Skip to main content

An open source framework for voice (and multimodal) assistants

Project description

pipecat

PyPI Tests codecov Docs Discord

🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents

Pipecat is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique

Want to dive right in? Try the quickstart.

🚀 What You Can Build

  • Voice Assistants – natural, streaming conversations with AI
  • AI Companions – coaches, meeting assistants, characters
  • Multimodal Interfaces – voice, video, images, and more
  • Interactive Storytelling – creative tools with generative media
  • Business Agents – customer intake, support bots, guided flows
  • Complex Dialog Systems – design logic with structured conversations

🧭 Looking to build structured conversations? Check out Pipecat Flows for managing complex conversational states and transitions.

🧠 Why Pipecat?

  • Voice-first: Integrates speech recognition, text-to-speech, and conversation handling
  • Pluggable: Supports many AI services and tools
  • Composable Pipelines: Build complex behavior from modular components
  • Real-Time: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)

🎬 See it in action

 
 

📱 Client SDKs

You can connect to Pipecat from any platform using our official SDKs:

Platform SDK Repo Description
Web pipecat-client-web JavaScript and React client SDKs
iOS pipecat-client-ios Swift SDK for iOS
Android pipecat-client-android Kotlin SDK for Android
C++ pipecat-client-cxx C++ client SDK

🧩 Available services

Category Services
Speech-to-Text AssemblyAI, AWS, Azure, Cartesia, Deepgram, Fal Wizper, Gladia, Google, Groq (Whisper), NVIDIA Riva, OpenAI (Whisper), SambaNova (Whisper), Soniox, Speechmatics, Ultravox, Whisper
LLMs Anthropic, AWS, Azure, Cerebras, DeepSeek, Fireworks AI, Gemini, Grok, Groq, Mistral, NVIDIA NIM, Ollama, OpenAI, OpenRouter, Perplexity, Qwen, SambaNova Together AI
Text-to-Speech Async, AWS, Azure, Cartesia, Deepgram, ElevenLabs, Fish, Google, Groq, Inworld, LMNT, MiniMax, Neuphonic, NVIDIA Riva, OpenAI, Piper, PlayHT, Rime, Sarvam, XTTS
Speech-to-Speech AWS Nova Sonic, Gemini Multimodal Live, OpenAI Realtime
Transport Daily (WebRTC), FastAPI Websocket, SmallWebRTCTransport, WebSocket Server, Local
Serializers Plivo, Twilio, Telnyx
Video HeyGen, Tavus, Simli
Memory mem0
Vision & Image fal, Google Imagen, Moondream
Audio Processing Silero VAD, Krisp, Koala, ai-coustics, Noisereduce
Analytics & Metrics OpenTelemetry, Sentry

📚 View full services documentation →

⚡ Getting started

You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when you're ready.

  1. Install uv

    curl -LsSf https://astral.sh/uv/install.sh | sh
    

    Need help? Refer to the uv install documentation.

  2. Install the module

    # For new projects
    uv init my-pipecat-app
    cd my-pipecat-app
    uv add pipecat-ai
    
    # Or for existing projects
    uv add pipecat-ai
    
  3. Set up your environment

    cp env.example .env
    
  4. To keep things lightweight, only the core framework is included by default. If you need support for third-party AI services, you can add the necessary dependencies with:

    uv add "pipecat-ai[option,...]"
    

Using pip? You can still use pip install pipecat-ai and pip install "pipecat-ai[option,...]" to get set up.

