
pipecat-flows
Open source conversation framework and visual editor for structured Pipecat dialogues
Stars: 443

Pipecat Flows is a framework designed for building structured conversations in AI applications. It allows users to create both predefined conversation paths and dynamically generated flows, handling state management and LLM interactions. The framework includes a Python module for building conversation flows and a visual editor for designing and exporting flow configurations. Pipecat Flows is suitable for scenarios such as customer service scripts, intake forms, personalized experiences, and complex decision trees.
README:
Pipecat Flows is an add-on framework for Pipecat that allows you to build structured conversations in your AI applications. It enables you to create both predefined conversation paths and dynamically generated flows while handling the complexities of state management and LLM interactions.
The framework consists of:
- A Python module for building conversation flows with Pipecat
- A visual editor for designing and exporting flow configurations
- Python 3.10 or higher
- Pipecat
-
Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
Need help? Refer to the uv install documentation.
-
Install the module
# For new projects uv init my-pipecat-flows-app cd my-pipecat-flows-app uv add pipecat-ai-flows # Or for existing projects uv add pipecat-ai-flows
Using pip? You can still use
pip install pipecat-ai-flows
to get set up.
See Quick Start README.
For more detailed examples and guides, visit our documentation.
The repository includes several complete example implementations demonstrating various features of Pipecat Flows.
- Static Flows: Pre-defined conversation paths including food ordering, movie exploration, patient intake, and travel planning
- Dynamic Flows: Runtime-generated flows including insurance quotes, restaurant reservations, and warm transfers
Each example is available in multiple LLM provider formats (OpenAI, Anthropic, Google Gemini, AWS Bedrock) to demonstrate cross-platform compatibility.
For detailed setup instructions, configuration, and running examples, see the Examples README.
Quick start:
# Install dependencies
uv sync
uv pip install "pipecat-ai[daily,openai,deepgram,cartesia,silero,examples]"
# Configure environment
cp env.example .env # Add your API keys
# Run an example
uv run python examples/static/food_ordering.py -u YOUR_DAILY_ROOM_URL
-
Clone the repository and navigate to it:
git clone https://github.com/pipecat-ai/pipecat-flows.git cd pipecat-flows
-
Install development dependencies:
uv sync --group dev
-
Install the git pre-commit hooks (these help ensure your code follows project rules):
uv run pre-commit install
The package is automatically installed in editable mode when you run
uv sync
.
The package includes a comprehensive test suite covering the core functionality.
-
Install Dependencies:
uv sync --group dev uv add "pipecat-ai[google,aws,openai,anthropic]"
Run all tests:
uv run pytest tests/
Run specific test file:
uv run pytest tests/test_state.py
Run specific test:
uv run pytest tests/test_state.py -k test_initialization
Run with coverage report:
uv run pytest tests/ --cov=pipecat_flows
The Flows Editor is deprecated and will be removed along with Static Flows.
We have future plans to introduce a new editor capability that supports dynamic flows, but we don't have a timeline yet. We're open to collaborating with any developers or teams who have an interesting in building out this type of UI! Reach out to us on Discord if you're interested.
A visual editor for creating and managing Pipecat conversation flows.
- Visual flow creation and editing
- Import/export of flow configurations
- Support for node and edge functions
- Merge node support for complex flows
- Real-time validation
While the underlying system is flexible with node naming, the editor follows these conventions for clarity:
- Start Node: Named after your initial conversation state (e.g., "greeting", "welcome")
- End Node: Conventionally named "end" for clarity, though other names are supported
- Flow Nodes: Named to reflect their purpose in the conversation (e.g., "get_time", "confirm_order")
These conventions help maintain readable and maintainable flows while preserving technical flexibility.
The editor is available online at flows.pipecat.ai.
- Node.js (v14 or higher)
- npm (v6 or higher)
Clone the repository
git clone [email protected]:pipecat-ai/pipecat-flows.git
Navigate to project directory
cd pipecat-flows/editor
Install dependencies
npm install
Start development server
npm run dev
Open the page in your browser: http://localhost:5173.
- Create a new flow using the toolbar buttons
- Add nodes by right-clicking in the canvas
- Start nodes can have descriptive names (e.g., "greeting")
- End nodes are conventionally named "end"
- Connect nodes by dragging from outputs to inputs
- Edit node properties in the side panel
- Export your flow configuration using the toolbar
The editor/examples/
directory contains sample flow configurations:
food_ordering.json
movie_explorer.py
patient_intake.json
restaurant_reservation.json
travel_planner.json
To use an example:
- Open the editor
- Click "Import Flow"
- Select an example JSON file
See the examples directory for the complete files and documentation.
-
npm start
- Start production server -
npm run dev
- Start development server -
npm run build
- Build for production -
npm run preview
- Preview production build locally -
npm run preview:prod
- Preview production build with base path -
npm run lint
- Check for linting issues -
npm run lint:fix
- Fix linting issues -
npm run format
- Format code with Prettier -
npm run format:check
- Check code formatting -
npm run docs
- Generate documentation -
npm run docs:serve
- Serve documentation locally
The Pipecat Flows Editor project uses JSDoc for documentation. To generate and view the documentation:
Generate documentation:
npm run docs
Serve documentation locally:
npm run docs:serve
View in browser by opening: http://localhost:8080
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.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for pipecat-flows
Similar Open Source Tools

pipecat-flows
Pipecat Flows is a framework designed for building structured conversations in AI applications. It allows users to create both predefined conversation paths and dynamically generated flows, handling state management and LLM interactions. The framework includes a Python module for building conversation flows and a visual editor for designing and exporting flow configurations. Pipecat Flows is suitable for scenarios such as customer service scripts, intake forms, personalized experiences, and complex decision trees.

