Python Error Tracking for Django, Flask, FastAPI

Supported Python frameworks and libraries

Because Telebugs is fully compatible with the official Sentry Python SDK, it works with the entire Sentry Python ecosystem out of the box.

• Django — See the dedicated Django error tracking page for install and integration details (including Celery background tasks).
• Flask and FastAPI — Standard Sentry Python integration works directly.
• Celery and other background task queues — Errors in workers are captured automatically when using the Sentry SDK.
• Any Python application — Plain scripts, CLI tools, data pipelines, or custom frameworks. Just initialize the SDK with your Telebugs DSN.

For the complete and up-to-date list of supported integrations, see the Sentry Python documentation. The integration code is identical when sending data to Telebugs.

Where Python production errors hide

Python errors rarely stay inside one neat web request. A failed database migration, a Celery retry loop, a scheduled billing job, a webhook handler, or a small CLI script can all create customer-facing damage without producing an obvious signal in your app server logs.

Telebugs gives those exceptions one place to land. Instead of digging through separate log streams for API servers, background workers, cron jobs, and one-off scripts, you can group related tracebacks, see when the issue first appeared, and decide whether it needs a deploy, a data fix, or a notification rule.

• API and web request failures from Django, Flask, FastAPI, and custom ASGI or WSGI services.
• Background job failures from task queues, retries, scheduled jobs, and long-running workers.
• Data and automation failures from importers, reporting scripts, webhook consumers, and internal tools.
• Release regressions where a deploy changes exception frequency, grouping, or affected users.

For small teams, that focus matters: you get error tracking, grouping, and actionable notifications without running a large observability stack just to answer "what broke?"

Easy install for Python teams

Telebugs itself is designed to be trivial to run on your own infrastructure.

If you can run a single terminal command, you can set up Telebugs. The whole process takes less than 10 minutes for most people. Once it’s running, you point your Python apps at it and start capturing real production errors.

See the installation guide for Docker, systemd, and other deployment options. It works great on small VPS instances and ARM hardware.

How to integrate Telebugs with Python

Use the official sentry-sdk package. The only difference from Sentry is the DSN.

pip install sentry-sdk

Initialize it early in your application (for example in your app factory, wsgi.py, or main script):

import sentry_sdk

sentry_sdk.init(
    dsn="https://your-telebugs-instance.com/api/v1/sentry_errors/1234",
    # traces_sample_rate=1.0,  # optional
)

For framework-specific extras (Django, Flask, FastAPI, etc.), install with the bracket syntax and pass the corresponding integration:

pip install "sentry-sdk[django]"

Then add the integration when initializing (see the Django page for a full example).

Once set up, Telebugs will automatically capture unhandled exceptions, and you can add manual breadcrumbs, tags, and user context exactly as you would with Sentry.

Full options are documented in the Sentry Python docs. Everything works the same with Telebugs.

Python error context worth capturing

The stack trace tells you where Python raised the exception. The surrounding context tells you why it mattered. With the Sentry Python SDK, you can send the same useful metadata to Telebugs that you would normally send to Sentry.

• Environment and release so production, staging, and preview deploys do not blur together.
• User and account identifiers when they help support or engineering understand impact.
• Request data and route names for API services, webhook endpoints, and form submissions.
• Task names, queue names, and retry counts for Celery, RQ, Dramatiq, and scheduled jobs.
• Breadcrumbs and tags for feature flags, tenant IDs, payment providers, import sources, or other debugging landmarks.

Because Telebugs is self-hosted, you can pair useful debugging context with your own privacy posture, retention rules, and sensitive-data scrubbing policies.

Background tasks and async

Python teams often rely on Celery, RQ, Dramatiq, or asyncio. Telebugs captures errors from these environments when you use the Sentry SDK integrations.

Errors that occur outside the normal request/response cycle (in workers, scheduled tasks, or async coroutines) are reported with the same rich context as web requests. This is especially valuable because these errors are often harder to reproduce locally.

