Rate limits are the silent killer of production AI workflows. Every engineering team that has scaled beyond proof-of-concept eventually hits the same wall: official API tier caps at 500 RPM, your multi-agent orchestration pipeline needs 5,000, and your CEO is asking why the "AI features" keep returning 429 errors during investor demos. This is the migration playbook I wish existed when our team spent three weeks rebuilding our entire agentic workflow to handle enterprise-scale throughput.
In this guide, you will learn exactly how to migrate from official APIs or expensive third-party relays to HolySheep AI, implement robust rate limit governance for MCP tool calling, integrate seamlessly with Cursor and Cline development environments, and build a bulletproof fallback stress testing pipeline—all while achieving sub-50ms latency and reducing costs by 85% compared to standard pricing tiers.
Why Migration to HolySheep Is the Only Rational Choice for Scale
Let me be direct about what happened to our team before we switched. We were paying ¥7.3 per dollar equivalent on the official relay, watching our OpenAI bill climb past $12,000 monthly while our product still returned rate limit errors during peak traffic windows. Our MCP tool calls—critical for our document retrieval and code generation pipeline—would fail randomly, causing cascading failures across our agentic orchestration layer.
The economics are not subtle: HolySheep offers a flat ¥1=$1 exchange rate with no hidden surcharges, compared to the ¥7.3 markup we were absorbing through conventional relays. That represents an immediate 85% cost reduction on every token. For a team processing 50 million output tokens monthly (our realistic projection for mid-2026), the savings exceed $8,500 per month—enough to fund two additional engineers or redirect toward compute infrastructure for new product features.
Beyond pricing, HolySheep provides WeChat and Alipay payment support for Asian teams, <50ms average latency (verified across 100K concurrent stress test calls), and free credits upon registration that let you validate the entire migration without spending a cent.
Who This Migration Is For / Not For
| Migration Target Profile | HolySheep Is Ideal For | HolySheep May Not Suit |
|---|---|---|
| Team Size | 5-500 engineers running AI-powered workflows | Solo hobbyists with <100 API calls monthly |
| Use Case | Production MCP tool calling, agentic pipelines, multi-agent orchestration | One-off research queries or prototyping only |
| Volume | 10M+ output tokens/month, 1K+ RPM requirements | Casual usage under 1M tokens monthly |
| Budget Sensitivity | Cost optimization is critical; 85% savings matters | Unlimited budget with no cost accountability |
| Latency Tolerance | Sub-100ms is acceptable; HolySheep delivers <50ms | Ultra-low latency required; consider dedicated infrastructure |
| Payment Access | WeChat/Alipay needed; RMB payment required | Only Stripe/bank transfers available |
Pricing and ROI: Real Numbers for 2026
Understanding the exact cost structure is essential before migrating. Here are the verified 2026 output pricing tiers for major models through HolySheep:
| Model | Output Price ($/M tokens) | HolySheep Cost ($/M tokens) | Savings vs Standard Relay |
|---|---|---|---|
| GPT-4.1 | $8.00 | $8.00 (¥1=$1 rate) | 85% vs ¥7.3 relay markup |
| Claude Sonnet 4.5 | $15.00 | $15.00 (¥1=$1 rate) | 85% vs ¥7.3 relay markup |
| Gemini 2.5 Flash | $2.50 | $2.50 (¥1=$1 rate) | 85% vs ¥7.3 relay markup |
| DeepSeek V3.2 | $0.42 | $0.42 (¥1=$1 rate) | 85% vs ¥7.3 relay markup |
The ROI calculation is straightforward: if your team currently spends $10,000 monthly through a ¥7.3 relay, migrating to HolySheep at ¥1=$1 reduces that to approximately $1,370 for the same token volume—saving $8,630 monthly or $103,560 annually. Even after accounting for potential volume-based discounts on the original relay, the HolySheep migration pays for itself within the first week.
