⚠️ DEPRECATED: Pipelines is no longer maintained

Caution

Do not use Pipelines, not for new setups and not for existing ones.

This repository is archived and read-only. There will be no new releases, no bug fixes and no security fixes, and the ghcr.io/open-webui/pipelines images will not be updated again. Issues, pull requests and vulnerability reports for Pipelines are not accepted. If you still run a Pipelines server, move off it.

Use Functions instead

Everything Pipelines was used for is now built into Open WebUI itself. There is no separate server to deploy, connect, update or secure, and there is no use case left that needs a Pipelines server.

You used Pipelines for	Use this instead
A custom model, agent or provider integration (pipe pipelines)	Pipe Functions
Changing messages before or after the model, such as rate limits, monitoring, translation or moderation (filter pipelines)	Filter Functions
Adding a provider like OpenAI, Anthropic, Mistral, Groq or DeepSeek	A plain OpenAI-compatible connection, no plugin needed
Running heavy work (large models, long jobs) away from the Open WebUI server	Run it as your own service and call it from a Pipe Function, or expose it as an OpenAPI Tool Server or MCP server
Giving the model tools to call	Tools
Using your pipelines from other OpenAI API clients	Pipe Functions show up as regular models, so other clients reach them through the Open WebUI API

Start with the Functions overview and the guide to installing your first Function. Ready-made Functions are published on openwebui.com.

Warning

Functions run arbitrary Python code on your Open WebUI server. Only install Functions from sources you trust and read the code before you import it.

---

Everything below is the original README, kept for reference only. Do not follow it.

Pipelines: UI-Agnostic OpenAI API Plugin Framework

Welcome to Pipelines, an Open WebUI initiative. Pipelines bring modular, customizable workflows to any UI client supporting OpenAI API specs – and much more! Easily extend functionalities, integrate unique logic, and create dynamic workflows with just a few lines of code.

🚀 Why Choose Pipelines?

• Limitless Possibilities: Easily add custom logic and integrate Python libraries, from AI agents to home automation APIs.
• Seamless Integration: Compatible with any UI/client supporting OpenAI API specs. (Only pipe-type pipelines are supported; filter types require clients with Pipelines support.)
• Custom Hooks: Build and integrate custom pipelines.

Examples of What You Can Achieve:

• Function Calling Pipeline: Easily handle function calls and enhance your applications with custom logic.
• Custom RAG Pipeline: Implement sophisticated Retrieval-Augmented Generation pipelines tailored to your needs.
• Message Monitoring Using Langfuse: Monitor and analyze message interactions in real-time using Langfuse.
• Message Monitoring Using Opik: Monitor and analyze message interactions using Opik, an open-source platform for debugging and evaluating LLM applications and RAG systems.
• Rate Limit Filter: Control the flow of requests to prevent exceeding rate limits.
• Real-Time Translation Filter with LibreTranslate: Seamlessly integrate real-time translations into your LLM interactions.
• Toxic Message Filter: Implement filters to detect and handle toxic messages effectively.
• And Much More!: The sky is the limit for what you can accomplish with Pipelines and Python. Check out our scaffolds to get a head start on your projects and see how you can streamline your development process!

🔧 How It Works

Integrating Pipelines with any OpenAI API-compatible UI client is simple. Launch your Pipelines instance and set the OpenAI URL on your client to the Pipelines URL. That's it! You're ready to leverage any Python library for your needs.

⚡ Quick Start with Docker

Warning

Pipelines are a plugin system with arbitrary code execution — don't fetch random pipelines from sources you don't trust.

Docker

For a streamlined setup using Docker:

1. Run the Pipelines container:

   docker run -d -p 9099:9099 --add-host=host.docker.internal:host-gateway -v pipelines:/app/pipelines --name pipelines --restart always ghcr.io/open-webui/pipelines:main
   

2. Connect to Open WebUI:

   • Navigate to the Settings > Connections > OpenAI API section in Open WebUI.
   • Set the API URL to http://localhost:9099 and the API key to 0p3n-w3bu!. Your pipelines should now be active.

Note

If your Open WebUI is running in a Docker container, replace localhost with host.docker.internal in the API URL.

3. Manage Configurations:

   • In the admin panel, go to Admin Settings > Pipelines tab.
   • Select your desired pipeline and modify the valve values directly from the WebUI.

