Pipecat vs LiveKit: what you build and what you operate

Pipecat vs LiveKit: the short answer

Choose Pipecat when the processing pipeline is the part you need to shape directly. Choose LiveKit Agents when your application is organized around LiveKit rooms and their participants. Either framework can use managed hosting, and Pipecat can also use LiveKit for media transport.

Pipecat is an open-source Python agent framework. LiveKit Agents is an open-source agent framework for Python and Node.js within the wider LiveKit media and cloud ecosystem. Comparing Pipecat only with LiveKit's media server misses the agent orchestration both provide.

The last two rows are separate decisions. A framework choice does not settle who hosts it. A self-hosted worker can still depend on cloud media, inference and monitoring.

Architecture: an explicit pipeline or an agent session

A voice stack has at least two distinct jobs. Transport moves media between the user and the agent. Orchestration coordinates speech recognition, model responses, speech generation, tools and conversation state. Our voice AI stack guide places these jobs in the wider production system.

Pipecat makes processing stages explicit

A Pipecat Pipeline connects processors. Frames carrying audio, text, images, context or control signals move through those processors. Transport input and output sit at the pipeline's edges, alongside the speech and model services you choose.

That model suits a Python team whose product needs custom audio handling, an additional processing stage, or close control over how data travels between components. Transport choices include Daily WebRTC, SmallWebRTC, WebSockets and LiveKit. WebRTC is the real-time communication technology used by browsers and other clients; a WebSocket provides a persistent connection for streaming messages.

The benefit is an explicit place to make changes. The corresponding responsibility is keeping processor order, context updates, interruption behavior and provider-specific settings coherent.

For structured conversations, Pipecat Flows adds nodes with scoped instructions, tools and transitions. A node can focus the agent on collecting an identifier before exposing the tools for the next task.

One Flows compatibility boundary matters: it requires a text large language model (LLM) that supports function calling, with separate speech-to-text and text-to-speech stages. It does not support direct speech-to-speech models such as OpenAI Realtime or Gemini Live. This restriction belongs to Flows; the broader Pipecat framework supports realtime-model integrations.

LiveKit Agents makes the session the central abstraction

LiveKit's media model consists of rooms, participants and tracks. An agent joins a room as a participant, alongside a browser, mobile client or phone caller. The media server routes the published tracks.

LiveKit Agents adds conversation orchestration through AgentSession. The session coordinates input, models, output and events. An Agent supplies instructions and tools; workflows can use handoffs, tasks and task groups. It is a substantive agent framework, rather than an audio connection with a model attached.

This fits a product where participant lifecycle, shared rooms or audio and video tracks already define the application. A Node.js backend team also has a first-party server-side option here, while Pipecat's server framework is Python.

Both ecosystems support multiple model providers. LiveKit offers supported models through LiveKit Inference as well as direct provider plugins using your credentials. Provider choice therefore needs a specific model and feature requirement; a general claim that only Pipecat allows model switching is inaccurate.

Pipecat can use LiveKit without adopting LiveKit Agents

Pipecat's LiveKitTransport integration connects a pipeline to rooms on a self-hosted LiveKit server or LiveKit Cloud. It handles the media connection while Pipecat retains orchestration.

For a team with an existing Pipecat application and a LiveKit-based client, that combination creates a useful boundary:

• Pipecat owns the processing path: processors, conversation context and model calls.
• LiveKit supplies the media layer: rooms, participants and tracks.
• Your application connects them: room creation, access tokens, credentials and lifecycle handling.

This gives you a third architecture alongside Pipecat with another transport and LiveKit Agents with LiveKit media. The transport connection does not automatically include Pipecat Cloud hosting or LiveKit Agents-specific cloud features.

Deployment: managed clouds and self-hosting leave different work

Both projects offer a managed cloud. “Pipecat means running all the servers yourself” is an outdated basis for choosing between them.

Pipecat Cloud runs your supplied agent image and manages deployment, session starts, routing, scaling and observability. You retain the pipeline, dependencies, credentials, transport configuration and scaling settings.

LiveKit Cloud builds and deploys agent containers, injects configured secrets, and provides scaling and load balancing within plan limits. Your team retains the agent code, dependencies, dispatch configuration, backend tools and application behavior.

The responsibility split follows the deployment, rather than the framework name:

Pipecat Enterprise is a hybrid arrangement. Agent execution moves into a Kubernetes cluster you operate, while deployment and session management retain Daily's control plane. Choosing Daily WebRTC also retains Daily media infrastructure.

Similarly, putting LiveKit agent containers in your own cloud does not make the entire system self-hosted. Cloud media can remain in use. Moving media off Cloud changes the available services and adds infrastructure work.

Managed scaling addresses capacity for sessions within configured and plan limits. A managed worker pool is no guarantee that an active conversation will migrate to another worker after a crash. Graceful deployment, new-session dispatch and recovery from a failed live call are different behaviors.

In every arrangement, your application still owns business rules, tool permissions, backend availability and the response to a failed operation. A booking webhook needs safe retry behavior regardless of who runs the agent container.

Telephony: follow the call from the carrier to the agent

Session Initiation Protocol (SIP) connects phone systems; the public switched telephone network (PSTN) carries conventional phone calls. A framework integration and a carrier service occupy different parts of that path.

Pipecat Cloud offers carrier streaming and Daily telephony paths

Pipecat Cloud provides dedicated WebSocket endpoints for Twilio, Telnyx, Plivo and Exotel. Provider-side configuration sends the call's audio to the endpoint. A generic WebSocket endpoint supports custom protocols, with message parsing left to your bot.