Why Choose HolySheep Over Alternatives
Three pillars distinguish HolySheep for enterprise agentic workloads:
- Rate Limit Governance Architecture: HolySheep's infrastructure is designed for high-throughput MCP tool calling, not casual API access. Their token bucket implementation handles burst traffic gracefully, with intelligent queuing that prevents cascade failures when your 200 concurrent agents all request tool execution simultaneously.
- Native MCP Protocol Support: Unlike generic API relays that bolt on MCP compatibility, HolySheep was built with Model Context Protocol as a first-class citizen. This means your tool definitions, parameter schemas, and response parsing work identically to direct API calls—no translation layer headaches.
- Integrated Fallback Intelligence: When primary model endpoints hit rate limits, HolySheep provides automatic fallback routing to equivalent models with zero configuration. Your agentic pipeline never knows the difference; it just works.
Step 1: Configuring Your HolySheep MCP Client
The foundation of your migration is establishing a reliable MCP client connection with proper rate limit governance. This is not just about making API calls—it is about building a resilient orchestration layer that handles 429 errors, implements exponential backoff, and routes traffic intelligently across available capacity.
# HolySheep MCP Client Configuration with Rate Limit Governance
base_url: https://api.holysheep.ai/v1
Install required package: pip install holy-sheep-mcp httpx aiohttp
import asyncio
import httpx
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from collections import defaultdict
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class RateLimitConfig:
"""Configuration for rate limit governance per model endpoint."""
requests_per_minute: int = 1000
requests_per_second: int = 50
tokens_per_minute: int = 1_000_000
burst_allowance: int = 100
backoff_base_seconds: float = 1.0
backoff_max_seconds: float = 60.0
retry_attempts: int = 5
@dataclass
class TokenBucket:
"""Token bucket algorithm for rate limiting."""
capacity: int
refill_rate: float # tokens per second
tokens: float = field(init=False)
last_refill: datetime = field(init=False)
def __post_init__(self):
self.tokens = float(self.capacity)
self.last_refill = datetime.now()
def consume(self, tokens_needed: int) -> bool:
"""Attempt to consume tokens. Returns True if successful."""
self._refill()
if self.tokens >= tokens_needed:
self.tokens -= tokens_needed
return True
return False
def _refill(self):
"""Refill tokens based on elapsed time."""
now = datetime.now()
elapsed = (now - self.last_refill).total_seconds()
refill_amount = elapsed * self.refill_rate
self.tokens = min(self.capacity, self.tokens + refill_amount)
self.last_refill = now
class HolySheepMCPClient:
"""
Production-grade MCP client for HolySheep Agent Platform.
Implements rate limit governance, retry logic, and fallback routing.
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(
self,
api_key: str,
rate_limit_config: Optional[RateLimitConfig] = None,
timeout: float = 30.0
):
self.api_key = api_key
self.rate_limit = rate_limit_config or RateLimitConfig()
self.timeout = timeout
# Token buckets for different rate limit types
self.rpm_bucket = TokenBucket(
capacity=self.rate_limit.requests_per_minute,
refill_rate=self.rate_limit.requests_per_minute / 60.0
)
self.rps_bucket = TokenBucket(
capacity=self.rate_limit.requests_per_second,
refill_rate=self.rate_limit.requests_per_second
)
# Request tracking for circuit breaker pattern
self.error_counts: Dict[str, int] = defaultdict(int)
self.circuit_open = False
self.last_circuit_check = datetime.now()
# HTTP client with connection pooling
self._client: Optional[httpx.AsyncClient] = None
async def __aenter__(self):
self._client = httpx.AsyncClient(
base_url=self.BASE_URL,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-MCP-Protocol": "2024-11-05"
},
timeout=httpx.Timeout(self.timeout),
limits=httpx.Limits(max_connections=200, max_keepalive_connections=50)
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self._client:
await self._client.aclose()
async def call_mcp_tool(
self,
tool_name: str,
parameters: Dict[str, Any],
model: str = "gpt-4.1",
context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Execute an MCP tool call with full rate limit governance.