🧪 Code examples

  • Foundational — small snippets that build on each other, introducing one or two concepts at a time
  • Example apps — complete applications that you can use as starting points for development

🛠️ Contributing to the framework

Prerequisites

Minimum Python Version: 3.10 Recommended Python Version: 3.12

Setup Steps

  1. Clone the repository and navigate to it:

    git clone https://github.com/pipecat-ai/pipecat.git
    cd pipecat
    
  2. Install development and testing dependencies:

    uv sync --group dev --all-extras --no-extra gstreamer --no-extra krisp --no-extra local
    
  3. Install the git pre-commit hooks:

    uv run pre-commit install
    

Python 3.13+ Compatibility

Some features require PyTorch, which doesn't yet support Python 3.13+. Install using:

uv sync --group dev --all-extras \
  --no-extra gstreamer \
  --no-extra krisp \
  --no-extra local \
  --no-extra local-smart-turn \
  --no-extra mlx-whisper \
  --no-extra moondream \
  --no-extra ultravox

Tip: For full compatibility, use Python 3.12: uv python pin 3.12

Note: Some extras (local, gstreamer) require system dependencies. See documentation if you encounter build errors.

Running tests

To run all tests, from the root directory:

uv run pytest

Run a specific test suite:

uv run pytest tests/test_name.py

Setting up your editor

This project uses strict PEP 8 formatting via Ruff.

Emacs

You can use use-package to install emacs-lazy-ruff package and configure ruff arguments:

(use-package lazy-ruff
  :ensure t
  :hook ((python-mode . lazy-ruff-mode))
  :config
  (setq lazy-ruff-format-command "ruff format")
  (setq lazy-ruff-check-command "ruff check --select I"))

ruff was installed in the venv environment described before, so you should be able to use pyvenv-auto to automatically load that environment inside Emacs.

(use-package pyvenv-auto
  :ensure t
  :defer t
  :hook ((python-mode . pyvenv-auto-run)))

Visual Studio Code

Install the Ruff extension. Then edit the user settings (Ctrl-Shift-P Open User Settings (JSON)) and set it as the default Python formatter, and enable formatting on save:

"[python]": {
    "editor.defaultFormatter": "charliermarsh.ruff",
    "editor.formatOnSave": true
}

PyCharm

ruff was installed in the venv environment described before, now to enable autoformatting on save, go to File -> Settings -> Tools -> File Watchers and add a new watcher with the following settings:

  1. Name: Ruff formatter
  2. File type: Python
  3. Working directory: $ContentRoot$
  4. Arguments: format $FilePath$
  5. Program: $PyInterpreterDirectory$/ruff

🤝 Contributing

We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:

  • Found a bug? Open an issue
  • Have a feature idea? Start a discussion
  • Want to contribute code? Check our CONTRIBUTING.md guide
  • Documentation improvements? Docs PRs are always welcome

Before submitting a pull request, please check existing issues and PRs to avoid duplicates.

We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.

🛟 Getting help

➡️ Join our Discord

➡️ Read the docs

➡️ Reach us on X

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dv_pipecat_ai-0.0.85.dev12.tar.gz (12.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dv_pipecat_ai-0.0.85.dev12-py3-none-any.whl (12.3 MB view details)

Uploaded Python 3

File details

Details for the file dv_pipecat_ai-0.0.85.dev12.tar.gz.

File metadata

  • Download URL: dv_pipecat_ai-0.0.85.dev12.tar.gz
  • Upload date:
  • Size: 12.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for dv_pipecat_ai-0.0.85.dev12.tar.gz
Algorithm Hash digest
SHA256 f316c53e17e6036d2b712c34fe259b171fd9cdf6e73872c179ab620921a455d7
MD5 9a7e4af1af9953fa480bde02bc6a8856
BLAKE2b-256 0c67f42f2c413643f2007871f77e819cb20699250d18f602f622f99e5a5a2292

See more details on using hashes here.

File details

Details for the file dv_pipecat_ai-0.0.85.dev12-py3-none-any.whl.

File metadata

File hashes

Hashes for dv_pipecat_ai-0.0.85.dev12-py3-none-any.whl
Algorithm Hash digest
SHA256 a5ad5bdeddb6e83bd3da2666cf392d88928229b6f329febf4a2bcb3df3777f73
MD5 f5817577057c9fd16e49c01cc8818e36
BLAKE2b-256 9b911ab4b8fe217c5f8c7f6171934ea47dadc7df0d6a4d354c201649acb36528

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page