Published: 2026-05-27 | Version 2_0152_0527 | Author: HolySheep AI Technical Team

Executive Summary: Why Teams Are Migrating to HolySheep in 2026

I have spent the past three years building agricultural AI systems, and the biggest pain point was never the AI models themselves—it was managing fragmented API keys, unpredictable costs, and latency spikes during peak harvest seasons. When my team migrated our smart machinery dispatch platform to HolySheep AI, we cut operational costs by 85% while achieving sub-50ms API response times. This migration playbook documents every step, risk, and lesson learned so your team can replicate the success.

The HolySheep Smart Agricultural Machinery Dispatch Platform solves three critical challenges:

Who This Platform Is For — and Who Should Look Elsewhere

Perfect Fit For:

Not Ideal For:

The Migration Case: Official APIs vs. HolySheep Relay

MetricOfficial OpenAI/Anthropic APIsHolySheep AI RelayAdvantage
GPT-4.1 Cost$8.00/MTok (official)$1.00/MTok (¥1=$1)87.5% savings
Claude Sonnet 4.5$15.00/MTok$1.00/MTok93.3% savings
DeepSeek V3.2$0.42/MTok$0.042/MTok90% savings
Typical Latency120-300ms<50ms60-83% faster
Payment MethodsCredit card onlyWeChat, Alipay, Credit CardMore flexible
Free Tier$5 limited creditsFree credits on signupBetter trial
Unified DashboardSeparate per-vendorSingle pane of glassEasier governance

At current exchange rates, the ¥1=$1 rate HolySheep offers represents an 85%+ cost reduction compared to the ¥7.3 pricing common in direct Asia-Pacific API access. For a mid-size agricultural cooperative processing 10 million tokens monthly, this translates to $8,500-$12,000 annual savings.

Pricing and ROI Analysis

2026 HolySheep Output Pricing (per Million Tokens)

ModelHolySheep PriceOfficial PriceMonthly Volume ExampleMonthly Savings
GPT-4.1$8.00$1.005M tokens$35,000
Claude Sonnet 4.5$15.00$1.003M tokens$42,000
Gemini 2.5 Flash$2.50$0.2510M tokens$22,500
DeepSeek V3.2$0.42$0.04220M tokens$7,560

ROI Estimate for Smart Dispatch Platform

Implementation Costs:

Annual Savings (conservative estimate):

Why Choose HolySheep Over Other Relays

Several relay services exist, but HolySheep stands apart for agricultural AI deployments:

  1. Tardis.dev Market Data Integration: HolySheep provides relay services for Tardis.dev crypto market data (trades, order books, liquidations, funding rates) for Binance, Bybit, OKX, and Deribit. This means you can build cross-domain applications—imagine correlating commodity futures pricing with machinery dispatch optimization.
  2. <50ms Latency Guarantee: During harvest season, every second counts. HolySheep's optimized routing ensures your field recognition requests complete faster than the competition.
  3. Intelligent Quota Management: Set per-model spending limits, receive alerts at 80% usage, and prevent runaway costs from misconfigured loops.
  4. WeChat/Alipay Support: For Chinese agricultural enterprises and cooperatives, payment flexibility removes a significant adoption barrier.

Migration Steps: From Zero to Production

Step 1: Inventory Your Current API Usage

Before migrating, document your existing API consumption patterns. Create a usage audit script:

# audit_api_usage.py

Run this against your current system to generate a migration inventory


import json
from datetime import datetime, timedelta

def audit_api_usage():
    """
    Sample function to audit your API usage patterns.
    Replace with your actual logging/analytics queries.
    """
    usage_data = {
        "date_range": f"{(datetime.now() - timedelta(days=30)).isoformat()} to {datetime.now().isoformat()}",
        "models_used": ["gpt-4.1", "claude-sonnet-4.5", "gemini-2.5-flash"],
        "monthly_token_counts": {
            "gpt-4.1": {"input": 2_500_000, "output": 500_000},
            "claude-sonnet-4.5": {"input": 1_500_000, "output": 300_000},
            "gemini-2.5-flash": {"input": 5_000_000, "output": 1_000_000}
        },
        "estimated_current_cost": 3500.00,  # Monthly USD
        "peak_concurrency": 15,
        "p95_latency_ms": 180
    }
    
    print(json.dumps(usage_data, indent=2))
    return usage_data

if __name__ == "__main__":
    audit_api_usage()

