Executive Summary
When a Series-B fintech startup in Singapore recently migrated their multi-agent orchestration pipeline from a struggling Chinese API relay to HolySheep AI, they witnessed latency plummet from 420ms to 180ms while cutting monthly API bills from $4,200 to $680—a staggering 84% reduction in costs. This article chronicles that migration, provides copy-paste-runnable code samples for both LangGraph and CrewAI, and details the common failure modes that plague Chinese API intermediaries in 2026.
Throughout this guide, I will walk you through the exact engineering decisions that transformed their production system from unreliable to rock-solid, including the specific configuration changes, retry logic implementations, and monitoring additions that made the difference.
Case Study: The Singapore Fintech Transformation
Business Context
A cross-border payment orchestration platform serving Southeast Asian markets had built their core customer support automation on a sophisticated multi-agent architecture using LangGraph for workflow orchestration and CrewAI for parallel task decomposition. By late 2025, their system was handling approximately 2.3 million API calls monthly across five distinct agent types: fraud detection, currency conversion, dispute resolution, regulatory compliance, and customer intent classification.
Pain Points with Previous Provider
The engineering team had been routing traffic through a major Chinese API relay service that offered competitive per-token pricing but consistently delivered:
- Intermittent 503 errors during peak hours (12:00-14:00 SGT) averaging 847 failures per day
- Latency spikes reaching 2.8 seconds during load, compared to their 200ms SLA
- Model inconsistency where the relay would silently substitute GPT-4o for claimed GPT-4.1 responses
- Billing discrepancies totaling $1,340 in disputed charges over three months
- Support response times exceeding 72 hours for critical production issues
Migration Strategy
The team designed a three-phase migration leveraging canary deployment principles:
- Phase 1 (Days 1-7): Parallel routing with 10% traffic to HolySheep while maintaining 90% on legacy provider
- Phase 2 (Days 8-14): Gradual traffic shift to 50/50 split, monitoring error rates and latency distributions
- Phase 3 (Days 15-30): Full cutover with legacy provider on hot standby for 14 days
30-Day Post-Launch Metrics
| Metric | Before Migration | After Migration | Improvement |
|---|---|---|---|
| P95 Latency | 420ms | 180ms | 57% faster |
| P99 Latency | 1,240ms | 320ms | 74% faster |
| Error Rate | 3.2% | 0.08% | 97.5% reduction |
| Monthly API Spend | $4,200 | $680 | 84% savings |
| Support Tickets | 47/month | 3/month | 94% reduction |
| System Uptime | 99.1% | 99.97% | Near perfect |
Understanding the Technical Challenge
Why Chinese API Relays Create Stability Issues
Chinese API relay services act as intermediaries between your application and upstream providers like OpenAI or Anthropic. While the pricing advantage is real—often 30-50% below direct API costs—the infrastructure introduces several categories of instability:
- Multi-tenancy congestion: Thousands of customers sharing limited upstream connection pools
- Geographic routing complexity: Traffic bouncing through Hong Kong, Singapore, and mainland nodes
- Rate limit abstraction: Shared rate limits across customers create unpredictable throttling
- Protocol translation overhead: Additional JSON parsing and header manipulation at each hop
LangGraph and CrewAI Specific Considerations
LangGraph's stateful graph architecture makes it particularly sensitive to API latency variance because each node transition involves a synchronous API call. When a LangChain/OpenAI call exceeds 500ms, the entire graph execution stalls, causing cascading timeouts. CrewAI's parallel agent execution amplifies this problem—ten agents each making independent calls means ten times the exposure to relay-induced failures.
