How does LiveKit Agents compare to Pipecat?

If you're building voice AI, you've probably come across both LiveKit Agents and Pipecat. They solve similar problems but take different approaches. Understanding those differences helps you pick the right tool for your project.

The core difference

Pipecat gives you building blocks. It's lower-level, feels like "just Python code," and lets you wire together different vendors however you want. This flexibility is great for prototyping or when you need fine-grained control over every piece of the pipeline.

The tradeoff? Pipecat's low-level frame-oriented design means more manual work. You're responsible for state management, orchestrating components, and handling edge cases. Depending on your use case, this can lead to more brittle code that requires ongoing maintenance.

LiveKit takes a different approach. The framework handles the undifferentiated heavy lifting (session orchestration, state management, interruption handling) so you can focus on your agent's behavior. It's focused on behaviors and tools rather than frame generation, which tends to produce more resilient code as requirements evolve.

The code tells the story

Here's a minimal voice agent in both frameworks. The difference in architecture becomes clear immediately.

LiveKit Agents: event-driven, declare what you want:

1
from livekit import agents
2
from livekit.agents import AgentServer, AgentSession, Agent
3

4
class Assistant(Agent):
5
    def __init__(self):
6
        super().__init__(instructions="You are a helpful assistant.")
7

8
server = AgentServer()
9

10
@server.rtc_session(agent_name="my-agent")
11
async def entrypoint(ctx: agents.JobContext):
12
    session = AgentSession(
13
        stt="deepgram/nova-3",
14
        llm="openai/gpt-4o-mini",
15
        tts="cartesia/sonic",
16
    )
17
    await session.start(agent=Assistant(), room=ctx.room)
18
    await session.generate_reply(instructions="Greet the user.")
19

20
if __name__ == "__main__":
21
    agents.cli.run_app(server)

Pipecat: pipeline-based, wire it yourself:

1
from pipecat.pipeline.pipeline import Pipeline
2
from pipecat.pipeline.runner import PipelineRunner
3
from pipecat.pipeline.task import PipelineTask
4
from pipecat.services.deepgram import DeepgramSTTService
5
from pipecat.services.openai import OpenAILLMService
6
from pipecat.services.cartesia import CartesiaTTSService
7
from pipecat.transports.services.daily import DailyTransport
8
from pipecat.frames.frames import LLMMessagesFrame
9

10
async def main():
11
    transport = DailyTransport(room_url, token, "Bot", DailyParams(...))
12
    stt = DeepgramSTTService(api_key=DEEPGRAM_KEY)
13
    llm = OpenAILLMService(api_key=OPENAI_KEY, model="gpt-4o-mini")
14
    tts = CartesiaTTSService(api_key=CARTESIA_KEY, voice_id="...")
15

16
    context = OpenAILLMContext([{"role": "system", "content": "You are a helpful assistant."}])
17
    context_aggregator = llm.create_context_aggregator(context)
18

19
    pipeline = Pipeline([
20
        transport.input(),
21
        stt,
22
        context_aggregator.user(),
23
        llm,
24
        tts,
25
        transport.output(),
26
        context_aggregator.assistant(),
27
    ])
28

29
    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
30

31
    # Initial greeting requires manually pushing a frame
32
    @transport.event_handler("on_first_participant_joined")
33
    async def on_first_participant_joined(transport, participant):
34
        await task.queue_frames([LLMMessagesFrame([
35
            {"role": "system", "content": "Greet the user."}
36
        ])])
37

38
    runner = PipelineRunner()
39
    await runner.run(task)

With LiveKit, you declare components and the framework orchestrates them. With Pipecat, you explicitly construct the pipeline and manage the data flow yourself, including triggering the initial greeting. Neither is wrong. They're optimized for different priorities.

Who's using what

PyPI downloads tell part of the story: LiveKit Agents sees roughly 4× the weekly downloads (around 400,000 compared to Pipecat's 90,000) as of Jan 2026.

The production story matters too. LiveKit's infrastructure runs voice AI for ChatGPT, Grok, Tesla, and Agentforce. That scale has shaped the framework's design choices around reliability and performance.

What this means in practice

Here's where the two frameworks diverge:

LiveKit has deeper tooling across the development lifecycle: testing, observability, and deployment, plus infrastructure battle-tested at scale.

The "vendor neutral" tradeoff

Pipecat markets itself as vendor neutral, supporting multiple transport providers. In practice, this claim deserves scrutiny.

The core Pipecat contributors work at Daily, which means the Daily transport gets first-class support while others may lag behind. When you choose a "vendor neutral" framework, you're often choosing one vendor's implementation that happens to have adapters for others. You'll likely find that one transport works significantly better than the rest.

LiveKit takes the opposite approach: it tightly integrates with a single transport layer, optimized end-to-end and fully open source. While this removes the flexibility to swap transports, it ensures consistency and reliability across the entire stack.

Client ecosystem

One of the less obvious differences is how agents interact with frontend applications.

LiveKit’s agents and client SDKs are designed to work together. Agent state is automatically synced to connected clients, so the frontend knows when the agent is thinking, speaking, or listening. To help visualize this, LiveKit provides Agents UI , a component library built with shadcn that offers high-quality, ready-to-use UI elements for agent interaction.

1
import { useSession, useVoiceAssistant } from '@livekit/components-react';
2
import { AgentSessionProvider } from '@/components/agents-ui/agent-session-provider';
3
import { AgentAudioVisualizerAura } from '@/components/agents-ui/agent-audio-visualizer-aura';
4

5
const TOKEN_SOURCE = TokenSource.sandboxTokenServer(
6
  process.env.MY_LK_SANDBOX_TOKEN_SERVER_ID
7
);
8

9
export function Demo() {
10
  const { audioTrack, state } = useVoiceAssistant();
11

12
  return (
13
    <AgentAudioVisualizerAura
14
      size="xl"
15
      state={state}
16
      color="#1FD5F9"
17
      colorShift={0.1}
18
      themeMode={themeMode}
19
      audioTrack={audioTrack}
20
    />
21
  );
22
}
23

24
export default function MyPage({ session }) {
25
  const session = useSession(TOKEN_SOURCE);
26

27
  return (
28
    <AgentSessionProvider session={session}>
29
      <Demo />
30
    </AgentSessionProvider>
31
  );
32
}

With Pipecat, you build this coordination yourself. The framework focuses on the agent pipeline; how you communicate state to clients is up to you.

If you're building a polished user experience with visual feedback, the integrated client ecosystem saves significant development time.

Avatar integrations

For applications that need visual AI personas, the frameworks differ substantially.

LiveKit has built-in support for avatar providers like Heygen or Hedra, with the avatar pipeline integrated into the same session as your voice agent. The agent, audio, and avatar video all coordinate automatically.

Pipecat supports avatars through separate integrations, but you manage the coordination between your voice pipeline and the avatar rendering yourself.

Choosing between them

The decision usually comes down to what you value most.

If you want to move fast with less code, get observability and testing out of the box, use native telephony without third-party setup, need integrated client SDKs with automatic state sync, want built-in avatar support, or need production infrastructure with a published SLA, LiveKit is the better fit.

If you prefer a lower-level "just Python" approach, want to experiment with multiple transport providers (with the understanding that support varies), or like visual flow editors for designing conversations, Pipecat might be the right choice.

Both can build production voice AI. The question is how much of the stack you want integrated versus how much you want to assemble yourself.