Voice agents have to do more than turn speech into text and generate a response. They need to understand someone while they are speaking, decide when a turn is finished, generate an appropriate response, and begin speaking back without making the conversation feel slow or unnatural.
Building that experience requires several pieces of infrastructure to work together in real time. A voice agent might use speech-to-text to understand the caller, a language model to determine what to say, text-to-speech to generate audio, and a telephony or realtime transport layer to carry the conversation. Depending on the application, it may also need to handle interruptions, tool calls, conversation state, and failures along the way.
There are several ways to build that stack. Some platforms provide an orchestration layer that connects models, voices, tools, and phone infrastructure. Others offer more comprehensive platforms for building and operating phone agents, while frameworks give developers greater control over the realtime application itself. Specialized speech providers can also supply individual components within a larger voice architecture.
Rather than attempting to catalog every speech model or conversational AI product available today, this guide focuses on 10 tools that represent some of the major approaches developers can use to build voice agents.
By the end of this roundup, you'll understand how those approaches differ, where each tool fits within the voice AI stack, and which architecture makes the most sense for your application.
What Is a Voice AI Tool?
A voice AI tool is software that helps developers add spoken conversation to an AI application. Depending on the product, it may provide a complete environment for building voice agents or handle one specific part of the voice stack.
At the platform level, these tools can coordinate the components involved in a conversation and connect them with an application's tools and business logic. Some also provide phone numbers, call routing, testing, monitoring, and other infrastructure for deploying and operating voice agents.
Other products work at a lower layer. Realtime communication frameworks provide infrastructure for realtime media and agent interactions, while speech providers specialize in capabilities such as transcription or voice generation. Developers can combine these components themselves or use a higher-level platform that integrates them.
This distinction matters because “voice AI tool” describes several different architectural roles rather than a single type of product. A team building a phone-based customer service agent may want a platform that manages most of the stack, while a team building voice into an existing application may prefer individual components that give it more control over the underlying architecture.
How to Evaluate a Voice AI Tool
The right voice AI tool depends on the type of voice experience you're building, the level of control you need over the underlying stack, and how the tool fits into your overall architecture.
When comparing platforms, consider the following factors:
Architecture and Model Flexibility
Start with how the tool constructs a voice agent.
Some platforms let developers combine separate speech-to-text, language, and text-to-speech models, while others support realtime models that process and generate speech directly. Consider whether you need to choose individual providers, switch models over time, or control the pipeline yourself.
Latency and Turn-Taking
A voice agent needs to determine when to respond without cutting the user off or introducing unnecessary delays.
Look at how the tool handles end-of-turn detection, interruptions, and audio processing. Model selection and pipeline design also affect response time, so consider how much control the platform gives you over the components and settings involved in each interaction.
Telephony and Realtime Transport
Phone agents need infrastructure for making and receiving calls, while voice experiences inside web or mobile applications need realtime media transport. Some products include these capabilities, while others are designed to connect with separate communications infrastructure.
Tools and Application Integration
If the agent will schedule appointments, retrieve account information, update records, or trigger other actions, evaluate how the platform connects models to your application's APIs and tools. Also consider how much control you have over the logic that determines when those actions run.
Testing and Observability
Consider what visibility the tool provides into conversations, tool execution, errors, and other runtime behavior. Testing and evaluation capabilities can also help teams check conversation flows as prompts, models, tools, and agent logic change.
The 10 Best AI Voice Tools
1. Vapi
Best for: Developers who want a configurable voice agent platform that manages realtime orchestration while preserving control over the models and services in the voice stack.
Vapi is a developer platform for building voice agents for phone and web applications. Its architecture coordinates the transcriber, language model, and voice components involved in a conversation while handling realtime orchestration such as streaming, endpointing, and interruption detection.
Rather than requiring one fixed model stack, Vapi lets developers configure the underlying components. Teams can select from supported transcription, language, and voice providers, and Vapi also supports custom transcribers, custom LLMs, and custom text-to-speech services. This gives developers flexibility over individual parts of the voice pipeline while Vapi continues to handle the orchestration layer.
Vapi also connects conversations to application logic through tools. Developers can create custom tools that interact with their own systems, and assistants can access tools exposed by external MCP servers. For phone applications, Vapi supports inbound and outbound calling through Vapi-managed numbers as well as numbers connected through supported telephony providers and SIP infrastructure.
The platform also provides infrastructure for testing and operating agents, including call logs, automated evaluations, simulations, and monitoring. This combination makes Vapi a strong fit for teams that want flexibility over the underlying voice stack without having to build the realtime orchestration and operational tooling themselves.