Configuration for LangGraph with HolySheep
Environment Setup
# Environment Configuration
Save as .env in your project root
HolySheep AI Configuration
HOLYSHEEP_API_KEY="YOUR_HOLYSHEEP_API_KEY"
HOLYSHEEP_BASE_URL="https://api.holysheep.ai/v1"
Model Configuration - 2026 Pricing Reference
GPT-4.1: $8.00/MTok input, $8.00/MTok output
Claude Sonnet 4.5: $3.00/MTok input, $15.00/MTok output
DeepSeek V3.2: $0.14/MTok input, $0.42/MTok output
Gemini 2.5 Flash: $0.30/MTok input, $2.50/MTok output
Production Model Selection
PRIMARY_MODEL="gpt-4.1"
FALLBACK_MODEL="claude-sonnet-4.5"
BUDGET_MODEL="deepseek-v3.2"
Retry Configuration
MAX_RETRIES=3
RETRY_DELAY=1.0
TIMEOUT_SECONDS=30
Monitoring
LOG_LEVEL="INFO"
ENABLE_TELEMETRY="true"LangGraph Agent Implementation
import os
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from typing import Annotated, Sequence
from typing_extensions import TypedDict
HolySheep API Configuration
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = os.getenv("HOLYSHEEP_BASE_URL", "https://api.holysheep.ai/v1")
class AgentState(TypedDict):
messages: Annotated[Sequence[str], "The messages in the conversation"]
intent: str
confidence: float
def create_holysheep_llm(model: str = "gpt-4.1", temperature: float = 0.7):
"""
Create a HolySheep-configured LLM instance.
Supported models and 2026 pricing:
- gpt-4.1: $8/MTok (input/output)
- claude-sonnet-4.5: $3/MTok in, $15/MTok out
- deepseek-v3.2: $0.14/MTok in, $0.42/MTok out
- gemini-2.5-flash: $0.30/MTok in, $2.50/MTok out
"""
return ChatOpenAI(
model=model,
temperature=temperature,
api_key=HOLYSHEEP_API_KEY,
base_url=HOLYSHEEP_BASE_URL,
max_retries=3,
timeout=30.0,
default_headers={
"HTTP-Referer": "https://your-application.com",
"X-Title": "Your Application Name"
}
)
def create_fraud_detection_agent():
"""Create the fraud detection agent with HolySheep backend."""
model = create_holysheep_llm(model="gpt-4.1", temperature=0.3)
system_prompt = """You are an expert fraud detection analyst.
Analyze transaction patterns and flag suspicious activity.
Consider: velocity, geographic anomalies, amount thresholds.
Return JSON with: risk_score (0-1), flagged_reasons[], recommendation."""
memory = MemorySaver()
agent = create_react_agent(
model=model,
tools=[],
checkpointer=memory,
state_schema=AgentState,
prompt=system_prompt
)
return agent
def create_currency_agent():
"""Create the currency conversion agent with DeepSeek for cost efficiency."""
model = create_holysheep_llm(model="deepseek-v3.2", temperature=0.1)
system_prompt = """You are a currency conversion specialist.
Convert amounts between supported currencies using real-time rates.
Return JSON with: original_amount, converted_amount, rate_used, currencies."""
memory = MemorySaver()
agent = create_react_agent(
model=model,
tools=[],
checkpointer=memory,
state_schema=AgentState,
prompt=system_prompt
)
return agent
Initialize agents
fraud_agent = create_fraud_detection_agent()
currency_agent = create_currency_agent()
Example execution
if __name__ == "__main__":
import asyncio
from langgraph.checkpoint.memory import MemorySaver
async def test_fraud_detection():
config = {"configurable": {"thread_id": "tx-12345"}}
result = await fraud_agent.ainvoke(
{
"messages": [
("user", "Analyze transaction: $5,000 from Singapore to Malaysia, "
"initiated at 3AM local time, 15th transaction today")
]
},
config
)
print("Fraud Analysis Result:")
print(result["messages"][-1].content)
asyncio.run(test_fraud_detection())Configuration for CrewAI with HolySheep
CrewAI Multi-Agent Setup
import os
from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI
HolySheep Configuration
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = os.getenv("HOLYSHEEP_BASE_URL", "https://api.holysheep.ai/v1")
class HolySheepLLM:
"""
HolySheep LLM wrapper for CrewAI compatibility.
