Graphsignal: Observability for AI Stack
=======================================

|License| |Version| |Status|

Graphsignal is an observability platform for AI agents and LLM-powered
applications. It helps developers ensure AI applications run as expected
and users have the best experience. With Graphsignal, developers can:

-  Trace requests, runs, and sessions with full AI context.
-  See latency breakdown by operations.
-  Analyze model API costs for deployments, models, or users.
-  Get notified about errors and anomalies.
-  Monitor API, compute, and GPU utilization.

|Dashboards|

Learn more at `graphsignal.com <https://graphsignal.com>`__.

Install
-------

Install Graphsignal library by running:

::

   pip install graphsignal

Or clone and install the `GitHub
repository <https://github.com/graphsignal/graphsignal-python>`__:

::

   git clone https://github.com/graphsignal/graphsignal-python.git
   python setup.py install

Configure
---------

Configure Graphsignal tracer by specifying your API key directly or via
``GRAPHSIGNAL_API_KEY`` environment variable.

.. code:: python

   import graphsignal

   graphsignal.configure(api_key='my-api-key', deployment='my-model-prod-v1') 

To get an API key, sign up for a free account at
`graphsignal.com <https://graphsignal.com>`__. The key can then be found
in your account’s `Settings / API
Keys <https://app.graphsignal.com/settings/api-keys>`__ page.

Alternatively, you can add Graphsignal tracer at command line, when
running your module or script. Environment variables
``GRAPHSIGNAL_API_KEY`` and ``GRAPHSIGNAL_DEPLOYMENT`` must be set.

.. code:: bash

   python -m graphsignal <script>

.. code:: bash

   python -m graphsignal -m <module>

Integrate
---------

Automatic integration
~~~~~~~~~~~~~~~~~~~~~

Graphsignal **auto-instruments** and traces libraries and frameworks,
such as `OpenAI <https://graphsignal.com/docs/integrations/openai/>`__,
`LangChain <https://graphsignal.com/docs/integrations/langchain/>`__,
`LlamaIndex <https://graphsignal.com/docs/integrations/llama-index/>`__,
`Hugging
Face <https://graphsignal.com/docs/integrations/hugging-face/>`__.
Traces, errors, and data, such as prompts and completions, are
automatically recorded and available for analysis at
`app.graphsignal.com <https://app.graphsignal.com/>`__.

Some integration examples are available in
`examples <https://github.com/graphsignal/examples>`__ repo.

User tracking
~~~~~~~~~~~~~

User tracking allows grouping and visualization of user-related traces,
interactions, metrics, and costs. It also enables detection of user
interaction outliers and other events.

To enable user tracking, set user identifier as ``user`` tag for every
request, e.g. in a request handler.

.. code:: python

   graphsignal.set_context_tag('user', user_id)

If you are running a single process per user and added Graphsignal at
command line, you can set the ``user`` tag in an environment variable.

.. code:: bash

   env GRAPHSIGNAL_TAGS="user=user_id" python -m graphsignal <script>

Tracing any operation
~~~~~~~~~~~~~~~~~~~~~

To measure and monitor operations that are not automatically
instrumented, e.g. any model inference or inference API calls, wrap the
code with
```start_trace()`` <https://graphsignal.com/docs/reference/python-api/#graphsignalstart_trace>`__
method or use
```@trace_function`` <https://graphsignal.com/docs/reference/python-api/#graphsignaltrace_function>`__
decorator.

.. code:: python

   with graphsignal.start_trace('predict'):
       pred = model(x)

.. code:: python

   @graphsignal.trace_function
   def predict(x):
       return model(x)

Enable profiling to additionally record code-level statistics. Profiling
is disabled by default due to potential overhead. To enable, provide
```TraceOptions`` <https://graphsignal.com/docs/reference/python-api/#graphsignaltraceoptions>`__
object.

.. code:: python

   with graphsignal.start_trace('predict', options=graphsignal.TraceOptions(enable_profiling=True)):
       pred = model(x)

The tracer will automatically choose a profiler depending on available
modules. Currently, CProfile, PyTorch Kineto and Yappi are supported.
The Kineto profiler is used if ``torch`` module is detected and Yappi
profiler is used if ``yappi`` module is detected. Otherwise, CProfile is
used. To properly profile ``asyncio`` coroutines, simply
``pip install yappi``.

See `API
reference <https://graphsignal.com/docs/reference/python-api/>`__ for
full documentation.

Exception tracking
~~~~~~~~~~~~~~~~~~

For auto-instrumented libraries, or when using ``@trace_function``
decorator, ``start_trace()`` method with ``with`` context manager or
callbacks, exceptions are **automatically** recorded. For other cases,
use
```Trace.add_exception`` <https://graphsignal.com/docs/reference/python-api/#graphsignalspanadd_exception>`__.

Data monitoring
~~~~~~~~~~~~~~~

Data, such as prompts and completions, is automatically monitored for
auto-instrumented libraries. To track data metrics and record data
profiles for other cases,
```Trace.set_data()`` <https://graphsignal.com/docs/reference/python-api/#graphsignalspanset_data>`__
method can be used.

.. code:: python

   with graphsignal.start_trace('predict') as span:
       span.set_data('input', input_data)

The following data types are currently supported: ``list``, ``dict``,
``set``, ``tuple``, ``str``, ``bytes``, ``numpy.ndarray``,
``tensorflow.Tensor``, ``torch.Tensor``.

Raw data samples, such as prompts and completions, are recorded by
default. To disable, set ``record_data_samples=False`` in
``graphsignal.configure``. Note, that data statistics, such as size,
shape or number of missing values will still be recorded.

Observe
-------

`Log in <https://app.graphsignal.com/>`__ to Graphsignal to monitor and
analyze your application and monitor for issues.

Overhead
--------

Graphsignal tracer is very lightweight. The overhead per trace is
measured to be less than 100 microseconds.

Security and Privacy
--------------------

Graphsignal tracer can only open outbound connections to
``signal-api.graphsignal.com`` and send data, no inbound connections or
commands are possible.

Raw data samples, e.g. prompts, are recorded by default. This feature
can be disabled at tracer initialization time, if necessary.

Troubleshooting
---------------

To enable debug logging, add ``debug_mode=True`` to ``configure()``. If
the debug log doesn’t give you any hints on how to fix a problem, please
report it to our support team via your account.

In case of connection issues, please make sure outgoing connections to
``https://signal-api.graphsignal.com`` are allowed.

.. |License| image:: http://img.shields.io/github/license/graphsignal/graphsignal-python
   :target: https://github.com/graphsignal/graphsignal-python/blob/main/LICENSE
.. |Version| image:: https://img.shields.io/github/v/tag/graphsignal/graphsignal-python?label=version
   :target: https://github.com/graphsignal/graphsignal-python
.. |Status| image:: https://img.shields.io/uptimerobot/status/m787882560-d6b932eb0068e8e4ade7f40c?label=SaaS%20status
   :target: https://stats.uptimerobot.com/gMBNpCqqqJ
.. |Dashboards| image:: https://graphsignal.com/external/screencast-dashboards.gif
   :target: https://graphsignal.com/