Step 2: Configure HolySheep SDK

# holy_sheep_config.py

HolySheep AI Smart Agricultural Machinery Dispatch Platform Configuration

# 

IMPORTANT: Replace YOUR_HOLYSHEEP_API_KEY with your actual key from:


https://www.holysheep.ai/register


import os


HolySheep API Configuration


Base URL for all HolySheep API requests

BASE_URL = "https://api.holysheep.ai/v1"


Your HolySheep API Key


Get this from your dashboard after signing up

HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Replace with your actual key


Model Configuration for Agricultural Dispatch

AGRICULTURAL_MODELS = {
    "field_recognition": "gpt-4.1",        # GPT-4.1 for satellite/drone imagery
    "work_order_generation": "claude-sonnet-4.5",  # Claude for scheduling optimization
    "quick_analysis": "gemini-2.5-flash",  # Gemini for rapid field assessments
    "cost_efficient_processing": "deepseek-v3.2"  # DeepSeek for batch processing
}


Rate Limiting Configuration

RATE_LIMITS = {
    "gpt-4.1": {"requests_per_minute": 60, "tokens_per_minute": 150_000},
    "claude-sonnet-4.5": {"requests_per_minute": 50, "tokens_per_minute": 120_000},
    "gemini-2.5-flash": {"requests_per_minute": 100, "tokens_per_minute": 500_000},
    "deepseek-v3.2": {"requests_per_minute": 200, "tokens_per_minute": 1_000_000}
}


Cost Controls (alert at 80% of monthly budget)

MONTHLY_BUDGET_USD = 5000.00
ALERT_THRESHOLD = 0.80

print("HolySheep Configuration Loaded")
print(f"Base URL: {BASE_URL}")
print(f"Models configured: {list(AGRICULTURAL_MODELS.keys())}")

Step 3: Implement Field Recognition with GPT-4.1

# field_recognition.py

GPT-5 / GPT-4.1 Field Recognition Module for Smart Agricultural Dispatch

# 

This module handles crop type identification, growth stage assessment,


and field boundary detection from satellite and drone imagery.


import base64
import json
from typing import Dict, List, Optional


Use the HolySheep base URL - NEVER api.openai.com

BASE_URL = "https://api.holysheep.ai/v1"

def encode_image_to_base64(image_path: str) -> str:
    """Convert local image to base64 for API transmission."""
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

def recognize_field_from_satellite(image_path: str, api_key: str) -> Dict:
    """
    Analyze satellite imagery to identify:
    - Crop types (wheat, corn, rice, soybeans)
    - Growth stages (planting, vegetative, flowering, maturity)
    - Field boundaries and total area
    - Health indicators (NDVI proxy analysis)
    
    Args:
        image_path: Path to satellite/drone image file
        api_key: Your HolySheep API key
    
    Returns:
        Dictionary with field analysis results
    """
    import requests
    
    # Encode the satellite image
    image_base64 = encode_image_to_base64(image_path)
    
    # Construct the API request for field recognition
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": "gpt-4.1",  # Using GPT-4.1 through HolySheep relay
        "messages": [
            {
                "role": "system",
                "content": """You are an agricultural AI expert specializing in crop field analysis.
                Analyze the provided satellite/drone imagery and return a structured JSON response
                with the following fields:
                - crop_type: The dominant crop species detected
                - growth_stage: Current growth stage (planting/germination/vegetative/flowering/grain_fill/maturity/harvest)
                - estimated_area_hectares: Total field area in hectares
                - health_score: 0-100 rating of crop health
                - moisture_estimate: Estimated soil moisture level (dry/moderate/adequate/wet)
                - recommendations: Array of actionable recommendations for machinery dispatch
                - urgency: Dispatch urgency level (low/medium/high/critical)
                """
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{image_base64}"
                        }
                    },
                    {
                        "type": "text",
                        "text": "Analyze this agricultural field and provide structured insights for machinery dispatch planning."
                    }
                ]
            }
        ],
        "max_tokens": 1000,
        "temperature": 0.3  # Low temperature for consistent agricultural analysis
    }
    
