ApiBench
Stress-test, benchmark, and optimize your endpoints with ruthless precision. Detect latency bottlenecks instantly, slash cloud costs, and guarantee rock-solid reliability with actionable, real-time analytics. Built for engineering teams ready to scale effortlessly. Don’t guess your throughput—prove it with ApiBench.
Read the technical deep-diveEliminating Endpoint Bottlenecks with ApiBench: A Field Guide#!/usr/bin/env python3
"""
ApiBench - High-Performance Endpoint Stress-Testing & Optimization Engine
A lightweight micro-SaaS framework for benchmarking API endpoints,
detecting latency anomalies, and analyzing throughput under heavy loads.
Features:
- Asynchronous load generation with precise concurrency controls
- Real-time latency percentile tracking (p50, p90, p99)
- Automated bottleneck detection and cost-efficiency scoring
- Zero-dependency standard library implementation for rapid deployment
Usage:
$ python main.py --target https://api.v1.internal/search --concurrency 50
"""
from __future__ import annotations
import asyncio
import enum
import logging
import time
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
logger = logging.getLogger("apibench")
class BenchmarkStrategy(enum.Enum):
"""Load distribution patterns for stress testing."""
CONSTANT = "constant"
RAMP_UP = "ramp_up"
BURST = "burst"
WAVE = "wave"
@dataclass(frozen=True)
class MetricSample:
"""Single HTTP request performance observation."""
status_code: int
latency_ms: float
timestamp: float
bytes_received: int
error: Optional[str] = None
class ApiBenchEngine:
"""Core orchestration engine for executing API benchmark scenarios."""
# ... the full file ships with your downloadQuick Start
This tool runs on pure Python 3.8+ using only the Standard Library. No external dependencies or pip install required.
1 — Direct Execution & Agentic Integration
Run it as a standalone utility or call it directly from your AI agents and scripts:
python main.py2 — Import in Your Code / Project
Drop the single file into your project directory and import it natively:
from main import AssetClassHow agents & programs use it: Since it relies purely on the Python Standard Library, your LLM agents (LangChain, AutoGen, or custom runtimes) can execute it as an isolated subprocess or import it directly — no dependency conflicts, no virtual environments.