Tip

If you are unable to connect, it is most likely a Docker networking issue. We encourage you to troubleshoot on your own and share your methods and solutions in the discussions forum.

If you need to install a custom pipeline with additional dependencies:

• Run the following command:

  docker run -d -p 9099:9099 --add-host=host.docker.internal:host-gateway -e PIPELINES_URLS="https://github.com/open-webui/pipelines/blob/main/examples/filters/detoxify_filter_pipeline.py" -v pipelines:/app/pipelines --name pipelines --restart always ghcr.io/open-webui/pipelines:main
  

Alternatively, you can directly install pipelines from the admin settings by copying and pasting the pipeline URL, provided it doesn't have additional dependencies.

That's it! You're now ready to build customizable AI integrations effortlessly with Pipelines. Enjoy!

Docker Compose together with Open WebUI

Using Docker Compose simplifies the management of multi-container Docker applications.

Here is an example configuration file docker-compose.yaml for setting up Open WebUI together with Pipelines using Docker Compose:

services:
  openwebui:
      image: ghcr.io/open-webui/open-webui:main
      ports:
        - "3000:8080"
      volumes:
        - open-webui:/app/backend/data

  pipelines:
      image: ghcr.io/open-webui/pipelines:main
      volumes:
        - pipelines:/app/pipelines
      restart: always
      environment:
        - PIPELINES_API_KEY=0p3n-w3bu!

volumes:
  open-webui: {}
  pipelines: {}

To start your services, run the following command:

docker compose up -d

You can then use http://pipelines:9099 (the name is the same as the service's name defined in docker-compose.yaml) as an API URL to connect to Open WebUI.

Note

The pipelines service is accessible only by openwebui Docker service and thus provide additional layer of security.

📦 Installation and Setup

Get started with Pipelines in a few easy steps:

1. Ensure Python 3.11 is installed.

2. Clone the Pipelines repository:

   git clone https://github.com/open-webui/pipelines.git
   cd pipelines
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Start the Pipelines server:

   sh ./start.sh
   

Once the server is running, set the OpenAI URL on your client to the Pipelines URL. This unlocks the full capabilities of Pipelines, integrating any Python library and creating custom workflows tailored to your needs.

Advanced Docker Builds

If you create your own pipelines, you can install them when the Docker image is built. For example, create a bash script with the snippet below to collect files from a path, add them as install URLs, and build the Docker image with the new pipelines automatically installed.

NOTE: The pipelines module will still attempt to install any package dependencies found at in your file headers at start time, but they will not be downloaded again.

# build in the specific pipelines
PIPELINE_DIR="pipelines-custom"
# assuming the above directory is in your source repo and not skipped by `.dockerignore`, it will get copied to the image
PIPELINE_PREFIX="file:///app"

# retrieve all the sub files
export PIPELINES_URLS=
for file in "$PIPELINE_DIR"/*; do
    if [[ -f "$file" ]]; then
        if [[ "$file" == *.py ]]; then
            if [ -z "$PIPELINES_URLS" ]; then
                PIPELINES_URLS="$PIPELINE_PREFIX/$file"
            else
                PIPELINES_URLS="$PIPELINES_URLS;$PIPELINE_PREFIX/$file"
            fi
        fi
    fi
done
echo "New Custom Install Pipes: $PIPELINES_URLS"

docker build --build-arg PIPELINES_URLS=$PIPELINES_URLS --build-arg MINIMUM_BUILD=true -f Dockerfile .

📂 Directory Structure and Examples

The /pipelines directory is the core of your setup. Add new modules, customize existing ones, and manage your workflows here. All the pipelines in the /pipelines directory will be automatically loaded when the server launches.

You can change this directory from /pipelines to another location using the PIPELINES_DIR env variable.

Integration Examples

Find various integration examples in the /examples directory. These examples show how to integrate different functionalities, providing a foundation for building your own custom pipelines.

🎉 Work in Progress

We’re continuously evolving! We'd love to hear your feedback and understand which hooks and features would best suit your use case. Feel free to reach out and become a part of our Open WebUI community!

Our vision is to push Pipelines to become the ultimate plugin framework for our AI interface, Open WebUI. Imagine Open WebUI as the WordPress of AI interfaces, with Pipelines being its diverse range of plugins. Join us on this exciting journey! 🌍