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RoomKit, Pipecat, TEN Framework, LiveKit Agents: Choosing the Right Conversational AI Framework

February 9, 2026 · 18 min read

The conversational AI space is booming. Whether you're building a voice assistant, a customer support system, or a multi-channel communication platform, there's no shortage of open-source frameworks to choose from. But with so many options, picking the right one can be overwhelming.

In this article, I compare four open-source frameworks that developers frequently encounter when building conversational AI systems: RoomKit, Pipecat, TEN Framework, and LiveKit Agents. Each takes a fundamentally different approach to the same problem space, and understanding their philosophies will save you weeks of going down the wrong path.

Full disclosure: I'm the creator of RoomKit. I'll do my best to keep this comparison fair and focused on helping you choose the right tool for your use case — including cases where RoomKit is not the right answer.

The Four Philosophies in 30 Seconds

Before diving into code, let's understand what makes each framework tick:

These aren't just implementation details — they're design philosophies that determine what's easy, what's possible, and what's painful with each framework.

Show Me the Code

The best way to understand a framework is to see it in action. Here's what a minimal voice AI setup looks like with each one.

RoomKit — Voice Pipeline Meets Multi-Channel Rooms

import asyncio
from roomkit import (
    RoomKit, VoiceChannel, ChannelCategory,
    HookTrigger, HookResult, HookExecution, create_vllm_provider, VLLMConfig,
)
from roomkit.channels.ai import AIChannel
from roomkit.voice.backends.local import LocalAudioBackend
from roomkit.voice.pipeline import AudioPipelineConfig, RecordingConfig, WavFileRecorder
from roomkit.voice.pipeline.vad.sherpa_onnx import SherpaOnnxVADProvider, SherpaOnnxVADConfig
from roomkit.voice.stt.sherpa_onnx import SherpaOnnxSTTProvider, SherpaOnnxSTTConfig
from roomkit.voice.tts.sherpa_onnx import SherpaOnnxTTSProvider, SherpaOnnxTTSConfig

async def main():
    kit = RoomKit()

    # --- Full audio pipeline: Mic → AEC → Denoiser → VAD → STT → LLM → TTS → Speaker ---
    backend = LocalAudioBackend(input_sample_rate=16000, output_sample_rate=22050)

    vad = SherpaOnnxVADProvider(SherpaOnnxVADConfig(
        model="ten-vad.onnx", model_type="ten",  # or "silero"
        threshold=0.5, silence_threshold_ms=600,
    ))
    stt = SherpaOnnxSTTProvider(SherpaOnnxSTTConfig(
        mode="transducer", encoder="encoder.onnx", decoder="decoder.onnx",
        joiner="joiner.onnx", tokens="tokens.txt",
    ))
    tts = SherpaOnnxTTSProvider(SherpaOnnxTTSConfig(
        model="en_US-amy-low.onnx", tokens="tokens.txt",
        data_dir="espeak-ng-data",
    ))
    pipeline = AudioPipelineConfig(
        vad=vad,
        recorder=WavFileRecorder(),
        recording_config=RecordingConfig(storage="./recordings"),
    )

    # Voice is a channel — same abstraction as SMS or Email
    voice = VoiceChannel("voice", stt=stt, tts=tts, backend=backend, pipeline=pipeline)
    ai = AIChannel("ai", provider=create_vllm_provider(
        VLLMConfig(model="qwen3:8b", base_url="http://localhost:11434/v1")
    ), system_prompt="You are a helpful voice assistant.")

    kit.register_channel(voice)
    kit.register_channel(ai)

    await kit.create_room(room_id="support-call")
    await kit.attach_channel("support-call", "voice")
    await kit.attach_channel("support-call", "ai", category=ChannelCategory.INTELLIGENCE)

    # Hooks intercept pipeline events — the same hook system used by all channels
    @kit.hook(HookTrigger.ON_TRANSCRIPTION)
    async def on_stt(text, ctx):
        print(f"User said: {text}")
        return HookResult.allow()