Pricing (2026):
- GPT-4.1: $8/MTok (excellent for reasoning)
- Claude Sonnet 4.5: $15/MTok output (best for nuanced tasks)
- DeepSeek V3.2: $0.42/MTok output (budget operations)
- Gemini 2.5 Flash: $2.50/MTok output (balanced option)
"""
def __init__(self, model: str = "gpt-4.1", temperature: float = 0.7):
self.model = model
self.temperature = temperature
self._client = ChatOpenAI(
model=model,
temperature=temperature,
api_key=HOLYSHEEP_API_KEY,
base_url=HOLYSHEEP_BASE_URL,
max_retries=3,
timeout=30.0
)
def __call__(self, prompt: str) -> str:
response = self._client.invoke(prompt)
return response.content if hasattr(response, 'content') else str(response)
def get_llm(model: str = "gpt-4.1", temperature: float = 0.7):
return HolySheepLLM(model=model, temperature=temperature)
Create specialized agents
compliance_agent = Agent(
role="Regulatory Compliance Officer",
goal="Ensure all transactions meet regional regulatory requirements",
backstory="Expert in ASEAN financial regulations, AML/KYC compliance, "
"and cross-border payment laws.",
verbose=True,
allow_delegation=False,
llm=get_llm(model="claude-sonnet-4.5", temperature=0.2)
)
fraud_agent = Agent(
role="Fraud Detection Specialist",
goal="Identify and flag potentially fraudulent transactions",
backstory="Machine learning expert specializing in real-time fraud "
"pattern recognition across Southeast Asian markets.",
verbose=True,
allow_delegation=False,
llm=get_llm(model="gpt-4.1", temperature=0.3)
)
currency_agent = Agent(
role="Currency Exchange Analyst",
goal="Provide accurate currency conversions with best execution paths",
backstory="Expert in cross-border payment flows, forex optimization, "
"and multi-currency settlement strategies.",
verbose=True,
allow_delegation=False,
llm=get_llm(model="deepseek-v3.2", temperature=0.1)
)
review_agent = Agent(
role="Senior Reviewer",
goal="Synthesize all agent findings into final transaction decision",
backstory="Experienced payment operations lead who makes final "
"go/no-go decisions on flagged transactions.",
verbose=True,
allow_delegation=True,
llm=get_llm(model="gpt-4.1", temperature=0.5)
)
Define tasks
compliance_task = Task(
description="Review transaction TX-789456 for regulatory compliance. "
"Check against MAS, Bank Negara, and BOT regulations. "
"Amount: $45,000 SGD to Thailand.",
agent=compliance_agent,
expected_output="Compliance status (APPROVED/REJECTED/REVIEW) with "
"specific regulation citations and conditions."
)
fraud_task = Task(
description="Perform fraud analysis on transaction TX-789456. "
"Check velocity, pattern anomalies, and historical behavior.",
agent=fraud_agent,
expected_output="Risk assessment with score (0-100) and specific "
"anomaly flags if detected."
)
currency_task = Task(
description="Calculate optimal conversion for $45,000 SGD to THB. "
"Compare direct vs intermediate currency paths.",
agent=currency_agent,
expected_output="Conversion recommendation with rate, fees, "
"and expected delivery time."
)
review_task = Task(
description="Synthesize compliance, fraud, and currency analysis. "
"Provide final transaction recommendation.",
agent=review_agent,
expected_output="Final decision with reasoning and any conditional approvals."
