8 best orchestration tools to build AI voice agents in 2026

Every AI voice agent is three models wearing a trench coat.

Speech-to-text hears the caller. An LLM figures out what they want. Text-to-speech says something back. That's the whole architecture, and on a whiteboard it takes about nine seconds to draw.

Then you try to ship it, and you discover the drawing left out everything hard. Who decides the caller is finished talking? What happens when they interrupt the agent mid-sentence? Where does the conversation history live? What calls your CRM, and what happens when the CRM takes four seconds to answer? What plays while everyone waits?

That layer—the timing, the turn-taking, the interruptions, the tool calls, the state—is orchestration. It's the part of a voice agent nobody demos and everybody rebuilds, and it's why a category of tools now exists to hand it to you.

The eight orchestration tools delivering results in production today are LiveKit, Pipecat, Telnyx, Vision Agents, Vapi, Retell, Synthflow, and Bland. They range from open-source frameworks you run yourself to no-code builders your ops team can drive, and they make genuinely different bets. Here's how to tell which bet matches your team.

What are AI voice agents?

The difference between a voice agent and an IVR tree is the difference between a conversation and a flowchart.

An IVR gives you four options and waits. Press 1 for sales. Press 2 for billing. Say your account number now. Miss the window and it starts over.

A voice agent takes "I need to check my recent order and also ask about your return policy" and handles both, in the order the caller raised them, without a menu anywhere. It holds context across turns, so "and how long does that take?" three sentences later still refers to the return. It handles being interrupted. It calls your order system mid-conversation and reads back a real tracking number.

That's what changed. Not the voice quality—the fact that the caller no longer has to translate what they want into the system's vocabulary. We've written a fuller primer on what AI voice agents are and how they work if you want the ground floor.

How do AI voice agents work?

Three models in a loop, plus the machinery that keeps the loop honest.

Around that loop, a production system also needs turn detection that knows the difference between a pause and a finished thought, interruption handling that stops the agent mid-word when the caller starts talking, context management across turns, tool calling into your own APIs, and error recovery for when any of the above misfires.

The three models are commodities you can swap. The machinery around them is where orchestration tools earn their keep—and where the differences between them actually show up.

One asymmetry is worth naming before you compare anything: errors at the STT layer are the only ones that can't be recovered downstream. A mediocre LLM response can be fixed with a better prompt. A flat TTS voice can be swapped in an afternoon. A misheard account number is simply gone, and every layer after it reasons confidently from the wrong input. That's why entity accuracy matters more than pooled word error rate when you're evaluating a transcriber for agents.

What to consider when choosing an orchestration tool

Technical fit. Do you want API and code-level control, or a no-code builder? How much of the conversation design do you need to own? Cloud, or self-hosted behind your own firewall? These aren't preferences, they're constraints—a regulated industry with a data residency requirement has already eliminated half this list.

Performance. Real-time latency low enough that the conversation doesn't feel laggy. Integration with the CRM and telephony you already run. Headroom to absorb your peak hour without quality falling apart.

Implementation capacity. This is the one teams get wrong. More customizable platforms give you more flexibility and demand more engineering to run—and teams almost always underestimate the technical debt of choosing a platform that exceeds their maintenance capacity. An open-source framework you can't staff is worse than a managed platform you find slightly constraining.

Pricing shape. Most tools charge on some mix of conversation minutes, API calls, and feature tiers. Usage-based plans scale with you, which is great in a pilot and unpredictable in production. Model the bill at 10x your pilot volume before you commit, and remember you'll be paying the orchestrator plus your STT, LLM, and TTS providers separately.

That last point is the whole argument for the other approach: skipping orchestration. Our Voice Agent API is a managed pipeline that replaces separate STT, LLM, and TTS providers with a single WebSocket connection at a flat $4.50/hr. The orchestration still happens—you just don't configure it. Whether that's a relief or a cage depends entirely on how much of the conversation design you need to own, which is the same question the four criteria above keep circling.

