retryflow 1.0.0

Smart retry engine with exponential backoff, jitter, per-exception rules, timeout, and async support

retryflow

Smart retry engine for Python — exponential backoff, jitter, per-exception rules, per-attempt timeout, hooks, and full async support.

---

Why retryflow?

Every app that talks to a network, a database, or an external API will eventually hit a transient failure. retryflow gives you a battle-tested, zero-dependency retry engine that handles the hard parts:

• Exponential backoff so you don't hammer a struggling service
• Jitter to avoid thundering-herd problems across multiple clients
• Per-attempt timeouts so a single hung call can't block forever
• Per-exception rules so you only retry errors that make sense to retry
• Lifecycle hooks (on_retry, on_success, on_failure) for logging, alerting, metrics
• Native async support — works seamlessly with async def functions
• Context manager API for one-off retry blocks without decorating a function

---

Installation

pip install retryflow

No dependencies. Requires Python 3.8+.

---

Quick Start

from retryflow import retry

@retry(max_attempts=3, delay=1.0, backoff=2.0)
def fetch_data(url):
    response = requests.get(url, timeout=5)
    response.raise_for_status()
    return response.json()

That's it. On failure, retryflow waits 1s, then 2s, then raises RetryError.

---

Usage

1. Basic Decorator

from retryflow import retry

@retry(max_attempts=5, delay=0.5, backoff=2.0, jitter=0.1)
def call_api():
    ...

Also works with no arguments (uses defaults):

@retry
def call_api():
    ...

2. Per-Attempt Timeout

Stop a single attempt if it hangs beyond a time limit:

@retry(max_attempts=3, delay=1.0, timeout=5.0)
def slow_query():
    # If this takes more than 5s, a TimeoutError is raised
    # and retryflow retries it
    return db.execute(heavy_query)

3. Retry Only Specific Exceptions

@retry(
    max_attempts=5,
    delay=1.0,
    exceptions=(ConnectionError, TimeoutError)  # Only retry these
)
def connect():
    ...

Non-matching exceptions (e.g., ValueError) propagate immediately without retrying.

4. Lifecycle Hooks

from retryflow import retry, RetryContext

def on_retry(ctx: RetryContext):
    print(f"Attempt {ctx.attempt}/{ctx.max_attempts} failed: {ctx.exception}")
    print(f"Retrying in {ctx.next_wait:.2f}s...")

def on_failure(ctx: RetryContext):
    alert_team(f"{ctx.func_name} failed after {ctx.attempts} attempts")

def on_success(ctx: RetryContext):
    metrics.record("api.success", tags={"attempt": ctx.attempt})

@retry(
    max_attempts=5,
    delay=1.0,
    on_retry=on_retry,
    on_failure=on_failure,
    on_success=on_success,
)
def critical_call():
    ...

5. Async Functions

Works exactly the same — retryflow detects async def automatically:

@retry(max_attempts=4, delay=0.5, timeout=3.0)
async def async_fetch(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as resp:
            return await resp.json()

6. Reusable Config Object

from retryflow import retry, RetryConfig

# Define once, apply everywhere
production_retry = RetryConfig(
    max_attempts=5,
    delay=1.0,
    backoff=2.0,
    jitter=0.3,
    timeout=10.0,
    exceptions=(ConnectionError, TimeoutError),
    log_retries=True,
)

@retry(config=production_retry)
def service_a(): ...

@retry(config=production_retry)
def service_b(): ...

7. Context Manager

For one-off retries without decorating a function:

from retryflow import attempt

with attempt(max_attempts=3, delay=1.0, timeout=5.0) as r:
    data = r.run(fetch, "https://api.example.com/data")

print(f"Completed in {r.elapsed:.2f}s")

---

API Reference

@retry decorator

retry(
    func=None,              # The function to wrap (when used without parentheses)
    *,
    max_attempts=3,         # Total attempts including the first call
    delay=1.0,              # Base delay in seconds between attempts
    backoff=2.0,            # Delay multiplier (1.0=constant, 2.0=exponential)
    jitter=0.0,             # Random seconds added to each wait (0 to jitter)
    timeout=None,           # Max seconds per attempt (None = no limit)
    exceptions=(Exception,),# Only retry on these exception types
    on_retry=None,          # Callable(RetryContext) called before each retry
    on_failure=None,        # Callable(RetryContext) called when all attempts fail
    on_success=None,        # Callable(RetryContext) called on success
    log_retries=True,       # Emit warning logs on each retry
    config=None,            # RetryConfig object (overrides all other params)
)

RetryConfig

All the same parameters as @retry, packaged into a reusable object.

cfg = RetryConfig(max_attempts=5, delay=1.0, backoff=2.0, timeout=10.0)

RetryContext

Passed to hook callbacks with information about the current state.

Attribute	Type	Description
attempt	int	Current attempt number (1-indexed)
max_attempts	int	Total configured attempts
exception	Exception	The exception that just occurred
elapsed	float	Total elapsed seconds since first attempt
next_wait	float | None	Seconds until next retry (None on final attempt)
func_name	str	Name of the decorated function

RetryError

Raised when all attempts are exhausted.

Attribute	Type	Description
last_exception	Exception	The final exception raised
attempts	int	Total number of attempts made

attempt context manager

with attempt(max_attempts=3, delay=1.0, ...) as r:
    result = r.run(func, *args, **kwargs)

After the with block, r.result, r.elapsed, r.last_error, and r.total_attempts are available.

---

Backoff Formula

wait = delay * (backoff ^ (attempt - 1)) + random(0, jitter)

attempt	delay=1, backoff=2	delay=0.5, backoff=3
1st retry	1.0s	0.5s
2nd retry	2.0s	1.5s
3rd retry	4.0s	4.5s

---

Running Tests

pip install pytest pytest-asyncio
pytest tests/ -v

---

License

MIT © prabhay759