action_mcp
Action MCP is a powerful tool for managing and automating your cloud infrastructure. It provides a user-friendly interface to easily create, update, and delete resources on popular cloud platforms. With Action MCP, you can streamline your deployment process, reduce manual errors, and improve overall efficiency. The tool supports various cloud providers and offers a wide range of features to meet your infrastructure management needs. Whether you are a developer, system administrator, or DevOps engineer, Action MCP can help you simplify and optimize your cloud operations.

BuildCLI
BuildCLI is a command-line interface (CLI) tool designed for managing and automating common tasks in Java project development. It simplifies the development process by allowing users to create, compile, manage dependencies, run projects, generate documentation, manage configuration profiles, dockerize projects, integrate CI/CD tools, and generate structured changelogs. The tool aims to enhance productivity and streamline Java project management by providing a range of functionalities accessible directly from the terminal.

codepair
CodePair is an open-source real-time collaborative markdown editor with AI intelligence, allowing users to collaboratively edit documents, share documents with external parties, and utilize AI intelligence within the editor. It is built using React, NestJS, and LangChain. The repository contains frontend and backend code, with detailed instructions for setting up and running each part. Users can choose between Frontend Development Only Mode or Full Stack Development Mode based on their needs. CodePair also integrates GitHub OAuth for Social Login feature. Contributors are welcome to submit patches and follow the contribution workflow.

pear-landing-page
PearAI Landing Page is an open-source AI-powered code editor managed by Nang and Pan. It is built with Next.js, Vercel, Tailwind CSS, and TypeScript. The project requires setting up environment variables for proper configuration. Users can run the project locally by starting the development server and visiting the specified URL in the browser. Recommended extensions include Prettier, ESLint, and JavaScript and TypeScript Nightly. Contributions to the project are welcomed and appreciated.

aigne-doc-smith
AIGNE DocSmith is a powerful AI-driven documentation generation tool that automates the creation of detailed, structured, and multi-language documentation directly from source code. It intelligently analyzes codebase to generate a comprehensive document structure, populates content with high-quality AI-powered generation, supports seamless translation into 12+ languages, integrates with AIGNE Hub for large language models, offers Discuss Kit publishing, automatically updates documentation with source code changes, and allows for individual document optimization.

eliza
Eliza is a versatile AI agent operating system designed to support various models and connectors, enabling users to create chatbots, autonomous agents, handle business processes, create video game NPCs, and engage in trading. It offers multi-agent and room support, document ingestion and interaction, retrievable memory and document store, and extensibility to create custom actions and clients. Eliza is easy to use and provides a comprehensive solution for AI agent development.

AutoAgent
AutoAgent is a fully-automated and zero-code framework that enables users to create and deploy LLM agents through natural language alone. It is a top performer on the GAIA Benchmark, equipped with a native self-managing vector database, and allows for easy creation of tools, agents, and workflows without any coding. AutoAgent seamlessly integrates with a wide range of LLMs and supports both function-calling and ReAct interaction modes. It is designed to be dynamic, extensible, customized, and lightweight, serving as a personal AI assistant.

pastemax
PasteMax is a modern file viewer application designed for developers to easily navigate, search, and copy code from repositories. It provides features such as file tree navigation, token counting, search capabilities, selection management, sorting options, dark mode, binary file detection, and smart file exclusion. Built with Electron, React, and TypeScript, PasteMax is ideal for pasting code into ChatGPT or other language models. Users can download the application or build it from source, and customize file exclusions. Troubleshooting steps are provided for common issues, and contributions to the project are welcome under the MIT License.

aisdk-prompt-optimizer
AISDK Prompt Optimizer is an open-source tool designed to transform AI interactions by optimizing prompts. It utilizes the GEPA reflective optimizer to evolve textual components of AI systems, providing features such as reflective prompt mutation, rich textual feedback, and Pareto-based selection. Users can teach their AI desired behaviors, collect ideal samples, run optimization to generate optimized prompts, and deploy the results in their applications. The tool leverages advanced optimization algorithms to guide AI through interactive conversations and refine prompt candidates for improved performance.

openai-kotlin
OpenAI Kotlin API client is a Kotlin client for OpenAI's API with multiplatform and coroutines capabilities. It allows users to interact with OpenAI's API using Kotlin programming language. The client supports various features such as models, chat, images, embeddings, files, fine-tuning, moderations, audio, assistants, threads, messages, and runs. It also provides guides on getting started, chat & function call, file source guide, and assistants. Sample apps are available for reference, and troubleshooting guides are provided for common issues. The project is open-source and licensed under the MIT license, allowing contributions from the community.

