbers

Create reusable call flow templates in Vapi for IVR, virtual agent, and fallback human transfer. Attaching templates during import ensures all numbers behave predictably from day one and reduces manual setup after import.

Importing Numbers into Vapi from Twilio

Choose between UI-driven imports and API-driven imports based on volume and automation needs.

Step-by-step import via Vapi UI using exported Twilio CSV/JSON

You will upload the CSV/JSON via the Vapi UI import page, map columns to the Vapi fields (phone_number → number, twilio_sid → external_id, project_tag → project), choose the environment, and preview the import. Resolve validation errors highlighted by Vapi and then confirm the import. Vapi will return a summary with successes and failures.

Step-by-step import via Vapi REST API with sample payload structure

Using Vapi’s REST API, POST to the import endpoint with a JSON array of numbers. A sample payload structure might look like: { “project”: “support-ai”, “environment”: “prod”, “numbers”: [ { “phone_number”: “+14155550123”, “external_id”: “PNXXXXXXXXXXXXXXXXX”, “country”: “US”, “capabilities”: [“voice”,”sms”], “number_type”: “local”, “assigned_flow”: “support-ivr-v1”, “metadata”: {“twilio_subaccount”: “SAxxxx”} } ] } Vapi will respond with import statuses per record so you can programmatically retry failures.

Mapping Twilio fields to Vapi fields and resolving schema mismatches

Map Twilio’s SID to Vapi’s external_id, phone_number to number, capabilities to arrays, and friendly_name to display_name. If Vapi expects a “region” while Twilio uses “state”, normalize those values during export. Create transformation scripts to handle these mismatches before import.

De-duplicating and resolving number conflicts during import

De-duplicate numbers by phone number (E.164) before import. If Vapi already has a number assigned, choose whether to update metadata, skip, or fail the import. Implement conflict resolution rules in your import process to avoid unintended reassignment.

Verifying successful import: status checks, test calls, and logs

After import, check Vapi’s import report and call logs. Perform test inbound and outbound calls to a sample of imported numbers, confirm that the correct AI flow executes, and validate voicemail, recordings, and webhook events are firing correctly.

Purchasing and Managing Trial Numbers in Vapi

You can buy trial or sandbox numbers in Vapi to test international calling behavior.

Buying trial numbers in Vapi to enable calling Canada, Australia, US and other supported countries

Within Vapi, purchase trial or sandbox numbers for countries you want to test (for example, US, Canada, Australia). Trial numbers let you simulate production behavior without full provisioning obligations; they’re useful to validate routing and AI flows.

Trial limits, sandbox behavior, and recommended use cases for testing

Trial numbers may have usage limits, reduced call duration, or restricted outbound destinations. Use them for functional tests, language checks, and flow validation, but not for high-volume live campaigns. Treat them as ephemeral and avoid exposing them to end users.

Assigning purchased numbers to projects, environments, or AI agents

Once purchased, assign trial numbers to the appropriate Vapi project and environment so your test agents respond. This ensures isolation from production data and enables safe iteration on AI models.

Managing renewal, release policies and how to upgrade to production numbers

Understand Vapi’s renewal cadence and release policies for trial numbers. When moving to production, buy full-production numbers or port existing Twilio numbers into Vapi. Plan a cutover process where you update DNS or webhook targets and verify traffic routing before decommissioning trial numbers.

Cost structure, currency considerations and how to monitor spend

Monitor recurring rental fees, per-minute costs, and cross-border charges. Vapi will bill in the currency you choose; account for FX differences if your billing account is in another currency. Set spending alerts and review usage dashboards regularly.

Handling European Numbers and Documentation Requirements

European provisioning often requires paperwork and extra lead time.

Country-by-country differences for European numbers and operator restrictions

You must research each EU country individually: some allow immediate provisioning, others require proving local presence or a legitimate business purpose. Operator restrictions might limit SMS or toll-free usage, or disallow certain outbound caller IDs. Design your rollout to accommodate these variations.

Accepted document types and verification workflow for EU number activation

Commonly accepted documents include company registration certificates, VAT registration, proof of address (utility bills), and identity documents for local representatives. Vapi’s verification workflow will ask you to upload these documents and may require translated or notarized copies, depending on the country.

Typical timelines and common causes for delayed approvals

EU number activation can take from a few days to several weeks. Delays commonly occur from incomplete documentation, mismatched company names/addresses, lack of local legal contact, or high demand for local number resources. Start the verification early and track status proactively.

Considerations for virtual presence, proof of address and identity verification

If you’re requesting numbers to show local presence, be ready to provide specific proof such as local lease agreements, office addresses, or appointed local representatives. Identity verification for the company or authorized person will often be required; ensure the person listed can sign or attest to usage.

Fallback strategies while awaiting EU number approval (alternative countries or temporary numbers)

While waiting, use alternative numbers from other supported countries or deploy temporary mobile numbers to continue development and testing. You can also implement call redirection or a virtual presence in nearby countries until verification completes.

Conclusion

You now have the roadmap to import phone numbers from Twilio into Vapi and run AI-driven voice automation reliably and compliantly.

Key takeaways for importing phone numbers into Vapi from Twilio for AI automation

Keep inventory metadata intact, use automated exports from Twilio where possible, secure credentials, and map fields accurately to Vapi’s schema. Prepare call flow templates and assign numbers to the correct projects and environments to minimize manual work post-import.

Recommended next steps to move from trial to production

Upgrade Twilio to a paid account if you’re still on trial, finalize KYC and documentation for regulated regions, purchase or port production numbers in Vapi, and run a staged cutover with monitoring in place. Validate AI flows end-to-end with test calls before full traffic migration.

Ongoing maintenance, monitoring and compliance actions to plan for

Schedule credential rotation, audit access and usage, maintain documentation for regulated numbers, and monitor spend and call quality metrics. Keep a process for re-verifying numbers and renewing required documents to avoid service interruption.

Where to get help: community forums, vendor support and professional services

If you need help, reach out to vendor support teams, consult community forums, or engage professional services for migration and regulatory guidance. Use your project and environment setup to iterate safely and involve legal or compliance teams early for country-specific requirements.

If you want to implement Chat and Voice Agents into your business to reduce missed calls, book more appointments, save time, and make more revenue, book a discovery call here: https://brand.eliteaienterprises.com/widget/bookings/elite-ai-30-min-demo-call

December 15, 2025

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