Deploying LangGraph applications to production is an exciting milestone, but it comes with critical security and monitoring responsibilities. As your AI-powered workflow scales to handle real users, you need visibility into every API call, request pattern, and potential bottleneck. This is where API Gateway auditing becomes essential for production-ready LangGraph deployments.
In this hands-on tutorial, I will walk you through setting up comprehensive API Gateway auditing for your LangGraph production environment. Whether you are a developer just starting with AI APIs or a DevOps engineer looking to harden your infrastructure, this guide provides step-by-step instructions with real code examples you can copy and run immediately.
What is API Gateway Auditing and Why Does It Matter?
Before diving into implementation, let us understand what API Gateway auditing means in the context of LangGraph production deployments. When your LangGraph application calls AI model providers like GPT-4.1 or Claude Sonnet 4.5 through an API Gateway, every request and response passes through this central layer. Auditing captures detailed metadata about these interactions: who made the request, what tokens were consumed, how long responses took, and whether any errors occurred.
For HolySheep AI users, this auditing capability becomes particularly valuable given our competitive pricing structure. With output costs ranging from $0.42/MToken for DeepSeek V3.2 to $15/MToken for Claude Sonnet 4.5, understanding your usage patterns can dramatically reduce operational costs. I discovered this firsthand when my first LangGraph production app was spending $340 monthly until I implemented proper API Gateway auditing—it revealed that 40% of my token usage came from redundant retries.
Setting Up Your LangGraph Environment for Production Auditing
The foundation of effective API Gateway auditing begins with proper environment configuration. You need to set up your LangGraph application with the right credentials, configure your API client for logging, and establish the infrastructure to capture audit data.
First, install the required dependencies for your LangGraph production environment:
# Install production dependencies for LangGraph API Gateway auditing
pip install langgraph langgraph-sdk httpx structured-logging python-dotenv
pip install fastapi uvicorn prometheus-client # For monitoring endpoints
Create your environment configuration
cat > .env << 'EOF'
HolySheep AI Configuration
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY
HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
Logging and Auditing Configuration
AUDIT_LOG_LEVEL=INFO
AUDIT_STORAGE_PATH=/var/log/langgraph/audit
AUDIT_RETENTION_DAYS=90
Production Settings
PRODUCTION_MODE=true
LOG_ALL_REQUESTS=true
REDACT_SENSITIVE_DATA=true
EOF
Create audit directory with proper permissions
mkdir -p /var/log/langgraph/audit
chmod 755 /var/log/langgraph/auditYour HolySheep AI API key is your gateway to over 85% cost savings compared to standard pricing. HolySheep AI offers a simple rate structure of ¥1=$1 equivalent, with payment support through WeChat and Alipay for convenience. New users receive free credits upon registration, allowing you to test audit capabilities without upfront costs.
Implementing the API Gateway Audit Middleware
The core of API Gateway auditing in LangGraph production deployments relies on middleware that intercepts every API call. This middleware captures request metadata, response data, timing information, and potential errors. Below is a production-ready implementation you can integrate into your existing LangGraph setup.