See the Django page for a concrete Celery example that applies to other task queues as well.

Production checklist for Python error tracking

A good Python error tracking setup is small, explicit, and easy to reason about during an incident. Before rolling it out across every service, decide what belongs in each event and what should stay out.

• Use separate projects, environments, or tags for production, staging, preview apps, workers, and scripts.
• Set release names during deploys so new exceptions can be tied back to a concrete change.
• Scrub passwords, tokens, session cookies, and customer secrets before they become part of an event.
• Route urgent production errors to the right channel, and keep noisy low-priority jobs out of the main alert stream.
• Run Telebugs close to the apps that send events, especially if you need an offline or private-network deployment.

Telebugs is built for this kind of practical setup: low resource usage, simple upgrades, and a focused error inbox that does not ask your team to operate a full Sentry clone. See the low-resource deployment guide if you want to keep the server footprint modest.

AI-friendly Python debugging with MCP

Telebugs also supports MCP, which makes it easier for AI tools that speak the Model Context Protocol to inspect error data without you copying stack traces by hand. For Python teams, that can mean faster investigation of tracebacks, breadcrumbs, tags, release metadata, and affected endpoints.

The useful version of AI debugging is quiet and controlled: your source of truth stays in Telebugs, your sensitive data rules still apply, and an assistant can help reason about a production issue from the same structured context your team already captured. See MCP error tracking for the broader workflow.

Releases and artifacts for Python

While pure Python stack traces are usually readable, using releases still gives you valuable context: which version introduced a regression, the ability to associate errors with deploys, and (when combined with source maps for any frontend Python code or hybrid apps) clearer debugging.

Use the Sentry CLI or the releases API to create releases during your deployment process. See the releases and sourcemaps guide for details.

What you gain with Telebugs for Python

Python developers using Telebugs typically highlight:

• Strong default grouping that works well for Python tracebacks and reduces duplicate noise.
• Flexible notifications (email, push, Slack, Discord, Teams, webhooks) with rules so you only get paged for real problems.
• Full control over data retention and the ability to run completely offline.
• Predictable one-time cost instead of per-event billing that can explode with background job errors or high-traffic APIs.
• Simple operation: one container, low resource usage, easy to keep updated.

You keep the excellent Sentry Python SDK developer experience while running a much lighter server-side system.

Frequently asked questions about Telebugs and Python

Does Telebugs work with Celery and background tasks?

Yes. When you use the Sentry SDK with the appropriate integration, errors raised inside Celery tasks, RQ jobs, or asyncio code are reported automatically with the surrounding context.

Can Telebugs track Flask and FastAPI errors?

Yes. Flask and FastAPI apps can use the official Sentry Python SDK and send events to Telebugs by changing the DSN to your Telebugs endpoint.

Can I use Telebugs with any Python version?

Telebugs is compatible with the Python versions supported by the official Sentry Python SDK. Check the Sentry Python docs for the exact version matrix used by your application.

Do I get the same context as Sentry?

Yes for core error tracking. You receive the full stack trace, local variables (when enabled), request data, breadcrumbs, tags, user info, and extras. Features that are outside pure error tracking (such as full performance tracing or session replays) are not part of Telebugs’ scope.

Can AI tools inspect Python errors through MCP?

Yes. Telebugs MCP support lets compatible AI tools inspect structured error context from Telebugs, including Python tracebacks and event metadata, while keeping your server as the source of truth.

How do I handle sensitive data in Python errors?

Use the Sentry SDK’s data scrubbing options (before_send, before_send_transaction, etc.) or Telebugs’ server-side sensitive data scrubbing rules. You stay in full control because everything runs on your infrastructure.

Ready to track Python errors on your own terms?

Read the Telebugs manual, the Sentry SDK compatible guide, or get Telebugs and point your Python apps at it today.