dbt-llm-agent
dbt-llm-agent is an LLM-powered agent designed for interacting with dbt projects. It offers features such as question answering, documentation generation, agentic model interpretation, Postgres integration with pgvector, dbt model selection, question tracking, and upcoming Slack integration. The agent utilizes dbt project parsing, PostgreSQL with pgvector, model selection syntax, large language models like GPT-4, and question tracking to provide its functionalities. Users can set up the agent by checking Python version, cloning the repository, installing dependencies, setting up PostgreSQL with pgvector, configuring environment variables, and initializing the database schema. The agent can be initialized in Cloud Mode, Local Mode, or Source Code Mode to load project metadata. Once set up, users can work with model documentation, ask questions, provide feedback, list models, get detailed model information, and contribute to the project.

manifold
Manifold is a powerful platform for workflow automation using AI models. It supports text generation, image generation, and retrieval-augmented generation, integrating seamlessly with popular AI endpoints. Additionally, Manifold provides robust semantic search capabilities using PGVector combined with the SEFII engine. It is under active development and not production-ready.

rclip
rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.

phospho
Phospho is a text analytics platform for LLM apps. It helps you detect issues and extract insights from text messages of your users or your app. You can gather user feedback, measure success, and iterate on your app to create the best conversational experience for your users.

xGitGuard
xGitGuard is an AI-based system developed by Comcast Cybersecurity Research and Development team to detect secrets (e.g., API tokens, usernames, passwords) exposed on GitHub repositories. It uses advanced Natural Language Processing to detect secrets at scale and with appropriate velocity. The tool provides workflows for detecting credentials and keys/tokens in both enterprise and public GitHub accounts. Users can set up search patterns, configure API access, run detections with or without ML filters, and train ML models for improved detection accuracy. xGitGuard also supports custom keyword scans for targeted organizations or repositories. The tool is licensed under Apache 2.0.
For similar tasks

pipecat-flows
Pipecat Flows is a framework designed for building structured conversations in AI applications. It allows users to create both predefined conversation paths and dynamically generated flows, handling state management and LLM interactions. The framework includes a Python module for building conversation flows and a visual editor for designing and exporting flow configurations. Pipecat Flows is suitable for scenarios such as customer service scripts, intake forms, personalized experiences, and complex decision trees.

burr
Burr is a Python library and UI that makes it easy to develop applications that make decisions based on state (chatbots, agents, simulations, etc...). Burr includes a UI that can track/monitor those decisions in real time.

llama_deploy
llama_deploy is an async-first framework for deploying, scaling, and productionizing agentic multi-service systems based on workflows from llama_index. It allows building workflows in llama_index and deploying them seamlessly with minimal changes to code. The system includes services endlessly processing tasks, a control plane managing state and services, an orchestrator deciding task handling, and fault tolerance mechanisms. It is designed for high-concurrency scenarios, enabling real-time and high-throughput applications.

agentscript
AgentScript is an open-source framework for building AI agents that think in code. It prompts a language model to generate JavaScript code, which is then executed in a dedicated runtime with resumability, state persistence, and interactivity. The framework allows for abstract task execution without needing to know all the data beforehand, making it flexible and efficient. AgentScript supports tools, deterministic functions, and LLM-enabled functions, enabling dynamic data processing and decision-making. It also provides state management and human-in-the-loop capabilities, allowing for pausing, serialization, and resumption of execution.

agents
Cloudflare Agents is a framework for building intelligent, stateful agents that persist, think, and evolve at the edge of the network. It allows for maintaining persistent state and memory, real-time communication, processing and learning from interactions, autonomous operation at global scale, and hibernating when idle. The project is actively evolving with focus on core agent framework, WebSocket communication, HTTP endpoints, React integration, and basic AI chat capabilities. Future developments include advanced memory systems, WebRTC for audio/video, email integration, evaluation framework, enhanced observability, and self-hosting guide.

dify-google-cloud-terraform
This repository provides Terraform configurations to automatically set up Google Cloud resources and deploy Dify in a highly available configuration. It includes features such as serverless hosting, auto-scaling, and data persistence. Users need a Google Cloud account, Terraform, and gcloud CLI installed to use this tool. The configuration involves setting environment-specific values and creating a GCS bucket for managing Terraform state. The tool allows users to initialize Terraform, create Artifact Registry repository, build and push container images, plan and apply Terraform changes, and cleanup resources when needed.

exospherehost
Exosphere is an open source infrastructure designed to run AI agents at scale for large data and long running flows. It allows developers to define plug and playable nodes that can be run on a reliable backbone in the form of a workflow, with features like dynamic state creation at runtime, infinite parallel agents, persistent state management, and failure handling. This enables the deployment of production agents that can scale beautifully to build robust autonomous AI workflows.
For similar jobs

promptflow
**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

deepeval
DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector
MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

leapfrogai
LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

llava-docker
This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

carrot
The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM
TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei
AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.