Voice Agent Orchestrators Compared: Vapi vs Pipecat vs LiveKit with AssemblyAI

Building a voice agent requires more than picking an LLM—you need an orchestration layer that connects speech recognition, language understanding, and speech synthesis in real time. But which orchestration layer? Vapi, Pipecat, and LiveKit are three of the most widely used platforms for this, and they take fundamentally different architectural approaches to the problem.

This article breaks down how each platform works, where they differ on transport, pipeline control, and speech recognition integration, and which use cases each one fits best. We'll also cover a fourth option that's gaining traction: skipping the orchestration layer entirely with a single API that handles the full Voice AI pipeline. Whether you're evaluating options for a new build or reconsidering your current stack, understanding these architectural trade-offs will help you make a more informed decision before you write a line of code.

What are Vapi, Pipecat, and LiveKit?

Vapi, Pipecat, and LiveKit are voice agent orchestration platforms—tools that help developers build software capable of speaking and listening in real time. All three connect the same core components: a speech-to-text model that hears the user, a large language model (LLM) that figures out what to say, and a text-to-speech model that speaks the response. But the way each platform connects those components is completely different, and that difference determines how much control you have over the conversation.

Think of it this way: Vapi hands you the finished product, Pipecat hands you the parts, and LiveKit hands you a communication room and lets your agent sit inside it.

It's worth noting upfront that AssemblyAI ships official plugins for both Pipecat and LiveKit, and integrates with Vapi as a supported STT provider.

Here's a quick orientation before going deeper:

How Vapi, Pipecat, and LiveKit differ architecturally

The architectural differences between these three platforms come down to three questions: Who controls the pipeline? How does audio travel? And how deeply can you configure speech recognition?

Orchestration model—managed service, explicit pipeline, or room-based events

The orchestration model is the logic layer that decides when to listen, when to call the LLM, and when to speak. Each platform handles this differently.

Vapi manages the pipeline for you. You write a system prompt, select a voice, and configure your tools through the API. Vapi's infrastructure executes the STT→LLM→TTS loop automatically. You see what the user said and what the agent replied—nothing in between. This is fast to set up but limits what you can modify. And because Vapi is orchestration middleware connecting third-party providers, your agent's accuracy, latency, and cost are only as good as the weakest link in that chain.

Pipecat makes the pipeline explicit. Every step—voice activity detection, streaming transcription, LLM call, speech synthesis—is code you write and control. You can insert logic between any two steps, run processes in parallel, or fork the conversation based on an intermediate result. Nothing is hidden from you.

LiveKit uses an event-driven model. Your agent joins a WebRTC "room" as a participant, subscribes to audio tracks, and responds to events like "new transcription received." The flow isn't a linear pipeline—it's reactions to what happens in the room. This model fits naturally when there are multiple speakers or when audio and video are happening at the same time.

The practical implication: Pipecat is the only one of the three where you can inspect and modify every step. Vapi is the only one where you don't need to.

Transport layer—WebSockets, WebRTC SFU, and transport-agnostic pipelines

The transport layer is how audio physically travels between the user's device and your agent. It affects latency, reliability, and how the platform scales.

Vapi uses WebSockets for bi-directional audio. Telephony connections (Twilio, SIP) are fully abstracted. You never configure transport directly—it's handled by the platform.

Pipecat is transport-agnostic. You choose the transport: Daily's WebRTC, Twilio Media Streams, a raw WebSocket server, or local audio capture. The pipeline logic stays the same regardless of how audio arrives. If you already have telephony infrastructure, this is a genuine advantage—you don't need to rebuild around a new transport layer.

LiveKit runs on WebRTC with a Selective Forwarding Unit (SFU) architecture. An SFU routes audio between participants without requiring direct peer-to-peer connections, which solves problems like NAT traversal and variable network conditions automatically. LiveKit Agents are tightly coupled to this infrastructure. Running them outside of it is technically possible but not the intended use case.

So the decision here isn't about which transport is "best"—it's about which constraint matters most to you:

• Need transport flexibility? Pipecat.
• Need reliable multi-party audio routing? LiveKit.
• Want to skip transport configuration entirely? Vapi.

STT, LLM, and TTS integration—pluggable components vs. configured providers vs. managed pipeline

This is the dimension that most directly affects conversation quality. A misheard word produces a wrong LLM response and a wrong spoken output—errors compound fast.

