Monitor

As mentioned in the debugging section, chatlas has support for gaining more insight into the behavior of your application through things like logging.

However, in a production setting, you may want to go beyond simple logging and use more sophisticated observability tools Datadog, Logfire, etc., to monitor your application. These tools can give you a more structured way to view and monitor app performance, including things like latency, error rates, and other metrics. These tools tend to integrate well with open standards like OpenTelemetry (OTel), meaning if you “instrument” your app with OTel, you can view your app’s telemetry data in any observability tool that supports OTel. There are at least a few different ways to do this, but we’ll cover some of the more simpler approaches here.

OpenLLMetry

The simplest (and most model agnostic) way to instrument your app with OTel is to leverage openllmetry, which can be as easy as adding the following code to your app:

pip install traceloop-sdk
from traceloop.sdk import Traceloop

Traceloop.init(
  app_name="my app name",
  disable_batch=True,
  telemetry_enabled=False
)

From here, a quick and easy way to get started visualizing your app’s telemetry data is to sign up for a (free) Traceloop account. Openllmetry does, however, integrate with many other observability platforms. If you want to avoid the Traceloop Python SDK, you can also use these OTel instrumentation libraries from the openllmetry project more directly (e.g., openai and anthropic).

OpenTelemetry

To use OpenTelemetry’s “official” instrumentation libraries, you’ll need to first install the relevant instrumentation packages for the model providers you are using.

OpenAI

More than a handful of chatlas’ model providers use the openai Python SDK under the hood (e.g., ChatOpenAI, ChatOllama, etc).

To be sure a particular provider uses the openai SDK, make sure the class of the .provider attribute is OpenAIProvider:

from chatlas import ChatOpenAI
chat = ChatOpenAI()
chat.provider
# <chatlas._openai.OpenAIProvider object at 0x103d2fdd0>

As a result, you can use the opentelemetry-instrumentation-openai-v2 package to add OTel instrumentation your app. It even provides a way to add instrumentation without modifying your code (i.e., zero-code). To tweak the zero-code example to work with chatlas, just change the requirements.txt and main.py files to use chatlas instead of openai directly:

main.py 
from chatlas import ChatOpenAI
chat = ChatOpenAI()
chat.chat("Hello world!")

You may also want to tweak the environment variables in .env to target the relevant OTel collector and service name.

Anthropic

Both the ChatAnthropic() and ChatBedrockAnthropic() providers use the anthropic Python SDK under the hood. As a result, you can use the opentelemetry-instrumentation-anthropic package to add OTel instrumentation your app.

To do this, you’ll need to install the package:

pip install opentelemetry-instrumentation-anthropic

Then, add the following instrumentation code to your app:

from opentelemetry.instrumentation.anthropic import AnthropicInstrumentor
AnthropicInstrumentor().instrument()

Google

Both the ChatGoogle() and ChatVertex() providers use the google-genai Python SDK under the hood. As a result, you can use the opentelemetry-instrumentation-google-genai package to add OTel instrumentation your app. It even provides a way to add instrumentation without modifying your code (i.e., zero-code). To tweak the zero-code example to work with chatlas, just change the requirements.txt and main.py files to use chatlas instead of google-genai directly:

main.py 
from chatlas import ChatGoogle
chat = ChatGoogle()
chat.chat("Hello world!")