SLNG speech and the Context Router

SLNG gives you speech to text, text to speech and the Context Router. You can use each one alone, or all three together. The library does not wrap the speech plugins. It only reads their metrics for telemetry.

Install

Add the slng extra beside your framework extra:

uv add "unmute-core[livekit,slng]"

The speech plugins are separate packages: livekit-plugins-slng for LiveKit and pipecat-slng for Pipecat. The installation page lists every extra.

Speech only

Use SLNG speech to text and text to speech with any LLM. This example uses Soniox to hear the caller and Fish Audio to speak.

import os

from livekit.plugins import slng

from unmute_core.slng import tracking_ids

world_part = os.environ["SLNG_WORLD_PART"]  # your SLNG project's region, like eu-north

stt = slng.STT(
    api_key=os.environ["SLNG_API_KEY"],
    slng_base_url=f"{world_part}.api.slng.ai",
    model="soniox/speech-ai:rt-v5",
    language="en",
    **tracking_ids(state.call),
)
tts = slng.TTS(
    api_key=os.environ["SLNG_API_KEY"],
    slng_base_url=f"{world_part}.api.slng.ai",
    voice="8d8aec63a56c460cb7def795bdf754fa",
    model="slng/fish/tts:s2.1-pro",
    language="en",
    **tracking_ids(state.call),
)

import os

from pipecat.transcriptions.language import Language
from pipecat_slng import SlngSTTService, SlngTTSService

from unmute_core.slng import tracking_ids

world_part = os.environ["SLNG_WORLD_PART"]  # your SLNG project's region, like eu-north

stt = SlngSTTService(
    api_key=os.environ["SLNG_API_KEY"],
    model="soniox/speech-ai:rt-v5",
    language=Language("en"),
    world_part=world_part,
    **tracking_ids(state.call),
)
tts = SlngTTSService(
    api_key=os.environ["SLNG_API_KEY"],
    voice="8d8aec63a56c460cb7def795bdf754fa",
    model="slng/fish/tts:s2.1-pro",
    language=Language("en"),
    world_part=world_part,
    **tracking_ids(state.call),
)

tracking_ids(state.call) gives both plugins the agent’s id and the call’s session id. SLNG groups usage by them. Build the call state first, so the ids exist.
Put both services in the session or the pipeline. On LiveKit, also call stt.attach_to_session(session) after you build the session.
SLNG_WORLD_PART must be your SLNG project’s region. A key used in another region gets 401 from the router.

Use the Context Router

RouterModelCall sends each model request to the SLNG Context Router. Give it to your framework through a small adapter.

from unmute_core.livekit import ModelCallLLM
from unmute_core.slng import RouterModelCall

router = RouterModelCall(
    state.call,
    model="gpt-5.6-luna",
    config=config,
    body={"reasoning_effort": "none"},
)
session = AgentSession(llm=ModelCallLLM(router, state=state), ...)

from unmute_core.pipecat import ModelCallLLMService
from unmute_core.slng import RouterModelCall

router = RouterModelCall(
    state.call,
    model="gpt-5.6-luna",
    config=config,
    body={"reasoning_effort": "none"},
)
llm = ModelCallLLMService(router, state=state)

The adapter sends the prompt with its {{placeholders}} still in it, and the values from state beside it. The router fills the prompt, so the model sees the same text it would see on a native LLM.
The router retries and fails over by its own rules. config names the models and your keys: see the next section.
OpenAI refuses function tools with reasoning on, so an agent with tools sends reasoning_effort "none". An agent with no tools may leave it out.

With UnmuteAgent, give the router to the agent’s spec instead: AgentSpec(PROMPT, model=router). Build the router per call, after the state, because it reads state.call. Arguments you leave out are read from the SLNG_* settings.

Bring your own model key

Send your own router config with every request. It names the model the router uses and holds your own provider key.

import os

from unmute_core.slng import OpenAIEndpoint, RouterConfig

config = RouterConfig.single(
    "gpt-5.6-luna",
    OpenAIEndpoint(
        url="https://api.openai.com/v1",
        api_key=os.environ["OPENAI_API_KEY"],
    ),
)

RouterConfig.single puts one model on your OpenAI key. RouterModelCall(config=config) sends it with every request.
Always pass a config. Without one, the router uses a stored config that may not select your model, and answers 400.
Only an SLNG key from a project on the Execution Layer may send a config. Any other key gets 403 project_mode_forbidden.

For weights, fallbacks and other providers, see Your router config.

Without the router

OpenAIModelCall is a native call for any OpenAI-compatible endpoint. It sends nothing to SLNG, and the library fills the prompt. See Use any OpenAI-compatible model. The complete files with speech and the router together are examples/parcel-desk/livekit/agent.py and examples/parcel-desk/pipecat/bot.py.

Where to go next

SLNG reference

Every RouterModelCall argument, header, config field, error and Gemini detail.

The model call

The interface the router implements, and FallbackModelCall.

Settings

The SLNG_* variables and how to check them at startup.

Context Router on Unmute

The agent.yaml side of the router.

Get started

Build a salon agent

On your framework

Guides

Example agents

Models

Phone calls

Run and observe

Reference

SLNG speech and the Context Router

Install

Speech only

Use the Context Router

Bring your own model key

Without the router

Where to go next

SLNG reference

The model call

Settings

Context Router on Unmute

​Install

​Speech only

​Use the Context Router

​Bring your own model key

​Without the router

​Where to go next

SLNG reference

The model call

Settings

Context Router on Unmute

Install

Speech only

Use the Context Router

Bring your own model key

Without the router

Where to go next