---
title: Open Source Observability & Monitoring for Mirascope
sidebarTitle: Mirascope
logo: /images/integrations/mirascope_icon.svg
description: Open source observability for Mirascope applications. Automatically capture detailed traces of how users interact with your application.
---

# Monitor Mirascope Applications with Langfuse

This guide shows you how to use Langfuse to track and debug Mirascope applications.

## What is Mirascope?

Mirascope ([GitHub](https://github.com/mirascope/mirascope)) is a Python toolkit for building LLM applications.

> Developing LLM-applications with Mirascope feels just like writing the Python code you’re already used to.
> Python Toolkit for LLMs: Mirascope simplifies the development of applications using Large Language Models (LLMs) by providing an intuitive interface similar to standard Python coding practices.

## What is Langfuse?

Langfuse ([GitHub](https://github.com/langfuse/langfuse)) is an open-source platform for LLM engineering. It provides tracing, evaluation, prompt management, and metrics to help debug and enhance your LLM application.

## How to use Mirascope with Langfuse

With this integration, you can automatically capture detailed traces of how users interact with your application.
You can install the necessary packages directly or using the `langfuse` extras flag:

```bash
pip install "mirascope[langfuse]"
```

Mirascope automatically passes the Langfuse [`observe()` decorator](/docs/sdk/python/decorators) to all relevant functions within Mirascope via its `with_langfuse` decorator.

```python
from mirascope.integrations.langfuse import with_langfuse
```

### Getting Started

#### 1. Calls

The `with_langfuse` decorator can be used on all Mirascope functions to automatically log calls across all of Mirascope's [supported LLM providers](https://docs.mirascope.io/latest/learn/calls/#supported-providers).

Here is a simple example using tools:

```python
from mirascope.core import anthropic, prompt_template
from mirascope.integrations.langfuse import with_langfuse


def format_book(title: str, author: str):
    return f"{title} by {author}"


@with_langfuse()
@anthropic.call(model="claude-3-5-sonnet-20240620", tools=[format_book])
@prompt_template("Recommend a {genre} book.")
def recommend_book(genre: str):
    ...


print(recommend_book("fantasy"))
# > Certainly! I'd be happy to recommend a fantasy book...
```

This will give you:

- A trace around the `recommend_book` function that captures items like the prompt template, and input/output attributes and more.
- Human-readable display of the conversation with the agent
- Details of the response, including the number of tokens used

[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/794523f3-fb98-47fa-96f6-04f99dae862a?observation=0be07a39-40e7-46e8-9b85-84d9fbc73e2c)

![Example trace in Langfuse](/images/cookbook/integration_mirascope_simple.png)

#### 2. Streams

You can capture streams exactly the same way:

```python
from mirascope.core import openai, prompt_template
from mirascope.integrations.langfuse import with_langfuse


@with_langfuse()
@openai.call(
    model="gpt-4o-mini",
    stream=True,
    call_params={"stream_options": {"include_usage": True}},
)
@prompt_template("Recommend a {genre} book.")
def recommend_book(genre: str):
    ...


for chunk, _ in recommend_book("fantasy"):
    print(chunk.content, end="", flush=True)
# > I recommend **"The Name of the Wind"** by Patrick Rothfuss. It's the first book...
```

For some providers, certain `call_params` will need to be set in order for usage to be tracked.

## Learn more

- [Notebook with examples](/guides/cookbook/integration_mirascope)
- [Mirascope Docs](https://docs.mirascope.io/latest/integrations/langfuse/)

<!-- 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/integrations/frameworks/mirascope.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>.