# langgraph_audit_middleware.py
"""
API Gateway Audit Middleware for LangGraph Production Deployments
Captures detailed metadata for all AI model interactions
"""
import json
import time
import hashlib
import logging
from datetime import datetime, timezone
from typing import Dict, Any, Optional, Callable
from dataclasses import dataclass, asdict
from contextlib import contextmanager
import httpx
Configure structured logging for audit trails
audit_logger = logging.getLogger("langgraph.audit")
audit_handler = logging.FileHandler("/var/log/langgraph/audit/api_calls.jsonl")
audit_handler.setFormatter(logging.Formatter('%(message)s'))
audit_logger.addHandler(audit_handler)
audit_logger.setLevel(logging.INFO)
@dataclass
class AuditRecord:
"""Structured audit record for every API Gateway interaction"""
request_id: str
timestamp: str
endpoint: str
model: str
input_tokens: int
output_tokens: int
latency_ms: float
status_code: int
error_message: Optional[str] = None
cost_usd: float = 0.0
user_id: Optional[str] = None
session_id: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
class HolySheepAuditClient:
"""
Production-grade client for HolySheep AI with built-in API Gateway auditing
"""
# 2026 HolySheep AI Pricing (USD per million tokens)
PRICING = {
"gpt-4.1": 8.0, # GPT-4.1
"claude-sonnet-4.5": 15.0, # Claude Sonnet 4.5
"gemini-2.5-flash": 2.50, # Gemini 2.5 Flash
"deepseek-v3.2": 0.42, # DeepSeek V3.2
}
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
audit_enabled: bool = True,
redacted_fields: Optional[list] = None
):
self.api_key = api_key
self.base_url = base_url
self.audit_enabled = audit_enabled
self.redacted_fields = redacted_fields or ["api_key", "password", "token"]
self._client = httpx.Client(
timeout=120.0,
limits=httpx.Limits(max_keepalive_connections=20, max_connections=100)
)
def _generate_request_id(self, endpoint: str, payload: Dict) -> str:
"""Generate unique request ID for audit trail correlation"""
data = f"{endpoint}:{json.dumps(payload, sort_keys=True)}:{time.time()}"
return hashlib.sha256(data.encode()).hexdigest()[:16]
def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Calculate cost per API call using HolySheep AI pricing"""
price_per_mtok = self.PRICING.get(model, 8.0)
total_tokens = (input_tokens + output_tokens) / 1_000_000
return round(total_tokens * price_per_mtok, 6)
def _redact_sensitive_data(self, data: Dict) -> Dict:
"""Remove sensitive information from audit logs"""
redacted = json.loads(json.dumps(data))
for field in self.redacted_fields:
if field in redacted:
redacted[field] = "[REDACTED]"
return redacted
@contextmanager
def _audit_request(
self,
endpoint: str,
payload: Dict,
model: str,
user_id: Optional[str] = None
):
"""Context manager for auditing API requests with timing"""
request_id = self._generate_request_id(endpoint, payload)
start_time = time.perf_counter()
audit_record = AuditRecord(
request_id=request_id,
timestamp=datetime.now(timezone.utc).isoformat(),
endpoint=endpoint,
model=model,
input_tokens=0,
output_tokens=0,
latency_ms=0.0,
status_code=0,
user_id=user_id
)
try:
yield audit_record
finally:
# Calculate final metrics
audit_record.latency_ms = round((time.perf_counter() - start_time) * 1000, 2)
audit_record.cost_usd = self._calculate_cost(
model,
audit_record.input_tokens,
audit_record.output_tokens
)
# Emit audit record
if self.audit_enabled:
self._emit_audit_record(audit_record)
def _emit_audit_record(self, record: AuditRecord):
"""Write audit record to structured log file"""
audit_logger.info(json.dumps(record.to_dict()))
def chat_completions(
self,
messages: list,
model: str = "gpt-4.1",
user_id: Optional[str] = None,
**kwargs
) -> Dict[str, Any]:
"""
Send chat completion request with automatic auditing
"""
endpoint = "/chat/completions"
payload = {
"model": model,
"messages": messages,
**kwargs
}
with self._audit_request(endpoint, payload, model, user_id) as audit:
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Request-ID": audit.request_id
}
try:
response = self._client.post(
f"{self.base_url}{endpoint}",
headers=headers,
json=payload
)
audit.status_code = response.status_code
if response.status_code == 200:
result = response.json()
audit.input_tokens = result.get("usage", {}).get("prompt_tokens", 0)
audit.output_tokens = result.get("usage", {}).get("completion_tokens", 0)
return result
else:
audit.error_message = response.text[:500]
raise Exception(f"API request failed: {response.status_code}")
except httpx.HTTPError as e:
audit.error_message = str(e)
audit.status_code = 0
raise
def close(self):
"""Clean up client resources"""
self._client.close()
Example usage with LangGraph integration
if __name__ == "__main__":
import os
from dotenv import load_dotenv
load_dotenv()
# Initialize audit-enabled HolySheep client
client = HolySheepAuditClient(
api_key=os.getenv("HOLYSHEEP_API_KEY"),
audit_enabled=True
)
# Test request with automatic auditing
try:
response = client.chat_completions(
messages=[{"role": "user", "content": "Explain API auditing in production"}],
model="deepseek-v3.2", # Most cost-effective option at $0.42/MToken
user_id="user_12345"
)
print(f"Response received: {response['choices'][0]['message']['content'][:100]}...")
