# Retries
source: https://docs.chalk.ai/docs/functions/retries

## Retry a Chalk Function automatically on failure with a retry count or a RetryPolicy.

Functions can automatically retry on failure. Pass a simple integer for
max-attempts with default exponential backoff, or use RetryPolicy for full
control:

```
import chalkcompute
from chalkcompute import RetryPolicy

# Simple: retry up to 3 times with exponential backoff
@chalkcompute.function(retries=3)
def flaky_api(query: str) -> str:
    ...

# Exponential backoff with custom parameters
@chalkcompute.function(
    retries=RetryPolicy.exponential(
        attempts=5,
        initial=0.5,
        max_wait=30.0,
    ),
)
def resilient_call(payload: str) -> str:
    ...

# Fixed delay, only retry on specific exceptions
@chalkcompute.function(
    retries=RetryPolicy.linear(
        attempts=3,
        delay=2.0,
        retry_on=(TimeoutError, ConnectionError),
    ),
)
def network_call(url: str) -> str:
    ...
```

### RetryPolicy.exponential()

Wait times grow geometrically: initial, initial * multiplier,
initial * multiplier², ..., capped at max_wait:

| Parameter    | Type               | Default        | Description                        |
| ------------ | ------------------ | -------------- | ---------------------------------- |
| `attempts`   | `int`              | `3`            | Max retry attempts                 |
| `initial`    | `float`            | `1.0`          | Base wait time in seconds          |
| `multiplier` | `float`            | `2.0`          | Backoff multiplier                 |
| `max_wait`   | `float`            | `60.0`         | Upper bound on wait time           |
| `jitter`     | `bool`             | `True`         | Add random jitter                  |
| `retry_on`   | `tuple[type, ...]` | `(Exception,)` | Exception types that trigger retry |

### RetryPolicy.linear()

Fixed delay between retries:

| Parameter  | Type               | Default        | Description                        |
| ---------- | ------------------ | -------------- | ---------------------------------- |
| `attempts` | `int`              | `3`            | Max retry attempts                 |
| `delay`    | `float`            | `1.0`          | Fixed wait in seconds              |
| `jitter`   | `bool`             | `False`        | Add random jitter                  |
| `retry_on` | `tuple[type, ...]` | `(Exception,)` | Exception types that trigger retry |

### Exception filtering

By default, all Exception subclasses trigger a retry. Pass a tuple of
exception classes to retry_on to restrict which failures are retried:

```
RetryPolicy.exponential(
    attempts=4,
    retry_on=(ConnectionError, TimeoutError, OSError),
)
```

Non-matching exceptions propagate immediately without consuming retry budget.