HolySheep Cline Workflow Productionization: Multi-Model Routing, SLA Monitoring & API Cost Governance for Development Teams

Step 2: Cost-Controlled API Client with Budget Guards

Now we build the actual API client with per-request budget limits and automatic circuit breakers when costs exceed thresholds.

#!/usr/bin/env python3
"""
HolySheep Cost-Controlled API Client
Implements budget guards, request tagging, and real-time cost tracking
"""

import httpx
import asyncio
from typing import Optional, Dict, List
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from collections import defaultdict

@dataclass
class CostBudget:
    max_monthly_usd: float
    max_per_request_usd: float
    alert_threshold_percent: float = 80.0

@dataclass
class CostTracker:
    total_spent: float = 0.0
    request_count: int = 0
    tokens_used: int = 0
    by_model: Dict[str, float] = field(default_factory=lambda: defaultdict(float))
    by_project: Dict[str, float] = field(default_factory=lambda: defaultdict(float))
    by_user: Dict[str, float] = field(default_factory=lambda: defaultdict(float))

class HolySheepClient:
    def __init__(
        self,
        api_key: str,
        budget: CostBudget,
        project_tag: str = "default"
    ):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.budget = budget
        self.project_tag = project_tag
        self.tracker = CostTracker()
        self.rate_limit_per_minute = 60
        
    def _estimate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
        """Estimate cost based on model pricing (input + output tokens)."""
        # Input tokens are typically 1/3 cost of output in most APIs
        input_cost = input_tokens * 0.001 * self._get_model_rate(model) / 3
        output_cost = output_tokens * 0.001 * self._get_model_rate(model)
        return input_cost + output_cost
    
    def _get_model_rate(self, model: str) -> float:
        """Get cost per million tokens for model."""
        rates = {
            "deepseek-v3.2": 0.42,
            "gemini-2.5-flash": 2.50,
            "gpt-4.1": 8.00,
            "claude-sonnet-4.5": 15.00
        }
        return rates.get(model, 2.50)
    
    def _check_budget_guard(self, estimated_cost: float) -> bool:
        """Check if request would exceed budget limits."""
        if self.tracker.total_spent + estimated_cost > self.budget.max_monthly_usd:
            print(f"⚠️ BLOCKED: Would exceed monthly budget (${self.tracker.total_spent:.2f}/${
self.budget.max_monthly_usd:.2f})")
            return False
        if estimated_cost > self.budget.max_per_request_usd:
            print(f"⚠️ BLOCKED: Single request cost ${estimated_cost:.4f} exceeds limit ${
self.budget.max_per_request_usd:.4f}")
            return False
        return True
    
    def _record_cost(
        self,
        model: str,
        input_tokens: int,
        output_tokens: int,
        actual_cost: float,
        user_id: Optional[str] = None
    ):
        """Record cost for tracking and alerting."""
        self.tracker.total_spent += actual_cost
        self.tracker.request_count += 1
        self.tracker.tokens_used += input_tokens + output_tokens
        self.tracker.by_model[model] += actual_cost
        
        if user_id:
            self.tracker.by_user[user_id] += actual_cost
        if self.project_tag:
            self.tracker.by_project[self.project_tag] += actual_cost
        
        # Check alert threshold
        budget_used_pct = (self.tracker.total_spent / self.budget.max_monthly_usd) * 100
        if budget_used_pct >= self.budget.alert_threshold_percent:
            print(f"🚨 ALERT: {budget_used_pct:.1f}% of monthly budget used (${self.tracker.total_spent:.2f})")
    
    async def chat_completion(
        self,
        model: str,
        messages: List[Dict],
        max_tokens: int = 1024,
        temperature: float = 0.7,
        user_id: Optional[str] = None
    ) -> Dict:
        """
        Send chat completion request with budget guards and cost tracking.
        """
        # Estimate input tokens (rough: ~4 chars per token)
        input_text = "".join([m.get("content", "") for m in messages])
        estimated_input_tokens = len(input_text) // 4
        estimated_output_tokens = max_tokens
        estimated_cost = self._estimate_cost(
            model, estimated_input_tokens, estimated_output_tokens
        )
        
        # Budget guard
        if not self._check_budget_guard(estimated_cost):
            return {"error": "Budget limit exceeded", "blocked": True}
        
        # Prepare headers with project tagging
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "X-Project-Tag": self.project_tag,
            "X-User-ID": user_id or "anonymous"
        }
        
        payload = {
            "model": model,
            "messages": messages,
            "max_tokens": max_tokens,
            "temperature": temperature
        }
        
        async with httpx.AsyncClient(timeout=30.0) as client:
            response = await client.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload
            )
            response.raise_for_status()
            result = response.json()
            
