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Python Error and Performance Monitoring

Python error monitoring with actionable insight into every exception. See the full picture behind any Python error, resolve performance bottlenecks, and get framework-specific coverage that spans Django application monitoring, Flask, FastAPI, and Celery.

Grab the Sentry Python SDK:

Bash
pip install --upgrade sentry-sdk

Configure your DSN:

Python
import sentry_sdk

sentry_sdk.init(
    "https://<key>@sentry.io/<project>",

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for Tracing.
    # We recommend adjusting this value in production.
    traces_sample_rate=1.0,
)

Getting Started is Simple

Grab the Sentry Python SDK:

Bash
pip install --upgrade sentry-sdk

Configure your DSN:

Python
import sentry_sdk

sentry_sdk.init(
    "https://<key>@sentry.io/<project>",

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for Tracing.
    # We recommend adjusting this value in production.
    traces_sample_rate=1.0,
)

Install our Python SDK using pip:

Bash
pip install --upgrade sentry-sdk

Initialize the Python SDK with the Django integration in your settings.py file.

Python
import sentry_sdk
from sentry_sdk.integrations.django import DjangoIntegration

sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",
    integrations=[DjangoIntegration()],

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for Tracing.
    # We recommend adjusting this value in production,
    traces_sample_rate=1.0,

    # If you wish to associate users to errors (assuming you are using
    # django.contrib.auth) you may enable sending PII data.
    send_default_pii=True
)

Install sentry-sdk from PyPI with the flask extra:

Bash
pip install --upgrade 'sentry-sdk[flask]'

Configure your DSN:

Python
import sentry_sdk
from flask import Flask
from sentry_sdk.integrations.flask import FlaskIntegration

sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",
    integrations=[FlaskIntegration()],

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for Tracing.
    # We recommend adjusting this value in production,
    traces_sample_rate=1.0
)

app = Flask(__name__)

Grab the Sentry Python SDK:

Bash
pip install --upgrade sentry-sdk

Configure your DSN:

Python
from fastapi import FastAPI

import sentry_sdk
sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for Tracing.
    # We recommend adjusting this value in production,
    traces_sample_rate=1.0,

    # If you wish to associate users to errors (assuming you are using
    # django.contrib.auth) you may enable sending PII data.
    send_default_pii=True,
)

app = FastAPI()

Install sentry-sdk from PyPI with the bottle extra:

Bash
pip install --upgrade 'sentry-sdk[bottle]==0.16.2'

To configure the SDK, initialize it with the integration before your app has been initialized:

Python
import sentry_sdk

from bottle import Bottle, run
from sentry_sdk.integrations.bottle import BottleIntegration

sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",
    integrations=[BottleIntegration()]
)

app = Bottle()

Just add CeleryIntegration() to your integrations list:

Python
import sentry_sdk
from sentry_sdk.integrations.celery import CeleryIntegration

sentry_sdk.init(
    dsn='https://<key>@sentry.io/<project>',
    integrations=[CeleryIntegration()]
)

Install sentry-sdk from PyPI:

Bash
pip install --upgrade sentry-sdk

To configure the SDK, initialize it with the integration before or after your app has been created:

Python
import sentry_sdk

from sentry_sdk.integrations.pyramid import PyramidIntegration

sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",
    integrations=[PyramidIntegration()]
)

from pyramid.config import Configurator

with Configurator() as config:
    # ...

Install our Python SDK using pip:

Bash
pip install --upgrade sentry-sdk

Initialize the Python SDK with the Tornado integration in your settings.py file.

Python
import sentry_sdk
from sentry_sdk.integrations.tornado import TornadoIntegration

sentry_sdk.init(
    dsn="https://examplePublicKey@o0.ingest.sentry.io/0",
    integrations=[
        TornadoIntegration(),
    ],

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for Tracing.
    # We recommend adjusting this value in production,
    traces_sample_rate=1.0,
)

# Your app code here, without changes

class MyHandler(...):
    # ...

Create a file called mysettings.py with the following content:

Python
import sentry_sdk
from sentry_sdk.integrations.rq import RqIntegration

sentry_sdk.init("https://<key>@sentry.io/<project>", integrations=[RqIntegration()])

Start your worker with:

Bash
rq worker \
-c mysettings \  # module name of mysettings.py
--sentry-dsn=""  # only necessary for RQ < 1.0

Install our Python SDK using pip:

Bash
pip install --upgrade sentry-sdk

Then use the AWS Lambda integration for the Python SDK like this:

Python
import sentry_sdk

sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",
    integrations=[AwsLambdaIntegration()],
    traces_sample_rate=1.0, # adjust the sample rate in production as needed
)

def my_function(event, context):
    # ...