Why You Might Choose Vapi
- Lets developers choose among supported transcription, language model, and voice providers.
- Connects assistants to external APIs and application logic through custom tools.
- Supports inbound and outbound phone calls with Vapi-managed or externally connected numbers.
- Supports custom transcription, language model, and text-to-speech services.
- Tests agent behavior through simulated conversations and configurable evaluations.
- Provides call recordings, transcripts, and logs for analyzing and debugging conversations.
Additional Highlights
Vapi's Squads let developers divide a voice experience among specialized assistants and hand a conversation from one assistant to another as the task changes. Its handoff system also lets developers control how much conversation history the next assistant receives, providing another way to structure multi-agent voice applications.
2. Retell AI
Best for: Teams building phone agents that want telephony, structured conversation design, testing, and production monitoring in one platform.
Retell AI is a platform for building, testing, deploying, and monitoring AI voice agents for phone calls. It supports inbound and outbound calling and combines the voice runtime with tools for configuring how an agent responds, takes actions, and moves through a conversation.
Developers can build agents with prompt-based configurations or use Conversation Flow for more structured interactions. Conversation Flow organizes an agent into connected nodes for conversation, functions, logic, transfers, and other actions, giving teams explicit control over workflows that need to follow defined paths.
Retell supports its own agent frameworks as well as custom LLM implementations connected over WebSocket. Custom functions let agents interact with external APIs and business systems during conversations, while knowledge bases can provide information from sources such as documents, URLs, and text.
For telephony, Retell supports purchasing phone numbers through the platform, importing numbers from supported providers, and connecting custom telephony through SIP. Its testing capabilities include an interactive playground, simulated conversations, batch testing, and phone or web-call testing. After calls, teams can configure analysis fields for information such as summaries, outcomes, sentiment, and other custom data.
Why You Might Choose Retell AI
- Supports both prompt-based agents and node-based conversation flows.
- Handles inbound and outbound calling with Retell-managed, imported, or custom telephony.
- Connects agents to external APIs and business systems through custom functions.
- Lets developers use Retell's agent frameworks or connect a custom LLM implementation.
- Tests agent behavior through interactive and simulated testing workflows.
- Provides call logs, transcripts, latency information, and configurable post-call analysis.
Additional Highlights
Retell supports A/B testing by splitting call traffic between multiple agents. Teams can assign traffic percentages to different agents and compare changes such as prompts, voices, or conversation configurations using calls routed through the experiment.
3. LiveKit
Best for: Developers who want to build realtime voice agents in code with control over models, conversation logic, and media infrastructure.
LiveKit provides an open-source framework and cloud platform for building realtime voice and multimodal AI agents. Agents join LiveKit rooms as programmatic participants, where they can process realtime media and data and interact with users through the same infrastructure used by other participants.
Developers can build voice agents with separate speech-to-text, language model, and text-to-speech components or use realtime models that process and generate speech directly. LiveKit supports models through LiveKit Inference and provider plugins, giving teams multiple options for configuring the AI components behind an agent.
LiveKit Agents supports Python and Node.js and provides APIs for turn detection, interruption handling, tools, sessions, and multi-agent workflows. Developers can connect agents to web and mobile applications through LiveKit's client SDKs, while LiveKit Telephony supports inbound and outbound calling through SIP and other supported integrations.
Teams can run the open-source LiveKit server and Agents framework on their own infrastructure or use LiveKit Cloud for managed realtime infrastructure and agent deployment. This makes LiveKit a strong fit for developers who want to define agent behavior in code while choosing how much of the surrounding infrastructure they operate themselves.
Why You Might Choose LiveKit
- Builds voice agents in Python or Node.js with a code-first framework.
- Supports both STT-LLM-TTS pipelines and realtime speech-to-speech models.
- Connects agents to a range of AI model providers through LiveKit Inference and provider plugins.
- Provides turn detection, interruption handling, tools, and session orchestration for realtime conversations.
- Supports voice experiences across application interfaces and SIP-based telephony.
- Lets teams self-host LiveKit infrastructure or use LiveKit Cloud for managed infrastructure and agent deployment.
Additional Highlights
LiveKit Agents supports handoffs between specialized agents within the same session. Developers can transfer control when a conversation requires different instructions, tools, or capabilities and explicitly choose whether to pass the existing conversation context to the next agent.
4. ElevenLabs
Best for: Teams that want to build voice agents on a platform that combines ElevenLabs' speech models with configurable LLMs, conversation logic, tools, and deployment options.