Vapi handles STT providers including AssemblyAI, Deepgram, and Whisper through API parameters. You pick a provider, and Vapi routes audio to it. What you can't do is intercept or modify the transcription before it reaches the LLM. The integration is fast but shallow—and because Vapi sits between you and the STT provider, you don't have direct access to configure advanced features like keyterms prompting or custom endpointing that can significantly improve accuracy for specialized vocabulary.

Pipecat treats STT as an explicit processor in your pipeline. You connect directly to a streaming transcription model—like AssemblyAI's Universal-3 Pro Streaming—handle partial results yourself, configure endpointing sensitivity, and insert custom logic between transcription and the LLM step. If you're building a medical agent that needs to handle drug names accurately, or a technical support agent with proprietary product codes, this level of control matters.

LiveKit integrates STT through a plugin system. It's more structured than raw API calls but less granular than Pipecat's explicit pipeline. AssemblyAI connects through this plugin interface, giving access to streaming features like speaker diarization and confidence scores without requiring low-level streaming code.

One thing worth calling out: both LiveKit and Pipecat handle interruption logic and back-channel suppression at the orchestrator level—that's not the ASR model's job. This means the quality of your turn detection and barge-in behavior depends as much on how well your orchestrator handles those events as it does on the underlying speech-to-text model. Teams running LiveKit, for example, benefit from built-in noise canceling at the transport layer, which can materially improve STT accuracy before the audio even reaches the model.

When to choose Vapi, Pipecat, or LiveKit

The right platform isn't the one with the most features—it's the one that matches how much ownership your team wants over the conversation pipeline. There's a real spectrum here, from full abstraction to full control, and the three platforms land at very different points on it.

Choose Vapi for speed and managed infrastructure

Vapi is the right choice when time-to-production matters more than architectural flexibility. If your use case fits a standard pattern—inbound customer support, outbound appointment reminders, lead qualification calls—you can have a working agent in days, not weeks.

Teams without real-time audio engineering experience benefit most from Vapi. You're not thinking about WebSocket stability, audio buffering, or pipeline orchestration. Vapi handles all of it.

Vapi charges a platform fee per minute on top of your AI provider costs. At early or moderate call volumes, this is often cheaper than the engineer-hours required to build and maintain a custom stack. The cost trade-off flips as volume scales.

The ceiling is real, though. When you need to insert custom logic mid-conversation, integrate with a CRM in a non-standard way, or modify how transcription results reach the LLM, Vapi's managed model becomes the constraint. As one developer put it, it's like trying to find the best pair of trousers that already fit instead of tailoring your own.

Choose Pipecat for full pipeline control

Pipecat is the right choice when you need to own the conversation logic—not just the inputs and outputs, but every step in between.

Good fits for Pipecat include:

• Domain-specific agents where speech accuracy for specialized vocabulary (medical terms, product names, technical codes) is critical and requires direct STT configuration
• Complex workflows where sentiment analysis, entity detection, or secondary LLM calls need to run in parallel with transcription
• Teams with existing transport infrastructure who want the orchestration layer only, without rebuilding around a new platform's transport
• Long-term cost optimization at scale, where eliminating a platform fee per minute adds up significantly

The trade-off is operational. Pipecat is open source and free to use, but you run the servers. Monitoring, reliability, and infrastructure scaling are your responsibility. That's a reasonable trade for teams with engineering capacity; it's a significant burden for teams without it.

Choose LiveKit for multi-participant real-time sessions

LiveKit is purpose-built for scenarios where multiple people are in the same session—and your agent needs to participate alongside them.

This makes it the natural fit for:

• AI meeting assistants that join video calls as a participant and respond to specific questions
• Group voice experiences like live Q&A, classrooms, or coaching sessions
• Any product roadmap that includes video alongside voice

The SFU architecture handles the hard parts of multi-party WebRTC automatically—NAT traversal, adaptive bitrate, packet loss recovery. Your agent gets reliable audio without building that infrastructure yourself.

But here's the flip side: you're coupled to LiveKit's WebRTC infrastructure. Point-to-point calling use cases or deployments with custom transport requirements don't benefit from the SFU model the same way. If you're building a standard two-party voice agent with no video and no multi-participant requirements, LiveKit's architecture is more than you need.

Skip orchestration entirely: AssemblyAI's Voice Agent API

There's a fourth option that sidesteps the orchestration question altogether. AssemblyAI's Voice Agent API is a single WebSocket connection that handles the full voice agent pipeline—speech understanding, LLM reasoning, voice generation, turn detection, and interruption handling—as invisible infrastructure. You connect to the WebSocket, stream audio in, and get audio back. No separate STT, LLM, and TTS providers to stitch together. No orchestration framework to learn.