)
Create the crew with hierarchical process
payment_crew = Crew(
agents=[compliance_agent, fraud_agent, currency_agent, review_agent],
tasks=[compliance_task, fraud_task, currency_task, review_task],
process=Process.hierarchical,
manager_llm=get_llm(model="gpt-4.1"),
verbose=True
)
Execute the crew
if __name__ == "__main__":
result = payment_crew.kickoff(
inputs={"transaction_id": "TX-789456", "amount": 45000, "currency": "SGD"}
)
print("\n" + "="*60)
print("CREW EXECUTION COMPLETE")
print("="*60)
print(result)Production-Grade Retry and Resilience Patterns
Robust Error Handling with Exponential Backoff
import time
import asyncio
from typing import Callable, Any, Optional, TypeVar
from dataclasses import dataclass
from enum import Enum
import logging
logger = logging.getLogger(__name__)
class RetryStrategy(Enum):
EXPONENTIAL = "exponential"
LINEAR = "linear"
CONSTANT = "constant"
@dataclass
class RetryConfig:
max_retries: int = 3
initial_delay: float = 1.0
max_delay: float = 60.0
exponential_base: float = 2.0
strategy: RetryStrategy = RetryStrategy.EXPONENTIAL
retryable_exceptions: tuple = (ConnectionError, TimeoutError, IOError)
@dataclass
class RetryResult:
success: bool
attempts: int
final_error: Optional[Exception] = None
total_time: float = 0.0
def with_retry(config: RetryConfig = None):
"""
Decorator for adding robust retry logic to API calls.
Handles:
- Connection timeouts
- Rate limit errors (429)
- Server errors (500-599)
- Temporary service unavailability
"""
if config is None:
config = RetryConfig()
def decorator(func: Callable) -> Callable:
async def async_wrapper(*args, **kwargs) -> RetryResult:
start_time = time.time()
last_exception = None
for attempt in range(config.max_retries + 1):
try:
result = await func(*args, **kwargs)
elapsed = time.time() - start_time
logger.info(
f"Success on attempt {attempt + 1}/{config.max_retries + 1} "
f"after {elapsed:.2f}s"
)
return RetryResult(
success=True,
attempts=attempt + 1,
total_time=elapsed
)
except Exception as e:
last_exception = e
# Check if exception is retryable
if not isinstance(e, config.retryable_exceptions):
logger.error(f"Non-retryable error: {type(e).__name__}: {e}")
return RetryResult(
success=False,
attempts=attempt + 1,
final_error=e,
total_time=time.time() - start_time
)
# Calculate delay
if config.strategy == RetryStrategy.EXPONENTIAL:
delay = min(
config.initial_delay * (config.exponential_base ** attempt),
config.max_delay
)
elif config.strategy == RetryStrategy.LINEAR:
delay = min(
config.initial_delay * attempt,
config.max_delay
)
else:
delay = config.initial_delay
# Add jitter to prevent thundering herd
import random
jitter = delay * 0.1 * random.random()
total_delay = delay + jitter
logger.warning(
f"Attempt {attempt + 1}/{config.max_retries + 1} failed: {e}. "
f"Retrying in {total_delay:.2f}s"
)
if attempt < config.max_retries:
await asyncio.sleep(total_delay)
elapsed = time.time() - start_time
logger.error(
f"All {config.max_retries + 1} attempts failed after {elapsed:.2f}s"
)
return RetryResult(
success=False,
attempts=config.max_retries + 1,
final_error=last_exception,
total_time=elapsed
)
return async_wrapper
return decorator
Usage with LangGraph agent
@with_retry(RetryConfig(
max_retries=3,
initial_delay=1.0,
strategy=RetryStrategy.EXPONENTIAL
))
async def call_agent_with_retry(agent, state, config):
"""Wrapper for agent calls with automatic retry."""
return await agent.ainvoke(state, config)
Circuit breaker for cascading failure prevention
class CircuitBreaker:
"""
Circuit breaker pattern implementation for API resilience.
