Build a salon agent

You will build one agent, step by step. It answers the phone for a hair salon called Sage and Stone. Each page adds one piece of unmute-core and ends with a call you can try. The last page maps your code to a real example in the repository. CI runs that example on every change.

The call

A caller phones the salon. The agent, Robin, answers.

The caller asks for a haircut on Friday. Robin checks the diary and offers times.
The caller gives their phone number. Robin saves it, reads it back and waits for a yes.
The caller picks a time. Robin asks “Shall I book it?” and books only after the caller agrees.
The caller says a colour went wrong. Robin hands the call to a complaint specialist, who takes it down.
The caller says goodbye. Robin says goodbye and hangs up.

The finished agent

The agent lives in one framework-neutral package, salon/. It imports only unmute-core. The LiveKit file and the Pipecat file build the pipeline around it.

What you need

Python 3.12 and uv.
An SLNG API key and its region, for speech. The pages use SLNG’s Soniox speech to text and Fish Audio text to speech.
An OpenAI API key for the model. Any LLM plugin works.
For LiveKit, the lk CLI. Console mode needs no LiveKit server.
For Pipecat, nothing more. The Pipecat runner serves a test page on your machine.
Optional, for the last step: Langfuse keys.

Install the library with the extra for your framework.

uv add "unmute-core[livekit]" livekit-plugins-openai livekit-plugins-silero livekit-plugins-slng

uv add "unmute-core[pipecat]" "pipecat-ai[openai,runner,silero,webrtc]" pipecat-slng

The files you will grow

salon-agent/
  salon/
    pyproject.toml
    src/salon/
      state.py       what one call remembers
      store.py       the demo diary, copied from the example
      agent.py       prompt, tools, tasks, prefetch, idle nudges
  livekit/
    pyproject.toml
    agent.py         the LiveKit pipeline
  pipecat/
    pyproject.toml
    bot.py           the Pipecat pipeline

Package the salon/ folder like the example does, and add it to each framework project as a path dependency. Copy the three pyproject.toml files from examples/salon-concierge-unmute/ and keep only what you use. store.py is plain Python with no framework in it, so copy it too. Code in salon/ has no framework in it, so it has no tabs. Where LiveKit and Pipecat differ, a page shows one tab for each.

The nine steps

Step	You add
Answer the phone	The smallest agent, with a prompt and a greeting
Remember the caller	Call state, with a phone number that is checked
Check the diary	A typed tool the model can call
Book step by step	A task and a group that cannot get stuck
Hand off complaints	A second agent and the handoff to it
Know the time	Today’s date and the caller’s number, read before hello
Quiet callers	Turn pace, idle nudges and a call time limit
See every call	One Langfuse trace per call
What you built	Your code next to the full example

Start with step 1: Answer the phone

The smallest agent that picks up and says hello.

Get started