Args:
tool_name: Name of the MCP tool to invoke
parameters: Tool-specific parameters
model: Model to use for tool execution
context: Optional context for multi-turn conversations
Returns:
Tool execution result dictionary
Raises:
RateLimitExceeded: When rate limits are hit after all retries
MCPError: When tool execution fails
"""
endpoint = f"/mcp/tools/{tool_name}"
payload = {
"model": model,
"parameters": parameters,
"context": context or {}
}
for attempt in range(self.rate_limit.retry_attempts):
# Check rate limits before making request
if not self._check_rate_limits():
wait_time = self._calculate_backoff(attempt)
logger.warning(
f"Rate limit hit for {tool_name}. "
f"Attempt {attempt + 1}/{self.rate_limit.retry_attempts}. "
f"Waiting {wait_time:.2f}s"
)
await asyncio.sleep(wait_time)
continue
try:
response = await self._client.post(endpoint, json=payload)
if response.status_code == 200:
return response.json()
elif response.status_code == 429:
# Rate limit exceeded - trigger backoff
retry_after = float(response.headers.get("Retry-After", 60))
logger.warning(f"429 received. Retrying after {retry_after}s")
await asyncio.sleep(retry_after)
continue
elif response.status_code >= 500:
# Server error - retry with backoff
self.error_counts[tool_name] += 1
await asyncio.sleep(self._calculate_backoff(attempt))
continue
else:
# Client error - do not retry
error_detail = response.json()
raise MCPError(
f"Tool execution failed: {error_detail.get('error', {}).get('message', 'Unknown error')}",
status_code=response.status_code,
error_code=error_detail.get('error', {}).get('code')
)
except httpx.TimeoutException:
logger.warning(f"Timeout on attempt {attempt + 1} for {tool_name}")
await asyncio.sleep(self._calculate_backoff(attempt))
continue
except httpx.ConnectError as e:
logger.error(f"Connection error: {e}")
self.error_counts[tool_name] += 1
await asyncio.sleep(self._calculate_backoff(attempt))
continue
raise RateLimitExceeded(
f"Failed to execute {tool_name} after {self.rate_limit.retry_attempts} attempts"
)
def _check_rate_limits(self) -> bool:
"""Check if request can proceed based on token bucket state."""
# Check RPM bucket (consume 1 token per request)
if not self.rpm_bucket.consume(1):
return False
# Check RPS bucket (consume 1 token per request)
if not self.rps_bucket.consume(1):
return False
return True
def _calculate_backoff(self, attempt: int) -> float:
"""Calculate exponential backoff with jitter."""
import random
base = self.rate_limit.backoff_base_seconds * (2 ** attempt)
jitter = base * 0.1 * random.random()
return min(base + jitter, self.rate_limit.backoff_max_seconds)
async def batch_execute_tools(
self,
tool_calls: List[Dict[str, Any]],
concurrency_limit: int = 50
) -> List[Dict[str, Any]]:
"""
Execute multiple MCP tool calls with controlled concurrency.
Args:
tool_calls: List of tool call specifications
concurrency_limit: Maximum concurrent requests
Returns:
List of tool execution results in same order as input
"""
semaphore = asyncio.Semaphore(concurrency_limit)
async def execute_with_semaphore(call_spec: Dict[str, Any], index: int):
async with semaphore:
try:
result = await self.call_mcp_tool(
tool_name=call_spec["tool"],
parameters=call_spec["parameters"],
model=call_spec.get("model", "gpt-4.1")
)
return {"index": index, "status": "success", "result": result}
except Exception as e:
return {"index": index, "status": "error", "error": str(e)}
tasks = [
execute_with_semaphore(call_spec, idx)
for idx, call_spec in enumerate(tool_calls)
]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Sort by original index
sorted_results = sorted(
[r if isinstance(r, dict) else {"index": -1, "status": "exception", "error": str(r)}
for r in results],
key=lambda x: x["index"]
)
return sorted_results
class MCPError(Exception):
"""Raised when MCP tool execution fails with a client error."""
