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This page describes the generated Pipecat project and its target settings.

Deploy to Pipecat Cloud

Start here to create a deployment, choose its destination, supply credentials, and update its code or secrets.

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Target fields

Write these inside targets.<name> in targets.yaml. The name is your target instance name, used by --target and the build/<name>/ output folder.
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string
required
Use pipecat for this target. Omission is refused.
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string
required
An exact x.y.z framework version from the supported window. Omission and unsupported versions are refused; there is no automatic upgrade.
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string
Accepts python. If omitted, the generated project still uses Python.
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string
Required for telephony: the stem of a file under connections/, using cloud-websocket or daily-sip with a supported carrier. See connection fields. Omit for a browser-only package; a connection without phone use is refused.
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string or list of strings
One non-empty platform region name, as a string or a one-item list. More than one region is refused. Unmute forwards region names as written and does not check them against the platform’s region list. If omitted, no region is passed and platform placement applies. The generated manifest and secret-set instructions use the same region. See deployment regions.
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map of model definitions
Overrides keyed by existing model names from agent.yaml, using the model fields. Omit to use the package’s models. An override replaces the entry, except that omitted pace, endpointing_delay, semantic_endpointing, and prompt_suffix carry forward. An override cannot author pace or a different prompt_suffix.
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map of strings
This field is accepted but Pipecat does not read it. Omit it; the framework version and model catalog determine the generated dependencies.
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integer
A non-negative count of instances to keep ready. A positive value becomes min_agents in the deploy manifest. Omitted or zero emits no minimum, so the platform can scale to zero.

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bot.py

One file holds the agent: the prompts as module constants, the model constructors, the tool wiring, and the pipeline. It imports Pipecat and your handlers, and nothing from Unmute. The generated startup check names missing required environment variables. See the build’s .env.example and credentials reference. Your architecture determines whether the pipeline uses separate speech services or a model that handles audio directly.

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Run it without Unmute

uv installs the pinned dependencies on the first run.

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Dependencies are pinned

The generated pyproject.toml follows the providers, turn detector, and tracing settings your package uses. The framework version comes from your target’s version: field. Change the package and compile again to update dependencies.

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Deploy

Follow Deploy to Pipecat Cloud for the complete sequence: package preparation, destination selection, runtime values, first deployment, updates, and a test interaction. The guide also covers secrets-only changes. Use the generated build/pipecat/ directory as the build context. Keep secret values out of the image. Supply the runtime values listed by its .env.example and runbook through the platform’s secret store.

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The deployed agent’s name

A package named my-agent with target pipecat deploys as my-agent-pipecat. This is the package’s name: joined to its target, and it is agent_name in pcc-deploy.toml. The secret set uses the same name. A rename creates another agent and leaves the old one running. Follow Renaming the agent to move traffic and remove the old deployment.
Do not edit files in build/. Change the source package and compile again.

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Where to go next

The LiveKit project

The same agent, the other runtime.

Going live

What to do with the project you were handed.