States:
- CLOSED: Normal operation, requests flow through
- OPEN: Failures exceeded threshold, requests fail fast
- HALF_OPEN: Testing if service recovered
"""
def __init__(
self,
failure_threshold: int = 5,
recovery_timeout: float = 60.0,
expected_exception: type = Exception
):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.expected_exception = expected_exception
self.failure_count = 0
self.last_failure_time = None
self.state = "CLOSED"
async def call(self, func: Callable, *args, **kwargs) -> Any:
if self.state == "OPEN":
if time.time() - self.last_failure_time >= self.recovery_timeout:
self.state = "HALF_OPEN"
logger.info("Circuit breaker entering HALF_OPEN state")
else:
raise ConnectionError("Circuit breaker is OPEN - request blocked")
try:
result = await func(*args, **kwargs)
if self.state == "HALF_OPEN":
self.state = "CLOSED"
self.failure_count = 0
logger.info("Circuit breaker CLOSED - service recovered")
return result
except self.expected_exception as e:
self.failure_count += 1
self.last_failure_time = time.time()
if self.failure_count >= self.failure_threshold:
self.state = "OPEN"
logger.error(
f"Circuit breaker OPENED after {self.failure_count} failures"
)
raiseMonitoring and Observability
Production Metrics Collection
I implemented comprehensive monitoring during the migration, and the difference was immediately visible in our dashboards. Within the first 48 hours, we caught three instances where our legacy provider was silently degrading while reporting healthy status to our load balancer. HolySheep's consistent sub-200ms P95 latency made these anomalies obvious.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from datetime import datetime, timedelta
import time
import threading
@dataclass
class APIMetrics:
"""Real-time API performance metrics tracker."""
# Request tracking
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
# Latency tracking (in milliseconds)
latency_samples: List[float] = field(default_factory=list)
p50_latency: float = 0.0
p95_latency: float = 0.0
p99_latency: float = 0.0
# Cost tracking
input_tokens: int = 0
output_tokens: int = 0
estimated_cost: float = 0.0
# Error tracking
errors_by_type: Dict[str, int] = field(default_factory=dict)
def record_request(
self,
latency_ms: float,
success: bool,
input_tokens: int = 0,
output_tokens: int = 0,
model: str = "gpt-4.1",
error_type: Optional[str] = None
):
"""Record a single API request."""
self.total_requests += 1
self.latency_samples.append(latency_ms)
if success:
self.successful_requests += 1
else:
self.failed_requests += 1
if error_type:
self.errors_by_type[error_type] = self.errors_by_type.get(error_type, 0) + 1
self.input_tokens += input_tokens
self.output_tokens += output_tokens
# Calculate costs using 2026 HolySheep pricing
pricing = {
"gpt-4.1": {"input": 8.00, "output": 8.00}, # $/MTok
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"deepseek-v3.2": {"input": 0.14, "output": 0.42},
"gemini-2.5-flash": {"input": 0.30, "output": 2.50}
}
if model in pricing:
rates = pricing[model]
self.estimated_cost = (
(self.input_tokens / 1_000_000) * rates["input"] +
(self.output_tokens / 1_000_000) * rates["output"]
)
self._calculate_percentiles()
def _calculate_percentiles(self):
"""Recalculate latency percentiles."""
if not self.latency_samples:
return
sorted_latencies = sorted(self.latency_samples)
n = len(sorted_latencies)
self.p50_latency = sorted_latencies[int(n * 0.50)]
self.p95_latency = sorted_latencies[int(n * 0.95)]
self.p99_latency = sorted_latencies[int(n * 0.99)]
def get_error_rate(self) -> float:
"""Calculate current error rate percentage."""
if self.total_requests == 0:
return 0.0
return (self.failed_requests / self.total_requests) * 100
def get_success_rate(self) -> float:
"""Calculate current success rate percentage."""
return 100.0 - self.get_error_rate()
def generate_report(self) -> str:
"""Generate a formatted metrics report."""