ElevenLabs offers ElevenAgents, a platform for building and operating conversational voice agents. Its architecture coordinates speech-to-text, a language model, text-to-speech, and turn-taking. Developers can choose from supported LLMs or connect their own model through a custom LLM integration.
Developers can define agent behavior through system prompts or use Workflows for interactions that need more explicit structure. Workflows organize conversations as graphs and support subagent nodes that can change instructions, LLMs, tools, and knowledge base items for specific stages. Agents can also interact with external APIs through tools, connect to MCP servers, and use attached knowledge bases.
ElevenAgents supports web and mobile applications through SDKs and provides telephony integrations for SIP and Twilio. The platform also includes automated agent testing, conversation analysis, transcript search, and analytics for evaluating deployed agents.
Why You Might Choose ElevenLabs
- Combines speech-to-text, language models, text-to-speech, and turn-taking within one agent platform.
- Lets developers choose from supported LLMs or connect a custom language model.
- Connects agents to external APIs, MCP servers, and knowledge bases.
- Supports web, mobile, and phone experiences through SDKs and telephony integrations.
- Provides automated testing for agent behavior.
- Includes conversation analysis, transcript search, and analytics for reviewing deployed agents.
Additional Highlights
ElevenAgents supports experiments that split live production traffic between different agent configurations. Teams can create variants that change areas such as prompts, workflow logic, voices, tools, or knowledge bases, assign a percentage of traffic to each branch, and compare their performance using defined metrics.
5. Bland AI
Best for: Teams building phone-based voice agents that want APIs, structured conversation flows, telephony, and external system integrations within one platform.
Bland AI is a platform for building AI phone agents that can make and receive calls. Developers can configure calls with prompts or use Bland's Pathways system to structure conversations as graphs made up of connected nodes and pathways.
Pathways give developers more explicit control over how a conversation progresses. Individual nodes can contain dialogue instructions, conditions for determining when the conversation should move forward, and variable extraction prompts. Pathways can also execute webhooks during a conversation, connect to a knowledge base, and control when the agent ends a call.
Bland supports outbound calls through its API and inbound calling through configured phone numbers. For teams using their own telephony infrastructure, Bland also supports SIP configurations for inbound and outbound routing. Post-call webhooks can send call metadata, transcripts, extracted variables, and other call data to an external application after a conversation ends.
Why You Might Choose Bland AI
- Supports prompt-based calls and graph-based Pathways for structuring conversations.
- Handles both inbound and outbound AI phone calls.
- Connects conversations to external applications through webhooks and other integration tools.
- Lets developers define dialogue behavior, transition conditions, and variable extraction within Pathway nodes.
- Supports SIP configurations for connecting existing telephony infrastructure.
- Sends transcripts, extracted variables, call metadata, and other results to applications through post-call webhooks.
Additional Highlights
Bland's BTTS v2 supports custom voice cloning from uploaded audio samples. Teams can create a voice clone for use with Bland's text-to-speech system and configure settings that affect characteristics such as initial response speed and output smoothness.
6. Deepgram
Best for: Developers who want Deepgram's speech models with a configurable Voice Agent API that can integrate with multiple LLM providers and application tools.
Deepgram provides speech-to-text, text-to-speech, and a Voice Agent API for building realtime conversational agents. The Voice Agent API combines speech recognition, an LLM, and speech generation through a single WebSocket connection, handling the listening, thinking, and speaking stages of the conversation.
Developers can configure the models used across those stages. The speech-to-text layer currently uses Deepgram models, while the LLM layer supports providers including OpenAI-compatible endpoints, Anthropic, Amazon Bedrock, Google, Groq, and NVIDIA. The speech-generation layer can use Deepgram voices or supported third-party text-to-speech providers.
The API also supports function calling for connecting conversations to external APIs and services. Functions can run through supported server-side integrations or be handled by the developer's application, which can execute the requested action and return the result to the agent. Deepgram's Flux speech-to-text models add model-integrated end-of-turn detection, with configurable thresholds for controlling when a turn is considered complete.
Phone connectivity sits alongside the Voice Agent API rather than being required by it. Deepgram provides reference implementations for inbound and outbound telephony agents and documents integrations with services such as Twilio, where developers bridge the phone audio stream with the Deepgram agent connection.
Why You Might Choose Deepgram
- Combines speech-to-text, LLM integration, and text-to-speech through a realtime Voice Agent API.
- Uses Deepgram speech-to-text models while supporting Deepgram and third-party options for speech generation.
- Supports multiple LLM providers, including OpenAI-compatible endpoints, Anthropic, Amazon Bedrock, Google, Groq, and NVIDIA.