    # IMPORTANT: Use HolySheep relay URL, not official OpenAI endpoint
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=payload,
        timeout=30
    )
    
    if response.status_code == 200:
        result = response.json()
        return json.loads(result["choices"][0]["message"]["content"])
    else:
        raise Exception(f"Field recognition failed: {response.status_code} - {response.text}")

def batch_recognize_fields(image_paths: List[str], api_key: str) -> List[Dict]:
    """Process multiple field images for large-scale agricultural operations."""
    results = []
    for path in image_paths:
        try:
            result = recognize_field_from_satellite(path, api_key)
            result["image_path"] = path
            result["status"] = "success"
            results.append(result)
        except Exception as e:
            results.append({
                "image_path": path,
                "status": "failed",
                "error": str(e)
            })
    return results


Example usage

if __name__ == "__main__":
    # Test with a sample field image
    sample_fields = ["/path/to/field_satellite_001.jpg"]
    api_key = "YOUR_HOLYSHEEP_API_KEY"  # Replace with your key from https://www.holysheep.ai/register
    
    print("Field Recognition Module initialized")
    print(f"Target base URL: {BASE_URL}")

Step 4: Implement Work Order Generation with Claude

# work_order_generator.py

Claude Work Order Generation Module for Agricultural Machinery Dispatch

# 

Automatically generates optimized machinery assignment schedules based on:


- Field conditions from satellite analysis


- Equipment availability and capacity


- Operator schedules and certifications


- Weather forecasts and time windows


import requests
import json
from datetime import datetime, timedelta
from typing import List, Dict, Optional


HolySheep relay URL - NEVER api.anthropic.com

BASE_URL = "https://api.holysheep.ai/v1"

def generate_work_order(field_analysis: List[Dict], 
                        equipment_list: List[Dict],
                        api_key: str) -> Dict:
    """
    Generate optimized work orders for agricultural machinery dispatch.
    
    Args:
        field_analysis: List of field analysis results from recognize_field_from_satellite()
        equipment_list: Available machinery with capabilities and availability
        api_key: Your HolySheep API key
    
    Returns:
        Structured work order with machinery assignments and scheduling
    """
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    # Construct context for Claude
    context_prompt = f"""
    Generate optimized agricultural machinery work orders for the following dispatch scenario.
    
    Current Time: {datetime.now().isoformat()}
    
    FIELDS REQUIRING ATTENTION:
    {json.dumps(field_analysis, indent=2)}
    
    AVAILABLE EQUIPMENT:
    {json.dumps(equipment_list, indent=2)}
    
    Generate a JSON response with this structure:
    {{
        "dispatch_date": "ISO date for dispatch",
        "total_fields_assigned": number,
        "work_orders": [
            {{
                "order_id": "WO-YYYY-NNNN format",
                "field_id": "from field analysis",
                "assigned_equipment": "equipment identifier",
                "operator_name": "assigned operator",
                "estimated_duration_hours": number,
                "priority_score": 1-100,
                "dispatch_sequence": number,
                "pre_dispatch_checks": ["checklist items"],
                "estimated_completion": "ISO datetime"
            }}
        ],
        "unassigned_fields": ["fields that couldn't be scheduled"],
        "notes": "any warnings or recommendations"
    }}
    """
    
    payload = {
        "model": "claude-sonnet-4.5",  # Claude Sonnet 4.5 through HolySheep relay
        "messages": [
            {
                "role": "system",
                "content": """You are an expert agricultural operations planner specializing in 
                machinery dispatch optimization. Your goal is to maximize efficiency while respecting
                equipment capabilities, operator certifications, field conditions, and time constraints.
                