The API is built on Universal-3 Pro Streaming, the #1 model on the Hugging Face Open ASR Leaderboard, with 92.7% mixed-entity accuracy on the things that matter most in voice agent conversations—emails, phone numbers, order IDs, and names. When the speech-to-text layer gets these right, the LLM responds to what was actually said, and the entire conversation quality improves downstream.

What makes it different from the orchestration approach:

• One API, one bill. $4.50/hr flat rate covers STT, LLM reasoning, and voice generation. No token math across three separate invoices, no per-minute platform fees on top of provider costs.
• No SDK required. Standard JSON messages over a single WebSocket. You can read the full API reference in 10 minutes. Most developers get a working agent running in an afternoon.
• Live configuration. Update system prompt, voice, tools, and VAD settings mid-conversation with a JSON message. No reconnection, no redeployment.
• Purpose-built turn detection. Speech-aware voice activity detection that distinguishes thoughtful pauses from conversation endings, with intelligent interruption handling and natural barge-in.
• Tool calling. Register custom functions with JSON Schema—look up an account, check the weather, book an appointment—and the agent calls them when appropriate.

The Voice Agent API supports six languages (English, Spanish, French, German, Italian, and Portuguese) and works natively with Twilio SIP for phone-based deployments.

So when does this make more sense than an orchestrator? When your goal is to build a voice-enabled product and you'd rather spend your engineering time on product logic than on pipeline plumbing. The orchestrators covered above give you more control over each individual component—but that control comes with the operational cost of managing multiple providers, debugging across service boundaries, and building your own turn detection and interruption handling. The Voice Agent API trades that granularity for a dramatically simpler integration surface where most of the hard problems are already solved.

Final words

The voice agent infrastructure landscape is splitting into two clear paths. On one side, orchestration frameworks like Pipecat and LiveKit give you full control over every component in the pipeline—ideal when you need to customize deeply or when your architecture demands it. On the other, managed approaches like Vapi and AssemblyAI's Voice Agent API abstract the complexity so you can focus on what your agent actually does rather than how the audio flows.

The interesting trend is that complexity is moving downward. A year ago, building any voice agent meant stitching together three or more providers. Today, teams can choose exactly how much of that plumbing they want to own—from all of it (Pipecat) to none of it (Voice Agent API). That's a meaningful shift for developers who'd rather ship a great product than become experts in WebSocket audio buffering.

Whichever path you choose, the speech recognition model underneath matters more than most teams realize at the start. A transcription error compounds through the LLM and TTS layers—get the input wrong and everything downstream degrades. AssemblyAI's Universal-3 Pro Streaming model integrates with all three orchestrators and powers the Voice Agent API, and supports features like keyterms prompting and Medical Mode for specialized vocabulary. The listening layer is the foundation. Everything else is built on top of it.

Frequently asked questions

What is the main difference between Vapi and Pipecat?

Vapi is a managed platform that runs the voice pipeline for you—you configure it, Vapi executes it. Pipecat is an open-source Python framework where you write the pipeline yourself, step by step. The choice comes down to how much control over conversation logic your build requires.

Can Pipecat and LiveKit be used together?

Yes. Pipecat is transport-agnostic, so it can run over LiveKit's WebRTC infrastructure using LiveKit's transport adapter. Some teams combine them to get Pipecat's pipeline control with LiveKit's media routing reliability.

Is LiveKit open source?

Yes—both LiveKit's media server and LiveKit Agents are open source under the Apache 2.0 license. You can self-host the full stack, or use LiveKit Cloud for a managed option with usage-based pricing.

Is Pipecat open source?

Yes. Pipecat is open source under the BSD 2-Clause License and free to use. Your costs come from infrastructure, transport, and the AI providers you connect to—not the framework itself.

What is the cheapest alternative to Vapi for voice agents?

Pipecat eliminates the platform fee per minute that Vapi charges, making it typically lower cost at scale—but it requires engineering time to build and operate. AssemblyAI's Voice Agent API at $4.50/hr is a managed alternative where one bill measured in hours replaces token math across three separate invoices—STT, LLM, and TTS all included in a single flat rate.

Which voice agent framework is easiest to get started with?

Vapi has the lowest barrier to entry for orchestration—you can have a working agent running through the API without writing pipeline code. But if you want the simplest path to a working voice agent overall, AssemblyAI's Voice Agent API requires no SDK, no framework, and no orchestration layer at all—just a WebSocket connection and JSON messages. Most developers get a working agent running the same afternoon.