Pipecat AI Prebuilt

A simple, ready-to-use prebuilt client supporting all Pipecat transports.

This prebuilt client provides a lightweight UI to quickly test and verify transport behavior without needing a custom implementation.

Ideal for development, debugging, and quick prototyping.

📦 Installation & Usage

If you just want to use the prebuilt client in your own Python project:

✅ Install from PyPI

pip install pipecat-ai-prebuilt

🧰 Example Usage

from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from pipecat_ai_prebuilt.frontend import PipecatPrebuiltUI

app = FastAPI()

# Mount the frontend at /client
app.mount("/client", PipecatPrebuiltUI)

@app.get("/", include_in_schema=False)
async def root_redirect():
    return RedirectResponse(url="/client/")

🧪 Try a Sample App

Want to see it in action? Check out our sample app demonstrating how to use this module:

• 👉 Sample App

⌨ Development Quick Start

If you want to work on the prebuilt client itself or use it locally in development:

📋 Prerequisites

• Node.js (for building the client)
• uv (recommended for Python dependency management)

🔧 Set Up the Environment

1. Clone the Repository

git clone https://github.com/pipecat-ai/pipecat-prebuilt.git
cd pipecat-ai-prebuilt

2. Build the Client

The Python package serves a built React client, so you need to build it first:

cd client
npm install
npm run build
cd ..

This creates the client/dist/ directory that the Python package will serve.

3. Try the Sample App

Now you can test the local package with the sample app:

cd test
uv sync  # Installs dependencies and the local package in editable mode
uv run bot.py

Then open http://localhost:7860 in your browser.

🚀 Publishing

Publishing is automated via GitHub Actions using trusted publishing (no API tokens needed).

Prerequisites

1. Update the version in pyproject.toml:

   version = "1.0.0"
   

2. Create a git tag:

   git tag -m v1.0.0 v1.0.0
   git push --tags origin
   

Publishing Process

1. Go to GitHub Actions in your repository
2. Select the "publish" workflow
3. Click "Run workflow"
4. Enter the git tag (e.g., v1.0.0)
5. Click "Run workflow"

The workflow will:

• Build the client (React/Vite)
• Bundle it into the Python package
• Build the Python package with version from pyproject.toml
• Publish to both Test PyPI and PyPI

Testing Before Production

To test publishing without creating a release:

1. Use the publish-test workflow (publishes to Test PyPI only):

   • Go to GitHub Actions → "publish-test" workflow
   • Click "Run workflow"
   • No git tag needed!

2. Install from Test PyPI:

   pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ pipecat-ai-prebuilt
   

3. Test your changes, then use the regular publish workflow for production

Local Build Testing

To test the build locally before publishing, use the provided build script. It builds the React client, bundles it into the Python package, and produces the distribution artifacts in dist/.

Run from the repo root:

./scripts/local_build.sh

The script will:

1. Clear any previous dist/ artifacts
2. Install client npm dependencies
3. Build the React client (client/dist/)
4. Copy the built client into the Python package
5. Build the Python package with uv build
6. Clean up the temporary client copy

The resulting .whl and .tar.gz files will be in dist/. You can install the wheel directly to test it:

pip install dist/pipecat_ai_prebuilt-*.whl