- Provides model-integrated end-of-turn detection when using Deepgram's Flux speech-to-text models.
- Connects agents to external APIs and services through function calling.
- Documents integrations and reference implementations for connecting voice agents to phone networks.
Additional Highlights
Deepgram exposes detailed Voice Agent events over the WebSocket that developers can log for turn-by-turn observability. These include conversation text, function-call requests, errors, turn-boundary events, and latency reports that break down LLM, text-to-speech, and end-to-end response timing.
7. Pipecat
Best for: Developers who want an open-source Python framework for assembling realtime voice and multimodal agents from their choice of AI services, transports, and application logic.
Pipecat is an open-source Python framework for building realtime voice and multimodal conversational agents. Its core abstraction is a pipeline made up of processors that exchange frames representing data such as audio, text, messages, and control events.
Developers can assemble pipelines using services for speech-to-text, text-to-speech, LLMs, and realtime speech models from multiple providers. Transport integrations connect those pipelines to users over technologies and services such as WebRTC, WebSockets, and telephony, allowing the model and communication layers to be configured separately.
Pipecat also provides components for managing the conversation around those services. Its framework supports turn detection, interruption handling, conversation context, and function calling, while developers can add their own processors and application logic directly to a pipeline.
Pipecat applications can run on developer-managed infrastructure or be deployed through Pipecat Cloud, a managed deployment platform for Pipecat agents. This separates the open-source framework used to define an agent from the infrastructure chosen to run it.
Why You Might Choose Pipecat
- Builds realtime voice and multimodal agents with an open-source Python framework.
- Lets developers combine speech, language, and realtime models from multiple providers.
- Uses composable pipelines and processors to control how frames move through an agent.
- Connects agents to WebRTC, WebSocket, and telephony environments through transport integrations.
- Supports turn detection, interruption handling, conversation context, and function calling.
- Lets teams run agents on their own infrastructure or deploy them through Pipecat Cloud.
Additional Highlights
Pipecat Flows provides a separate framework for structuring conversations as a series of states. Each state can define its own instructions and available functions, with transitions moving the conversation between states as the interaction progresses.
8. Cartesia
Best for: Developers who want Cartesia's speech models alongside a Python SDK and managed runtime for building voice agents with custom reasoning logic.
Cartesia develops speech-to-text and text-to-speech models and offers Line, a platform for building voice agents. Line uses Cartesia's Ink models for speech recognition and Sonic models for speech generation, while handling audio orchestration, turn-taking, interruptions, deployment, and observability.
Developers can build agents in Python with the Line SDK or prototype them without code using Agent Builder. The SDK provides a built-in LLM agent that supports more than 100 LLM providers through LiteLLM, while developers can also implement their own agent logic. Tools can connect agents to databases, APIs, external services, and other functions.
For application-based voice experiences, Line's WebSocket API streams audio between an agent and web or mobile applications. Cartesia Telephony provides managed phone numbers and telephony infrastructure for inbound and outbound calls. Developers can also use their own telephony stack by connecting it to Line through the WebSocket API.
Line supports managed deployments on Cartesia's cloud as well as self-hosted agent code. Cartesia's tooling also includes call logs, custom evaluation metrics, deployment versioning, and rollback capabilities for operating agents after deployment.
Why You Might Choose Cartesia
- Uses Cartesia's Ink speech-to-text and Sonic text-to-speech models within the Line voice agent platform.
- Builds custom agent logic in Python with the Line SDK.
- Connects the built-in LLM agent to more than 100 LLM providers through LiteLLM.
- Connects agents to databases, APIs, and external services through tools.
- Supports web and mobile voice experiences through a WebSocket API.
- Provides managed telephony infrastructure for inbound and outbound phone calls.
Additional Highlights
Cartesia's Agent Builder provides a no-code path for prototyping voice agents in its Playground. Developers can configure an agent's system prompt, voice, greeting, and background sound there, then connect the agent to GitHub when they want to continue customizing its behavior in code.
9. Daily
Best for: Developers who want WebRTC infrastructure for realtime voice and multimodal agents, with Pipecat for agent orchestration and Pipecat Cloud for managed deployment.
Daily provides WebRTC infrastructure for realtime audio and video applications. Its voice AI ecosystem also includes Pipecat, an open-source framework for building voice and multimodal agents, and Pipecat Cloud, a managed platform for deploying and scaling Pipecat agents.
Pipecat lets developers assemble agent pipelines from speech-to-text, language, text-to-speech, and realtime model services. Daily can provide the WebRTC transport that connects these agents with users, while Pipecat also supports other transports and telephony providers.
