Raja SubramanianandClaude Fable 5.1 9b20c1da57 sfu/buffer: stop allocating per ingested packet (#4951)
* sfu/buffer: stop allocating per ingested packet

Buffer.Write allocated six objects for every RTP packet. They were
the parsed rtp.Packet, a copy of it in patchExtPacket, and the flow
state, whose address two rare log calls took. The rest were an
AbsCaptureTime, present on about one packet per second, and the
extension slice that pion grows with append.

Unmarshal into a pooled scratch packet. Give ExtPacket its own backing
rtp.Packet and an [8]rtp.Extension array, and copy the header into
them. Patch the payload in place. If the abs-capture-time extension is
present, parse it. Log a copy of the flow state.

Per packet, Write plus ReadExtended. Opus: 6 allocs, 448 B -> 0.
VP8: 6 allocs, 416 B -> 1 alloc, 32 B. Opus 240 -> 142 ns, VP8 269 -> 204 ns.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* sfu/buffer: point extension payloads at the bucket copy on read

Queued ExtPackets kept extension payload slices into the write input,
which the transport reuses for the next packet. ReadExtended now parses
the header again from the bucket copy, so extensions and CSRCs point
into RawPacket like the payload does.

The test writes every packet through one input buffer and checks the
audio level of each packet read back.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* sfu/buffer: test that a dropped padding packet keeps the shifted sequence number on read

The bucket stores the shifted number, so re-parsing the header on read
returns it. This test guards that.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
2026-10-06 18:21:11 +05:30
2026-09-16 22:11:45 -07:00
2023-01-11 14:49:50 -07:00
2026-10-06 16:16:48 +05:30
2026-10-06 16:16:48 +05:30
2021-06-03 23:22:19 -07:00
2023-07-27 16:43:19 -07:00
2026-09-08 15:48:01 -04:00

The LiveKit icon, the name of the repository and some sample code in the background.

LiveKit: Realtime infrastructure for voice, video, and AI agents

LiveKit is an open source platform for building voice, video, and physical AI agents. This repository is the LiveKit server: a scalable, distributed WebRTC SFU that moves realtime audio, video, and data between people, devices, and AI models. The SDKs, agents frameworks, and companion services are linked in the table at the bottom of this page.

LiveKit's server is written in Go, using the awesome Pion WebRTC implementation.

GitHub stars Slack community Twitter Follow Ask DeepWiki GitHub release (latest SemVer) GitHub Workflow Status License

Important

If you're building Voice AI, LiveKit Agents is the SDK for code-first realtime voice agents. STT, LLM, TTS, turn detection, expressive speech, keyterm accuracy, tool usage, and telephony all come bundled in the framework. It's available in both Python and Node.js.

# agent.py
from livekit import agents
from livekit.agents import Agent, AgentServer, AgentSession, STTContextOptions, TurnHandlingOptions, inference

server = AgentServer()


@server.rtc_session(agent_name="my-agent")
async def my_agent(ctx: agents.JobContext):
    session = AgentSession(
        stt=inference.STT(model="deepgram/nova-3", language="multi"),
        llm=inference.LLM(model="google/gemma-4-31b-it"),
        tts=inference.TTS(model="inworld/inworld-tts-2", voice="Ashley"),
        turn_handling=TurnHandlingOptions(turn_detection=inference.TurnDetector()),
        stt_context_options=STTContextOptions(keyterms=["LiveKit", "Acme Corp"]),
        expressive=True,
    )
    await session.start(room=ctx.room, agent=Agent(instructions="You are a helpful voice AI assistant."))
    await session.generate_reply(instructions="Greet the user and offer your assistance.")


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

Models come from LiveKit Inference with no per-provider API keys, and LiveKit Cloud handles deployment and observability. Visit the docs for more info at docs.livekit.io/agents.

Used in production by

LiveKit carries billions of calls a year for companies including Salesforce, Nvidia, Oracle, SAP, Deutsche Telekom, Spotify, Tinder, Coursera, Headspace, Skydio, Retell, Decagon, Cresta, and HeyGen. Read how Assort Health, Playback, and Polymath Robotics use it, or see more customers.

Features

Documentation & Guides

https://docs.livekit.io

Working with a coding agent? Give it the LiveKit Docs MCP server, or start with the coding agents guide.

Live Demos

Install

Tip

We recommend installing LiveKit CLI along with the server. It lets you access server APIs, create tokens, generate test traffic, and scaffold and deploy agents.

The following will install LiveKit's media server:

MacOS

brew install livekit

Linux

curl -sSL https://get.livekit.io | bash

Windows

Download the latest release here

Getting Started

Starting LiveKit

Start LiveKit in development mode by running livekit-server --dev. It'll use a placeholder API key/secret pair.

API Key: devkey
API Secret: secret

To customize your setup for production, refer to our deployment docs

Creating access token

A user connecting to a LiveKit room requires an access token. Access tokens (JWT) encode the user's identity and the room permissions they've been granted. You can generate a token with our CLI:

lk token create \
    --api-key devkey --api-secret secret \
    --join --room my-first-room --identity user1 \
    --valid-for 24h

Test with example app

Head over to our example app and enter a generated token to connect to your LiveKit server.

Once connected, your video and audio are now being published to your new LiveKit instance!

Simulating a test publisher

lk room join \
    --url ws://localhost:7880 \
    --api-key devkey --api-secret secret \
    --identity bot-user1 \
    --publish-demo \
    my-first-room

This command publishes a looped demo video to a room. Due to how the video clip was encoded (keyframes every 3s), there's a slight delay before the browser has sufficient data to begin rendering frames. This is an artifact of the simulation.

Adding an agent

Agents join rooms as participants, the same way a browser or a phone does. Follow the Voice AI quickstart to build one. An agent connects to a self-hosted server the same way it connects to LiveKit Cloud; when running without Cloud, use model plugins in place of LiveKit Inference.

Deployment

Use LiveKit Cloud

LiveKit Cloud is the fastest and most reliable way to run LiveKit. It runs in 19+ regions with 99.99% uptime and adds agent hosting, model inference, telephony, and observability on top of the server. The Build plan is free, with no credit card required.

Sign up for LiveKit Cloud.

Self-host

Read our deployment docs for more information. Official Docker images and Helm charts are available.

Building from source

Pre-requisites:

  • Go 1.26+ is installed
  • GOPATH/bin is in your PATH

Then run

git clone https://github.com/livekit/livekit
cd livekit
./bootstrap.sh
mage

Contributing

We welcome your contributions toward improving LiveKit! Please join us on Slack or in the Developer Community to discuss your ideas and/or PRs.

License

LiveKit server is licensed under Apache License v2.0.


LiveKit Ecosystem
Agents SDKsPython · Node.js
LiveKit SDKsBrowser · Swift · Android · Flutter · React Native · Rust · Node.js · Python · Unity · Unity (WebGL) · ESP32 · C++
Starter AppsPython Agent · TypeScript Agent · React App · SwiftUI App · Android App · Flutter App · React Native App · Web Embed
UI ComponentsReact · Android Compose · SwiftUI · Flutter
Server APIsNode.js · Golang · Ruby · Java/Kotlin · Python · Rust · PHP (community) · .NET (community)
ResourcesDocs · Docs MCP Server · CLI · LiveKit Cloud
LiveKit Server OSSLiveKit server · Egress · Ingress · SIP
CommunityDeveloper Community · Slack · X · YouTube

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