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FR-AI / ANALYSISWhy it is worth attention
It consolidates multiple error-handling patterns (retry with various backoff strategies, circuit breaker, conditional retry, stateful retry) and modern features (sync/async, metrics, profiling, mock error injection) into one consistent API, reducing boilerplate significantly.
Who it is for
- Python developers building resilient APIs or microservices
- Engineers who want to replace repetitive try/except blocks with declarative decorators
- Teams needing circuit breaker patterns for external service calls
- Testers and developers writing robust error-handling logic who require mock error injection
Use cases
- Retrying transient network failures in API clients with exponential backoff and jitter
- Protecting a service from cascading failures using a circuit breaker
- Batch processing where retry state must persist across multiple invocations
- Logging and reporting errors to multiple destinations (file, Sentry, Slack) from a single decorator
Strengths
- Clean decorator and context manager APIs that keep business logic uncluttered
- Rich retry policies: exponential, linear, Fibonacci backoff, max delay, jitter, and conditional retry on exception or result
- Built-in sync and async circuit breakers with configurable thresholds and half-open requests
- Integrated metrics, profiling, and pluggable reporters for observability and testing
Considerations
- Requires Python 3.9 or newer, excluding older Python versions
- Relatively new library with a smaller community and ecosystem compared to tenacity or backoff
- Some advanced features (e.g., stateful retry, custom backoff callbacks) may require deeper understanding of configuration
README quick start
🌿 tranq
Calm error handling for Python – decorator-based, zero boilerplate.
🧘 Why tranq?
Writing repetitive try / except blocks clutters your code and hides the business logic.
tranq gives you declarative error handling with decorators, context managers, and a rich set of retry strategies – so you can focus on what your code does, not how it recovers from failures.
| Feature | Description |
|---|---|
| 🧘 Tranquil | Clean, readable, and maintainable. |
| 🔁 Smart retries | Exponential, linear, Fibonacci backoff, jitter, and max delay. |
| 🚦 Circuit Breaker | Prevent cascading failures (sync & async). |
| 🧪 Conditional retry | On specific exceptions or result values. |
| 📦 Retry groups | All-or-nothing execution for multiple functions. |
| 📊 Built‑in metrics & profiling | Monitor performance and error rates. |
| 📝 Pluggable reporters | Send errors to files, Sentry, Slack, or custom destinations. |
| 🧩 Context manager API | Use tranq.retry(...) when decorators aren't ideal. |
| 🔧 Stateful retry | Persist attempt count across calls. |
| 🎭 Mock error injection | Test your error handling with ease. |
📦 Installation
pip install tranq
Requires Python 3.9 or later.
⚡ Quick Start
Decorator (@handle)
import tranq
@tranq.handle(on=ValueError, retry=3, delay=0.5, backoff=2.0)
def risky():
# This will be retried up to 3 times with exponential backoff
...
Async (@handle_async)
@tranq.handle_async(on=ConnectionError, retry=2, fallback=lambda: "offline")
async def fetch_data():
...
Circuit Breaker
cb = tranq.CircuitBreaker(failure_threshold=5, timeout=60)
@tranq.handle(circuit_breaker=cb)
def call_unstable_service():
...
Context Manager
with tranq.retry(on=ValueError, retry=2) as ctx:
result = ctx.run(my_function, arg1, arg2)
Retry Group (all‑or‑nothing)
group = tranq.retry_group(step1, step2, step3, on=Exception, retry=1)
results = group.run() # if any step fails, all are retried together
🔍 Features in Depth
1. Retry with Backoff
Choose from exponential, linear, or Fibonacci backoff. Add jitter to avoid thundering herds.
@tranq.handle(
on=TimeoutError,
ret
Description
Calm error handling for Python
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