This integration connects Sentry with the OpenAI Python SDK .
Once you've installed this SDK, you can use Sentry AI Agents Monitoring, a Sentry dashboard that helps you understand what's going on with your AI requests.
Sentry AI Monitoring will automatically collect information about prompts, tools, tokens, and models. Learn more about the AI Agents Dashboard.
InstallInstall sentry-sdk from PyPI with the openai extra:
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pip install "sentry-sdk[openai]"
If you have the openai package in your dependencies, the OpenAI integration will be enabled automatically when you initialize the Sentry SDK.
An additional dependency, tiktoken, is required if you want to calculate token usage for streaming chat responses.
Error Monitoring Logs Tracing Profiling
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import sentry_sdk
sentry_sdk.init(
dsn="https://examplePublicKey@o0.ingest.sentry.io/0",
send_default_pii=True,
traces_sample_rate=1.0,
profile_session_sample_rate=1.0,
profile_lifecycle="trace",
_experiments={
"enable_logs": True,
},
)
Verify that the integration works by making a chat request to OpenAI.
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import sentry_sdk
from openai import OpenAI
sentry_sdk.init(...)
client = OpenAI(api_key="(your OpenAI key)")
def my_llm_stuff():
with sentry_sdk.start_transaction(
name="The result of the AI inference",
op="ai-inference",
):
print(
client.chat.completions.create(
model="gpt-3.5", messages=[{"role": "system", "content": "say hello"}]
)
.choices[0]
.message.content
)
After running this script, the resulting data should show up in the "AI Spans" tab on the "Explore" > "Traces" page on Sentry.io.
If you manually created an Invoke Agent Span (not done in the example above) the data will also show up in the AI Agents Dashboard.
It may take a couple of moments for the data to appear in sentry.io .
BehaviorThe OpenAI integration will connect Sentry with all supported OpenAI methods automatically.
All exceptions leading to an OpenAIException are reported.
The supported modules are currently responses.create, chat.completions.create, and embeddings.create.
Sentry considers LLM and tokenizer inputs/outputs as PII (Personally identifiable information) and doesn't include PII data by default. If you want to include the data, set send_default_pii=True in the sentry_sdk.init() call. To explicitly exclude prompts and outputs despite send_default_pii=True, configure the integration with include_prompts=False as shown in the Options section below.
By adding OpenAIIntegration to your sentry_sdk.init() call explicitly, you can set options for OpenAIIntegration to change its behavior:
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import sentry_sdk
from sentry_sdk.integrations.openai import OpenAIIntegration
sentry_sdk.init(
send_default_pii=True,
integrations=[
OpenAIIntegration(
include_prompts=False,
tiktoken_encoding_name="cl100k_base",
),
],
)
You can pass the following keyword arguments to OpenAIIntegration():
include_prompts:
Whether LLM and tokenizer inputs and outputs should be sent to Sentry. Sentry considers this data personal identifiable data (PII) by default. If you want to include the data, set send_default_pii=True in the sentry_sdk.init() call. To explicitly exclude prompts and outputs despite send_default_pii=True, configure the integration with include_prompts=False.
The default is True.
tiktoken_encoding_name:
If you want to calculate token usage for streaming chat responses you need to have an additional dependency, tiktoken installed and specify the tiktoken_encoding_name that you use for tokenization. See the OpenAI Cookbook for possible values.
The default is None.
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