Pipecat Init

Scaffold a new Pipecat project by collecting configuration from the user and running pipecat init in non-interactive mode.

Arguments

/init [<target-dir>]

• <target-dir> (optional): Directory to create the project in. Defaults to a new directory named after the project. Use . for the current directory.

Prerequisites

Check if pipecat is installed by running pipecat --version. If not installed, tell the user to install it with uv tool install pipecat-ai-cli and stop.

Discover Available Options

Before asking the user any questions, run pipecat init --list-options to get the current valid values for all fields. The output is JSON:

{
  "bot_type": ["web", "telephony"],
  "transports": {
    "web": ["daily", "smallwebrtc"],
    "telephony": ["twilio", "telnyx", ...]
  },
  "stt": ["deepgram_stt", "openai_stt", ...],
  "llm": ["openai_llm", "anthropic_llm", ...],
  "tts": ["cartesia_tts", "elevenlabs_tts", ...],
  "realtime": ["openai_realtime", "gemini_live_realtime", ...],
  "video": ["heygen_video", "tavus_video", "simli_video"]
}

Use this data to populate the choices in every question below. Do NOT hardcode service lists — always use the values from --list-options.

Configuration Flow

Walk through the following questions to build the project configuration. After collecting all answers, show a summary and run the command.

Choosing the right interaction method:

• AskUserQuestion — Use for questions with a small, fixed set of options (bot type, pipeline mode, client framework, yes/no questions). This gives a clean clickable UI.
• Show list as text — Use for questions with many options (STT, LLM, TTS, realtime, video, transports). Display the full list of available options from --list-options formatted as a readable list, then let the user reply with their choice in chat.

Step 1: Project Name

Ask the user for a project name. This is passed as the target directory (positionally) — the project is scaffolded in place in a directory of that name, and the project identifier is derived from it.

Step 2: Bot Type

Ask the user to choose a bot type:

• Web/Mobile (web) - Browser or mobile app
• Telephony (telephony) - Phone calls

Step 3: Client Framework (web only)

If the bot type is web, ask the user to choose a client framework:

• React (react)
• Vanilla JS (vanilla)
• None (none) - Server only, no client generated

If the user chose React, ask which dev server:

• Vite (vite)
• Next.js (nextjs)

Skip this step entirely for telephony bots.

Step 4: Transport

Show the user the full list of available transports from --list-options, filtered by the selected bot type. Let the user reply with their choice.

If the user chose a daily_pstn transport, ask for mode:

• Dial-in (receive calls) → use --daily-pstn-mode dial-in
• Dial-out (make calls) → use --daily-pstn-mode dial-out

If the user chose a twilio_daily_sip transport, ask for mode:

• Dial-in (receive calls) → use --twilio-daily-sip-mode dial-in
• Dial-out (make calls) → use --twilio-daily-sip-mode dial-out

Then ask if they want to add an additional transport for local testing. This is common — e.g. a telephony bot that also supports WebRTC for development.

Step 5: Pipeline Mode

Ask the user to choose a pipeline architecture:

• Cascade (cascade) - STT → LLM → TTS pipeline
• Realtime (realtime) - Speech-to-speech model

Step 6: AI Services

If cascade mode, show the full list of available options from --list-options for each service and let the user reply with their choice:

1. Speech-to-Text (STT) — Show all available STT services
2. Language Model (LLM) — Show all available LLM services
3. Text-to-Speech (TTS) — Show all available TTS services

If realtime mode, show all available realtime services and let the user reply with their choice.

For each service question, display the options as a numbered vertical list (one per line) so the user can easily scan and pick one.

Step 7: Features

Show the user the default feature settings and ask if they want to customize:

Defaults:

• Audio recording: No
• Transcription logging: No
• Video avatar service: None
• Video input: No (web only)
• Video output: No (web only)
• Observability: No

If they want to customize, ask about each feature. For video avatar service (web bots only), use the video options from --list-options.

If a video avatar service is selected, video output is automatically enabled.

Step 8: Deployment

Ask if they want to generate Pipecat Cloud deployment files (Dockerfile, pcc-deploy.toml). Default is yes.

If deploying to cloud, ask if they want to enable Krisp noise cancellation. Default is no.

Building the Command

After collecting all answers, build the pipecat init command using non-interactive flags. Lead with the project name as the positional target directory — the project is scaffolded in place there:

pipecat init <project_name> \
  --bot-type <web|telephony> \
  --transport <transport> \
  --mode <cascade|realtime> \
  [--stt <service>] \
  [--llm <service>] \
  [--tts <service>] \
  [--realtime <service>] \
  [--video <service>] \
  [--client-framework <react|vanilla|none>] \
  [--client-server <vite|nextjs>] \
  [--daily-pstn-mode <dial-in|dial-out>] \
  [--twilio-daily-sip-mode <dial-in|dial-out>] \
  [--recording | --no-recording] \
  [--transcription | --no-transcription] \
  [--video-input | --no-video-input] \
  [--video-output | --no-video-output] \
  [--deploy-to-cloud | --no-deploy-to-cloud] \
  [--enable-krisp | --no-enable-krisp] \
  [--observability | --no-observability] \
  [--eval | --no-eval]

Passing scaffold flags makes pipecat init run non-interactively (no prompts) and scaffold the runnable bot in place, alongside the coding-agent guide files it writes (AGENTS.md, CLAUDE.md).

Use . as the target to scaffold into the current directory instead of a new one.

For multiple transports, repeat the --transport flag (e.g. --transport twilio --transport smallwebrtc).

Confirmation

Before running the command, show the user a summary of their choices:

• Project name
• Bot type
• Client framework (if web)
• Transport(s)
• Pipeline mode and services
• Features enabled
• Deployment target

Ask the user to confirm before proceeding. If they want to change something, go back and re-ask that specific question.

Running the Command

Run the pipecat init command. The positional target directory controls where the project is created — use the <target-dir> argument if the user provided one, otherwise default to a new directory named after the project.

If the command succeeds, show the user what was generated and suggest next steps:

1. cd <project_name>/server
2. Copy .env.example to .env and fill in API keys
3. Run the bot

If deploying to cloud, also mention they can use /pipecat-cloud:deploy to deploy.

Error Handling

• If pipecat init fails with validation errors, show the error and help the user fix their choices.
• If the target directory already exists and is not empty, warn the user before proceeding.