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Ready to build your own bot? The Pipecat CLI takes you from zero to a runnable project, whether you build with an AI coding agent or scaffold it by hand.
New to Pipecat? We recommend completing the Quickstart first to understand Pipecat basics before scaffolding your own project.

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Install the Pipecat CLI

Install the CLI globally with uv:
Verify installation:

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Start with pipecat init

pipecat init is the starting point. It writes the Pipecat coding-agent guide (AGENTS.md + CLAUDE.md) so your coding agent works well with Pipecat, then asks how you want to build:
  • Build with a coding agent (recommended). Let an AI coding assistant (Claude Code, Codex, …) do the building. Continue with Build with a coding agent below.
  • Scaffold a runnable bot now. Prefer to drive the CLI yourself? Skip to Scaffold it yourself.

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Build with a coding agent

AI coding tools like Claude Code and Codex write your agent code. The agent follows the AGENTS.md guide that init wrote, so it uses Pipecat conventions, scaffolds the app for you, and verifies its own work. This path also writes GETTING_STARTED.md, a short guide to driving the agent well.

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The Pipecat Context Hub

The Context Hub indexes Pipecat docs, examples, and API source into a local database, so your agent queries live context instead of stale training data. It ships with the CLI, and init sets it up for you: it registers the MCP server with the coding agents it finds, then offers to build the index. The build takes upwards of three minutes and about 900 MB of disk, so init asks rather than assuming. If you skipped it, run:
If init found no coding agent CLI to register with — the case for editors configured by hand, like Cursor, VS Code, and Zed — run pipecat context-hub install instead, which prints the config block to paste and builds the index. Do it before opening your coding session — MCP servers load at session start, and the server won’t start against an empty index.

Pipecat Context Hub reference

Full setup, the tool list, CLI lookups, and MCP config for Cursor and VS Code

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Start a coding session

Open Claude Code or Codex in your project and prompt it to build. The agent follows AGENTS.md, queries the Context Hub for accurate APIs, and scaffolds and iterates on your bot.
Prefer to feed context by hand? Pipecat docs support the llms.txt standard: llms.txt is a structured index of all pages, and llms-full.txt is the full documentation in a single file, useful for tools that ingest docs in bulk.

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Scaffold it yourself

Prefer to drive the CLI directly? When you pick Scaffold a runnable bot now, init runs an interactive setup wizard that walks you through your platform (phone or web/mobile), transport provider, AI services (STT, LLM, TTS), and deployment target, then scaffolds the project in place alongside AGENTS.md + CLAUDE.md. The generated README includes Pipecat Context Hub setup, so the project is ready for a coding agent too. Once scaffolding completes, the CLI provides specific next steps for your generated project:
  • How to configure your API keys
  • Installing dependencies
  • Running your bot locally
  • Customizing the bot logic and behavior

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Test with evals

Before deploying, pin down what the agent should do with an eval: a scripted conversation, run against the real pipeline, judged on what the bot actually said. On the coding-agent path above this pays for itself quickly — the assistant can run the evals itself and keep iterating until they pass.

Evals Quickstart

Write a scenario and run it against your agent

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Deploy to Production

Ready to deploy your bot? Choose between managed cloud hosting or self-hosted infrastructure.

Pipecat Cloud

Deploy and manage your agents with the CLI - purpose-built for Pipecat

Self-Hosted Options

Deploy to Fly.io, Modal, AWS, or your own infrastructure

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Learn More

Core Concepts

Understand pipelines, processors, and transports

Browse Examples

30+ production-ready examples for inspiration