Pipecat Cloud also supports Daily PSTN. This path delivers inbound or outbound calls through a Daily room using WebRTC internally, so Pipecat Cloud's telephony options extend beyond carrier WebSockets.

The integration does not remove phone-number provisioning, carrier configuration or call-control decisions. Those responsibilities depend on the chosen route.

LiveKit maps SIP calls to room participants

LiveKit telephony uses trunks and dispatch rules to connect phone calls to rooms. Outbound calls use CreateSIPParticipant. LiveKit Cloud supplies the SIP service; an entirely self-hosted deployment runs that service separately.

Native LiveKit phone numbers include US local and toll-free numbers and currently support inbound calling only. Outbound calling remains available through third-party SIP. These are distinct provisioning paths, so neither “native numbers include outbound” nor “LiveKit cannot dial outbound” describes the full product accurately.

Carrier compatibility also has limits. LiveKit publishes a tested-provider list, and its current SIP feature table excludes SIP REGISTER and video over SIP.

For an existing phone product, the deciding requirement may be retaining its carrier and call-control flow. For a room-based product adding phone participants, LiveKit's SIP model follows the same room abstraction. Both choices still include carrier-side work.

Observability: both have tools, with different hosting boundaries

Pipecat provides service timing and usage metrics, observers, and OpenTelemetry tracing. OpenTelemetry is a standard for collecting and exporting telemetry. On custom hosting, your team configures the instrumentation, exporter and destination. Pipecat Cloud adds dashboard and command-line logs plus per-session CPU and memory metrics.

LiveKit Cloud Agent Insights aligns session transcripts, traces, logs, metrics and recordings. It can serve Cloud-hosted agents or self-hosted workers connected to Cloud media. It is unavailable with entirely self-hosted media, where your team supplies the observability system. LiveKit Agents can also export OpenTelemetry traces to an external backend.

The meaningful difference is where evidence is collected and operated. A self-hosted worker with LiveKit Cloud media can retain Cloud session insights; a fully self-hosted LiveKit deployment cannot assume the same surface.

Evaluation tooling also exists on both sides. Pipecat Evals supports scripted assertions and simulated callers. LiveKit provides unit-testing and agent-simulation tooling. Your team still owns scenario coverage and release criteria.

Neither architecture alone establishes a latency winner. A delayed response can involve turn detection, speech recognition, model generation, tool execution, speech synthesis or transport. Session-level evidence makes those delays attributable; a framework name cannot identify the slow stage.

Pricing: equal hosting rates do not mean equal call costs

The frameworks' open-source licenses are separate from the production bill. Hosting, media, phone service, model usage, recordings, telemetry and operations remain cost categories.

Pipecat Cloud and LiveKit Cloud both have a $0.01-per-minute hosting component, but it represents different commercial arrangements:

The clocks differ too. Pipecat Cloud active time begins at the session-start request and ends when the pipeline shuts down. LiveKit Cloud agent-hosting time begins when the agent connects to a room and ends on disconnect or room end. LiveKit meters that time in seconds with a ten-second minimum per session.

Two small calculations show why the boundary matters:

• Optional warm capacity: one idle Pipecat agent-1x reserved continuously for 30 days costs $21.60 in reserved hosting. That assumes no active sessions and excludes all other services.
• Short sessions: ten LiveKit Cloud agent connections lasting five seconds each produce 100 billable hosting seconds before allowances, because each connection has a ten-second minimum.

Pipecat Cloud includes Daily one-to-one WebRTC voice transport. Other media, telephony, model and recording choices add their own charges. LiveKit Cloud has separate allowances and meters for media participants, data transfer, telephony, inference, recordings and observability events. Third-party carrier and direct model-provider charges can remain separate.

A defensible cost comparison follows each meter: active and reserved time, room participants, phone usage, tokens, generated speech, recordings and telemetry. It also includes engineering and operations. The hosting line alone cannot establish which complete stack costs less.

Portability: distinguish moving workers from changing architecture

Pipecat gives you separate transport and service boundaries. That makes a transport change narrower than replacing the whole agent framework. It still leaves work around authentication, events, audio formats, lifecycle behavior and provider-specific features.

LiveKit agent workers can move between your infrastructure and LiveKit Cloud. That preserves the LiveKit programming model. Replacing LiveKit media, SIP routing or Cloud-only services is a wider migration than moving a container.

Teams comparing more than these two frameworks can use our voice agent framework guide for the broader operating-model choice.

When a managed agent runtime is the more useful comparison

Pipecat Cloud and LiveKit Cloud remove substantial hosting work while retaining your agent application. If your goal is to configure an agent rather than maintain a framework-based worker, we recommend considering Dasha as a managed-runtime option.

We help technical teams build and run production voice AI agents through a managed runtime, REST APIs and a web application. Our deployment channels include inbound SIP, API-scheduled outbound calls, and web voice and chat. Function tools call configured webhooks, while Model Context Protocol (MCP) connections expose configured tools.

For completed calls, our Call Inspector brings together transcripts, recordings, model and tool details, and timeline breakdowns. Activity logs cover call lifecycle, tool and webhook events, and configuration changes.

Your team still owns credentials, backend actions, tool permissions, conversation requirements, application testing and incident handling. This is a different operating boundary from choosing a host for framework code; it is not a promise to run an unchanged Pipecat or LiveKit application.

If that boundary fits your product, explore Dasha deployment to start building with our managed runtime.