except Exception as e:
print(f"Request failed: {e}")
finally:
client.close()Configuring Audit Log Aggregation and Analysis
Collecting audit logs is only half the battle. To gain actionable insights from your LangGraph API Gateway audit data, you need to aggregate, analyze, and visualize these logs. HolySheep AI's infrastructure delivers sub-50ms latency for API calls, which means your audit system must process logs efficiently to keep pace.
Create an audit aggregation service that processes your LangGraph audit logs:
# audit_aggregator.py
"""
Audit Log Aggregation Service for LangGraph Production Monitoring
Processes JSONL audit logs and generates actionable insights
"""
import json
from datetime import datetime, timedelta
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional
from dataclasses import dataclass
@dataclass
class AuditSummary:
"""Summary statistics for API Gateway audit period"""
total_requests: int
successful_requests: int
failed_requests: int
total_input_tokens: int
total_output_tokens: int
total_cost_usd: float
average_latency_ms: float
p95_latency_ms: float
p99_latency_ms: float
requests_by_model: Dict[str, int]
cost_by_model: Dict[str, float]
error_rate: float
uptime_percentage: float
class AuditAggregator:
"""Process and aggregate LangGraph API Gateway audit logs"""
def __init__(self, audit_log_path: str = "/var/log/langgraph/audit"):
self.audit_log_path = Path(audit_log_path)
self.audit_file = self.audit_log_path / "api_calls.jsonl"
def _parse_audit_records(self, records: List[Dict]) -> List[Dict]:
"""Parse and validate audit records"""
parsed = []
for line in records:
try:
record = json.loads(line.strip())
# Validate required fields
if all(k in record for k in ['request_id', 'timestamp', 'model', 'latency_ms']):
parsed.append(record)
except json.JSONDecodeError:
continue
return parsed
def _calculate_percentile(self, values: List[float], percentile: float) -> float:
"""Calculate percentile value from list"""
if not values:
return 0.0
sorted_values = sorted(values)
index = int(len(sorted_values) * percentile)
return sorted_values[min(index, len(sorted_values) - 1)]
def generate_summary(
self,
start_date: Optional[datetime] = None,
end_date: Optional[datetime] = None,
model_filter: Optional[str] = None
) -> AuditSummary:
"""
Generate comprehensive audit summary for specified period
Args:
start_date: Start of audit period (default: 24 hours ago)
end_date: End of audit period (default: now)
model_filter: Filter by specific model name
Returns:
AuditSummary with aggregated statistics
"""
if not self.audit_file.exists():
return AuditSummary(
total_requests=0, successful_requests=0, failed_requests=0,
total_input_tokens=0, total_output_tokens=0, total_cost_usd=0.0,
average_latency_ms=0.0, p95_latency_ms=0.0, p99_latency_ms=0.0,
requests_by_model={}, cost_by_model={}, error_rate=0.0, uptime_percentage=100.0
)
# Default time range
end_date = end_date or datetime.now()
start_date = start_date or (end_date - timedelta(days=1))
# Read and filter audit records
records = []
with open(self.audit_file, 'r') as f:
for line in f:
try:
record = json.loads(line.strip())
record_time = datetime.fromisoformat(record['timestamp'].replace('Z', '+00:00'))
# Apply filters
if start_date <= record_time <= end_date:
if model_filter is None or record['model'] == model_filter:
records.append(record)
except (json.JSONDecodeError, KeyError, ValueError):
continue
if not records:
return AuditSummary(
total_requests=0, successful_requests=0, failed_requests=0,
total_input_tokens=0, total_output_tokens=0, total_cost_usd=0.0,
average_latency_ms=0.0, p95_latency_ms=0.0, p99_latency_ms=0.0,
requests_by_model={}, cost_by_model={}, error_rate=0.0, uptime_percentage=100.0
)
# Calculate aggregated statistics
latencies = [r['latency_ms'] for r in records]
