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jargo is a framework for real-time voice agents in Go: audio in over WebRTC, a streaming transcription → reasoning → speech pipeline with turn-taking and barge-in, and audio back out.

Why?

Pipecat is great, and jargo is a port of it. The architecture and many design decisions are Pipecat's.

Python might not be the way

This port exists for one reason: I'd rather not run a voice agent on Python.

Python is the right tool when you need the AI/data-science ecosystem. A real-time voice server doesn't: the models run as services or as ONNX, and what's left is plumbing: audio framing, WebRTC, concurrency, and shipping a binary. For that, Go is a better fit: one static binary to deploy, low and predictable memory, fast startup, and real concurrency for many simultaneous sessions without a GIL. The heavy numerics stay where they belong (the ONNX Runtime, the remote services), so giving up Python costs little here. See the benchmarks for the honest performance picture.

Features

• Transports: WebRTC (Pion), WebSockets, LiveKit and local audio.
• Audio, pure Go: Opus encode and decode via pion/opus, resampling via go-resample. No cgo, and no build tag that adds any.
• Streaming voice pipeline: STT → LLM → TTS, with prompt caching.
• Speech-to-speech: single-model voice agents (OpenAI Realtime, Gemini Live, AWS Nova Sonic).
• Turn-taking & barge-in: Silero VAD + Smart Turn v3, local ONNX. Both models are embedded in the binary.
• Telephony (optional): inbound/outbound phone calls over Twilio Media Streams.
• User-idle watchdog: re-engage or hang up when the caller goes silent.
• RTVI data channel: works with existing RTVI clients.
• Pluggable services: swap any STT/LLM/TTS behind a small interface.
• Concurrent by design: independent processors; interruptions are frames.

Providers

Pick any per category; each is a small Config + constructor.

• STT: Deepgram, AssemblyAI, Gladia, Speechmatics, Soniox, Whisper (OpenAI/Groq/local), Azure, xAI, ElevenLabs, Cartesia, NVIDIA.
• LLM: Anthropic (direct + Bedrock), OpenAI (chat + Responses), Google Gemini (direct + Vertex), Groq, Together, Fireworks, DeepSeek, Cerebras, Perplexity, OpenRouter, xAI, Ollama, NVIDIA, Mistral, Nebius, SambaNova, Qwen, Azure OpenAI.
• TTS: ElevenLabs, Cartesia, Rime, LMNT, Kokoro, Piper, Pocket TTS, Deepgram, OpenAI, Azure, Hume, Fish, MiniMax, xAI, NVIDIA, Soniox.
• Speech-to-speech: OpenAI Realtime (direct + Azure), Gemini Live (direct + Vertex), AWS Nova Sonic, xAI Realtime.
• Memory: mem0.

Usage

go get github.com/gojargo/jargo

A bot is an STT → LLM → TTS pipeline over a WebRTC transport. The heart of it:

stt := chat.NewSTT(chat.STTConfig{APIKey: key, SampleRate: opus.SampleRate})
llm := chat.NewLLM(chat.LLMConfig{APIKey: key})
tts := chat.NewTTS(chat.TTSConfig{APIKey: key})

t := rtc.NewTransport(conn, transport.DefaultParams())
agg := aggregators.New(frames.NewLLMContext("You are a helpful voice assistant."))

task := pipeline.NewWorker(pipeline.New(
	t.Input(), stt, agg.User(), llm, tts, t.Output(), agg.Assistant(),
), pipeline.WorkerConfig{})
task.Run(ctx)

examples/voice/openai is that pipeline as a complete server (WebRTC signaling, VAD/turn-taking, barge-in).

Run it in Docker: build on the gojargo/jargo-build base and ship on the distroless gojargo/jargo runtime (it bundles the ONNX Runtime), then:

docker run --rm -p 8080:8080 -e OPENAI_API_KEY=$OPENAI_API_KEY my-bot

See Deploy with Docker for the Dockerfile and the Quickstart for the full setup.

Examples

Runnable bots live in examples/:

• echo: hear yourself back, no API keys.
• voicebot: the full voice agent (STT → LLM → TTS over WebRTC) with turn-taking, long-term memory, and tracing.
• voice/: one headless backend per provider, each wiring its STT/LLM/TTS explicitly and exposing the WebRTC /offer endpoint (no web UI). Run with go run ./examples/voice/<provider> (e.g. deepgram, cartesia, openai) and drive it from a browser client, the nextjs-voicebot in jargo-client-react.
• twiliobot: a phone agent over Twilio Media Streams, with the idle watchdog.

The fastest way to try them (locally or with Docker) is the Quickstart.

go run ./examples/echo                 # then open http://localhost:8080

Documentation

gojargo.github.io/jargo is the full documentation. The same pages live in docs/ and read fine on GitHub.

Start with Architecture for the model, or Frames and Processors for the engine. Writing a processor covers extending it. The API reference is the Go reference.

What you need to install

Nothing, to build: the default build is cgo-free, so CGO_ENABLED=0 go build ./... works with no C toolchain and no system packages.

To run a voice bot you need one shared library, and only for turn-taking:

Both libraries are loaded at run time through purego, so they are never needed at build time. Point jargo at them with JARGO_ONNXRUNTIME_LIB and JARGO_RNNOISE_LIB, or leave them on the loader's default search path. The base images bundle all of them.

License & attribution

jargo is a Go port of Pipecat, distributed under the same BSD 2-Clause License. The upstream copyright (Copyright (c) 2024–2026, Daily) is preserved verbatim in LICENSE; see NOTICE for details. jargo is an independent project, not affiliated with or endorsed by Daily.