    @kit.hook(HookTrigger.BEFORE_TTS)
    async def before_tts(text, ctx):
        print(f"Assistant: {text}")
        return HookResult.allow()

asyncio.run(main())

What stands out: RoomKit has a full audio pipeline (VAD → STT → LLM → TTS) with optional AEC, denoiser, and WAV recording — but it's still a channel in a room. The same hook system that filters SMS spam can intercept speech events (ON_SPEECH_START, ON_TURN_COMPLETE, ON_VAD_SILENCE, etc.). You could add an SMS or WhatsApp channel to this same room and every message would flow across all channels automatically.

The audio backend is pluggable: LocalAudioBackend (microphone), FastRTCVoiceBackend (WebRTC), or RTPVoiceBackend (for SIP/telephony integration — RTP is already supported, with a SIP module in progress). VAD supports both TEN-VAD and Silero models via sherpa-onnx, and semantic turn detection is built into the pipeline.

Pipecat — A Linear Pipeline of Processors

from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.transports.daily.transport import DailyTransport, DailyParams
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext

async def main():
    transport = DailyTransport(
        room_url="https://your-domain.daily.co/room",
        params=DailyParams(vad_analyzer=SileroVADAnalyzer()),
    )
    stt = DeepgramSTTService(api_key="...")
    llm = OpenAILLMService(api_key="...", model="gpt-4o")
    tts = CartesiaTTSService(api_key="...", voice_id="...")

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

    # The pipeline: data flows left to right as "frames"
    pipeline = Pipeline([
        transport.input(),          # Audio from user
        stt,                        # Speech → Text
        context_aggregator.user(),  # Collect user turn
        llm,                        # Generate response
        tts,                        # Text → Speech
        transport.output(),         # Audio back to user
        context_aggregator.assistant(),
    ])

    task = PipelineTask(pipeline)
    runner = PipelineRunner()
    await runner.run(task)

What stands out: the pipeline metaphor is immediately intuitive. Audio goes in, gets transcribed, processed by an LLM, synthesized back to speech, and sent out. Each processor in the chain does one thing. Swapping Deepgram for Whisper or Cartesia for ElevenLabs is a one-line change.

TEN Framework — A Graph of Extensions in JSON

{
  "ten": {
    "predefined_graphs": [{
      "name": "voice_assistant",
      "nodes": [
        {
          "name": "agora_rtc",
          "addon": "agora_rtc",
          "extension_group": "default",
          "property": { "app_id": "${env:AGORA_APP_ID}" }
        },
        {
          "name": "stt",
          "addon": "deepgram_asr_python",
          "extension_group": "default",
          "property": { "api_key": "${env:DEEPGRAM_API_KEY}" }
        },
        {
          "name": "llm",
          "addon": "openai_chatgpt_python",
          "extension_group": "default",
          "property": { "api_key": "${env:OPENAI_API_KEY}", "model": "gpt-4o" }
        },
        {
          "name": "tts",
          "addon": "elevenlabs_tts_python",
          "extension_group": "default",
          "property": { "api_key": "${env:ELEVENLABS_TTS_KEY}" }
        }
      ],
      "connections": [
        {
          "extension": "agora_rtc",
          "audio_frame": [{ "name": "pcm_frame", "dest": [{ "extension": "stt" }] }]
        },
        {
          "extension": "stt",
          "data": [{ "name": "text_data", "dest": [{ "extension": "llm" }] }]
        },
        {
          "extension": "llm",
          "data": [{ "name": "text_data", "dest": [{ "extension": "tts" }] }]
        },
        {
          "extension": "tts",
          "audio_frame": [{ "name": "pcm_frame", "dest": [{ "extension": "agora_rtc" }] }]
        }
      ]
    }]
  }
}

What stands out: there's no Python to write for a standard setup. You define your agent as a graph of extensions connected by typed messages (audio frames, text data, commands). The visual TMAN Designer lets you drag-and-drop these nodes. Extensions can be written in C++, Go, or Python, and they all run in the same process.

