LiveKit vs Pipecat in 2026: Which Fits Your Use Case?

LiveKit is the closer fit for teams building realtime media products at scale; Pipecat is the closer fit for developers prototyping voice agents quickly with minimal infrastructure. LiveKit is an open-source realtime media platform with an agents framework, while Pipecat is an open-source framework specifically for voice and multimodal agents. The main dividing line: LiveKit provides full media infrastructure (WebRTC, SFU, WHIP/WHEP) alongside its agents SDK, according to LiveKit's documentation, whereas Pipecat focuses on agent orchestration and integrates with external transport layers, according to Pipecat's documentation.

This comparison covers build model, telephony, model and voice choice, integrations, compliance and pricing. Prices checked October 2026.

What is the difference between LiveKit and Pipecat?

LiveKit is a realtime media platform with an agents framework; Pipecat is an agent orchestration framework. LiveKit provides WebRTC infrastructure, a selective forwarding unit (SFU), TURN servers and an agents SDK, according to LiveKit's documentation, whereas Pipecat provides pipeline primitives for building voice and multimodal agents but requires the developer to bring or integrate a transport layer (WebRTC, WebSocket, telephony gateway), according to Pipecat's documentation. LiveKit offers a managed cloud service and self-hosted deployment; Pipecat is a library developers run in their own infrastructure.

What is LiveKit?

LiveKit is an open-source realtime media platform that includes a selective forwarding unit, WebRTC server, TURN infrastructure and an agents framework (LiveKit Agents). The platform supports voice, video and data streaming for applications including telehealth, live events, contact centers and voice agents, according to LiveKit's homepage. LiveKit Agents, available in Python and TypeScript, provides speech-to-text, language model and text-to-speech integrations alongside pipeline controls for building conversational agents, according to LiveKit Agents documentation.

What is Pipecat?

Pipecat is an open-source Python framework for building voice and multimodal agents, released under the MIT license by Daily. The framework provides pipeline abstractions (frames, processors, transports) for orchestrating speech-to-text, language models, text-to-speech and function calling, according to Pipecat's GitHub repository. Pipecat integrates with external services for media transport (Daily, Twilio, WebSocket), speech recognition (Deepgram, AssemblyAI, Whisper), language models (OpenAI, Anthropic, Google) and voice synthesis (ElevenLabs, Cartesia, PlayHT), according to Pipecat's documentation.

LiveKit vs Pipecat side by side

Pricing and features checked October 2026. Sources linked in the sections below.

Which is easier to build with?

Pipecat is easier to build with for developers prototyping a voice agent quickly, because it provides high-level pipeline abstractions and requires fewer infrastructure decisions. LiveKit is easier for teams that need both media infrastructure and agent logic in one platform, because it bundles WebRTC, SFU, TURN and the agents SDK, according to LiveKit's documentation.

Pipecat's pipeline model uses frames and processors: a developer defines a sequence of processing steps (speech-to-text, language model, text-to-speech) and Pipecat handles frame routing, according to Pipecat's documentation. The framework includes pre-built processors for common tasks (VAD, transcription aggregation, LLM streaming, TTS caching) and supports custom processors in Python. A minimal Pipecat agent can be built in under 100 lines of Python, according to Pipecat's quickstart guide. The trade-off: developers must integrate and manage their own transport layer (Daily for WebRTC, Twilio for telephony, or a custom WebSocket server), which adds setup steps.

LiveKit Agents provides a similar pipeline abstraction (speech-to-text, LLM, text-to-speech) but includes the media infrastructure. A developer using LiveKit Cloud can create a room via API, connect an agent to that room using the Python or TypeScript SDK, and handle inbound or outbound calls without configuring WebRTC or SIP separately, according to LiveKit Agents documentation. The framework supports both voice-only and multimodal (voice, video, screen share) agents. LiveKit's full-stack approach means more components to understand (rooms, tracks, participants, SFU behavior) if a developer only needs voice agent logic.

