resilient-sdk-core 0.1.1

Adaptive retry SDK with persistent failure memory

resilient-sdk

Adaptive retry SDK + CLI for production Python applications.

Every developer writes ad-hoc retry logic - fixed attempts, guessed backoff values, no observability. resilient-sdk replaces that with a zero-config decorator that learns from your failure history and surfaces actionable insights via a CLI.

from resilient import retry

@retry.auto
def call_openai(prompt: str):
    return openai.chat.completions.create(...)

$ resilient report
$ resilient explain openai
$ resilient anomalies

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How it works

• @retry.auto - wraps any function (sync or async), classifies exceptions automatically, and applies exponential backoff with jitter
• Postgres persistence - every retry event is written to resilient.events, multi-pod safe
• CLI - queries that data and gives you plain-English reports powered by Gemini

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Installation

Python SDK

pip install resilient-sdk-core

Requires Python 3.10+ and a Postgres database.

CLI

Download the binary from GitHub Releases or install with Go:

go install github.com/abhishekgit03/resilient-sdk/cli@latest

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Setup

1. Configure

resilient init --dsn postgresql://user:pass@host/dbname --gemini-key AIza...

This writes ~/.resilient/config.toml. The SDK reads the same file.

2. Use the decorator

from resilient import retry

# Works with any external call - HTTP, DB, queue
@retry.auto
def call_stripe():
    return stripe.PaymentIntent.create(...)

@retry.auto
async def call_openai(prompt: str):
    return await openai.chat.completions.create(...)

3. Optional - Circuit Breaker

Pair with the circuit breaker to stop retrying a service that's fully down:

from resilient import retry
from resilient.circuit import CircuitBreaker

cb = CircuitBreaker(failure_threshold=5, recovery_timeout=30)

@retry.auto
@cb.protect
def call_openai(prompt: str):
    ...

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CLI Commands

Command	Description
resilient init --dsn <dsn> --gemini-key <key>	One-time setup
resilient report	Failure summary, last 24h
resilient report --app openai --last 7d	Scoped report
resilient explain <service>	AI-powered analysis
resilient explain <service> --last 7d	Scoped explanation
resilient anomalies	Services that spiked vs yesterday
resilient top	Worst offenders in the last hour

Example output

$ resilient explain openai

Analysing openai (last 7d)...

OpenAI calls are failing at 4.2% over the last 7 days, up from 1.1% the week
before. Failures cluster between 14:00–16:00 UTC. The rate_limit errors suggest
you are retrying inside the same rate-limit window. Recommendation: add a 60s
cooldown after 3 consecutive 429s and consider request batching during peak hours.

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Error Classification

The SDK classifies exceptions automatically - no configuration needed.

HTTP Status	Error Type	Strategy
429	rate_limit	Exponential backoff + jitter, 5 attempts
500/502/503/504	server_error	Backoff + jitter, 4 attempts
400/401/403/404	client_fault	No retry - fail immediately
Timeout exceptions	transient	Short jitter, 3 attempts

Works with any HTTP library (httpx, requests, aiohttp) without importing them.

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Database Schema

Auto-created on first run:

resilient.events  -- one row per retry attempt
resilient.stats   -- aggregated windows (populated by CLI queries)

Compatible with any existing Postgres instance. Uses a dedicated resilient schema to avoid conflicts.

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Tech Stack

Layer	Technology
SDK	Python + Poetry
CLI	Go + Cobra
Storage	PostgreSQL
AI	Gemini 2.5 Flash

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License

MIT