---
date: 2024-03-05
title: "Uptrain.ai integration (cookbook)"
description: Langfuse adds >20 open source evaluators through cookbook integration with open source project and fellow Y Combinator startup UpTrain.ai
author: Clemens
ogImage: /images/blog/uptrain/uptrain-ai.png
---

> **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the current documentation (https://langfuse.com/docs) and the API/SDK reference (https://api.reference.langfuse.com).

## How-to

<Steps>

### Trace your application with Langfuse

Use any Langfuse [integration](/integrations).

### Pull (a sample) via our Python SDK

Define which traces/LLM-calls you want to evaluate and pull them via our Python SDK.

### Run Uptrain OSS evals

You can find the complete list of UpTrain evals [here](https://github.com/uptrain-ai/uptrain?tab=readme-ov-file#pre-built-evaluations-we-offer-).

### Log scores to Langfuse

Use Langfuse [Scores](/docs/scores) to track the eval results in Langfuse.

</Steps>

We have set up an end-to-end cookbook which makes evaluating your traces with UpTrain happen in a jiffy:

- [External evaluation pipelines cookbook](/guides/cookbook/example_external_evaluation_pipelines)

## Sneak peak

We are currently working on an evaluation service to automatically score all incoming observations by running custom evaluation templates. Ping us if you want to be among the first to try it: early-access@langfuse.com

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2024-03-05-uptrain-integration.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.