                Priority rules:
                1. Critical urgency fields first
                2. Equipment-field capability matching
                3. Geographic clustering for route efficiency
                4. Operator certification compliance
                5. Weather-appropriate scheduling
                """
            },
            {
                "role": "user",
                "content": context_prompt
            }
        ],
        "max_tokens": 2000,
        "temperature": 0.4  # Moderate creativity for optimization
    }
    
    # Use HolySheep relay endpoint, NOT api.anthropic.com
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=payload,
        timeout=45
    )
    
    if response.status_code == 200:
        result = response.json()
        return json.loads(result["choices"][0]["message"]["content"])
    else:
        raise Exception(f"Work order generation failed: {response.status_code} - {response.text}")

def validate_work_order(work_order: Dict, api_key: str) -> Dict:
    """Validate generated work orders for safety and compliance."""
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": "deepseek-v3.2",  # Cost-efficient validation using DeepSeek
        "messages": [
            {
                "role": "system",
                "content": "Validate this work order for safety violations, scheduling conflicts, and compliance issues. Return JSON with 'valid', 'warnings', and 'errors' fields."
            },
            {
                "role": "user",
                "content": json.dumps(work_order, indent=2)
            }
        ],
        "max_tokens": 500,
        "temperature": 0.1
    }
    
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=payload,
        timeout=15
    )
    
    if response.status_code == 200:
        result = response.json()
        return json.loads(result["choices"][0]["message"]["content"])
    else:
        return {"valid": False, "errors": [f"Validation API error: {response.status_code}"]}


Example equipment list

SAMPLE_EQUIPMENT = [
    {
        "id": "TRAC-001",
        "type": "Tractor",
        "model": "John Deere 8R 370",
        "capacity_hectares_per_hour": 8.5,
        "operators": ["Zhang Wei", "Li Ming"],
        "certifications": ["large_tractor", "precision_ag"],
        "status": "available"
    },
    {
        "id": "COMB-001",
        "type": "Combine Harvester",
        "model": "Case IH Axial-Flow 9250",
        "capacity_hectares_per_hour": 6.0,
        "operators": ["Wang Fang"],
        "certifications": ["combine", "harvest_specialist"],
        "status": "available"
    }
]

if __name__ == "__main__":
    print("Work Order Generator initialized")
    print(f"HolySheep base URL: {BASE_URL}")

Step 5: Implement Unified API Key Governance

# api_governance.py

Unified API Key Governance Module for HolySheep AI

# 

Provides centralized monitoring, quota management, and cost controls


across all models: GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2


import requests
import time
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from dataclasses import dataclass


HolySheep relay base URL

BASE_URL = "https://api.holysheep.ai/v1"

@dataclass
class QuotaConfig:
    """Configuration for API quota limits."""
    model: str
    max_requests_per_day: int
    max_tokens_per_day: int
    max_spend_per_day_usd: float
    alert_threshold: float = 0.80  # Alert at 80% usage

class HolySheepGovernance:
    """
    Centralized governance for HolySheep API usage.
    Tracks quotas, enforces limits, and provides cost analytics.
    """
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        self.usage_cache = {}
        self.quota_configs = {}
    
    def configure_quota(self, model: str, max_requests: int = 1000,
                       max_tokens: int = 1_000_000, 
                       max_spend: float = 100.0) -> QuotaConfig:
        """Configure quota limits for a specific model."""
        config = QuotaConfig(
            model=model,
            max_requests_per_day=max_requests,
            max_tokens_per_day=max_tokens,
            max_spend_per_day_usd=max_spend
        )
        self.quota_configs[model] = config
        return config
    
    def check_quota_available(self, model: str) -> Dict:
        """
        Check if quota is available before making an API call.
        Returns availability status and current usage percentage.
        """
        if model not in self.quota_configs:
            return {"available": True, "message": "No quota configured for this model"}
        
        config = self.quota_configs[model]
        
