LiveKit vs Pipecat: Choosing an Open-Source Voice AI Framework

Two frameworks, two philosophies

Both frameworks solve the same core problem: keeping a real-time conversation flowing between a person and an AI, with streaming speech recognition, a language model that can call tools, streaming speech synthesis, and handling of interruptions and turn-taking. Both let you choose your own providers for each stage, and both support speech-to-speech models as an alternative to the separate stages.

The difference is in what surrounds that core. LiveKit Agents is part of a larger real-time platform: the LiveKit media server, client SDKs for web and mobile, and LiveKit SIP for phone calls. An agent is a worker that joins a room like any other participant. Pipecat is a focused Python framework: you build a pipeline of processors and choose a transport to connect it to users, whether that is WebRTC, a WebSocket from a telephony provider or a bridge from your PBX.

Put simply, LiveKit gives you a full stack with strong opinions about media; Pipecat gives you a flexible pipeline that fits into whatever media stack you already have.

Both communities are active and both frameworks release often, adding new model integrations, better turn detection and new deployment options every few months. That pace is good news for capability but means you should pin versions in production, read release notes before upgrading, and keep a test suite that tells you whether an upgrade changed behaviour.

How each one is built

LiveKit Agents vs Pipecat, side by side

Both projects move quickly, so check current documentation for specifics such as supported providers and features before committing.

Telephony: the deciding factor for many teams

For voice agents that answer phone calls, how each framework reaches the phone network often decides the choice.

Detailed walk-throughs: LiveKit with Asterisk and FreeSWITCH and Pipecat with Asterisk and FreeSWITCH.

What the code looks like

A minimal LiveKit agent: a worker that joins a room, greets the user and runs a speech-to-text, language model and text-to-speech session.

# agent.py — pip install "livekit-agents[openai,deepgram,cartesia,silero]"
from livekit import agents
from livekit.agents import Agent, AgentSession
from livekit.plugins import cartesia, deepgram, openai, silero

class Receptionist(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a friendly receptionist for Acme Dental. Keep replies short.")

async def entrypoint(ctx: agents.JobContext):
    session = AgentSession(
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4o-mini"),
        tts=cartesia.TTS(),
        vad=silero.VAD.load(),
    )
    await session.start(room=ctx.room, agent=Receptionist())
    await session.generate_reply(instructions="Greet the caller and ask how you can help.")

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))

A minimal Pipecat pipeline does the same with an explicit chain of processors. The transport here is a placeholder for whichever one you use: WebRTC, a telephony WebSocket or a PBX bridge.

# bot.py — pip install "pipecat-ai[deepgram,openai,cartesia,silero]"
# Import paths vary between Pipecat releases; check them against your version.
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService

async def run_bot(transport):  # transport created for WebRTC, telephony or a PBX bridge
    stt = DeepgramSTTService(api_key=DEEPGRAM_KEY)
    llm = OpenAILLMService(api_key=OPENAI_KEY, model="gpt-4o-mini")
    tts = CartesiaTTSService(api_key=CARTESIA_KEY, voice_id=VOICE_ID)

    context = OpenAILLMContext([{"role": "system",
        "content": "You are a friendly receptionist for Acme Dental. Keep replies short."}])
    agg = llm.create_context_aggregator(context)

    pipeline = Pipeline([
        transport.input(), stt, agg.user(), llm, tts, transport.output(), agg.assistant(),
    ])
    await PipelineRunner().run(PipelineTask(pipeline))

The shapes reveal the philosophies: LiveKit hides the plumbing behind a session that lives in a room; Pipecat exposes the pipeline so you can insert your own processors anywhere, for example a custom filter between the language model and speech synthesis.

Turn-taking, interruptions and latency

The hardest part of any voice agent is deciding when the caller has finished speaking and handling interruptions gracefully. Both frameworks provide voice activity detection, interruption handling and options for smarter end-of-turn detection, and both stream every stage to keep latency down.

• Latency is dominated by model choice, streaming settings and hosting location, not by the framework.
• Tune end-of-turn detection for your callers: too eager and the agent interrupts, too patient and it feels slow.
• Decide how short acknowledgements like “yeah” or “okay” are treated, so they don't stop the agent mid-sentence.
• Measure turn latency on real phone calls at the 90th percentile, with your chosen providers, before choosing.

Our voice agent testing and monitoring guide covers how to measure this properly.

Speech-to-speech models, where one model listens and speaks directly, are available in both frameworks and can reduce latency further. They trade some control, for example over the exact voice, retrieval and tool behaviour, for speed and natural-sounding conversation, so test them on your own use case rather than assuming they are always better.

Hosting, scaling and operations

For teams that already operate WebRTC or SIP infrastructure, self-hosting either is routine. For teams without that experience, a managed option for the media layer, or a partner who runs it, avoids a steep learning curve. See our self-hosted LiveKit deployment guide.

Whichever you choose, plan for regional deployment early if you serve callers in several countries. Placing media servers or pipeline processes close to your carriers and callers reduces latency, and some data rules require audio to stay in a particular region.

Which one should you choose?

Many teams could succeed with either. The better question is often which one your team can operate confidently in production, and which fits the telephony and client apps you already have.

Keeping your options open

Whichever you choose, a few design habits make it far easier to switch frameworks, or move from a managed platform, later:

• Keep prompts, tool definitions and business logic in your own modules, not spread through framework callbacks.
• Put your systems behind your own API layer so tools don't depend on the framework.
• Store transcripts, outcomes and metrics in your own database in a framework-neutral format.
• Keep a test suite of simulated conversations you can run against any implementation.

With those in place, the framework becomes a replaceable component rather than a long-term lock-in, which is one of the main reasons to choose open source in the first place.

It is also worth revisiting the choice as your product grows. A team that starts with Pipecat behind a telephony provider may later want multi-party rooms and mobile apps, which favour LiveKit; a team on LiveKit may want a lighter footprint for one simple phone line. Neither choice is permanent if the business logic stays your own.

Common production requirements, and how each handles them

Most voice agent projects end up needing the same handful of capabilities beyond the core conversation. Both frameworks can meet them; the effort and the approach differ.

If several of these matter, especially supervisor listen-in, transfers and outbound calling across phone and web, LiveKit's room model tends to make them simpler. If your provider or PBX already handles them, Pipecat lets you keep that and focus on the conversation itself.

What each approach costs

Neither framework charges for the software itself. The cost differences come from what you run around it and how much engineering it takes.

For a full breakdown of build and per-minute costs, see our AI voice agent cost guide. In most projects we see, the choice between LiveKit and Pipecat changes total cost far less than choices about models, voice tiers, call length and hosting.

Frequently asked questions