python-resilience
Makes your agent handle temporary failures better. It retries actions that failed, making the program more reliable.
What it does
This tool helps make computer programs more reliable. It makes the program automatically try again if something temporarily goes wrong, like a network issue, and wait a bit before retrying.
When to use it
- ✓When a part of your program needs to connect to another service and might fail.
- ✓If you want your program to keep working even with minor temporary glitches.
- ✓To avoid overwhelming a struggling service by retrying gradually.
Example of what to ask your AI
“Make my program retry up to 3 times if the website connection fails, waiting a bit longer each time.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
Under the hood, the code uses a technique called 'retry'. If an operation fails, it waits for a duration that increases with each attempt (exponential backoff) before trying again, preventing the program from crashing.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill python-resilienceOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/python-development/skills/python-resilience/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Python Resilience Patterns
Build fault-tolerant Python applications that gracefully handle transient failures, network issues, and service outages. Resilience patterns keep systems running when dependencies are unreliable.
When to Use This Skill
- Adding retry logic to external service calls
- Implementing timeouts for network operations
- Building fault-tolerant microservices
- Handling rate limiting and backpressure
- Creating infrastructure decorators
- Designing circuit breakers
Core Concepts
1. Transient vs Permanent Failures
Retry transient errors (network timeouts, temporary service issues). Don't retry permanent errors (invalid credentials, bad requests).
2. Exponential Backoff
Increase wait time between retries to avoid overwhelming recovering services.
3. Jitter
Add randomness to backoff to prevent thundering herd when many clients retry simultaneously.
4. Bounded Retries
Cap both attempt count and total duration to prevent infinite retry loops.
Quick Start
from tenacity import retry, stop_after_attempt, wait_exponential_jitter
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential_jitter(initial=1, max=10),
)
def call_external_service(request: dict) -> dict:
return httpx.post("https://api.example.com", json=request).json()
Fundamental Patterns
Pattern 1: Basic Retry with Tenacity
Use the tenacity library for production-grade retry logic. For simpler cases, consider built-in retry functionality or a lightweight custom implementation.
from tenacity import (
retry,
stop_after_attempt,
stop_after_delay,
wait_exponential_jitter,
retry_if_exception_type,
)
TRANSIENT_ERRORS = (ConnectionError, TimeoutError, OSError)
@retry(
retry=retry_if_exception_type(TRANSIENT_ERRORS),
stop=stop_after_attempt(5) | stop_after_delay(60),
wait=wait_exponential_jitter(initial=1, max=30),
)
def fetch_data(url: str) -> dict:
"""Fetch data with automatic retry on transient failures."""
response = httpx.get(url, timeout=30)
response.raise_for_status()
return response.json()
Pattern 2: Retry Only Appropriate Errors
Whitelist specific transient exceptions. Never retry:
ValueError,TypeError- These are bugs, not transient issuesAuthenticationError- Invalid credentials won't become valid- HTTP 4xx errors (except 429) - Client errors are permanent
from tenacity import retry, retry_if_exception_type
import httpx
# Define what's retryable
RETRYABLE_EXCEPTIONS = (
ConnectionError,
TimeoutError,
httpx.ConnectTimeout,
httpx.ReadTimeout,
)
@retry(
retry=retry_if_exception_type(RETRYABLE_EXCEPTIONS),
stop=stop_after_attempt(3),
wait=wait_exponential_jitter(initial=1, max=10),
)
def resilient_api_call(endpoint: str) -> dict:
"""Make API call with retry on network issues."""
return httpx.get(endpoint, timeout=10).json()
Pattern 3: HTTP Status Code Retries
Retry specific HTTP status codes that indicate transient issues.
from tenacity import retry, retry_if_result, stop_after_attempt
import httpx
RETRY_STATUS_CODES = {429, 502, 503, 504}
def should_retry_response(response: httpx.Response) -> bool:
"""Check if response indicates a retryable error."""
return response.status_code in RETRY_STATUS_CODES
@retry(
retry=retry_if_result(should_retry_response),
stop=stop_after_attempt(3),
wait=wait_exponential_jitter(initial=1, max=10),
)
def http_request(method: str, url: str, **kwargs) -> httpx.Response:
"""Make HTTP request with retry on transient status codes."""
return httpx.request(method, url, timeout=30, **kwargs)
Pattern 4: Combined Exception and Status Retry
Handle both network exceptions and HTTP status codes.
from tenacity import (
retry,
retry_if_exception_type,
retry_if_result,
stop_after_attempt,
wait_exponential_jitter,
before_sleep_log,
)
import logging
import httpx
logger = logging.getLogger(__name__)
TRANSIENT_EXCEPTIONS = (
ConnectionError,
TimeoutError,
httpx.ConnectError,
httpx.ReadTimeout,
)
RETRY_STATUS_CODES = {429, 500, 502, 503, 504}
def is_retryable_response(response: httpx.Response) -> bool:
return response.status_code in RETRY_STATUS_CODES
@retry(
retry=(
retry_if_exception_type(TRANSIENT_EXCEPTIONS) |
retry_if_result(is_retryable_response)
),
…
FAQ
What are transient failures?
These are problems that fix themselves, like a network that was briefly slow or a service that was down but has since recovered.
Why wait longer with each retry?
To give the failing service time to recover. Trying too many times in quick succession can worsen the problem.
What is 'jitter'?
It's adding a small random amount of time to the waits. This prevents many programs from retrying at the exact same moment, which could overload the service.
Details
Original description by the author
Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4933
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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