Add the Sentry SDK to your requirements.txt:

Bash
pip install sentry-sdk

Then use the GCP Functions integration for the Python SDK like this:

Python
import sentry_sdk
from sentry_sdk.integrations.gcp import GcpIntegration

sentry_sdk.init(
    dsn="https://<key>@sentry.io/<project>",
    integrations=[GcpIntegration()],
    traces_sample_rate=1.0, # adjust the sample rate in production as needed
)

def http_function_entrypoint(request):
    # ...

Check our documentation for the latest instructions.

See All Platforms

How to install the Python SDK

More than 150K Organizations Trust Sentry with Their Application Monitoring

Python Performance Monitoring

Within minutes after installing Sentry, software teams can trace Python performance issues back to a poor-performing API call and surface every related code error. From Django application monitoring to FastAPI performance, engineering managers and developers get a single tool to optimize their code and deliver fast customer experiences.
See a Sample Transaction

Complete Stack Traces, With Local Variables

See local variables in the stack for prod errors, just like in your dev environment. Introspect more deeply into the runtime and jump into the frame to get additional data for any local variable. Filter and group Python exceptions intuitively to eliminate noise, whether you're doing Flask error handling, tracking Django views, or monitoring Celery jobs.
See a Sample Stack Trace

Fill In the Blanks

Expose the important events that led to each Python exception: SQL queries, debug logs, network requests, past errors. Improve debugging workflow with a full view of releases so you can mark errors as resolved and prioritize live issues.
Check out Breadcrumbs

Profiling, Down to the Function

Profiling lets you see what parts of your code are consuming the most resources, like CPU or memory, in your application— so you can optimize them before end user experience is impacted. Test your application performance in any environment, including in production, without writing manual tests or extensive troubleshooting.
Try Profiling
Sentry Logs showing structured log output alongside errors and traces

Your Logs, Next to the Trace

Ship your Python logging output to Sentry and query it next to the trace that produced it. The logging calls you already write become searchable context on the exact request that failed, so retries, third-party API failures, and internal state are visible without a second tool.

Learn more about Logs in Sentry.

Explore Logs

"The time to resolve errors went from days to minutes."

Vaidik Kapoor
VP of Engineering at Grofers
Grofers

See the Full Picture of Any Exception

Aggregate errors by details like HTTP request, hostname, and app version to see what's new, a priority, or a trend.

Assign custom tags to reproduce the error environment specific to your application, business, and users.

Answer the most important questions: In which app release did the Python bug occur? Was it the kraken?

FAQs

Traditional logging provides you with a trail of events. Some of those events are errors, but many times they're simply informational. Sentry is fundamentally different because we focus on exceptions, or in other words, we capture application crashes. We discuss in more detail here and on our blog.

Sentry supports every major language, framework, and library. You can browse each of them here.

You can get started for free. Pricing depends on the number of monthly events, transactions, and attachments that you send Sentry. For more details, visit our pricing page.

Sentry doesn't impact a web site's performance.

If you look at the configuration options for when you initialize Sentry in your code, you'll see there's nothing regarding minimizing its impact on your app's performance. This is because our team of SDK engineers already developed Sentry with this in mind.

Sentry is a listener/handler for errors that asynchronously sends out the error/event to Sentry.io. This is non-blocking. The error/event only goes out if this is an error.

Global handlers have almost no impact as well, as they are native APIs provided by the browsers.

Yes. Sentry Logs lets you send, view, and query structured logs from your application alongside your errors and traces. Because logs sit next to the error and the trace that produced them, you get the granular context — retries, API failures, internal state — that spans and stack traces alone can miss, without switching tools.

Learn more about Logs in Sentry.

Seer is Sentry's AI debugging agent. It reads the full context Sentry already has — the error, its stack trace, the surrounding trace, logs, and profiles — to find the root cause of an issue and propose a fix. Seer can open a pull request with that fix, hand the work to an external coding agent, or review your PRs before they ship.

Learn more about Seer and Autofix.

Supporting Resources

Debugging Python Errors

How Sentry Helps Grofers Meet Unprecedented Delivery Demand

Monitor your MCP server with Sentry

Resolve Python errors with max efficiency, not max effort