> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inferable.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Langfuse

> Use Langfuse to monitor and evaluate your Inferable cluster

# Integration with Langfuse

[Langfuse](https://langfuse.com) is an open-source observability and analytics platform designed for LLM applications. It helps teams monitor, evaluate, and improve their LLM implementations through comprehensive tracing and evaluation tools.

## Key Features

### Observability

* **Detailed Tracing**: Capture and analyze every LLM interaction, including:
  * Prompts and completions
  * Token usage and costs
  * Latency metrics
  * Metadata and custom properties
* **Structured Logging**: Track complex chains of LLM calls with nested traces
* **Real-time Monitoring**: View live performance metrics and usage patterns

### Evaluation & Scoring

* **Quality Metrics**: Track and measure LLM output quality through:
  * Model-based evaluations
  * User feedback collection
  * Custom scoring criteria
* **Score Analysis**: Analyze quality scores across different:
  * Models
  * Prompt versions
  * Use cases
* **Performance Tracking**: Monitor success rates and identify areas for improvement

## Using Langfuse with Inferable

Inferable provides native integration with Langfuse, allowing you to get complete telemetry for your Runs. To enable Langfuse integration:

1. Get your Langfuse API keys from the [Langfuse dashboard](https://cloud.langfuse.com)
2. Configure Inferable with your Langfuse credentials in the Integrations tab of your preferred cluster.

## Configuration

![Langfuse Configuration](https://mintlify.s3.us-west-1.amazonaws.com/inferable/images/integrations/langfuse-config.png)

* **Secret API Key**: Your Langfuse Secret API Key.
* **Public API Key**: Your Langfuse Public API Key.
* **Base URL**: Your Langfuse Base URL. Depending on your deployment and data region, this will be something like `https://<region>.cloud.langfuse.com`.
* **Send Message Payloads**: Whether to send inputs and outputs of LLM calls and function calls to Langfuse.

### Message Payloads

By default, Inferable will only send metadata about LLM calls and function calls. This includes the model, Run ID, token usage, latency etc.

If you have **Send Message Payloads** enabled, Inferable will also send the inputs and outputs of the LLM calls and function calls. This includes the prompt, response, tool calls, tool call arguments, tool call results etc.

## Features

### Tracing

Once you have enabled Langfuse integration, you will start to see traces in the Langfuse dashboard.

Every Run in Inferable will be mapped to its own trace in Langfuse.

![Langfuse Run](https://mintlify.s3.us-west-1.amazonaws.com/inferable/images/integrations/langfuse-run.png)

You will find two types of spans in the trace:

* **Tool Calls**: Denoted by function name. These are spans created for each tool call made in the Run by the LLM.
* **LLM Calls**: Denoted by `GENERATION`. This is the span created for the LLM call. Inferable will create a new span for each LLM call in the Run, including:
  * Agent loop reasoning
  * Utility calls (e.g. Summarization, Title generation)

### Evaluation

Whenever you submit a evaluation on a Run via the Playground or the API, we will send a score to Langfuse on the trace for that Run.

If you're using Langfuse for evaluation, this will help you correlate the evaluation back to the specific Trace in Langfuse.

![Langfuse Evaluation](https://mintlify.s3.us-west-1.amazonaws.com/inferable/images/integrations/langfuse-score.png)