LiveKit Agents — An Agent Joins the Room

from livekit.agents import Agent, AgentSession, JobContext, cli, WorkerOptions
from livekit.plugins import silero, deepgram, openai, cartesia

async def entrypoint(ctx: JobContext):
    await ctx.connect()

    session = AgentSession(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-3"),
        llm=openai.LLM(model="gpt-4.1-mini"),
        tts=cartesia.TTS(voice="9626c31c-..."),
    )

    await session.start(
        agent=Agent(instructions="You are a helpful voice assistant."),
        room=ctx.room,
    )

    await session.generate_reply(
        instructions="Greet the user and offer your assistance."
    )

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

What stands out: the code is remarkably concise. The AgentSession handles the entire voice pipeline (VAD → STT → LLM → TTS) internally. The agent joins a LiveKit room as a regular participant, which means you get all of LiveKit's WebRTC infrastructure for free — noise cancellation, SIP telephony, multi-participant rooms.

Comparing on Voice (Apples to Apples)

All four frameworks can power a voice AI agent, so let's compare them where they overlap.

Capability RoomKit Pipecat TEN Framework LiveKit Agents
Pipeline model Full audio pipeline + hook intercepts Linear frame chain Directed graph (JSON) Session-managed pipeline
STT/TTS Pluggable (Deepgram, ElevenLabs, SherpaOnnx local) 40+ services Deepgram, Azure, Whisper, etc. Deepgram, OpenAI, Cartesia, etc.
Speech-to-speech OpenAI Realtime, Gemini Live OpenAI, Gemini OpenAI, Gemini OpenAI Realtime API
VAD TEN-VAD, Silero (sherpa-onnx), semantic turn detection Silero, custom Smart Turn Proprietary TEN VAD Silero + semantic turn detection
Audio transport FastRTC (WebRTC), RTP, WebSocket, local mic Daily.co (WebRTC) Agora RTC LiveKit (WebRTC)
Barge-in Via hook pipeline events Built-in interruption handling Interrupt detector extension Built-in with min_words config
SIP/Telephony RTP backend supported, SIP module in progress Twilio WebSocket SIP extension Native SIP trunking
Audio processing AEC (Speex), denoiser (GTCRN), WAV recorder — Noise reduction extension BVC noise cancellation
Install pip install roomkit[all] pip install pipecat-ai Docker + Agora SDK pip install livekit-agents

On voice alone, Pipecat and LiveKit Agents are the most polished ecosystems with the widest service integrations. But RoomKit has a surprisingly complete audio pipeline under the hood: neural VAD (TEN-VAD or Silero via sherpa-onnx), semantic turn detection, echo cancellation (Speex AEC), neural denoising (GTCRN), WAV recording, and pluggable backends (WebRTC, RTP, local mic). The difference is architectural: RoomKit's pipeline lives inside a channel that coexists with SMS, Email, and WhatsApp in the same room. TEN goes deepest on real-time media with proprietary VAD and avatar lip-sync.

Where Each Framework Shines Alone

This is where the real differences emerge.

RoomKit: Multi-Channel Conversation Orchestration

RoomKit's unique strength is that voice is just one of many channels in a room. You can have a conversation that spans SMS, Email, WhatsApp, Voice, Microsoft Teams, and AI — all in the same room, with automatic content transcoding between them. A rich card sent to WhatsApp becomes plain text over SMS. An AI response broadcasts to every channel simultaneously.

But don't mistake "multi-channel" for "voice-light." The voice subsystem has a full audio pipeline: neural VAD (TEN-VAD or Silero), AEC, denoiser, STT, TTS, semantic turn detection, and WAV recording with stereo/mixed/separate modes. The pipeline fires granular events (ON_SPEECH_START, ON_SPEECH_END, ON_VAD_SILENCE, ON_TURN_COMPLETE, ON_TURN_INCOMPLETE, ON_BACKCHANNEL, ON_DTMF) that the hook system can intercept — the same hook system used by all channels. Audio backends are pluggable: local mic, FastRTC (WebRTC), or RTP (for telephony integration).