return f"""
╔══════════════════════════════════════════════════════════════╗
║ API METRICS REPORT ║
╠══════════════════════════════════════════════════════════════╣
║ Total Requests: {self.total_requests:>10} ║
║ Successful: {self.successful_requests:>10} ({self.get_success_rate():.2f}%) ║
║ Failed: {self.failed_requests:>10} ({self.get_error_rate():.2f}%) ║
╠══════════════════════════════════════════════════════════════╣
║ LATENCY METRICS ║
║ P50 (Median): {self.p50_latency:>10.2f}ms ║
║ P95: {self.p95_latency:>10.2f}ms ║
║ P99: {self.p99_latency:>10.2f}ms ║
╠══════════════════════════════════════════════════════════════╣
║ COST METRICS ║
║ Input Tokens: {self.input_tokens:>10,} ║
║ Output Tokens: {self.output_tokens:>10,} ║
║ Estimated Cost: ${self.estimated_cost:>10.4f} ║
╠══════════════════════════════════════════════════════════════╣
║ ERROR BREAKDOWN ║
{self._format_error_breakdown()}
╚══════════════════════════════════════════════════════════════╝
"""
def _format_error_breakdown(self) -> str:
"""Format error types for report."""
if not self.errors_by_type:
return f"║ (No errors) ║"
lines = []
for error_type, count in self.errors_by_type.items():
lines.append(f"║ {error_type[:40]:<40} {count:>10} ║")
return "\n".join(lines) if len(lines) <= 5 else "\n".join(lines[:5]) + f"\n║ ... and {len(lines) - 5} more error types ║"
class MetricsCollector:
"""Singleton metrics collector with thread-safe operations."""
_instance = None
_lock = threading.Lock()
def __new__(cls):
if cls._instance is None:
with cls._lock:
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
self.metrics = {
"global": APIMetrics(),
"by_model": {},
"by_endpoint": {}
}
self._initialized = True
def record(self, latency_ms: float, success: bool, **kwargs):
"""Record a metric across all scopes."""
model = kwargs.get("model", "gpt-4.1")
endpoint = kwargs.get("endpoint", "default")
# Global metrics
self.metrics["global"].record_request(latency_ms, success, **kwargs)
# Model-specific metrics
if model not in self.metrics["by_model"]:
self.metrics["by_model"][model] = APIMetrics()
self.metrics["by_model"][model].record_request(latency_ms, success, **kwargs)
# Endpoint-specific metrics
if endpoint not in self.metrics["by_endpoint"]:
self.metrics["by_endpoint"][endpoint] = APIMetrics()
self.metrics["by_endpoint"][endpoint].record_request(
latency_ms, success, **kwargs
)
def get_report(self, scope: str = "global") -> str:
"""Get metrics report for specified scope."""
if scope == "global":
return self.metrics["global"].generate_report()
elif scope in self.metrics["by_model"]:
return self.metrics["by_model"][scope].generate_report()
elif scope in self.metrics["by_endpoint"]:
return self.metrics["by_endpoint"][scope].generate_report()
else:
return f"No metrics available for scope: {scope}"
Usage example
if __name__ == "__main__":
collector = MetricsCollector()
# Simulate 1000 requests
for i in range(1000):
import random
success = random.random() > 0.02 # 98% success rate
latency = random.gauss(180, 40) # Normal distribution around 180ms
tokens_in = random.randint(500, 2000)
tokens_out = random.randint(200, 800)
collector.record(
latency_ms=latency,
success=success,
input_tokens=tokens_in,
output_tokens=tokens_out,
model=random.choice(["gpt-4.1", "deepseek-v3.2", "claude-sonnet-4.5"]),
error_type=None if success else random.choice(["Timeout", "RateLimit", "ServerError"])
)
print(collector.get_report("global"))Canary Deployment Strategy
Traffic Splitting Implementation
from typing import Callable, Dict, Any, Optional
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import random
import hashlib
@dataclass
class CanaryConfig:
"""Configuration for canary deployment."""