Both frameworks support local development and testing. Pipecat agents can run locally and connect to a tunneling service (ngrok, localtunnel) for inbound call testing. LiveKit provides a local server (livekit-server) and CLI tools for testing agents against simulated rooms, according to LiveKit's local development guide.

Documentation quality is strong for both. LiveKit's documentation covers media concepts (SFU, TURN, adaptive bitrate) alongside agent examples, according to LiveKit documentation. Pipecat's documentation focuses on agent patterns (function calling, context management, multi-turn conversations) with code samples for each supported service integration, according to Pipecat documentation.

Which handles telephony and call quality controls better?

LiveKit handles telephony and call quality controls better for teams that need SIP trunking, WebRTC infrastructure and adaptive bitrate controls in one platform. Pipecat handles telephony better for teams that already use a specific provider (Twilio, Daily) and want to keep that relationship, because Pipecat integrates with external telephony services rather than providing its own.

LiveKit Cloud includes SIP endpoints for inbound and outbound PSTN calls, according to LiveKit SIP documentation. Developers can bring their own SIP trunk (Twilio Elastic SIP, Telnyx, Bandwidth) or use LiveKit's bundled SIP service. The platform provides TURN servers for NAT traversal, adaptive bitrate controls for network congestion, and simulcast for multi-quality streams, according to LiveKit's architecture documentation. Call quality controls include configurable jitter buffers, packet loss concealment and dynamic bandwidth allocation. LiveKit's SFU architecture supports hundreds of participants in a single room, which is relevant for contact-center use cases where supervisors monitor agent calls, according to LiveKit's scalability documentation.

Pipecat does not provide telephony infrastructure.

Developers integrate Pipecat with a telephony provider's SDK: the Twilio transport for PSTN calls via Twilio, the Daily transport for WebRTC calls via Daily's infrastructure, or a custom WebSocket transport for other providers. Call quality controls (echo cancellation, noise suppression, adaptive bitrate) are provided by the integrated service, not by Pipecat. The advantage: teams that already use Twilio or Daily can keep their existing telephony setup and add Pipecat's agent logic on top. The disadvantage: switching telephony providers requires rewriting the transport integration.

Both frameworks support voice activity detection (VAD) for turn-taking. LiveKit Agents includes Silero VAD and supports custom VAD models, according to LiveKit Agents VAD documentation. Pipecat supports VAD via its pipeline processors and integrates with Silero, Deepgram VAD and custom implementations, according to Pipecat VAD documentation. Endpointing (how long to wait after speech stops before triggering the next turn) is configurable in both frameworks.

LiveKit supports warm transfer (handing a call from an agent to a human) via SIP REFER or by bridging two rooms, according to LiveKit SIP documentation. Pipecat supports warm transfer by invoking the integrated telephony provider's transfer API (Twilio's transfer, Daily's room bridging), according to Pipecat's documentation.

Which offers more model and voice choice?

Both LiveKit and Pipecat offer comparable model and voice choice, because both integrate with the same set of major language model and text-to-speech providers. The difference is in how integrations are added: LiveKit provides official plugins for a curated set of services, while Pipecat's plugin ecosystem is broader but includes community-maintained integrations.

LiveKit Agents supports OpenAI (GPT-4o, GPT-4, GPT-3.5), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku), Google (Gemini 1.5 Pro, Gemini 1.5 Flash), Groq, Cerebras and Ollama for local models, according to LiveKit Agents LLM documentation. Text-to-speech integrations include ElevenLabs, Cartesia, OpenAI TTS, Azure Speech, Google Text-to-Speech and Amazon Polly, according to LiveKit Agents TTS documentation. Speech-to-text integrations include Deepgram, AssemblyAI, Azure Speech and Google Speech-to-Text, according to LiveKit Agents STT documentation. All integrations are maintained by the LiveKit team and documented with code examples.

Pipecat supports OpenAI, Anthropic, Google, Groq, Together AI, Fireworks and custom LLM endpoints, according to Pipecat LLM documentation. Text-to-speech integrations include ElevenLabs, Cartesia, PlayHT, Deepgram Aura, OpenAI TTS, Azure Speech and XTTS (open-source), according to Pipecat TTS documentation. Speech-to-text integrations include Deepgram, AssemblyAI, Whisper (local and API), Azure Speech and Google Speech-to-Text, according to Pipecat STT documentation. Pipecat's plugin architecture allows developers to add custom integrations by implementing the processor interface, according to Pipecat's custom processor guide.

Both frameworks support function calling (tool use) during conversations. LiveKit Agents passes function definitions to the LLM and handles function execution via custom processors, according to LiveKit Agents function calling documentation. Pipecat supports function calling via the LLM's native tool-use API and routes function results back into the pipeline, according to Pipecat function calling documentation.

Language coverage depends on the chosen speech-to-text and text-to-speech providers. LiveKit's documentation states that Deepgram supports 36 languages, ElevenLabs supports 29 languages, and Google Text-to-Speech supports 40+ languages, giving LiveKit Agents coverage for 100+ languages when integrations are combined, according to LiveKit documentation. Pipecat's language coverage is similar, because it integrates with the same providers, according to Pipecat documentation.

Neither framework locks developers into a single model or voice provider. Both allow switching providers by changing configuration, though the ease of switching depends on whether the new provider's API matches the existing integration pattern.

Which integrates better with your CRM and tools?

Both LiveKit and Pipecat integrate with CRMs and external tools via custom code rather than pre-built connectors, so neither offers a clear advantage. The integration pattern differs: LiveKit uses webhooks and API calls from within agent code, while Pipecat uses Python functions and pipeline processors.

LiveKit Agents can call external APIs (Salesforce, HubSpot, Google Calendar, Zendesk) during a conversation by defining custom processors or using function calling, according to LiveKit Agents documentation. A developer writes Python or TypeScript code to authenticate with the CRM's API, fetch or update records, and return data to the agent's context. LiveKit provides webhook endpoints for call events (call started, call ended, recording ready), which can trigger workflows in external systems, according to LiveKit webhooks documentation. The platform does not publish pre-built integrations for specific CRMs or calendars.

Pipecat integrates with external tools by defining Python functions that the LLM can call during a conversation, according to Pipecat function calling documentation. A developer writes a function to query a CRM, book a calendar appointment or update a ticket, registers it with the LLM, and Pipecat routes the function's output back into the conversation pipeline. Pipecat's pipeline architecture allows developers to insert custom processors that listen for specific events (call connected, user mentioned a product name) and trigger API calls, according to Pipecat custom processor documentation. The framework does not include pre-built CRM or calendar connectors.

Both frameworks support real-time data enrichment: looking up a caller's account details mid-conversation, checking inventory, or fetching the next available appointment slot. The implementation is custom code in both cases. LiveKit's TypeScript SDK may be a better fit for teams with existing Node.js integrations; Pipecat's Python-only approach fits teams with Python-based backend services.

Neither framework provides a visual workflow builder or no-code integration layer. Teams that need pre-built CRM connectors or a drag-and-drop integration UI should consider a platform like Vapi or Retell AI instead, according to the Vapi review and Retell AI alternatives pages.

Which is stronger on security and compliance?

LiveKit is stronger on security and compliance for regulated industries, because it is SOC 2 Type II certified and offers HIPAA BAA availability on enterprise plans, according to LiveKit's security page. Pipecat's compliance posture depends on the services the developer integrates and how the agent is deployed, because Pipecat is a framework rather than a managed service.

LiveKit Cloud is SOC 2 Type II certified, meaning an independent auditor has verified its security controls for confidentiality, availability and processing integrity, according to LiveKit's security page. The platform offers HIPAA Business Associate Agreements (BAAs) for enterprise customers handling protected health information, according to the same source. LiveKit's infrastructure includes end-to-end encryption for media streams (DTLS-SRTP for WebRTC, TLS for SIP), encrypted storage for recordings, and role-based access controls for API keys and room permissions, according to LiveKit's security documentation. The platform is GDPR-compliant and provides data residency options (US, EU, Asia-Pacific regions), according to LiveKit's privacy policy.

Pipecat is an open-source framework that developers run in their own infrastructure, so compliance depends on the deployment environment and the external services integrated. A developer using Pipecat with Twilio for telephony, OpenAI for the language model and ElevenLabs for text-to-speech inherits the compliance posture of those three services. Twilio is HIPAA-eligible and SOC 2 certified, according to Twilio's compliance page; OpenAI offers BAAs for HIPAA compliance on enterprise plans, according to OpenAI's enterprise page; ElevenLabs states it is SOC 2 Type II certified, according to ElevenLabs' security page. A developer deploying Pipecat on AWS or Google Cloud can inherit those platforms' certifications (SOC 2, ISO 27001, HIPAA) by configuring the environment correctly, but the developer is responsible for the overall compliance program, including data handling, access controls and audit logging.

Both frameworks support call recording with consent. LiveKit provides recording APIs that capture audio, video and transcripts, with configurable storage (S3, Google Cloud Storage, Azure Blob), according to LiveKit recording documentation. Pipecat can trigger recording via the integrated telephony provider's API (Twilio's recording, Daily's recording), according to Pipecat documentation. Recording-consent requirements (one-party, two-party, all-party) vary by jurisdiction; neither framework enforces consent rules, so developers must implement consent prompts and logging according to local law.

PCI DSS compliance (for handling payment card data) is not documented for either framework. Developers building agents that collect payment information should use a PCI-compliant payment processor (Stripe, Braintree) and avoid passing card numbers through the voice agent, according to PCI DSS guidance.

Which costs less?

Pipecat costs less in total for low-volume prototypes and small-scale deployments, because the framework itself is free and developers pay only for the external services they use. LiveKit costs less for high-volume production deployments that need media infrastructure, because LiveKit Cloud's per-minute pricing includes WebRTC relay, TURN and SFU, which would otherwise require separate services.

LiveKit Cloud pricing starts at $0.01 per participant-minute for media relay (the SFU forwarding audio and video between participants), according to LiveKit's pricing page as of October 2026. Additional charges apply for egress (data sent out of LiveKit's network, starting at $0.10 per GB), recording ($0.004 per minute), and agents compute (billed per vCPU-hour, starting at $0.05 per hour), according to the same source. The pricing page does not specify whether the participant-minute rate includes speech-to-text, language model or text-to-speech costs; those are billed separately by the integrated providers (Deepgram, OpenAI, ElevenLabs). LiveKit offers a free tier with 10,000 participant-minutes per month and 100 GB egress, according to LiveKit's pricing page. Enterprise plans include HIPAA BAA, dedicated infrastructure, SLA and volume discounts; pricing is not publicly listed.

Pipecat is free (MIT license), according to Pipecat's GitHub repository. Developers pay for the external services they integrate: telephony (Twilio, Daily), speech-to-text (Deepgram, AssemblyAI), language model (OpenAI, Anthropic) and text-to-speech (ElevenLabs, Cartesia). A typical Pipecat agent using Twilio for telephony, Deepgram for STT, OpenAI GPT-4o for the LLM and ElevenLabs for TTS incurs the following costs, based on published pricing as of October 2026:

An illustrative cost calculation for a 5-minute inbound call using the stack above:

• Twilio Voice: 5 minutes × $0.0085 = $0.0425 (illustrative assumption)
• Deepgram STT: 5 minutes × $0.0043 = $0.0215 (illustrative assumption)
• OpenAI GPT-4o: assuming 2,000 input tokens and 500 output tokens per call (illustrative assumption): (2,000 / 1,000,000 × $2.50) + (500 / 1,000,000 × $10) = $0.005 + $0.005 = $0.01
• ElevenLabs TTS: assuming 500 characters spoken by the agent (illustrative assumption): 500 / 1,000 × $0.30 = $0.15
• Total per call: $0.0425 + $0.0215 + $0.01 + $0.15 = $0.224

Actual costs depend on call length, conversation complexity, model choice and voice provider. The formula is:

Total cost per call ≈ (call minutes × telephony rate) + (call minutes × STT rate) + (tokens × LLM rate) + (characters × TTS rate)

Pipecat's cost advantage appears at low volumes (under 1,000 minutes per month), where the free framework and pay-as-you-go service pricing beat LiveKit's minimum infrastructure costs. LiveKit's cost advantage appears at high volumes (over 100,000 minutes per month), where the bundled media relay, TURN and SFU reduce the need for separate WebRTC infrastructure, and volume discounts on enterprise plans lower the effective per-minute rate.

Self-hosting is an option for both. LiveKit provides a self-hosted server (Apache 2.0 license) that eliminates LiveKit Cloud fees but requires the developer to provision and manage compute, storage and network infrastructure, according to LiveKit's self-hosting documentation. Pipecat runs in any Python environment (AWS Lambda, Google Cloud Run, a VPS), so self-hosting costs are the compute and network charges from the cloud provider.

What do users say about LiveKit and Pipecat?

Public review volume for LiveKit is limited; the recurring themes below are drawn from the available G2 / Trustpilot / TrustRadius coverage. Pipecat, as a newer open-source framework, has minimal third-party review coverage; the summary below is based on GitHub discussions and community feedback.

LiveKit

LiveKit is rated 4.8 out of 5 on G2 from 12 reviews as of October 2026, according to LiveKit's G2 page. Reviewers praise the platform's low latency, scalability and open-source model. Reported limitations include the learning curve for teams unfamiliar with WebRTC concepts (SFU, TURN, simulcast) and the need to manage multiple pricing components (media relay, egress, recording, agents compute) when estimating costs. Another reviewer mentions that LiveKit Agents, while powerful, requires more setup than a no-code platform like Vapi or Synthflow.

Pipecat

Pipecat's review coverage is limited to GitHub stars (2,100+ as of October 2026, according to Pipecat's GitHub repository) and community discussions. Developers on GitHub and the Pipecat Discord highlight the framework's flexibility, the quality of the pipeline abstraction and the ease of swapping service providers (changing from OpenAI to Anthropic, or from ElevenLabs to Cartesia) without rewriting agent logic. One GitHub discussion thread praises Pipecat's support for custom processors, which allows developers to insert business logic (CRM lookups, compliance checks) into the conversation flow.

Reported limitations include the Python-only SDK (teams with TypeScript or Go backends must run Pipecat as a separate service) and the lack of pre-built telephony infrastructure (developers must integrate Twilio, Daily or another provider). One GitHub issue notes that Pipecat's documentation, while improving, is less detailed than LiveKit's for advanced topics like multi-turn context management and error recovery.

Which use cases fit each?

When should you choose LiveKit?

• You are building a realtime media product (telehealth platform, live event streaming, contact center) that needs WebRTC infrastructure, SFU, TURN and agent logic in one platform, according to LiveKit's use cases.
• You need SOC 2 or HIPAA compliance from the media infrastructure provider, and your use case requires a Business Associate Agreement, according to LiveKit's security page.
• You expect high call volumes (over 100,000 minutes per month) and want to consolidate media relay, recording and agents compute under one vendor with volume pricing, according to LiveKit's pricing page.
• You need multimodal agents (voice, video, screen share) or supervisor monitoring (multiple listeners in one room), which LiveKit's SFU architecture supports, according to LiveKit's architecture documentation.

When should you choose Pipecat?

• You are prototyping a voice agent quickly and want a lightweight framework with minimal infrastructure decisions, according to Pipecat's quickstart guide.
• You already use a specific telephony provider (Twilio, Daily) and want to keep that relationship while adding agent logic, according to Pipecat's transports documentation.
• You need maximum flexibility in service choices (swapping LLMs, TTS providers or STT engines without rewriting core logic), which Pipecat's plugin architecture supports, according to Pipecat's documentation.
• You prefer Python for backend development and want to integrate agent logic into an existing Python codebase, according to Pipecat's GitHub repository.

How do you decide between LiveKit and Pipecat?

What is your team's technical depth?

LiveKit requires familiarity with WebRTC concepts (SFU, TURN, simulcast) and media infrastructure, according to LiveKit's documentation. Pipecat requires Python fluency and the ability to integrate external APIs (telephony, STT, LLM, TTS), according to Pipecat's documentation. Both are developer-first platforms; neither offers a no-code builder. Teams without engineering resources should consider a no-code platform like Synthflow or Thoughtly instead.

Do you need bundled telephony or bring-your-own-carrier flexibility?

LiveKit Cloud includes SIP endpoints for PSTN calls and supports bring-your-own-trunk, according to LiveKit SIP documentation. Pipecat requires the developer to integrate a telephony provider (Twilio, Daily, custom SIP gateway), according to Pipecat's transports documentation. If you already use Twilio or Daily and want to keep that setup, Pipecat fits. If you want telephony and media infrastructure from one vendor, LiveKit fits.

How important is model and voice choice?

Both LiveKit and Pipecat support the same major LLM and TTS providers (OpenAI, Anthropic, ElevenLabs, Cartesia, Deepgram), according to their respective documentation. The difference is in how integrations are maintained: LiveKit provides official plugins, while Pipecat's ecosystem includes community-maintained integrations. If you need a provider not yet supported by LiveKit, Pipecat's custom processor interface may offer faster integration.

What compliance requirements apply?

LiveKit is SOC 2 Type II certified and offers HIPAA BAAs on enterprise plans, according to LiveKit's security page. Pipecat's compliance depends on the services you integrate and your deployment environment. If you handle protected health information or need a vendor-signed BAA, LiveKit is the stronger fit. If you deploy on a compliant cloud platform (AWS, Google Cloud) and integrate compliant services (Twilio, OpenAI enterprise), Pipecat can meet the same standards, but the compliance program is your responsibility.

What is your expected call volume and budget?

Pipecat is cheaper for low volumes (under 1,000 minutes per month), because the framework is free and you pay only for external services. LiveKit is cheaper for high volumes (over 100,000 minutes per month), because the bundled media infrastructure and volume discounts reduce the effective per-minute cost. Run the cost formula above with your expected call length, conversation complexity and service choices to compare.

Decision rule: If you need media infrastructure (WebRTC, SFU, TURN) alongside agent logic, or if you require SOC 2 / HIPAA from the platform vendor, choose LiveKit. If you want maximum flexibility in service choices, already use a specific telephony provider, or are prototyping at low volume, choose Pipecat. If you need a no-code builder or pre-built CRM integrations, neither fits; consider Vapi or Retell AI instead, according to the Vapi alternatives page.

Frequently asked questions

Is LiveKit cheaper than Pipecat?

LiveKit is cheaper for high-volume production deployments (over 100,000 minutes per month) that need media infrastructure, because LiveKit Cloud's per-minute pricing includes WebRTC relay, TURN and SFU, according to LiveKit's pricing page. Pipecat is cheaper for low-volume prototypes and small-scale deployments (under 1,000 minutes per month), because the framework itself is free and developers pay only for external services (telephony, STT, LLM, TTS), according to Pipecat's GitHub repository. The crossover point depends on call volume, service choices and whether you need LiveKit's bundled infrastructure or prefer Pipecat's bring-your-own-services model.

Which is easier for non-developers?

