Ravn captures every exception and tells you exactly what broke — concrete AI explanations and actionable fix steps, not just a stack trace. Two lines of code. Nothing else needed.
import ravn
ravn.init(api_key="your_api_key")
# That's it. Errors are captured automatically.
Every unhandled exception is captured and reported the moment it happens. Full stack trace, request context, and metadata — automatically, with no try/except wrappers needed.
Every error comes with a concrete explanation of what broke, why it happened, and how to fix it. Actionable answers and specific fix suggestions — not a stack trace you have to interpret yourself.
Get notified via email when new errors are detected or error rates spike. Configurable thresholds per project.
Send specific exceptions or log messages with custom metadata whenever you need fine-grained control.
Decorate any function with @ravn.measure to track execution time and get alerted when it exceeds your threshold. Lightweight function-level timing — no agents or tracing overhead.
Organize errors by project, invite team members, and control access with role-based permissions.
pip install ravn-sdk
import ravn
ravn.init(api_key="your_key")
Every unhandled exception is sent to your Ravn dashboard with full stacktrace and metadata.
Lightweight Python SDK. No dependencies you don't need. Python 3.8+.
try:
risky_operation()
except Exception as e:
ravn.capture_exception(
e,
metadata={"user_id": 42}
)
ravn.capture_message(
"Payment processed",
level="info",
metadata={"amount": 99.99}
)
@ravn.measure
def slow_database_query():
# Warns if execution > 1000ms
...
No complex setup. No sprawling dashboards. If you've outgrown print() debugging but Sentry feels like too much, Ravn is for you.
| Ravn | Traditional tools | |
|---|---|---|
| Time to first event | ~30 seconds | 5–15 minutes of SDK config |
| AI root cause analysis | ✓ Every error, built-in | Add-on or paid tier |
| Performance tracking | ✓ One decorator | Distributed tracing setup |
| Dashboard complexity | Minimal, focused | Feature-rich, steep learning curve |