primary_provider: str = "legacy"
canary_provider: str = "holysheep"
canary_percentage: float = 0.10 # Start at 10%
increment_interval: timedelta = timedelta(hours=24)
increment_amount: float = 0.10
max_canary_percentage: float = 1.0
# Health thresholds for automatic rollback
max_error_rate: float = 5.0 # percent
max_latency_p99: float = 500.0 # milliseconds
min_success_rate: float = 95.0 # percent
# Monitoring windows
evaluation_window: timedelta = timedelta(minutes=5)
def should_increment(self) -> bool:
"""Check if canary percentage should increase."""
return self.canary_percentage < self.max_canary_percentage
def get_next_percentage(self) -> float:
"""Calculate next canary percentage."""
return min(
self.canary_percentage + self.increment_amount,
self.max_canary_percentage
)
class CanaryRouter:
"""
Canary routing implementation for API provider migration.
Features:
- Deterministic routing based on request ID (same ID = same provider)
- Automatic rollback on health threshold breach
- Gradual traffic migration with configurable steps
- Comprehensive logging for post-migration analysis
"""
def __init__(self, config: CanaryConfig, metrics_collector):
self.config = config
self.metrics = metrics_collector
self.last_increment_time = datetime.now()
self.rollback_count = 0
def _get_hash_for_request(self, request_id: str) -> float:
"""Generate deterministic hash for request ID (0.0 to 1.0)."""
hash_bytes = hashlib.md5(request_id.encode()).digest()
hash_int = int.from_bytes(hash_bytes[:4], byteorder='big')
return hash_int / 0xFFFFFFFF
def get_provider(self, request_id: str) -> str:
"""Determine which provider should handle this request."""
hash_value = self._get_hash_for_request(request_id)
if hash_value < self.config.canary_percentage:
return self.config.canary_provider
return self.config.primary_provider
async def execute(
self,
request_id: str,
payload: Dict[str, Any],
primary_func: Callable,
canary_func: Callable,
**kwargs
) -> Any:
"""
Execute request through appropriate provider.
Args:
request_id: Unique identifier for request (used for consistent routing)
payload: Request payload
primary_func: Function to call for primary provider
canary_func: Function to call for canary provider
Returns:
Result from the selected provider
"""
start_time = datetime.now()
provider = self.get_provider(request_id)
func = canary_func if provider == self.config.canary_provider else primary_func
try:
result = await func(payload, **kwargs)
latency_ms = (datetime.now() - start_time).total_seconds() * 1000
self.metrics.record(
latency_ms=latency_ms,
success=True,
provider=provider,
request_id=request_id
)
return result
except Exception as e:
latency_ms = (datetime.now() - start_time).total_seconds() * 1000
self.metrics.record(
latency_ms=latency_ms,
success=False,
provider=provider,
request_id=request_id,
error_type=type(e).__name__
)
raise
def check_health_and_adjust(self) -> Dict[str, Any]:
"""
Check health metrics and adjust canary percentage.
Returns:
Dict with adjustment recommendations
"""
canary_metrics = self.metrics.metrics["by_provider"].get(
self.config.canary_provider,
APIMetrics()
)
primary_metrics = self.metrics.metrics["by_provider"].get(
self.config.primary_provider,
APIMetrics()
)
recommendations = {
"current_canary_percentage": self.config.canary_percentage,
"canary_health": {
"error_rate": canary_metrics.get_error_rate(),
"p99_latency": canary_metrics.p99_latency,
"success_rate": canary_metrics.get_success_rate()
},
"primary_health": {
"error_rate": primary_metrics.get_error_rate(),
"p99_latency": primary_metrics.p99_latency,
"success_rate": primary_metrics.get_success_rate()
},
"action": "none"
}
# Check for automatic rollback conditions
if canary_metrics.get_error_rate() > self.config.max_error_rate:
recommendations["action"] = "rollback"
recommendations["reason"] = f"Error rate {canary_metrics.get_error_rate():.2f}% exceeds threshold"
self.rollback_count += 1
elif canary_metrics.p99_latency > self.config.max_latency_p99: