---
date: 2024-04-23
badge: Launch Week 1 🚀
title: LLM playground
description: Test and iterate on your prompts with the new LLM playground directly in Langfuse.
ogImage: /images/changelog/2024-04-23-prompt-playground.png
showOgInHeader: false
author: Hassieb
canonical: /docs/prompt-management/features/playground
---

> **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 Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated.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 canonical documentation for this feature (https://langfuse.com/docs/prompt-management/features/playground) and the API/SDK reference (https://api.reference.langfuse.com).

On Day 2 of [Launch Week 1](/blog/launch-week-1), we're excited to introduce the [**LLM playground**](/docs/playground) to Langfuse. By making prompt engineering possible directly in Langfuse, we take another step in our mission to build a feature-complete AI engineering platform that helps you along the full live cycle of your LLM application.

With the LLM playground, you can now test and iterate your prompts directly in Langfuse. Either start from scratch or jump into the playground from an existing prompt in your project. You can then tweak the prompt and the model parameters to see how the model responds to different inputs. This way, you can quickly iterate on your prompts to get the best results for your LLM app.

The LLM playground currently support all major models from both OpenAI and Anthropic, and we're planning on adding more model providers in the future.

We hope you enjoy using the **LLM playground**. Let us know what you think in the [GitHub discussion](https://github.com/orgs/langfuse/discussions/1170), and stay tuned for more updates during [Langfuse Launch Week 1](/blog/launch-week-1) 🚀

### Learn more

- [Playground docs](/docs/playground)

<!-- 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/2024-04-23-prompt-playground.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>.