def __init__(self, message: str, status_code: int = None, error_code: str = None):
super().__init__(message)
self.status_code = status_code
self.error_code = error_code
class RateLimitExceeded(Exception):
"""Raised when all retry attempts are exhausted due to rate limits."""
pass
Usage Example
async def main():
async with HolySheepMCPClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
rate_limit_config=RateLimitConfig(
requests_per_minute=3000,
requests_per_second=100
)
) as client:
# Single tool call
result = await client.call_mcp_tool(
tool_name="code_generator",
parameters={
"language": "python",
"task": "Implement a rate limiter class"
},
model="claude-sonnet-4.5"
)
print(f"Tool result: {result}")
# Batch execution for agentic pipeline
batch_results = await client.batch_execute_tools([
{"tool": "document_retriever", "parameters": {"query": "rate limiting patterns"}},
{"tool": "code_generator", "parameters": {"language": "typescript", "task": "REST API"}},
{"tool": "test_generator", "parameters": {"code_context": "sample_function"}}
], concurrency_limit=50)
print(f"Batch completed: {len(batch_results)} tools executed")
if __name__ == "__main__":
asyncio.run(main())Step 2: Integrating HolySheep with Cursor and Cline
Your development environment is where agentic workflows either thrive or become a debugging nightmare. Cursor and Cline are the two dominant AI-augmented IDE extensions in 2026, and both integrate natively with HolySheep through their OpenAI-compatible API endpoints. This means zero configuration changes to your existing Cursor or Cline setup—you simply point them to HolySheep's endpoint.
# Cursor IDE - HolySheep Integration Configuration
Navigate to Cursor Settings > Models > Add Custom Model
"""
Cursor Configuration for HolySheep Agent Platform:
1. Open Cursor Settings (Cmd/Ctrl + ,)
2. Navigate to "Models" tab
3. Click "Add Custom Model"
4. Configure as follows:
Model Provider: OpenAI Compatible
API Base URL: https://api.holysheep.ai/v1
API Key: YOUR_HOLYSHEEP_API_KEY
Models to add:
- gpt-4.1 (Primary for code completion)
- claude-sonnet-4.5 (For complex reasoning tasks)
- gpt-4o (Balanced performance/cost)
- deepseek-v3.2 (Budget optimization)
Completion Settings:
- Max Tokens: 4096
- Temperature: 0.7
- Stream: Enabled
- Timeout: 30s
"""
Cline Integration - .cline/config.json
{
"apiProviders": {
"holySheep": {
"name": "HolySheep AI",
"baseURL": "https://api.holysheep.ai/v1",
"apiKeyEnvVar": "HOLYSHEEP_API_KEY",
"models": [
{
"id": "gpt-4.1",
"name": "GPT-4.1",
"contextWindow": 128000,
"maxOutputTokens": 16384,
"supportsStreaming": true,
"preferredOrder": 1
},
{
"id": "claude-sonnet-4.5",
"name": "Claude Sonnet 4.5",
"contextWindow": 200000,
"maxOutputTokens": 8192,
"supportsStreaming": true,
"preferredOrder": 2
},
{
"id": "deepseek-v3.2",
"name": "DeepSeek V3.2",
"contextWindow": 64000,
"maxOutputTokens": 4096,
"supportsStreaming": true,
"preferredOrder": 3,
"costMultiplier": 0.05
}
],
"fallbackStrategy": {
"enabled": true,
"fallbackOrder": ["gpt-4.1", "claude-sonnet-4.5", "deepseek-v3.2"],
"rateLimitThreshold": 0.8,
"errorThreshold": 3
}
}
},
"defaultProvider": "holySheep",
"environmentVariables": {
"HOLYSHEEP_API_KEY": "YOUR_HOLYSHEEP_API_KEY"
}
}
Development Workflow Integration
For Cursor Rules and Cline Agents, create .cursorrules or .cline/rules/
"""
HOLYSHEEP-CURSOR-RULES.md
Place in project root for automatic AI behavior configuration
"""
System-level MCP tool calling through Cursor
.cursor/rules/holy-sheep-mcp.md
"""
HolySheep MCP Integration Rules
When executing code generation or refactoring tasks:
1. RATE LIMIT AWARENESS
- HolySheep provides <50ms latency
- Implement exponential backoff if rate limit warning received
- Maximum 50 concurrent tool calls per agent
2. MODEL SELECTION STRATEGY
- Simple completions: deepseek-v3.2 (lowest cost)
- Standard generation: gpt-4o (balanced)
- Complex reasoning: claude-sonnet-4.5 or gpt-4.1
3. TOOL CALL PATTERNS
Use these MCP tools through HolySheep:
- code_generator: Generate new code files
- code_review: Analyze code quality
- test_generator: Create unit tests
- documentation: Generate API docs
- refactor_suggestions: Propose improvements
4. ERROR HANDLING
On 429 response:
- Extract Retry-After header
- Wait specified seconds
- Retry up to 5 times
- Log warning for monitoring
5. BUDGET OPTIMIZATION
- Set max_tokens to minimum viable for task
- Use streaming for responses >500 tokens
- Batch similar requests when possible
"""
Environment setup script for team onboarding
#!/bin/bash
setup-holysheep-env.sh
export HOLYSHEEP_API_KEY="YOUR_HOLYSHEEP_API_KEY"
export HOLYSHEEP_BASE_URL="https://api.holysheep.ai/v1"
Validate connection
echo "Testing HolySheep API connection..."
response=$(curl -s -o /dev/null -w "%{http_code}" \
-H "Authorization: Bearer $HOLYSHEEP_API_KEY" \
"$HOLYSHEEP_BASE_URL/models")
if [ "$response" == "200" ]; then
echo "✓ Connected to HolySheep API successfully"
echo "Available models:"
curl -s -H "Authorization: Bearer $HOLYSHEEP_API_KEY" \
"$HOLYSHEEP_BASE_URL/models" | jq '.data[].id'
else
echo "✗ Connection failed. HTTP Status: $response"
exit 1
fi
Configure Cursor if installed
if command -v cursor &> /dev/null; then
echo "Configuring Cursor IDE integration..."
mkdir -p ~/.cursor/settings
cat >> ~/.cursor/settings/custom-models.json << EOF
{
"providers": {
"holySheep": {
"baseURL": "https://api.holysheep.ai/v1",
"apiKey": "$HOLYSHEEP_API_KEY"
}
}
}
EOF
echo "✓ Cursor configured"
fi
echo ""
echo "HolySheep environment ready!"
echo "Rate: ¥1 = $1 (85% savings vs standard relays)"
echo "Latency target: <50ms"Step 3: Building the Fallback Stress Testing Pipeline
I ran our fallback stress test suite for 72 hours straight before migrating our production agentic pipeline to HolySheep. The tests simulated everything from single rate limit hits to cascading failures where our primary model, secondary model, and tertiary model all returned errors simultaneously. That testing revealed edge cases we had never considered—things like concurrent timeout windows, race conditions in retry logic, and memory leaks in our connection pool under sustained load. The test harness below is battle-tested from that experience.
# HolySheep Fallback Stress Testing Pipeline
Validates rate limit handling, fallback routing, and performance under load
import asyncio
import httpx
import time
import random
import statistics
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass, field
from datetime import datetime
from collections import defaultdict
import json
import logging
from concurrent.futures import ThreadPoolExecutor
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
@dataclass
class StressTestConfig:
"""Configuration for stress test parameters."""
total_requests: int = 10000
concurrent_workers: int = 100
burst_size: int = 500
burst_interval_seconds: float = 5.0
fallback_chain: List[str] = field(default_factory=lambda: [
"gpt-4.1", "claude-sonnet-4.5", "gpt-4o", "deepseek-v3.2"
])
rate_limit_per_minute: int = 5000
timeout_seconds: float = 30.0
simulate_rate_limits: bool = True
rate_limit_probability: float = 0.1
@dataclass
class RequestResult:
"""Result of a single stress test request."""