Choose RoomKit when: you're building a multi-channel conversation system (contact center, B2B2C messaging, omnichannel support) where voice is one touchpoint among many, or when you need the full audio pipeline integrated with hooks and identity resolution across channels.

Pipecat: The Widest Ecosystem for Voice AI

Pipecat's frame-based pipeline is the most intuitive model for building voice agents. With 40+ service integrations, client SDKs for every platform (React, Swift, Kotlin, C++), and debugging tools like Whisker and Tail, it has the most mature developer ecosystem for pure voice AI.

Pipecat Flows adds structured conversation state management on top, letting you build complex multi-step interactions (patient intake, order tracking) without reinventing the wheel.

Choose Pipecat when: you're building a voice-first AI agent and want the widest choice of AI services with the fastest path to production.

TEN Framework: Visual Builder + Multi-Language Extensions

TEN is the most ambitious in scope. The TMAN Designer lets non-developers visually wire together voice agents. Extensions can be written in C++, Go, or Python and run in the same process. The proprietary VAD and turn detection models are highly optimized. And the lip-sync avatar integrations (with Trulience, HeyGen, Tavus) make it the clear choice for "wow" demos with visual characters.

The tradeoff is weight: TEN requires Docker, an Agora account, and a Go server. It's a full platform, not a library.

Choose TEN when: you need visual agent building, multi-language extensions, or real-time avatar experiences — and you're okay with the Agora ecosystem.

LiveKit Agents: Production-Grade Voice with Observability

LiveKit Agents is the most developer-friendly framework for getting a production voice agent running. The v1.0 AgentSession API is beautifully clean. Semantic turn detection (using a custom open-weights model) reduces false interruptions. The built-in test framework and metrics collection make it easy to iterate. And with native SIP trunking, you can deploy phone agents without extra infrastructure.

LiveKit's open-source media server means you can self-host the entire stack, which matters for compliance-sensitive deployments.

Choose LiveKit Agents when: you're building a production voice AI agent and need the best balance of developer experience, observability, and deployment flexibility.

Decision Tree

Still not sure? Here's a quick guide:

"I need voice + SMS + Email + WhatsApp in one conversation"
→ RoomKit. None of the others do multi-channel.

"I want the fastest path to a working voice agent"
→ LiveKit Agents or Pipecat. Both get you to "hello world" in under 50 lines.

"I need to visually build agents without writing code"
→ TEN Framework's TMAN Designer.

"I need lip-sync avatars and video in real-time"
→ TEN Framework.

"I need the widest choice of AI providers"
→ Pipecat (40+ integrations).

"I need to self-host everything for compliance"
→ LiveKit Agents (open-source media server) or RoomKit (no external dependencies for core).

"I need SIP/telephony integration"
→ LiveKit Agents (native SIP) or Pipecat (Twilio WebSocket). RoomKit has RTP support already, with a SIP module in development.

"I'm building a hook/middleware system around conversations"
→ RoomKit. The hook pipeline (PRE_INBOUND, POST_DELIVERY, BEFORE_BROADCAST, etc.) is purpose-built for this.

Conclusion

These four frameworks are not in direct competition — they solve overlapping but different problems. Pipecat, TEN, and LiveKit Agents are voice/AI agent frameworks optimized for real-time audio interactions. RoomKit is a multi-channel conversation framework with a full voice pipeline that happens to coexist with SMS, Email, WhatsApp, and a dozen other channels in the same room abstraction.

The landscape is evolving fast. Speech-to-speech models (OpenAI Realtime, Gemini Live) are making traditional STT → LLM → TTS pipelines less necessary. All four frameworks are adapting. What won't change is the underlying architectural bet each one makes: pipelines vs. graphs vs. rooms vs. sessions. Choose the abstraction that matches your problem, and you'll be building on solid ground.

All four frameworks are open source. Go explore: