Pipecat realtime voice/multimodal bots. Covers pipelines/frames, transports, RTVI, Pipecat Cloud deploy. Use when building real-time voice bots (STT/LLM/TTS pipelines), multimodal AI agents, WebRTC/WebSocket transports, or deploying to Pipecat Cloud. Keywords: pipecat, pipecat-ai, RTVI, WebRTC, voice bot.
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Pipecat realtime voice/multimodal bots. Covers pipelines/frames, transports, RTVI, Pipecat Cloud deploy. Use when building real-time voice bots (STT/LLM/TTS pipelines), multimodal AI agents, WebRTC/WebSocket transports, or deploying to Pipecat Cloud. Keywords: pipecat, pipecat-ai, RTVI, WebRTC, voice bot.
metadata
{"version":"1.3.0","release_date":"2026-05-28"}
Pipecat
Pipecat is an open-source Python framework for building real-time voice and multimodal bots.
It composes streaming speech/LLM/TTS services into a low-latency pipeline, connected via transports (WebRTC/WebSocket) and client SDKs using the RTVI message standard.
Put provider API keys (LLM/STT/TTS) only on the server/bot container.
The client should call a server start endpoint (startBot / startBotAndConnect) to receive transport credentials (e.g., a room URL + token), not provider keys.
2) Use WebRTC for production voice
Prefer a WebRTC transport (e.g., Daily) for resilience and media quality.
Use a WebSocket transport mostly for server↔server, prototypes, or constrained environments.
2b) Design for streaming + overlap
Keep the pipeline fully streaming (avoid batching whole turns when you can).
If your services support it, start TTS from partial LLM output to reduce perceived latency.
3) Initialize and evolve context via RTVI
Initialize the bot’s pipeline context from the server start request payload.
For ongoing interaction, prefer a dedicated “send text” style API (when available) instead of deprecated context append methods.
4) Function calling: end-to-end flow
LLM requests a function call.
Client registers a handler by function name.
Client returns a function-call result message back to the bot.
5) Pipecat Cloud deployment basics
Build/push an image that matches the expected platform (Pipecat Cloud requires linux/arm64 in the docs).
Use a deployment config file for repeatability.
Configure pool sizing with min_agents (warm capacity) and max_agents (hard limit).
Critical gotchas / prohibitions
Do not embed sensitive API keys in client apps.
Expect and handle “at capacity” responses (HTTP 429) when the pool is exhausted.
Plan for cold-start latency if min_agents = 0.
Ensure secrets and image-pull credentials are created in the same region as the deployed agent.
Do not assume deprecated import shims or service-specific context classes still exist in 1.0.0; audit imports before upgrading.
Do not keep VAD/turn-detection logic on transport params; current releases route that control through LLMUserAggregator strategies.
Do not assume OpenAIResponsesLLMService is HTTP-based anymore; WebSocket is now the default implementation.
Release Highlights (0.0.109 -> 1.2.0)
Runtime and service additions
OpenAIResponsesLLMService now defaults to a persistent WebSocket connection; the prior HTTP behavior moved to OpenAIResponsesHttpLLMService.
Inworld Realtime LLM adds a WebSocket cascade STT/LLM/TTS path with semantic VAD and function calling.
MistralTTSService adds streaming Voxtral TTS, and TTS/STT services gained more runtime-update and sample-rate options.
The development runner now exports a module-level FastAPI app for custom routes before main().
Tooling and context changes
Function calling now supports grouped parallel tool batches, async tool completion after interruption, and streaming intermediate tool results.
Context editing now has LLMMessagesTransformFrame, and the framework standardizes on universal LLMContext / LLMContextAggregatorPair.
OpenAI tool schemas can now include provider-specific custom_tools.
1.2.0 adds add_tool_change_messages for LLM aggregators, widens tool_resources into deprecated app_resources, and extends async-tool compatibility across more realtime providers.
RTVI grows first-class UI Agent Protocol support with ui-event, ui-snapshot, ui-cancel-task, ui-command, and ui-task, bumping the protocol to 1.3.0.
The development runner and runner arguments now carry a stable session_id, which is useful for per-session tracing across local and cloud-like flows.
Breaking migrations
Deprecated service-specific context classes, transport params, RTVI shims, frame aliases, and interruption/VAD helpers were removed across the stack.
Turn detection and mute behavior moved toward LLMUserAggregator strategies instead of transport-level configuration.
Some legacy providers and helpers were removed entirely (OpenPipeLLMService, TTSService.say(), FrameProcessor.wait_for_task(), older beta/alias modules).
Release Highlights (1.3.0)
Workers and multi-agent pipelines: PipelineTask / PipelineRunner are renamed toward PipelineWorker / WorkerRunner, and pipecat.workers makes pipelines peers on a typed-message bus for @job dispatch, handoffs, sidecars, UI workers, and distributed Redis/PGMQ patterns.
UIWorker and RTVI UI protocol: UIWorker can observe client accessibility snapshots and drive UI commands over RTVI; the UI worker vocabulary moves from task/agent to job/worker (ui-task -> ui-job-group, cancelUITask -> cancelUIJobGroup).
Development runner: one runner can serve WebRTC, Daily, telephony, and plain WebSocket clients; /start accepts a transport field, /ws-client supports protobuf WebSocket clients, /status reports enabled transports, and the Daily redirect moved to /daily.
Service surface: adds Vonage Video Connector transport, Inception Mercury 2 LLM service, Cartesia turn-based STT, Rime coda TTS defaults, Soniox endpoint-delay settings, LLMService.append_system_instruction(), and STTService.supports_ttfs.
Operational migration notes: optional service/transport extras now raise ImportError, transformers is no longer a base dependency, Rime defaults to coda, OpenRouter defaults to openai/gpt-4.1 and maps developer messages to user unless explicitly supported.