Top 8 orchestration tools for building AI voice agents

1. LiveKit: open-source with maximum control

LiveKit is fully open-source, which means no third-party hosting constraints and no ceiling on what you can change. Teams modify the codebase directly and tailor agents to use cases a hosted platform would never prioritize.

It handles voice, video, and text in one framework, with function calling, natural turn detection, and native telephony support. The plugin ecosystem has grown quickly, and AssemblyAI ships a drop-in plugin—adding Universal-3.5 Pro Realtime for real-time transcription is a one-line addition to your agent definition.

Best for: teams with real engineering capacity who need control over every layer and expect to keep customizing after launch. LiveKit was also one of the early adopters of Universal-3.5 Pro Realtime, which tells you something about how quickly the framework absorbs new models.

2. Pipecat: flexible open-source orchestration

Pipecat, originally from Daily, is a vendor-neutral Python framework built for mixing and matching. Every component in the pipeline is a swap, so you can choose each model on performance, cost, and fit rather than taking whatever a platform bundles.

It manages multi-turn context and real-time media transport natively, and handles phrase endpointing and multimodal input. AssemblyAI integrates cleanly with a dedicated plugin, and Pipecat's open STT benchmark is the one we publish Universal-3.5 Pro Realtime's numbers against—a useful property when you're comparing transcribers inside the framework you're going to run them in.

Best for: Python teams who want full pipeline customization without vendor lock-in, and who'd rather assemble than accept defaults.

3. Telnyx: full-stack orchestration with telephony built in

Telnyx runs voice agent orchestration on the same private carrier network that carries the calls. That removes the third-party audio hops between telephony and inference, which is a real latency advantage if your agent lives on a phone line rather than in a browser.

Phone numbers, SIP trunking, and programmable call control sit in one platform alongside turn detection and tool calling. You choose your STT engine per call, and AssemblyAI is among the supported providers.

Best for: receptionists, appointment setting, and support lines—anything where the phone network is the product surface and you'd rather not stitch a telephony vendor to an orchestration vendor.

4. Vision Agents: video-first with native voice support

Vision Agents, from Stream, starts from video rather than retrofitting it onto a voice system. That inversion matters more than it sounds: agents that need to see—a coaching app watching form, an accessibility tool describing a room, a support agent looking at what the customer is looking at—are badly served by frameworks where video is an afterthought.

It runs on Stream's low-latency WebRTC edge network with direct connections to LLM and vision-language model providers, and supports pluggable computer-vision processors including YOLO, Roboflow, and custom PyTorch or ONNX models. Turn-taking and speaker diarization are built in, and it integrates natively with AssemblyAI for the speech layer.

Best for: multimodal agents where vision is a first-class input, not a bolt-on.

5. Vapi: developer-friendly with visual design options

Vapi is the one that refuses to pick a side between no-code and code, and that turns out to be its strongest feature. Flow Studio lets a product or ops person map a conversation visually; the API lets an engineer define the same assistant in JSON and check it into source control. Both edit the same agent.

Cross-channel consistency—web, phone, in-app—makes it a natural fit for customer service, and the integration catalog is large enough that most CRMs and helpdesks are already wired. Tool calling, multi-language support, and A/B testing are built in.

The AssemblyAI integration is native. Add your key under Settings → Transcriber Providers, then set the transcriber block:

{
  "transcriber": {
    "provider": "assembly-ai",
    "speechModel": "universal-3-5-pro",
    "language": "en",
    "keytermsPrompt": ["YourBrand", "SpecialTerm"]
  }
}

That's Universal-3.5 Pro Realtime, with keyterm prompting included at the base rate. There's a full walkthrough in our Vapi voice agent tutorial, and a head-to-head if you're still deciding between Vapi, Pipecat, and LiveKit.

Best for: teams that need to move fast without giving up the ability to drop into code when the visual builder runs out of road.

6. Retell: best for natural conversation

Retell has aimed most of its engineering at one problem: making the conversation not feel like one. Proprietary turn-taking models target the awkward pause—the half-second of dead air after a caller stops talking that instantly tells them they're speaking to software.