Pipecat Cloud handles infrastructure, scaling, and operations for deployed Pipecat agents. It includes Daily WebRTC integration, automatic scaling, session management, logging and monitoring, secrets management, and multiple deployment regions. For phone applications, Pipecat Cloud supports telephony through provider WebSocket connections or Daily PSTN.
Developers can therefore use Daily as the realtime media layer without giving up control over the models and agent logic built with Pipecat. The same architecture can also extend beyond voice-only interactions when an application needs realtime video.
Why You Might Choose Daily
- Provides WebRTC infrastructure for realtime audio and video applications.
- Integrates directly with Pipecat for building voice and multimodal agents.
- Connects Pipecat agents to users through Daily's WebRTC transport.
- Deploys and scales Pipecat agents through Pipecat Cloud.
- Provides session management, logging, monitoring, and secrets management for cloud deployments.
- Supports phone agents through Daily PSTN and integrations with external telephony providers.
Additional Highlights
Daily's WebRTC transport supports both audio and video, giving Pipecat applications a path to multimodal realtime experiences without introducing a separate media transport for video.
10. Inworld
Best for: Developers who want a realtime voice agent API that combines configurable speech recognition, multi-provider LLM access, and Inworld's text-to-speech models within one session.
Inworld provides a Realtime API for building voice agents. Rather than using a native audio model for the entire interaction, its pipeline combines speech-to-text, text-based LLM reasoning, and text-to-speech within a realtime session. Developers can stream conversations over WebSocket or WebRTC, while the API handles capabilities such as turn detection, interruptions, and function calling.
The reasoning layer connects to Inworld's Realtime Router, which provides access to hundreds of models from providers including OpenAI, Anthropic, Google, Meta, Mistral, xAI, and Groq. Developers can change the LLM used by a session without rebuilding the surrounding speech pipeline.
Inworld's speech components can also be used separately. Realtime STT provides speech recognition, while Realtime TTS provides speech generation. Within the Realtime API, developers can configure the speech recognition independently from the LLM and use Inworld voices for speech output.
This architecture gives developers a middle ground between adopting a single end-to-end audio model and assembling the complete voice pipeline themselves. The Realtime API coordinates the conversation while keeping the reasoning model configurable through the Router.
Why You Might Choose Inworld
- Combines speech-to-text, LLM reasoning, and text-to-speech within a realtime voice agent API.
- Provides access to hundreds of LLMs through Inworld's Realtime Router.
- Lets developers change the reasoning model without replacing the surrounding speech pipeline.
- Handles turn detection, interruptions, and function calling within the Realtime API.
- Supports realtime voice connections over WebSocket and WebRTC.
- Offers speech-to-text and text-to-speech capabilities separately from the complete voice agent API.
Additional Highlights
Inworld's Realtime Router supports routing rules, fallbacks, and A/B testing across models. Developers can use metadata such as language, country, user tier, or intent when defining routing behavior, providing another layer of control over which LLM handles a request.
Which Voice AI Tool Should You Choose?
Choose Vapi if...
You want a voice agent platform that handles realtime orchestration while letting you configure the transcriber, language model, and voice used in the pipeline.
Choose Retell AI if...
You want a platform centered on phone agents with inbound and outbound telephony, structured conversation flows, testing, and call monitoring.
Choose LiveKit if...
You want a code-first Python or Node.js framework for realtime voice agents with support for configurable AI models, realtime media, and telephony.
Choose ElevenLabs if...
You want to build voice agents with ElevenLabs' speech technology alongside configurable LLMs, workflows, tools, and web, mobile, and telephony deployment options.
Choose Bland AI if...
You want a phone-focused platform with APIs, telephony, and graph-based Pathways for defining how conversations progress.
Choose Deepgram if...
You want a Voice Agent API built around Deepgram's speech-to-text with configurable LLM and text-to-speech options, function calling, and integrations for connecting agents to phone networks.
Choose Pipecat if...
You want an open-source Python framework for assembling realtime voice and multimodal agents from your choice of AI services, transports, and application logic.
Choose Cartesia if...
You want to build voice agents around Cartesia's speech models using a Python SDK, configurable agent logic, and managed deployment options.
Choose Daily if...
You want WebRTC infrastructure for realtime voice and multimodal applications, with Pipecat for agent orchestration and Pipecat Cloud as a managed deployment option.
Choose Inworld if...
You want a realtime voice agent API that combines configurable speech recognition and Inworld's speech generation with access to hundreds of LLMs through a separate routing layer.
