Paul WellsandClaude Opus 5 0da02c4717 agent endpoints: add a path router that matches in declaration order
Manifest matching runs a compiled regex per route over a linear scan, once per
candidate registration, on the serving path. At the 256-route cap that is up to
256 RE2 executions per replica per request.

The router compiles a manifest into a compressed trie instead. Starlette
selects the first route declared that matches, so the trie cannot resolve
static before wildcard the way net/http's does: every node carries the lowest
route index in its subtree, the walk tracks the lowest full match found and
prunes any subtree that cannot improve on it, and inserting in declaration
order leaves edges and leaves sorted by that index with no sort pass.

Params are not segment-aligned - starlette allows /f/{name}.{ext}, a {p:path}
anywhere, and a float whose fraction backtracks - so the walk must search. An
edge is single when nothing below it can start with a byte its own convertor
could have consumed, which is a property of the target node and so is settled
at build time; a single edge takes its greedy run and descends once. Templates
whose params are segment-aligned are entirely single and never search. What
remains is bounded by a step budget, and exhausting it returns ResultOverBudget
rather than a route the cut-short search cannot vouch for.

Templates parse without regexp. Scanning the grammar by hand keeps a brace that
opens nothing well-formed as an ordinary literal, which is what starlette's
finditer does. Anchoring is `\n?$` rather than `$`: python's '$' matches before
one trailing newline where Go's does not, and [^/] accepts a newline where .
refuses one, so the two convertors diverge in opposite directions on a decoded
%0A.

The regex implementation moves into the tests as an oracle, carrying its own
parser so it references the hand-rolled scanner as well as the trie.
FuzzMatchAgainstOracle generates a table and a request and asserts the two
agree on both the winning template and the result. Matching allocates nothing,
asserted by AllocsPerRun on hit, 405, miss and on the searching path.

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-15 10:21:50 -07:00
2023-01-11 14:49:50 -07:00
2026-08-26 17:58:15 +05:30
2026-08-26 17:58:15 +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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