Retell reports responses typically under 500ms, and the platform emphasizes conversational flow across every component rather than optimizing each in isolation. Interruptibility, adaptive error recovery, multi-language support, and web, mobile, and telephony deployment are all in the box. Retell was also an early adopter of Universal-3.5 Pro Realtime.

Best for: consumer-facing agents where the caller's first impression decides whether they stay on the line.

7. Synthflow: no-code for faster deployment

Synthflow's bet is that most voice agents are variations on a small number of patterns, and that starting from a template beats starting from a blank canvas. The drag-and-drop builder and template library let a business team ship a working agent without an engineer in the loop.

Around 200 pre-built integrations, multi-language support, enterprise security features, and usage-based pricing round it out.

Best for: SMBs and individual departments who need something live this quarter and don't have engineering capacity to allocate. The tradeoff is real—you're accepting the platform's conversation model—but "shipped and imperfect" beats "perfect and unstaffed."

8. Bland: self-hosted security for enterprise

Bland's differentiator is architectural: the whole stack runs on infrastructure you control. Transcription, processing, and response generation all execute behind your firewall, which changes the compliance conversation from a vendor questionnaire into an infrastructure decision.

You still get human-like voice output, custom prompts and guardrails, 24/7 availability with redundancy, an analytics dashboard, and warm transfer to a human.

Best for: financial services, healthcare, and government—anywhere the data governance requirement rules out sending audio to a third party. For healthcare specifically, note that whichever platform you choose, the model provider handling PHI needs a signed Business Associate Addendum in place; AssemblyAI offers a BAA that can be signed without a sales call.

How AssemblyAI fits in the voice AI ecosystem

We don't make an orchestration tool. We make the layer underneath all eight of them.

A voice agent is only as good as its ability to understand what people say, and that's where the speech model does its work. Universal-3.5 Pro Realtime (universal-3-5-pro) is our flagship streaming model, released June 23, 2026, and it was built specifically for agent conversations rather than adapted from a dictation model.

The accuracy. 6.99% pooled word error rate and a 15.31% entity error rate on Pipecat's open STT benchmark, built from real agent conversations. Deepgram Flux posts 15.58% and 50.50% on the same set. Phone numbers come back at 3.55% versus Flux's 10.41%. For an agent that reads a callback number back to a caller, that second column is the whole ballgame.

The turn-taking. Partial and final transcripts stream in a few hundred milliseconds, and end-of-turn detection reads the punctuation it predicts—not just silence duration—at roughly 300ms. That's what keeps an agent from interrupting a caller who paused to look at their bill. Three modes let you tune it: min_latency, balanced (the default), and max_accuracy for noisy or far-field audio.

The context. Your agent already knows what it just asked. Pass that question in through agent_context and the model resolves short replies—spelled-out emails, account IDs, one-word confirmations—against it. Across 20,000 voice agent audio files, that cut WER by 10.2%, with fabrications down 18.3% and short-utterance errors down 13.7%. Context Carryover keeps a rolling conversation memory on top of it, on by default.

The rest. Keyterm prompting for domain vocabulary, included at the base rate. voice_focus with near-field and far-field speaker isolation. Live speaker diarization with a single re-clustering correction after the stream ends. 18 languages with native mid-sentence code-switching, Hinglish included.

It's $0.45/hr base, billed per second, with no concurrency limits.

And if you'd rather not run an orchestrator at all, our Voice Agent API bundles STT, LLM, and TTS into a single WebSocket at a flat $4.50/hr with roughly 1s end-to-end latency. It runs on Universal-3.5 Pro Realtime—the same speech model you'd get plugging us into any tool on this list. That's deliberate. The accuracy shouldn't depend on which architecture you picked.

