AI-Assisted Development

Use AI coding tools with Pipecat documentation and source code.

Pipecat offers several ways to set up AI coding tools and feed them accurate, up-to-date context, helping you build voice and multimodal applications faster.

Make your project agent-ready (pipecat init)

If you’re building with an AI coding assistant, run pipecat init in your project first:

uv tool install "pipecat-ai[cli]"
pipecat init

This writes a Pipecat coding-agent guide (AGENTS.md + CLAUDE.md) and developer guidance (GETTING_STARTED.md) into the project. Your agent picks up AGENTS.md automatically — it follows Pipecat conventions and scaffolds the app for you with pipecat create. Pair it with the Pipecat Context Hub below so the agent works from live API context instead of stale training data.

Build Your Next Bot

The full agent-driven workflow, start to finish

Pipecat Context Hub (MCP Server)

Pipecat Context Hub is a local-first MCP server that indexes Pipecat documentation, code examples, and framework API source on your machine. It runs a local vector database and is optimized for coding workflows. It returns relevant source code and snippets rather than synthesized answers, giving your AI coding tool the raw context it needs to solve implementation problems.

Because it runs locally, you can also add your own repositories (public or private) to the index for a more customized context.

Available tools include search_docs, search_examples, search_api, get_code_snippet, and check_deprecation.

Setup

The hub is published to PyPI as pipecat-ai-context-hub and runs with uv — no clone required. Build the local index once:

uvx pipecat-ai-context-hub refresh   # build local index (~2 min first run)

Add to your coding tool

Claude Code

claude mcp add pipecat-context-hub -- uvx pipecat-ai-context-hub serve

Codex

codex mcp add pipecat-context-hub -- uvx pipecat-ai-context-hub serve

Cursor

Add to your Cursor MCP config:

{
  "mcpServers": {
    "pipecat-context-hub": {
      "command": "uvx",
      "args": ["pipecat-ai-context-hub", "serve"]
    }
  }
}

VS Code

Add to .vscode/mcp.json:

{
  "servers": {
    "pipecat-context-hub": {
      "command": "uvx",
      "args": ["pipecat-ai-context-hub", "serve"]
    }
  }
}

A newly added MCP server loads when your coding tool starts a session, so add it before opening the session you want to use it in.

Use it without MCP

The same tools are available as one-shot CLI commands, so you can query the index from a shell with no MCP setup — handy in a session where the MCP server isn’t connected yet:

uvx pipecat-ai-context-hub check-deprecation PipelineTask
uvx pipecat-ai-context-hub search-api "WebsocketServerParams"
uvx pipecat-ai-context-hub status            # index health / freshness

Each command prints the tool’s JSON to stdout. The package name is pipecat-ai-context-hub; the command and MCP server are named pipecat-context-hub (both spellings of the command resolve once installed).

See the repo docs for additional configuration options, including pinning to a specific Pipecat version.

CLAUDE.md in the framework repo

Separate from the project guide pipecat init writes, the Pipecat framework repository ships its own CLAUDE.md with architecture guidance, coding conventions, and development patterns for the framework codebase itself. When you work in a cloned copy of the Pipecat repo — for example, contributing to the framework — Claude Code reads it automatically.

Markdown docs

Every page on this docs site has a Copy page button (in the top-right area) that copies the page content as markdown. This is useful for pasting into any AI chat or coding tool to provide context about a specific topic.

llms.txt

Pipecat docs support the llms.txt standard, which provides a machine-readable index of documentation content. This is useful for tools that can ingest documentation in bulk.

• https://docs.pipecat.ai/llms.txt — structured index of all pages
• https://docs.pipecat.ai/llms-full.txt — full documentation content in a single file