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Four example agents show unmute-core on calls that are nothing like the salon. Each one is a real project in the repo’s examples/ folder. Each one runs on LiveKit and on Pipecat from the same code, and each one is checked with whole scripted calls on its real model. Use them when:
  • You want to see a whole agent, not one feature.
  • Your call has a shape the salon does not have: a long intake, an outbound call, a tools-only finder, two languages.
  • You want to copy a working setup for SLNG speech and Gemini in the EU.

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Pick the closest one

All four follow one idea: type the agent’s path. The model reads what the caller meant and turns it into a typed tool call. Code checks it, saves it, and says the next line. Type the path explains it and how to apply it to your own agent.

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What they have in common

  1. One package per agent. It holds the State, the tools, the tasks and the prompts. It imports no framework.
  2. Two small projects. livekit/agent.py and pipecat/bot.py are about 20 lines each. They import the package’s APP and run it.
  3. One voice stack. examples/voice-stack builds the framework side once: SLNG speech to text and text to speech, Gemini on Vertex AI, the turn pace, the greeting and the handoffs. Use it as a starting point for your own pipeline.
Every value can be changed in the project’s .env. The .env.example of each project lists them.

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Run one

Each example’s README lists what to say on a call, and the demo data it ships with.

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Check one

Every example has whole calls written as JSON in its scenarios/ folder. A scripted caller says each line, and the check reads what the agent said, the tools it called, the model requests each turn made, and the state at the end.
A scenario can also send its start values the way a real dispatch does: in the LiveKit job’s metadata, or in the Pipecat runner’s request body. A scenario with refused checks that a call with a missing or wrong start value never starts, and that the log says why. The --audio run speaks each caller line with SLNG text to speech and sends it through the real pipeline: voice detection, turn detection, speech to text, the model and text to speech.

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In this section

Type the path

Spend the model on understanding, and let code do the rest.

Nosh: a tools-only finder

One agent, three tools, and one model request per caller turn.

Kestrel: a long intake

Code asks every question and reads the whole claim back.

Wasla: an outbound reminder

Verify the person first, then record a promise or a concern.

Faro: two languages

One agent per language, and a handoff to switch.