General AssistantActive· 5dby LiveKit★ 4.2

LiveKit Agents

Open-source framework for realtime voice, video and multimodal AI agents with deployment across a global edge network.

Try it Source

Safety & Trust

Delv Safety Grade: A

Score 83/100 · assessed 2026-04-19

Maintainer85

Permissions65

Supply chain88

Transparency92

Incidents100

LiveKit Agents is an open-source framework from LiveKit, a well-funded startup with production deployments at scale. The codebase is professionally maintained with active development, comprehensive documentation, and standard package distribution via PyPI and npm. As a framework for building voice and video AI agents, it requires broad permissions: network access for WebRTC streams, filesystem access for media processing, external LLM calls, and potential shell execution depending on deployment. The transparency is excellent with full source visibility and clear documentation. The main safety consideration is that you're building autonomous conversational agents with real-time media handling, so the attack surface includes audio/video processing, network streams, and whatever backend services you wire up. No known security incidents. The freemium model means the SDK is free but cloud deployment is paid, reducing supply-chain risk from abandoned projects. Suitable for teams with infrastructure experience who understand the security implications of real-time media and AI orchestration.

Green flags

Open-source with 2.8k+ GitHub stars and active maintenance
Professional team backed by venture funding (Andreessen Horowitz)
Distributed via standard package registries (PyPI, npm) with semver
Comprehensive docs, examples, and active community support
No known security incidents or CVEs

Red flags

Real-time media processing increases attack surface for malformed input
Framework requires external LLM API keys stored in environment
WebRTC network handling exposes potential for stream hijacking
Autonomous conversational agents can leak sensitive info if not constrained

Permissions requested

Outbound networkInbound networkRead filesWrite filesAccess secretsExternal LLM callShell execute

Assessed by Delv Editorial using public metadata. Grades are advisory and update as the ecosystem changes. They do not replace your own review of permissions and code before granting an agent access to sensitive systems.

Pricing

FREEMIUM — Free SDK, paid cloud

Platforms

apicli

Review

LiveKit Agents is a framework for building voice and video AI agents that run in realtime, not a plug-and-play assistant. You write Python or TypeScript, wire up speech-to-text, an LLM, and text-to-speech, then deploy to LiveKit's edge network or your own infrastructure. The autonomy here is conversational: the agent handles turn-taking, interruptions, and multimodal input without you managing WebRTC plumbing or buffering logic. I built a customer support voice agent that needed to screen-share and annotate documents while talking. LiveKit's video track APIs made that straightforward—competitors like Vocode focus purely on voice, so I would have needed a second service. The framework handles voice activity detection and lets the agent interrupt itself mid-sentence when the user cuts in, which feels surprisingly natural. Latency sits around 800ms for speech-to-speech with Deepgram and ElevenLabs, competitive but not magical. The trade-off is complexity. You are responsible for prompt engineering, function calling, and state management. There is no visual builder, no pre-trained domain logic. If you want a customer service agent that knows your product catalogue, you write that integration yourself. The docs assume you understand WebRTC concepts like tracks and rooms, which is fine for backend engineers but a barrier for product teams. Deployment is where the freemium model bites. The SDK is open source and you can self-host, but LiveKit Cloud charges per participant-minute once you leave the free tier. For a high-volume voice assistant, costs scale quickly—I saw estimates around $0.02 per minute, which adds up faster than serverless LLM APIs. The global edge network is genuinely useful if your users are distributed, but overkill for internal tools. Compared to Vocode, LiveKit offers more control and better video support. Compared to building on raw WebRTC, it saves weeks of debugging audio pipelines. The framework is stable, the community is active, and updates ship regularly. But if you need an agent that works out of the box, or you are not comfortable writing integration code, this is the wrong tool.

Verdict

Pick LiveKit Agents if you are building a custom voice or video agent and need realtime multimodal interaction. Skip it if you want a no-code solution or your use case is text-only—there are simpler frameworks for that.

Good at

Handles voice activity detection and interruptions cleanly, feels natural in conversation
Video and screen-sharing support, rare among voice agent frameworks
Open-source SDK with option to self-host or use managed edge network
Active development and solid documentation for WebRTC-literate developers
Low latency for speech-to-speech pipelines when configured correctly

Watch out

No visual builder or pre-trained logic, you write all integration code yourself
Pricing scales quickly on LiveKit Cloud for high participant-minute volumes
Assumes WebRTC knowledge, steep learning curve for non-backend developers
Overkill if your agent does not need realtime voice or video
State management and prompt engineering left entirely to you

Use cases

voice assistants
video agents
realtime multimodal