---
date: 2024-04-24
badge: Launch Week 1 🚀
title: Decorator-based integration for Python
description: The decorator now supports all Langfuse features and is the recommended way to use Langfuse in Python.
ogImage: /images/changelog/2024-03-24-python-decorator.png
showOgInHeader: false
author: Marc
canonical: /docs/observability/sdk/instrumentation
---

> **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 Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated.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/observability/sdk/instrumentation) and the API/SDK reference (https://api.reference.langfuse.com).

On Day 3 of [Launch Week 1](/blog/launch-week-1), we're happy to share that the Decorator-based integration for Python now supports all Langfuse features and is the recommended way to use Langfuse in Python.

The decorator makes integrating with Langfuse so much easier. Head over to the [Python Decorator docs](/docs/sdk/python/decorators) to learn more. All inputs, outputs, timings are captured automatically, and it works with all other [Langfuse integrations](/integrations) (LangChain, LlamaIndex, OpenAI SDK, ...).

To celebrate this milestone, we wrote a [blog post](/blog/2024-04-python-decorator) on the technical details and created the example notebook shown in the video as it demonstrates what's really cool about the decorator. Let us know what you think in the [GitHub discussion](https://github.com/orgs/langfuse/discussions/1009), and stay tuned for more updates during [Langfuse Launch Week 1](/blog/launch-week-1) 🚀

Thanks again to [@lshalon](https://github.com/lshalon) and [@AshisGhosh](https://github.com/AshisGhosh) for your contributions to this!

### Learn more

- [Decorator docs](/docs/sdk/python/decorators)
- [Notebook demonstrating all features](/docs/sdk/python/example)

<!-- 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/2024-04-24-decorator.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>.