Neither LiveKit nor Pipecat is designed for non-developers; both are code-first platforms that require Python or TypeScript fluency, API integration skills and infrastructure knowledge. LiveKit requires understanding WebRTC concepts (SFU, TURN, simulcast), according to LiveKit's documentation. Pipecat requires Python development skills and the ability to integrate external APIs, according to Pipecat's documentation. Teams without engineering resources should consider a no-code platform like Synthflow, Thoughtly or Bland AI instead.

Can I bring my own phone numbers to LiveKit or Pipecat?

LiveKit supports bring-your-own-trunk (BYOT) for SIP, allowing developers to use their existing phone numbers from Twilio, Telnyx, Bandwidth or another carrier, according to LiveKit SIP documentation. Pipecat supports bring-your-own-numbers by integrating with the telephony provider that holds the numbers (Twilio, Daily, a custom SIP gateway), according to Pipecat's transports documentation. Both platforms allow number portability; the process depends on the carrier's porting requirements.

Are LiveKit and Pipecat HIPAA compliant?

LiveKit offers HIPAA Business Associate Agreements (BAAs) on enterprise plans, according to LiveKit's security page, and is SOC 2 Type II certified. Pipecat's HIPAA compliance depends on the services the developer integrates (Twilio, OpenAI, ElevenLabs) and the deployment environment (AWS, Google Cloud). A developer using Pipecat with HIPAA-eligible services and a compliant infrastructure can meet HIPAA requirements, but the developer is responsible for the overall compliance program, including data handling, access controls and audit logging. Compliance with HIPAA depends on how the platform is deployed and configured; consult legal counsel before handling protected health information with either framework.

Which handles outbound calling campaigns?

Both LiveKit and Pipecat handle outbound calling campaigns. LiveKit supports outbound calls via SIP trunking or API, according to LiveKit SIP documentation. Pipecat supports outbound calls by integrating with a telephony provider's outbound API (Twilio's Programmable Voice, Daily's call creation API), according to Pipecat's documentation. Neither platform includes a built-in campaign manager (contact list upload, call scheduling, retry logic); developers must build or integrate campaign orchestration separately. For pre-built outbound campaign features, consider a sales-focused platform like Bland AI or 11x.

Can I migrate from LiveKit to Pipecat?

Migrating from LiveKit to Pipecat requires rewriting the agent logic, because the two frameworks use different pipeline abstractions and APIs. LiveKit Agents uses room-based sessions and track subscriptions, according to LiveKit Agents documentation, while Pipecat uses frame-based pipelines and processors, according to Pipecat's documentation. The migration involves porting the conversation flow, function definitions and service integrations (STT, LLM, TTS) to Pipecat's processor model. Telephony setup also changes: LiveKit's SIP endpoints must be replaced with a Pipecat-compatible transport (Twilio, Daily, custom WebSocket). The reverse migration (Pipecat to LiveKit) requires similar rewriting. Both frameworks are open-source, so vendor lock-in is limited to the effort of porting code.

Which supports more languages?

LiveKit and Pipecat support comparable language coverage, because both integrate with the same speech-to-text and text-to-speech providers (Deepgram, ElevenLabs, Google, Azure). LiveKit's documentation states coverage for 100+ languages when integrations are combined, according to LiveKit documentation. Pipecat's language coverage depends on the STT and TTS providers the developer chooses; using Deepgram (36 languages), ElevenLabs (29 languages) and Google Text-to-Speech (40+ languages) gives Pipecat similar reach, according to Pipecat documentation. Neither framework limits language support beyond what the integrated providers offer.

Do LiveKit and Pipecat support function calling during calls?

LiveKit Agents supports function calling (tool use) via the LLM's native API, allowing the agent to invoke external functions (CRM lookups, calendar booking, database queries) during a conversation, according to LiveKit Agents function calling documentation. Pipecat supports function calling via the LLM's tool-use API and routes function results back into the conversation pipeline, according to Pipecat function calling documentation. Both frameworks allow developers to define custom functions in code and register them with the LLM; the agent can call those functions mid-conversation and use the results to continue the dialogue.

Last updated: October 2026.