            # Record actual cost
            usage = result.get("usage", {})
            actual_input = usage.get("prompt_tokens", estimated_input_tokens)
            actual_output = usage.get("completion_tokens", 0)
            actual_cost = self._estimate_cost(model, actual_input, actual_output)
            
            self._record_cost(model, actual_input, actual_output, actual_cost, user_id)
            
            return {
                **result,
                "cost_recorded": actual_cost,
                "cumulative_spend": self.tracker.total_spent
            }

Usage example with budget control
budget = CostBudget(
    max_monthly_usd=500.00,
    max_per_request_usd=0.50,
    alert_threshold_percent=80.0
)

client = HolySheepClient(
    api_key="YOUR_HOLYSHEEP_API_KEY",
    budget=budget,
    project_tag="ecommerce-chatbot"
)

async def main():
    messages = [{"role": "user", "content": "Help me find my order from last week"}]
    
    result = await client.chat_completion(
        model="deepseek-v3.2",  # Routed to budget tier
        messages=messages,
        user_id="user_12345"
    )
    
    print(f"Response: {result.get('choices', [{}])[0].get('message', {}).get('content', '')}")
    print(f"Cost: ${result.get('cost_recorded', 0):.4f}")
    print(f"Cumulative spend: ${result.get('cumulative_spend', 0):.2f}")

asyncio.run(main())

#!/usr/bin/env python3
"""
HolySheep SLA Monitor
Tracks latency, success rates, and costs with alerting
"""

import time
import asyncio
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from collections import deque
import statistics

@dataclass
class SLAMetrics:
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    timeout_requests: int = 0
    latencies_ms: deque = field(default_factory=lambda: deque(maxlen=1000))
    model_costs: Dict[str, float] = field(default_factory=dict)
    p50_latency_ms: float = 0.0
    p95_latency_ms: float = 0.0
    p99_latency_ms: float = 0.0
    success_rate_percent: float = 100.0
    cost_per_request_usd: float = 0.0

class SLAMonitor:
    def __init__(
        self,
        target_p99_latency_ms: float = 500.0,
        target_success_rate: float = 99.0,
        cost_alert_threshold: float = 100.0
    ):
        self.metrics = SLAMetrics()
        self.target_p99 = target_p99_latency_ms
        self.target_success = target_success_rate
        self.cost_alert = cost_alert_threshold
        self.start_time = datetime.now()
    
    def record_request(
        self,
        model: str,
        latency_ms: float,
        success: bool,
        cost_usd: float,
        error_type: Optional[str] = None
    ):
        """Record a request for SLA tracking."""
        self.metrics.total_requests += 1
        self.metrics.latencies_ms.append(latency_ms)
        
        if success:
            self.metrics.successful_requests += 1
        else:
            self.metrics.failed_requests += 1
            if error_type == "timeout":
                self.metrics.timeout_requests += 1
        
        # Track costs by model
        if model not in self.metrics.model_costs:
            self.metrics.model_costs[model] = 0.0
        self.metrics.model_costs[model] += cost_usd
        
        # Recalculate percentiles
        self._recalculate_metrics()
        
        # Check alerts
        self._check_alerts()
    
    def _recalculate_metrics(self):
        """Recalculate SLA metrics."""
        if self.metrics.latencies_ms:
            sorted_latencies = sorted(self.metrics.latencies_ms)
            n = len(sorted_latencies)
            
            self.metrics.p50_latency_ms = sorted_latencies[int(n * 0.50)]
            self.metrics.p95_latency_ms = sorted_latencies[int(n * 0.95)]
            self.metrics.p99_latency_ms = sorted_latencies[int(n * 0.99)]
        
        if self.metrics.total_requests > 0:
            self.metrics.success_rate_percent = (
                self.metrics.successful_requests / self.metrics.total_requests
            ) * 100
        
        total_cost = sum(self.metrics.model_costs.values())
        if self.metrics.total_requests > 0:
            self.metrics.cost_per_request_usd = (
                total_cost / self.metrics.total_requests
            )
    
    def _check_alerts(self):
        """Check for SLA violations and emit alerts."""
        alerts = []
        
        if self.metrics.p99_latency_ms > self.target_p99:
            alerts.append(
                f"🚨 LATENCY: P99 {self.metrics.p99_latency_ms:.0f}ms exceeds target {
self.target_p99:.0f}ms"
            )
        
        if self.metrics.success_rate_percent < self.target_success:
            alerts.append(
                f"🚨 AVAILABILITY: Success rate {self.metrics.success_rate_percent:.2f}% below target {
self.target_success:.1f}%"
            )
        
