nopanic 0.2.1

Unified resilience toolkit for Python: retries, circuit breakers, timeouts, rate limits, bulkheads and request hedging. Zero dependencies, sync + async, one decorator API.

nopanic

The unified resilience toolkit for Python.

Retries, circuit breakers, timeouts, rate limits, bulkheads and hedged requests: one decorator-based API, identical for sync and async code, with zero runtime dependencies.

from nopanic import retry, circuit_breaker, timeout, backoff

openai_breaker = circuit_breaker(failure_threshold=0.5, reset_timeout=15.0, name="openai")

@retry(attempts=4, on=ConnectionError, backoff=backoff.full_jitter(base=0.2, cap=10.0))
@openai_breaker
@timeout(30.0)
async def call_llm(prompt: str) -> str:
    ...

Why

Java has resilience4j. .NET has Polly. Python has a drawer of single-purpose parts:

Need	Today in Python	nopanic
Retry + backoff	tenacity / backoff / stamina	✅ retry
Circuit breaker	pybreaker (sync-oriented, aging)	✅ circuit_breaker (sliding window, sync + async)
Timeout	roll your own per framework	✅ timeout
Client-side rate limit	roll your own token bucket	✅ rate_limit
Concurrency bulkhead	raw semaphores	✅ bulkhead
Fallback / graceful degradation	try/except sprawl	✅ fallback
Hedged requests (tail latency)	nothing	✅ hedge
Adaptive (AIMD) rate limiting	nothing	✅ adaptive_rate_limit
Cache that survives outages	roll your own	✅ cache (stale-while-failing)
One event stream for all of it	per-library logging	✅ events.subscribe
All of the above, composable	none	✅ compose

These patterns only pay off when they compose: a timeout per attempt, retries that respect an open circuit, a fallback that catches whatever is left. Wiring that out of three libraries with three philosophies is exactly the code nobody wants to own. Every service that calls another service needs this, and in the LLM era every app calls flaky, rate-limited, high-latency remote APIs.

Install

pip install nopanic

Python 3.10+. No dependencies. Fully typed (py.typed).

The toolkit

Every policy is a decorator that works on both def and async def functions. Policies with shared state (breaker, bulkhead, rate limit) are created once and applied to every call site that talks to the same dependency.

retry: try again, politely

from nopanic import retry, backoff

@retry(
    attempts=5,
    on=(ConnectionError, TimeoutError),          # or a predicate: lambda e: ...
    backoff=backoff.full_jitter(base=0.1, cap=30.0),
    giveup=lambda e: getattr(e, "status", 0) == 401,   # don't retry hopeless errors
    before_sleep=lambda a: log.warning("attempt %d failed: %s", a.attempt, a.exception),
)
def fetch(): ...

On exhaustion the original exception is re-raised, so there is no wrapper to unwrap. Backoff strategies: fixed, exponential, full_jitter (default, AWS-style), decorrelated_jitter, or any iterable of floats.

Exceptions carrying a numeric retry_after attribute (build one from an HTTP 429's Retry-After header, or let CircuitOpen provide it) raise the wait to at least that long; disable with honor_retry_after=False.

circuit_breaker: stop hammering what's already down

from nopanic import circuit_breaker, CircuitOpen

payments = circuit_breaker(
    failure_threshold=0.5,   # open at >=50% failures...
    min_calls=10,            # ...once 10 outcomes are in the window
    window=30.0,             # sliding time window (seconds)
    reset_timeout=15.0,      # then let a probe through
    name="payments",
    on_state_change=lambda b, old, new: log.warning("%s: %s -> %s", b.name, old, new),
)

@payments
async def charge(order): ...

While open, calls fail instantly with CircuitOpen(retry_after=...) instead of stacking up on a dead dependency. One instance = one dependency; decorate as many functions with it as you like, and check payments.state anytime.

timeout: bound every attempt

@timeout(2.0)
async def lookup(): ...   # cancelled cleanly via the event loop

Sync functions run on a worker thread and are abandoned (not killed) on timeout. This is documented and deliberate, and still usually what you want at a system boundary.

rate_limit: be a good client

rl = rate_limit(90, per=60.0, burst=10)   # 90 calls/min, bursts of 10

@rl
async def embed(text): ...

Token bucket; throttled calls wait by default (roughly FIFO). Set max_wait to fail fast with RateLimited instead.

bulkhead: contain the blast radius

bh = bulkhead(20, max_wait=0)   # at most 20 in flight, reject the 21st

@bh
async def render_report(): ...

fallback: degrade, don't die

@fallback(lambda exc: CACHED_ANSWER, on=(ConnectionError, CircuitOpen))
async def recommendations(user): ...

hedge: fix the p99, not the median

@hedge(delay=0.8, max_hedges=1)
async def complete(prompt): ...   # if slow after 800ms, race a duplicate call

The first success wins; losers are cancelled ("The Tail at Scale"). For idempotent async calls only.

adaptive_rate_limit: find the server's real limit

arl = adaptive_rate_limit(50, per=1.0, min_rate=5)   # start at 50/s, never below 5/s

@arl
async def call_api(): ...

