build a voice agent with livekit

building on livekit agents? use livekit-plugins-rumik-ai, our official livekit TTS plugin. it drops rumik straight into an AgentSession with streaming audio and interruption handling already wired up.

pip install livekit-plugins-rumik-ai

set your key from the dashboard:

export RUMIK_API_KEY="rk_live_•••••••••"

add it to an agent

the TTS class plugs into a livekit AgentSession next to your STT and LLM:

from livekit.agents import AgentSession
from livekit.plugins import rumik_ai

# muga: expressive, tone-tagged hinglish
session = AgentSession(
    stt=...,   # your speech-to-text plugin
    llm=...,   # your llm plugin
    tts=rumik_ai.TTS(model="muga"),
)

steer mulberry with a natural-language description, or pin a preset speaker:

# mulberry: description-driven voice
tts = rumik_ai.TTS(
    model="mulberry",
    description="a female 30s hindi voice, warm timbre, conversational pacing, like a podcast host",
)

# or pin a named studio voice. description still goes with it.
tts = rumik_ai.TTS(
    model="mulberry",
    description="a female 30s hindi voice, warm timbre, conversational pacing, like a podcast host",
    speaker="ira",
)

constructor options

argument	applies to	notes
`model`	both	`"muga"` or `"mulberry"`. default `"muga"`.
`api_key`	both	defaults to the `RUMIK_API_KEY` environment variable.
`base_url`	both	defaults to `https://silk-api.rumik.ai`.
`full_response_aggregation`	both	buffer the full reply before synthesis. default `True` for muga, `False` for mulberry.
`tone`	muga	fallback tone when the input text has no `[tone]` marker.
`description`	mulberry	required. natural-language voice description.
`speaker`	mulberry	optional. a named voice, e.g. `"ira"`. send `description` too. see preset voices.

it also accepts the shared sampling params (temperature, top_p, top_k, repetition_penalty, max_new_tokens).

muga aggregates the full tagged reply before speaking (full_response_aggregation is True by default), so it never tries to synthesize a half-tagged sentence. see prompting muga for why.

which model?

muga for short, expressive reactions you steer with [tone] tags. see prompting muga.
mulberry for low-latency conversational agents you steer with a description. see prompting mulberry.

prefer a full walkthrough? the livekit cookbook builds a working voice agent from scratch. or hand your coding agent the rumik TTS skill so it wires all of this up on the first try.

documentation

SDK

integrations

cookbook

API reference

build a voice agent with livekit

add it to an agent

constructor options

which model?

​add it to an agent

​constructor options

​which model?

add it to an agent

constructor options

which model?