        total_cost = sum(self.metrics.model_costs.values())
        if total_cost > self.cost_alert:
            alerts.append(
                f"🚨 COST: Total spend ${total_cost:.2f} exceeds alert threshold"
            )
        
        for alert in alerts:
            print(alert)
    
    def get_report(self) -> Dict:
        """Generate SLA status report."""
        uptime = (datetime.now() - self.start_time).total_seconds() / 3600
        total_cost = sum(self.metrics.model_costs.values())
        
        return {
            "uptime_hours": round(uptime, 2),
            "total_requests": self.metrics.total_requests,
            "success_rate": f"{self.metrics.success_rate_percent:.2f}%",
            "latency_p50_ms": round(self.metrics.p50_latency_ms, 1),
            "latency_p95_ms": round(self.metrics.p95_latency_ms, 1),
            "latency_p99_ms": round(self.metrics.p99_latency_ms, 1),
            "total_cost_usd": round(total_cost, 2),
            "cost_per_request": round(self.metrics.cost_per_request_usd, 4),
            "cost_by_model": {
                model: round(cost, 2) 
                for model, cost in self.metrics.model_costs.items()
            },
            "sla_compliant": (
                self.metrics.p99_latency_ms <= self.target_p99 and
                self.metrics.success_rate_percent >= self.target_success
            )
        }

Usage
monitor = SLAMonitor(
    target_p99_latency_ms=500.0,
    target_success_rate=99.0
)

Simulate production traffic
for i in range(100):
    latency = 45 + (i % 50)  # Simulated latency 45-95ms
    success = i % 20 != 0    # 95% success rate
    cost = 0.0012 * (1 + i % 5)  # Varying costs
    
    monitor.record_request(
        model="deepseek-v3.2",
        latency_ms=latency,
        success=success,
        cost_usd=cost,
        error_type="timeout" if not success and i % 40 == 0 else None
    )

report = monitor.get_report()
print("\n📊 SLA Report:")
for key, value in report.items():
    print(f"  {key}: {value}")

#!/usr/bin/env python3
"""
HolySheep Team API Key Manager
Creates scoped API keys per developer with budget limits
"""

import hashlib
import secrets
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import List, Optional, Dict

@dataclass
class TeamMember:
    user_id: str
    name: str
    email: str
    role: str  # "admin", "developer", "readonly"
    monthly_budget_usd: float
    project_tags: List[str]
    is_active: bool = True

class TeamKeyManager:
    def __init__(self, master_api_key: str):
        self.master_key = master_api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.team_members: Dict[str, TeamMember] = {}
        self.scoped_keys: Dict[str, str] = {}  # key_hash -> user_id
    
    def create_scoped_key(
        self,
        user_id: str,
        expires_days: int = 90,
        restrict_models: Optional[List[str]] = None,
        max_requests_per_day: Optional[int] = None
    ) -> str:
        """
        Create a scoped API key for team member.
        Note: In production, this would call HolySheep's key management API.
        """
        # Generate secure random key
        raw_key = secrets.token_urlsafe(32)
        key_hash = hashlib.sha256(raw_key.encode()).hexdigest()
        
        # Store mapping (in production, store in database)
        self.scoped_keys[key_hash] = user_id
        
        metadata = {
            "key_prefix": raw_key[:8],
            "user_id": user_id,
            "expires_at": (datetime.now() + timedelta(days=expires_days)).isoformat(),
            "restrict_models": restrict_models or ["deepseek-v3.2", "gemini-2.5-flash"],
            "max_requests_per_day": max_requests_per_day,
            "created_at": datetime.now().isoformat()
        }
        
        print(f"✅ Created scoped key for {user_id}")
        print(f"   Key preview: {raw_key[:8]}...")
        print(f"   Expires: {metadata['expires_at']}")
        print(f"   Restricted models: {metadata['restrict_models']}")
        
        # Return full key (in production, only shown once)
        return raw_key
    
    def rotate_key(self, old_key_hash: str) -> str:
        """Rotate an API key, invalidating the old one."""
        user_id = self.scoped_keys.get(old_key_hash)
        if not user_id:
            raise ValueError("Key not found")
        
        del self.scoped_keys[old_key_hash]
        return self.create_scoped_key(user_id)
    
    def get_team_spend_report(self) -> Dict:
        """Generate spending report by team member."""
        # In production, this would query HolySheep's analytics API
        report = {
            "total_team_spend": 0.0,
            "by_member": {},
            "by_project": {},
            "budget_utilization": {}
        }
        
        for user_id, member in self.team_members.items():
            spend = 0.0  # Would be fetched from API
            report["by_member"][user_id] = {
                "name": member.name,
                "spend_usd": spend,
                "budget_usd": member.monthly_budget_usd,
                "utilization_pct": (spend / member.monthly_budget_usd * 100) 
                    if member.monthly_budget_usd > 0 else 0
            }
            report["total_team_spend"] += spend
        
        return report

Usage
manager = TeamKeyManager(master_api_key="YOUR_HOLYSHEEP_API_KEY")

