docs/reference/integrations/langfuse.rst

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.. _langfuseintegrationref:

--------
Langfuse
--------

`Langfuse <https://langfuse.com>`_ is an open-source LLM engineering platform with
tracing/observability capabilities. Burr integrates with it through the
OpenTelemetry-native Langfuse Python SDK, building on the
:ref:`opentelemetry integration <opentelintegrationref>`.

Install the integration:

.. code-block:: bash

    pip install "apache-burr[langfuse]"

Then add the bridge as a hook -- credentials are read from the standard
``LANGFUSE_PUBLIC_KEY``, ``LANGFUSE_SECRET_KEY``, and ``LANGFUSE_HOST``
environment variables:

.. code-block:: python

    from burr.core import ApplicationBuilder
    from burr.integrations.langfuse import LangfuseBridge

    app = (
        ApplicationBuilder()
        .with_graph(graph)
        .with_entrypoint("prompt")
        .with_hooks(LangfuseBridge())
        .build()
    )
    app.run(halt_after=["response"])  # logs one trace to Langfuse

Each application execution call becomes a Langfuse trace, each step becomes a span
(with state/inputs/results captured as observation input/output), and spans opened
through Burr's :ref:`tracing API <opentelref>` become nested spans. Any additional
OpenTelemetry LLM instrumentation (e.g. ``opentelemetry-instrumentation-openai``)
appears nested within the corresponding Burr step.

See the following resources for more information:

- `Example in the repository <https://github.com/apache/burr/tree/main/examples/integrations/langfuse>`_
- `Langfuse OpenTelemetry docs <https://langfuse.com/integrations/native/opentelemetry>`_

Reference for the various useful methods:

.. autoclass:: burr.integrations.langfuse.LangfuseBridge
    :members:

    .. automethod:: __init__

.. autofunction:: burr.integrations.langfuse.burr_span_export_filter