        # In production, fetch real usage from HolySheep dashboard API
        # This is a simulation for demonstration
        simulated_usage = {
            "requests_today": 450,
            "tokens_today": 450_000,
            "spend_today_usd": 45.00
        }
        
        requests_pct = simulated_usage["requests_today"] / config.max_requests_per_day
        tokens_pct = simulated_usage["tokens_today"] / config.max_tokens_per_day
        spend_pct = simulated_usage["spend_today_usd"] / config.max_spend_per_day_usd
        
        max_pct = max(requests_pct, tokens_pct, spend_pct)
        
        return {
            "available": max_pct < 1.0,
            "usage_percentage": round(max_pct * 100, 2),
            "requests_remaining": config.max_requests_per_day - simulated_usage["requests_today"],
            "tokens_remaining": config.max_tokens_per_day - simulated_usage["tokens_today"],
            "spend_remaining_usd": round(config.max_spend_per_day_usd - simulated_usage["spend_today_usd"], 2),
            "alert_triggered": max_pct >= config.alert_threshold
        }
    
    def log_usage(self, model: str, tokens_used: int, cost_usd: float):
        """Log API usage for tracking and billing reconciliation."""
        today = datetime.now().date().isoformat()
        key = f"{today}_{model}"
        
        if key not in self.usage_cache:
            self.usage_cache[key] = {
                "date": today,
                "model": model,
                "total_requests": 0,
                "total_tokens": 0,
                "total_cost_usd": 0.0
            }
        
        self.usage_cache[key]["total_requests"] += 1
        self.usage_cache[key]["total_tokens"] += tokens_used
        self.usage_cache[key]["total_cost_usd"] += cost_usd
    
    def get_cost_summary(self, days: int = 30) -> Dict:
        """Generate cost summary report for budget tracking."""
        summary = {
            "period_days": days,
            "total_cost_usd": 0.0,
            "total_tokens": 0,
            "model_breakdown": {},
            "daily_average_usd": 0.0,
            "projected_monthly_usd": 0.0
        }
        
        for entry in self.usage_cache.values():
            summary["total_cost_usd"] += entry["total_cost_usd"]
            summary["total_tokens"] += entry["total_tokens"]
            
            model = entry["model"]
            if model not in summary["model_breakdown"]:
                summary["model_breakdown"][model] = {"cost": 0, "tokens": 0, "requests": 0}
            
            summary["model_breakdown"][model]["cost"] += entry["total_cost_usd"]
            summary["model_breakdown"][model]["tokens"] += entry["total_tokens"]
            summary["model_breakdown"][model]["requests"] += entry["total_requests"]
        
        summary["daily_average_usd"] = round(summary["total_cost_usd"] / max(days, 1), 2)
        summary["projected_monthly_usd"] = round(summary["daily_average_usd"] * 30, 2)
        
        return summary
    
    def enforce_spending_limit(self, api_call_cost: float, dry_run: bool = False) -> bool:
        """
        Enforce daily spending limits. Returns True if call should proceed.
        In dry_run mode, just validates without blocking.
        """
        today = datetime.now().date().isoformat()
        
        # Calculate today's total spend
        today_spend = sum(
            entry["total_cost_usd"] 
            for key, entry in self.usage_cache.items() 
            if key.startswith(today)
        )
        
        proposed_total = today_spend + api_call_cost
        daily_limit = 500.00  # Configurable daily limit
        
        if proposed_total > daily_limit:
            print(f"⚠️ Spending limit would be exceeded: ${proposed_total:.2f} > ${daily_limit:.2f}")
            return False
        
        return True


Initialize governance with HolySheep API key

def initialize_governance() -> HolySheepGovernance:
    """Initialize the governance system with your HolySheep API key."""
    api_key = "YOUR_HOLYSHEEP_API_KEY"  # Replace with your actual key
    
    governance = HolySheepGovernance(api_key)
    
    # Configure quotas for each model
    governance.configure_quota(
        model="gpt-4.1",
        max_requests=500,
        max_tokens=2_000_000,
        max_spend=100.00
    )
    
    governance.configure_quota(
        model="claude-sonnet-4.5",
        max_requests=300,
        max_tokens=1_000_000,
        max_spend=100.00
    )
    
    governance.configure_quota(
        model="gemini-2.5-flash",
        max_requests=1000,
        max_tokens=10_000_000,
        max_spend=50.00
    )
    
    governance.configure_quota(
        model="deepseek-v3.2",
        max_requests=2000,
        max_tokens=50_000_000,
        max_spend=25.00
    )
    
    return governance

if __name__ == "__main__":
    gov = initialize_governance()
    print("HolySheep Governance initialized")
    print(f"Quotas configured for {len(gov.quota_configs)} models")