Add team members
manager.team_members["dev_alice"] = TeamMember(
    user_id="dev_alice",
    name="Alice Chen",
    email="[email protected]",
    role="developer",
    monthly_budget_usd=100.0,
    project_tags=["frontend", "chatbot"]
)

manager.team_members["dev_bob"] = TeamMember(
    user_id="dev_bob",
    name="Bob Martinez",
    email="[email protected]",
    role="developer",
    monthly_budget_usd=200.0,
    project_tags=["backend", "data-pipeline"]
)

Create scoped keys
alice_key = manager.create_scoped_key(
    user_id="dev_alice",
    expires_days=30,
    restrict_models=["deepseek-v3.2"],
    max_requests_per_day=1000
)

bob_key = manager.create_scoped_key(
    user_id="dev_bob",
    expires_days=60,
    restrict_models=["deepseek-v3.2", "gemini-2.5-flash", "gpt-4.1"],
    max_requests_per_day=500
)

print("\n📋 Team Spend Report:")
print(manager.get_team_spend_report())

# ❌ WRONG: Using OpenAI-style endpoint
"https://api.openai.com/v1/chat/completions"

✅ CORRECT: Use HolySheep base URL
"https://api.holysheep.ai/v1/chat/completions"

Full Python fix:
import httpx

headers = {
    "Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
    "Content-Type": "application/json",
    "X-Project-Tag": "your-project"
}

async def correct_request():
    async with httpx.AsyncClient(timeout=30.0) as client:
        response = await client.post(
            "https://api.holysheep.ai/v1/chat/completions",  # HolySheep endpoint
            headers=headers,
            json={
                "model": "deepseek-v3.2",
                "messages": [{"role": "user", "content": "Hello"}]
            }
        )
        return response.json()

# Problem: Monthly budget limit reached
Error: {"error": "Budget limit exceeded", "blocked": true}

Solution 1: Increase budget limit
client.budget.max_monthly_usd = 1000.00  # Raise limit

Solution 2: Rotate to more cost-effective models
Instead of routing everything to gpt-4.1:
route = router.route(user_message, force_tier=RequestTier.TIER1_BUDGET)
This sends simple queries to DeepSeek V3.2 ($0.42/MTok vs $8.00)

Solution 3: Check current spend and reset if needed
print(f"Current spend: ${client.tracker.total_spent:.2f}")
print(f"Requests: {client.tracker.request_count}")
print(f"By model: {dict(client.tracker.by_model)}")

# Problem: P99 latency exceeding 500ms target
Response includes: {"latency_ms": 850, "error": "timeout"}

Solution 1: Implement request caching
cache = {}

def get_cache_key(model, messages):
    import hashlib
    content = "".join([m.get("content","") for m in messages])
    return hashlib.md5(f"{model}:{content}".encode()).hexdigest()

async def cached_request(model, messages):
    cache_key = get_cache_key(model, messages)
    if cache_key in cache:
        print("⚡ Cache hit!")
        return cache[cache_key]
    
    result = await client.chat_completion(model, messages)
    cache[cache_key] = result
    return result

Solution 2: Use fallback model on timeout
async def resilient_request(messages):
    models_to_try = ["deepseek-v3.2", "gemini-2.5-flash"]
    
    for model in models_to_try:
        try:
            result = await client.chat_completion(model, messages)
            if "error" not in result:
                return result
        except httpx.TimeoutException:
            print(f"⏱️ Timeout on {model}, trying next...")
            continue
    
    return {"error": "All models failed"}

# Problem: Model not found
Error: {"error": "Unknown model: gpt-5"}

Solution: Use supported models only
SUPPORTED_MODELS = [
    "deepseek-v3.2",      # $0.42/MTok
    "gemini-2.5-flash",   # $2.50/MTok
    "gpt-4.1",            # $8.00/MTok
    "claude-sonnet-4.5"   # $15.00/MTok
]

def validate_model(model: str) -> bool:
    if model not in SUPPORTED_MODELS:
        print(f"❌ Invalid model: {model}")
        print(f"   Supported: {SUPPORTED_MODELS}")
        return False
    return True

Usage
if validate_model("deepseek-v3.2"):
    result = await client.chat_completion("deepseek-v3.2", messages)