Classic AIMD, the scheme TCP uses for congestion control: a 429/503 (or any exception carrying retry_after) cuts the rate multiplicatively, every success earns a small additive recovery, and an explicit Retry-After blocks the bucket for exactly that long. You converge on whatever rate the server actually sustains instead of hardcoding a guess. Inspect it live via arl.current_rate.

cache: an outage is not your outage

@cache(ttl=60.0, stale_ttl=3600.0, on=(ConnectionError, TimeoutError))
def exchange_rates(base): ...

Fresh values are served from memory; when a refresh fails, the expired value is served for up to stale_ttl more seconds instead of the error. LRU-bounded, per-arguments keying, injectable clock.

Observability: one stream for everything

from nopanic import events

cancel = events.subscribe(
    lambda e: log.info("%s name=%s %s", e.kind, e.name, dict(e.data))
)

Every policy emits typed events (retry.attempt_failed, breaker.state_change, ratelimit.throttled, cache.stale_served, ...). A listener is all it takes to wire OpenTelemetry, StatsD or metrics counters; listeners can never break the calls they observe, and with no listeners the overhead is a single attribute read.

Composition: the whole point

Stack decorators (innermost runs closest to the call), or name the stack once and reuse it:

from nopanic import compose, fallback, retry, circuit_breaker, timeout, backoff, CircuitOpen

llm = circuit_breaker(failure_threshold=0.5, min_calls=8, reset_timeout=20.0, name="llm")

resilient = compose(                              # first = outermost
    fallback(lambda e: "Sorry, try again later.", on=(ConnectionError, CircuitOpen, TimeoutError)),
    retry(attempts=3, on=(ConnectionError, TimeoutError), backoff=backoff.full_jitter(0.2)),
    llm,
    timeout(30.0),
)

@resilient
async def ask(prompt: str) -> str: ...

Reading inside-out: each attempt gets 30s -> outcomes feed the breaker -> transient failures retry with jitter -> anything left becomes a graceful answer.

Recipe: retrying HTTP the right way

Retry transport errors, timeouts, 429 and 5xx; never retry other 4xx (they are permanent, retrying them is just noise):

import requests
from nopanic import retry, backoff

def _retriable(e):
    if isinstance(e, (requests.ConnectionError, requests.Timeout)):
        return True
    return (isinstance(e, requests.HTTPError) and e.response is not None
            and (e.response.status_code == 429 or e.response.status_code >= 500))

@retry(attempts=4, on=_retriable, backoff=backoff.full_jitter(base=0.2, cap=10.0))
def get_json(url):
    r = requests.get(url, timeout=10)   # always set a transport timeout too
    r.raise_for_status()
    return r.json()

Design principles

• Zero dependencies. A resilience library must not be a reliability risk itself.
• Sync and async are equals. One API; the wrapper flavour is chosen at decoration time, not per call.
• No wrapper exceptions. Your except ConnectionError: keeps working; policies only add their own precise signals (CircuitOpen, BulkheadFull, RateLimited).
• Composition over configuration. Small orthogonal policies + compose, instead of one mega-object with 40 kwargs.
• Testable time. Breakers and rate limiters take an injectable clock, so your test suite never sleeps.

Roadmap

• OpenTelemetry helper package on top of events (spans + metrics)
• Single-flight deduplication for cache refreshes
• Trio/AnyIO support

Performance

Resilience must not become its own latency problem. Per-call overhead on the hot (success) path, single thread, measured with benchmarks/bench.py:

Policy	Overhead per call
retry	~0.15 us
rate_limit	~0.6 us
adaptive_rate_limit	~0.9 us
cache (fresh hit)	~0.9 us
circuit_breaker	~1.1 us
bulkhead	~1.3 us
events.emit, no listeners	~0.1 us

For scale: a fast HTTP round trip costs 5,000+ us, an LLM call millions. Numbers vary by machine; run the benchmark yourself. Design rules that keep it this way: success paths allocate nothing (retry builds its backoff iterator only after the first failure), the breaker's failure window is bucketed counters with constant memory no matter the traffic, event emission with zero listeners is a single attribute read, and locks are held for nanoseconds and never while user code runs.

Security & hardening

• Zero runtime dependencies means no transitive supply chain; CI runs pip-audit, ruff's flake8-bandit rules and a python -O smoke check (no assert in library code).
• All numeric parameters reject NaN/infinity/booleans at construction time: a bad config fails at import, not by silently never tripping a breaker in production.
• Observability hooks (before_sleep, on_state_change) and event listeners can never break the call they observe: their exceptions are logged and suppressed.
• Server-controlled delays are capped: a hostile Retry-After: 999999999 cannot park your client (retry_after_cap on retry, max_block on adaptive_rate_limit, both defaulting to 60s).
• hedge cancels and reaps losing attempts, leaving no orphaned tasks and no "exception was never retrieved" noise.
• Under untrusted load, set max_wait on rate_limit/bulkhead so backpressure becomes fast failure instead of unbounded queueing.

Vulnerability reports: see SECURITY.md. Contributions: CONTRIBUTING.md.

For AI coding agents

A compact, machine-oriented API reference lives in llms.txt. Point your agent at it for the full surface in one read.

Development

pip install -e .[dev]
pytest
ruff check .
mypy src

License

MIT.