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Add Langfuse Integration tutorial

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Added guide on how to monitor and debug OpenWebUI sessions with the Langfuse pipeline.
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---
sidebar_position: 20
title: "Monitoring and debugging with Langfuse"
---
# Langfuse Integration with OpenWebUI
[Langfuse](https://langfuse.com/) ([GitHub](https://github.com/langfuse/langfuse)) offers open source observability and evaluations for OpenWebUI. By enabling the Langfuse integration, you can trace your application data with Langfuse to develop, monitor, and improve the use of OpenWebUI, including:
- Application [traces](https://langfuse.com/docs/tracing)
- Usage patterns
- Cost data by user and model
- Replay sessions to debug issues
- [Evaluations](https://langfuse.com/docs/scores/overview)
## How to integrate Langfuse with OpenWebUI:
![Langfuse Integration](https://langfuse.com/images/docs/openwebui-integration.gif)
_Langfuse integration steps_
[Pipelines](https://github.com/open-webui/pipelines/) in OpenWebUi is an UI-agnostic framework for OpenAI API plugins. It enables the injection of plugins that intercept, process, and forward user prompts to the final LLM, allowing for enhanced control and customization of prompt handling.
To trace your application data with Langfuse, you can use the [Langfuse pipeline](https://github.com/open-webui/pipelines/blob/d4fca4c37c4b8603be7797245e749e9086f35130/examples/filters/langfuse_filter_pipeline.py), which enables real-time monitoring and analysis of message interactions.
## Quick Start Guide
### Step 1: Setup OpenWebUI
Make sure to have OpenWebUI running. To do so, have a look at the [OpenWebUI documentation](https://docs.openwebui.com/).
### Step 2: Set Up Pipelines
Launch [Pipelines](https://github.com/open-webui/pipelines/) by using Docker. Use the following command to start Pipelines:
```bash
docker run -p 9099:9099 --add-host=host.docker.internal:host-gateway -v pipelines:/app/pipelines --name pipelines --restart always ghcr.io/open-webui/pipelines:main
```
### Step 3: Connecting OpenWebUI with Pipelines
In the _Admin Settings_, create and save a new connection of type OpenAI API with the following details:
- **URL:** http://host.docker.internal:9099 (this is where the previously launched Docker container is running).
- **Password:** 0p3n-w3bu! (standard password)
![OpenWebUI Settings](https://langfuse.com/images/docs/openwebui-setup-settings.png)
### Step 4: Adding the Langfuse Filter Pipeline
Next, navigate to _Admin Settings_ -> _Pipelines_ and add the Langfuse Filter Pipeline. Specify that Pipelines is listening on http://host.docker.internal:9099 (as configured earlier) and install the [Langfuse Filter Pipeline](https://github.com/open-webui/pipelines/blob/main/examples/filters/langfuse_filter_pipeline.py) by using the _Install from Github URL_ option with the following URL:
```
https://github.com/open-webui/pipelines/blob/main/examples/filters/langfuse_filter_pipeline.py
```
Now, add your Langfuse API keys below. If you haven't signed up to Langfuse yet, you can get your API keys by creating an account [here](https://cloud.langfuse.com).
![OpenWebUI add Langfuse Pipeline](https://langfuse.com//images/docs/openwebui-add-pipeline.png)
_**Note:** Capture usage (token counts) for OpenAi models while streaming is enabled, you have to navigate to the model settings in OpenWebUI and check the "Usage" [box](https://github.com/open-webui/open-webui/discussions/5770#discussioncomment-10778586) below *Capabilities*._
### Step 5: See your traces in Langfuse
You can now interact with your OpenWebUI application and see the traces in Langfuse.
[Example trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/904a8c1f-4974-4f8f-8a2f-129ae78d99c5?observation=fe5b127b-e71c-45ab-8ee5-439d4c0edc28) in the Langfuse UI:
![OpenWebUI Example Trace in Langfuse](https://langfuse.com/images/docs/openwebui-example-trace.png)
## Learn more
For a comprehensive guide on OpenWebUI Pipelines, visit [this post](https://ikasten.io/2024/06/03/getting-started-with-openwebui-pipelines/).