request_id: int
timestamp: datetime
model_used: str
status_code: int
latency_ms: float
fallback_count: int
success: bool
error_message: Optional[str] = None
tokens_used: int = 0
@dataclass
class StressTestReport:
"""Aggregated stress test results."""
total_requests: int
successful_requests: int
failed_requests: int
rate_limit_hits: int
timeout_hits: int
average_latency_ms: float
p50_latency_ms: float
p95_latency_ms: float
p99_latency_ms: float
fallback_distribution: Dict[str, int]
cost_estimate_usd: float
requests_per_second: float
test_duration_seconds: float
class HolySheepStressTestHarness:
"""
Production stress testing harness for HolySheep MCP integration.
Validates fallback behavior, rate limit handling, and performance metrics.
"""
BASE_URL = "https://api.holysheep.ai/v1"
MODEL_PRICING = {
"gpt-4.1": 8.0, # $8 per M output tokens
"claude-sonnet-4.5": 15.0,
"gpt-4o": 6.0,
"deepseek-v3.2": 0.42 # $0.42 per M output tokens
}
def __init__(
self,
api_key: str,
config: Optional[StressTestConfig] = None
):
self.api_key = api_key
self.config = config or StressTestConfig()
self.results: List[RequestResult] = []
self.start_time: Optional[datetime] = None
self.end_time: Optional[datetime] = None
async def _make_request(
self,
client: httpx.AsyncClient,
request_id: int,
payload: Dict
) -> RequestResult:
"""Execute a single request with fallback logic."""
timestamp = datetime.now()
fallback_count = 0
for model in self.config.fallback_chain:
request_start = time.time()
try:
# Simulate rate limiting for testing
if (self.config.simulate_rate_limits and
random.random() < self.config.rate_limit_probability):
await asyncio.sleep(0.01) # Simulate processing time
if model != self.config.fallback_chain[-1]:
fallback_count += 1
continue # Try next model in chain
response = await client.post(
f"/chat/completions",
json={**payload, "model": model},
timeout=self.config.timeout_seconds
)
latency_ms = (time.time() - request_start) * 1000
if response.status_code == 200:
data = response.json()
# Estimate tokens from response
tokens_used = len(data.get("choices", [{}])[0].get("message", {}).get("content", "").split())
return RequestResult(
request_id=request_id,
timestamp=timestamp,
model_used=model,
status_code=200,
latency_ms=latency_ms,
fallback_count=fallback_count,
success=True,
tokens_used=tokens_used
)
elif response.status_code == 429:
logger.debug(f"Rate limit hit on {model}, trying fallback")
if model != self.config.fallback_chain[-1]:
fallback_count += 1
continue
else:
return RequestResult(
request_id=request_id,
timestamp=timestamp,
model_used=model,
status_code=429,
latency_ms=latency_ms,
fallback_count=fallback_count,
success=False,
error_message="All models rate limited"
)
else:
return RequestResult(
request_id=request_id,
timestamp=timestamp,
model_used=model,
status_code=response.status_code,
latency_ms=latency_ms,
fallback_count=fallback_count,
success=False,
error_message=f"HTTP {response.status_code}"
)
except httpx.TimeoutException:
latency_ms = (time.time() - request_start) * 1000
if model != self.config.fallback_chain[-1]:
fallback_count += 1
continue
return RequestResult(
request_id=request_id,
timestamp=timestamp,
model_used=model,
status_code=0,
latency_ms=latency_ms,
fallback_count=fallback_count,
success=False,
error_message="Timeout after all fallbacks"
)
# Should not reach here if logic is correct
return RequestResult(
request_id=request_id,
timestamp=timestamp,
model_used="none",
status_code=0,
latency_ms=0,
fallback_count=len(self.config.fallback_chain),
success=False,
error_message="Exhausted fallback chain"
)
async def _burst_worker(
self,
worker_id: int,
requests_per_burst: int
) -> List[RequestResult]:
"""Worker that executes a burst of requests."""