Find the right tool for your voice strategy

Quick version, if you skipped down:

• Telnyx for agents that live on real phone infrastructure.
• LiveKit and Pipecat when maximum customization matters and you have the engineers to use it.
• Vision Agents when the agent needs to see, not just hear.
• Vapi for teams balancing speed against flexibility.
• Retell if conversation naturalness is the thing you're being judged on.
• Synthflow for no-code speed.
• Bland when the data can't leave your infrastructure.
• Voice Agent API if you'd rather not maintain an orchestrator at all.

Now the part these lists usually skip.

The orchestrator is the layer you'll change. Teams migrate off no-code builders when they outgrow them, off frameworks when they can't staff them, and between platforms when pricing shifts. That's normal, and every tool on this list is designed to be replaceable—which is exactly why picking one is a lower-stakes decision than it feels like in week one.

What you don't change casually is the speech layer, because everything downstream is calibrated to it. Your prompts are tuned around how the transcripts actually read. Your keyterm lists, your confidence thresholds, your escalation rules, the specific failure modes your support team has learned to spot—all of it is fitted to one model's behavior. Swap the transcriber and you're re-tuning the entire agent, not swapping a component.

So spend your evaluation time asymmetrically. Pick the orchestrator that matches your team today, and accept that you'll probably move. Pick the speech model like you're going to live with it, because you are. Our own take on where voice agent stacks start showing their limits goes deeper on which seams give first—and it's rarely the one teams brace for.

Frequently asked questions

What is an orchestration tool for AI voice agents?

An orchestration tool connects the AI models in a voice agent—speech-to-text, an LLM, and text-to-speech—and manages the real-time flow of data between them. It handles the timing, turn-taking, interruptions, and tool calls that make a conversation feel natural rather than transactional. Without one, you're writing that coordination layer yourself, and it's the hardest part of the build to get right.

What is the best orchestration tool for building a voice agent?

It depends on what your team can maintain. Vapi balances a visual builder with full API access, LiveKit and Pipecat give open-source teams maximum customization, Retell optimizes for conversation naturalness, Synthflow gets non-technical teams live fastest, Telnyx builds on its own carrier network, and Bland runs entirely self-hosted for regulated industries. If you'd rather not maintain an orchestrator at all, AssemblyAI's Voice Agent API bundles STT, LLM, and TTS into one WebSocket connection at a flat $4.50/hr.

How are AI voice agents different from traditional IVR?

Traditional IVR follows rigid, menu-based decision trees—press 1 for sales, press 2 for billing—and fails the moment a caller's need doesn't match a preset branch. AI voice agents understand natural language, so a caller can describe what they want in their own words and get handled without navigating a menu. They also hold context across a conversation, handle interruptions, and call external systems mid-call to retrieve real data.

What is the most important component of an AI voice agent?

Speech-to-text, because it's the only component whose errors can't be recovered downstream. If the transcript says the wrong account number, the LLM reasons correctly about the wrong input and the TTS says the wrong thing in a very pleasant voice. On Pipecat's open STT benchmark of real agent conversations, Universal-3.5 Pro Realtime posts a 6.99% word error rate and a 15.31% entity error rate against Deepgram Flux's 15.58% and 50.50%—and it's that entity column that shows up in your support tickets.

Do orchestration tools work with any speech-to-text provider?

Most orchestration tools are vendor-neutral and let you choose your STT, LLM, and TTS providers independently. Vapi, LiveKit, and Pipecat all offer native AssemblyAI integrations running Universal-3.5 Pro Realtime, so you can plug in low-latency transcription with minimal code regardless of framework. In Vapi that's a transcriber block with "provider": "assembly-ai" and "speechModel": "universal-3-5-pro"; in LiveKit and Pipecat it's a drop-in plugin.

How long does it take to build and deploy a voice agent?

Simple agents can go live in days using a no-code tool like Synthflow, while heavily customized systems built on LiveKit or Pipecat take weeks to months once you include testing and tuning. The variable that dominates the timeline isn't the build—it's integration with your existing telephony and CRM, and the iteration cycle on conversation design. Using a bundled API like AssemblyAI's Voice Agent API shortens it further by removing three separate model integrations from the critical path.