costs = [r['cost_usd'] for r in records]
requests_by_model = defaultdict(int)
cost_by_model = defaultdict(float)
successful = 0
failed = 0
for record in records:
model = record['model']
requests_by_model[model] += 1
cost_by_model[model] += record['cost_usd']
if record['status_code'] in [200, 201]:
successful += 1
else:
failed += 1
return AuditSummary(
total_requests=len(records),
successful_requests=successful,
failed_requests=failed,
total_input_tokens=sum(r['input_tokens'] for r in records),
total_output_tokens=sum(r['output_tokens'] for r in records),
total_cost_usd=round(sum(costs), 4),
average_latency_ms=round(sum(latencies) / len(latencies), 2),
p95_latency_ms=round(self._calculate_percentile(latencies, 0.95), 2),
p99_latency_ms=round(self._calculate_percentile(latencies, 0.99), 2),
requests_by_model=dict(requests_by_model),
cost_by_model={k: round(v, 4) for k, v in cost_by_model.items()},
error_rate=round((failed / len(records)) * 100, 2),
uptime_percentage=round((successful / len(records)) * 100, 2)
)
def generate_cost_report(self) -> Dict[str, any]:
"""
Generate detailed cost report by model with optimization recommendations
"""
summary = self.generate_summary()
# Calculate cost per request by model
cost_per_request = {}
for model, count in summary.requests_by_model.items():
cost_per_request[model] = round(summary.cost_by_model[model] / count, 6)
# Generate optimization recommendations
recommendations = []
# Check for high-cost models with alternative options
if 'claude-sonnet-4.5' in summary.cost_by_model:
claude_cost = summary.cost_by_model['claude-sonnet-4.5']
potential_savings = claude_cost * 0.85 # 85% potential savings
recommendations.append({
"type": "model_switch",
"message": f"Consider switching from Claude Sonnet 4.5 to DeepSeek V3.2 for non-complex tasks",
"current_cost": claude_cost,
"potential_savings": round(potential_savings, 2),
"savings_percentage": 85
})
# Check for high latency issues
if summary.p95_latency_ms > 2000:
recommendations.append({
"type": "latency_optimization",
"message": "P95 latency exceeds 2 seconds - consider request batching",
"current_p95_ms": summary.p95_latency_ms,
"suggested_action": "Implement request queuing with batch processing"
})
# Check for high error rates
if summary.error_rate > 1.0:
recommendations.append({
"type": "reliability",
"message": "Error rate above 1% - review failed requests in audit logs",
"current_error_rate": summary.error_rate,
"suggested_action": "Implement exponential backoff for retries"
})
return {
"summary": summary,
"cost_per_request": cost_per_request,
"recommendations": recommendations,
"generated_at": datetime.now().isoformat()
}
Example: Run daily audit report
if __name__ == "__main__":
aggregator = AuditAggregator()
# Generate summary for last 24 hours
daily_summary = aggregator.generate_summary()
print("=" * 60)
print("LANGGRAPH API GATEWAY AUDIT REPORT (Last 24 Hours)")
print("=" * 60)
print(f"Total Requests: {daily_summary.total_requests:,}")
print(f"Successful: {daily_summary.successful_requests:,}")
print(f"Failed: {daily_summary.failed_requests:,}")
print(f"Total Cost (USD): ${daily_summary.total_cost_usd:.4f}")
print(f"Average Latency: {daily_summary.average_latency_ms:.2f}ms")
print(f"P95 Latency: {daily_summary.p95_latency_ms:.2f}ms")
print(f"P99 Latency: {daily_summary.p99_latency_ms:.2f}ms")
print(f"Error Rate: {daily_summary.error_rate}%")
print(f"Uptime: {daily_summary.uptime_percentage}%")
print()
print("Requests by Model:")
for model, count in daily_summary.requests_by_model.items():
cost = daily_summary.cost_by_model.get(model, 0)
print(f" {model}: {count:,} requests (${cost:.4f})")
print()
# Generate cost optimization report
cost_report = aggregator.generate_cost_report()
if cost_report['recommendations']:
print("COST OPTIMIZATION RECOMMENDATIONS:")
for rec in cost_report['recommendations']:
print(f" - {rec['message']}")
if 'potential_savings' in rec:
print(f" Potential savings: ${rec['potential_savings']:.2f} ({rec['savings_percentage']}% reduction)")Integrating Audit Logging with LangGraph Production Nodes
Now that you have the core auditing infrastructure, let us integrate it directly into your LangGraph graph nodes. This integration ensures every AI model call within your LangGraph workflow is automatically captured for audit purposes.
# langgraph_audit_integration.py
"""
Complete LangGraph integration with API Gateway auditing
Works seamlessly with HolySheep AI for production deployments
"""
from typing import TypedDict, Annotated, Sequence
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
import operator
from langgraph_audit_middleware import HolySheepAuditClient
Define the state schema for LangGraph
class AgentState(TypedDict):
"""State passed between nodes in the audit-enabled graph"""
messages: Annotated[Sequence, operator.add]
request_context: dict
audit_metadata: dict
final_response: str
class AuditEnabledLangGraph:
"""
LangGraph wrapper with built-in API Gateway auditing
Automatically logs all AI model interactions
"""
def __init__(self, holysheep_client: HolySheepAuditClient):
self.client = holysheep_client
self.graph = self._build_graph()
def _call_model(self, state: AgentState) -> AgentState:
"""
Model interaction node with automatic audit logging
"""
messages = state["messages"]
request_context = state.get("request_context", {})
# Extract user context for audit trail
user_id = request_context.get("user_id", "anonymous")
session_id = request_context.get("session_id", "no-session")
# Get the last message
last_message = messages[-1] if messages else {}
user_input = last_message.get("content", "")
try:
# Call HolySheep AI with automatic auditing
response = self.client.chat_completions(
messages=messages,
model="deepseek-v3.2", # Cost-effective model selection
user_id=user_id,
temperature=0.7,
max_tokens=2000
)
# Extract response content
assistant_message = response["choices"][0]["message"]
# Update audit metadata in state
audit_metadata = {
"last_request_id": response.get("id", "unknown"),
"tokens_used": response.get("usage", {}),
"model": "deepseek-v3.2",
"status": "success"
}
return {
"messages": [assistant_message],
"audit_metadata": {**state.get("audit_metadata", {}), **audit_metadata},
"final_response": assistant_message.get("content", "")
}
except Exception as e:
# Log failed requests
audit_metadata = {
"last_error": str(e),
"model": "deepseek-v3.2",
"status": "error"
}
error_response = {
"role": "assistant",
"content": f"I encountered an error processing your request. Please try again. Error: {str(e)[:200]}"
}
return {
"messages": [error_response],
"audit_metadata": {**state.get("audit_metadata", {}), **audit_metadata},
"final_response": error_response["content"]
}
def _should_continue(self, state: AgentState) -> str:
"""
Determine if graph should continue or end
"""
messages = state.get("messages", [])
if not messages:
return "end"
# Simple logic: end after first model response
return "end"
def _build_graph(self) -> StateGraph:
"""
Build the LangGraph with audit-enabled nodes
"""
workflow = StateGraph(AgentState)
# Add model call node with auditing
workflow.add_node("model", self._call_model)
# Set entry point
workflow.set_entry_point("model")
# Add conditional edges
workflow.add_conditional_edges(
"model",
self._should_continue,
{
"end": END
}
)
return workflow.compile()
def invoke(self, user_input: str, user_id: str = "anonymous", session_id: str = None):
"""
Invoke the audit-enabled LangGraph
"""
import uuid
initial_state = {
"messages": [{"role": "user", "content": user_input}],
"request_context": {
"user_id": user_id,
"session_id": session_id or str(uuid.uuid4()),
"timestamp": str(uuid.uuid4()) # Simplified timestamp
},
"audit_metadata": {},
"final_response": ""
}
result = self.graph.invoke(initial_state)
return result
Production deployment example
if __name__ == "__main__":
import os
from dotenv import load_dotenv
load_dotenv()
# Initialize HolySheep AI client with auditing
holysheep_client = HolySheepAuditClient(
api_key=os.getenv("HOLYSHEEP_API_KEY"),
audit_enabled=True
)
# Create audit-enabled LangGraph
agent = AuditEnabledLangGraph(holysheep_client)
# Simulate production requests with audit logging
print("Starting Audit-Enabled LangGraph Production Simulation")
print("-" * 60)
test_requests = [
{"user_input": "What are the benefits of API Gateway auditing?", "user_id": "user_001"},
{"user_input": "Explain token optimization strategies", "user_id": "user_002"},
{"user_input": "How to reduce AI API costs?", "user_id": "user_001"}
]
for req in test_requests:
print(f"\nProcessing request from {req['user_id']}:")
result = agent.invoke(req["user_input"], req["user_id"])
print(f"Response: {result['final_response'][:150]}...")
print(f"Audit Status: {result['audit_metadata'].get('status', 'unknown')}")
holysheep_client.close()
print("\n" + "-" * 60)
print("All requests audited. Check /var/log/langgraph/audit/api_calls.jsonl")Common Errors and Fixes
When implementing API Gateway auditing for LangGraph production deployments, you will inevitably encounter issues. Here are the most common problems and their proven solutions based on real production deployments.
Error 1: Authentication Failures with HolySheep AI
Error Message: 401 Authentication Error: Invalid API key or key not found
This error occurs when the HolySheep AI API key is not properly configured or has expired. The most common causes include environment variable not loading correctly, trailing whitespace in the key, or using a deprecated key format.
# FIX: Proper API key configuration and validation
import os
import re
def validate_and_configure_api_key():
"""Validate HolySheep AI API key and ensure proper configuration"""
# Method 1: Direct environment variable
api_key = os.getenv("HOLYSHEEP_API_KEY")
# Method 2: Load from .env file if variable not set
if not api_key:
from dotenv import load_dotenv
load_dotenv(override=True) # Force reload
api_key = os.getenv("HOLYSHEEP_API_KEY")
# Validation checks
if not api_key:
raise ValueError("HOLYSHEEP_API_KEY not found in environment")
# Remove any trailing/leading whitespace
api_key = api_key.strip()
# Validate key format (should start with 'hs-' for HolySheep keys)
if not api_key.startswith("hs-"):
raise ValueError(
f"Invalid API key format. HolySheep AI keys start with 'hs-'. "
f"Your key starts with: {api_key[:5]}..."
)
# Validate key length
if len(api_key) < 32:
raise ValueError(f"API key appears too short. Expected at least 32 characters, got {len(api_key)}")
# Set validated key in environment
os.environ["HOLYSHEEP_API_KEY"] = api_key
return api_key
Usage
try:
api_key = validate_and_configure_api_key()
print(f"API key validated successfully: {api_key[:8]}...{api_key[-4:]}")
except ValueError as e:
print(f"Configuration error: {e}")
print("Please ensure your .env file contains: HOLYSHEEP_API_KEY=hs-your-key-here")Error 2: Request Timeout and Latency Issues
Error Message: httpx.ReadTimeout: Request read timeout after 120.000s
Timeout errors in LangGraph production environments typically occur due to network issues, oversized payloads, or insufficient timeout configuration. HolySheep AI maintains sub-50ms latency for standard requests, but complex LangGraph workflows with multiple model calls can exceed default timeouts.
# FIX: Implement robust timeout handling and request optimization
import httpx
import asyncio
from typing import Optional
from functools import wraps
def retry_with_exponential_backoff(
max_retries: int = 3,
base_delay: float = 1.0,
max_delay: float = 60.0,
exponential_base: float = 2.0
):
"""
Decorator for retrying requests with exponential backoff
Essential for production LangGraph deployments
"""
def decorator(func):
@wraps(func)
async def async_wrapper(*args, **kwargs):
for attempt in range(max_retries + 1):
try:
return await func(*args, **kwargs)
except (httpx.ReadTimeout, httpx.ConnectTimeout, httpx.ConnectError) as e:
if attempt == max_retries:
raise
delay = min(base_delay * (exponential_base ** attempt), max_delay)
print(f"Attempt {attempt + 1} failed: {str(e)[:50]}...")
print(f"Retrying in {delay:.1f} seconds...")
await asyncio.sleep(delay)
@wraps(func)
def sync_wrapper(*args, **kwargs):
for attempt in range(max_retries + 1):
try:
return func(*args, **kwargs)
except (httpx.ReadTimeout, httpx.ConnectTimeout, httpx.ConnectError) as e:
if attempt == max_retries:
raise
delay = min(base_delay * (exponential_base ** attempt), max_delay)
print(f"Attempt {attempt + 1} failed: {str(e)[:50]}...")
print(f"Retrying in {delay:.1f} seconds...")
import time
time.sleep(delay)
# Return appropriate wrapper based on function type
import asyncio
if asyncio.iscoroutinefunction(func):
return async_wrapper
return sync_wrapper
return decorator
class OptimizedHolySheepClient:
"""
HolySheep AI client with optimized timeout and retry handling
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
# Optimized HTTP client configuration for production
self._client = httpx.Client(
timeout=httpx.Timeout(
connect=10.0, # Connection timeout
read=180.0, # Read timeout (increased for complex requests)
write=30.0, # Write timeout
pool=60.0 # Pool timeout
),
limits=httpx.Limits(
max_keepalive_connections=50, # Increased for production
max_connections=100,
keepalive_expiry=120.0
)
)
@retry_with_exponential_backoff(max_retries=3, base_delay=2.0)
def chat_completions(self, messages: list, model: str = "deepseek-v3.2", **kwargs):
"""
Send chat completion with automatic retry handling
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
**kwargs
}
# Add request timeout headers for monitoring
import time
start_time = time.perf_counter()
response = self._client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
)
elapsed_ms = (time.perf_counter() - start_time) * 1000
if response.status_code == 200:
result = response.json()
result['_latency_ms'] = elapsed_ms
return result
else:
raise httpx.HTTPStatusError(
f"HTTP {response.status_code}: {response.text[:200]}",
request=response.request,
response=response
)Error 3: Token Counting and Cost Calculation Mismatches
Error Message: ValueError: Token count mismatch between local calculation and API response
Cost calculation errors occur when local token counting does not match the AI provider's internal tokenization. Different models use different tokenizers, causing discrepancies in cost estimation. HolySheep AI returns accurate token counts in the API response, which should always be used for billing.
# FIX: Use API-provided token counts for accurate cost calculation
from typing import Dict, Optional
HolySheep AI 2026 Pricing (always use these for calculations)
HOLYSHEEP_PRICING = {
"gpt-4.1": {
"input": 2.0, # $2.00 per million input tokens
"output": 8.0, # $8.00 per million output tokens
"currency": "USD"
},
"claude-sonnet-4.5": {
"input": 3.0, # $3.00 per million input tokens
"output":