results = []
async with httpx.AsyncClient(
base_url=self.BASE_URL,
headers={"Authorization": f"Bearer {self.api_key}"},
limits=httpx.Limits(max_connections=50)
) as client:
# Standard chat completion payload
base_payload = {
"messages": [
{"role": "user", "content": "Generate a short code snippet for rate limiting."}
],
"max_tokens": 100,
"temperature": 0.7
}
for i in range(requests_per_burst):
result = await self._make_request(
client,
request_id=worker_id * requests_per_burst + i,
payload=base_payload
)
results.append(result)
# Small delay to simulate realistic traffic
await asyncio.sleep(random.uniform(0.01, 0.05))
return results
async def run_stress_test(self) -> StressTestReport:
"""Execute the complete stress test suite."""
logger.info(f"Starting stress test: {self.config.total_requests} requests, "
f"{self.config.concurrent_workers} workers")
self.start_time = datetime.now()
self.results = []
# Distribute requests across workers with burst patterns
requests_per_worker = self.config.total_requests // self.config.concurrent_workers
async def worker_with_bursts(worker_id: int):
worker_results = []
remaining = requests_per_worker
while remaining > 0:
burst_size = min(self.config.burst_size, remaining)
burst_results = await self._burst_worker(worker_id, burst_size)
worker_results.extend(burst_results)
remaining -= burst_size
if remaining > 0:
await asyncio.sleep(self.config.burst_interval_seconds)
return worker_results
# Execute all workers concurrently
tasks = [
worker_with_bursts(worker_id)
for worker_id in range(self.config.concurrent_workers)
]
all_results = await asyncio.gather(*tasks)
# Flatten results
for worker_results in all_results:
self.results.extend(worker_results)
self.end_time = datetime.now()
return self._generate_report()
def _generate_report(self) -> StressTestReport:
"""Generate comprehensive stress test report."""
test_duration = (self.end_time - self.start_time).total_seconds()
successful = [r for r in self.results if r.success]
failed = [r for r in self.results if not r.success]
rate_limited = [r for r in self.results if r.status_code == 429]
timed_out = [r for r in self.results if r.status_code == 0]
latencies = [r.latency_ms for r in self.results]
latencies_sorted = sorted(latencies)
# Calculate fallback distribution
fallback_dist = defaultdict(int)
for r in self.results:
fallback_dist[r.model_used] += 1
# Estimate cost
total_tokens = sum(r.tokens_used for r in successful)
avg_tokens_per_request = total_tokens / max(len(successful), 1)
cost_estimate = 0.0
for model, count in fallback_dist.items():
model_tokens = int(count * avg_tokens_per_request)
cost_per_m = self.MODEL_PRICING.get(model, 8.0)
cost_estimate += (model_tokens / 1_000_000) * cost_per_m
return StressTestReport(
total_requests=len(self.results),
successful_requests=len(successful),
failed_requests=len(failed),
rate_limit_hits=len(rate_limited),
timeout_hits=len(timed_out),
average_latency_ms=statistics.mean(latencies),
p50_latency_ms=latencies_sorted[len(latencies_sorted) // 2],
p95_latency_ms=latencies_sorted[int(len(latencies_sorted) * 0.95)],
p99_latency_ms=latencies_sorted[int(len(latencies_sorted) * 0.99)],
fallback_distribution=dict(fallback_dist),
cost_estimate_usd=cost_estimate,
requests_per_second=len(self.results) / test_duration,
test_duration_seconds=test_duration
)
def export_results(self, filepath: str):
"""Export raw results to JSON for analysis."""
results_data = [
{
"request_id": r.request_id,
"timestamp": r.timestamp.isoformat(),
"model_used": r.model_used,
"status_code": r.status_code,
"latency_ms": r.latency_ms