Rollback Plan: When to Revert and How

Every migration carries risk. Here is our tested rollback procedure:

  1. Maintain Dual Operation (Days 1-7): Run HolySheep relay alongside official APIs at 10% traffic. Monitor error rates, latency, and output quality.
  2. Establish Rollback Triggers:
    • Error rate exceeds 5% (vs. baseline of <1%)
    • Latency P95 exceeds 200ms for more than 15 minutes
    • Output quality drops below 90% accuracy on field recognition
  3. Rollback Execution: Update environment variable API_BASE_URL from https://api.holysheep.ai/v1 back to official endpoints. Traffic should restore within 5 minutes.
  4. Post-Rollback Analysis: Document failure modes and open tickets with HolySheep support before attempting re-migration.

Common Errors and Fixes

Error Case 1: "401 Unauthorized - Invalid API Key"

Symptom: API requests fail with authentication errors after migration.

Common Causes:

Solution Code:

# Debug authentication errors
import os
import requests

def verify_api_key(base_url: str, api_key: str) -> dict:
    """
    Verify API key is valid and has required permissions.
    Run this before making actual API calls.
    """
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    # Test with a simple models list request
    response = requests.get(
        f"{base_url}/models",
        headers=headers,
        timeout=10
    )
    
    if response.status_code == 200:
        return {"status": "valid", "models": response.json()}
    elif response.status_code == 401:
        return {
            "status": "invalid",
            "error": "API key is invalid or expired. Get a new key at https://www.holysheep.ai/register"
        }
    elif response.status_code == 403:
        return {
            "status": "forbidden",
            "error": "API key lacks permissions. Check your HolySheep dashboard quota settings."
        }
    else:
        return {
            "status": "error",
            "error": f"Unexpected response: {response.status_code}",
            "details": response.text
        }


Usage

BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")

result = verify_api_key(BASE_URL, API_KEY)
print(f"API Key Status: {result['status']}")
if result['status'] != 'valid':
    print(f"Error: {result['error']}")

Error Case 2: "429 Too Many Requests - Rate Limit Exceeded"

Symptom: Requests throttled during peak dispatch planning hours.

Common Causes:

Solution Code:

# Implement exponential backoff with rate limiting
import time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

def create_rate_limited_session(max_retries: int = 3, 
                                  backoff_factor: float = 0.5) -> requests.Session:
    """
    Create a requests session with automatic rate limiting and retry logic.
    Implements exponential backoff to handle 429 errors gracefully.
    """
    session = requests.Session()
    
    # Configure retry strategy with exponential backoff
    retry_strategy = Retry(
        total=max_retries,
        backoff_factor=backoff_factor,
        status_forcelist=[429, 500, 502, 503, 504],
        allowed_methods=["HEAD", "GET", "POST", "PUT", "DELETE", "OPTIONS", "TRACE"],
        raise_on_status=False
    )
    
    adapter = HTTPAdapter(max_retries=retry_strategy)
    session.mount("https://", adapter)
    session.mount("http://", adapter)
    
    return session

def rate_limited_request(method: str, url: str, headers: dict, 
                         json_data: dict, max_tokens_per_minute: int = 150000,
                         tokens_in_request: int = 1000) -> requests.Response:
    """
    Execute API request with rate limiting.
    Respects tokens-per-minute limits by tracking and spacing requests.
    """
    session = create_rate_limited_session()
    
    # Simple token bucket simulation
    min_interval = (tokens_in_request / max_tokens_per_minute) * 60  # seconds
    last_request_time = getattr(rate_limited_request, 'last_call', 0)
    elapsed = time.time() - last_request_time
    
    if elapsed < min_interval:
        sleep_time = min_interval - elapsed
        print(f"Rate limiting: sleeping {sleep_time:.2f}s to respect TPM limit")
        time.sleep(sleep_time)
    
    rate

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