HolySheep + Cline Workflow: Agent Multi-Model API Quota Governance and Circuit Breaker Protection Best Practices (2026)

Our AI-powered data pipeline had burned through $340 in API credits in 6 hours—three times the daily budget—because the fallback model chain didn't have proper circuit breakers. The GPT-4.1 calls were timing out, triggering cascading retries to Claude Sonnet 4.5, which then hit its quota ceiling, and the whole system spiraled into an expensive death loop.

That night, I rebuilt our HolySheep + Cline integration with proper quota governance and circuit breaker protection. This guide walks you through exactly what I implemented, with working code you can copy-paste today.

What This Guide Covers

• How to configure multi-model API routing through HolySheep's unified endpoint
• Implementing token budget enforcement per agent session
• Building adaptive circuit breakers that fail fast when quotas are exhausted
• Cost optimization strategies that reduced our API spend by 85%
• Complete working code for Node.js and Python integrations

Why HolySheep + Cline?

HolySheep AI provides a unified API gateway that aggregates 15+ LLM providers including OpenAI, Anthropic, Google, DeepSeek, and dozens of regional models—all accessible through a single endpoint. For Cline agents that need to intelligently route requests across multiple models, this eliminates the complexity of managing separate API keys and rate limits for each provider.

The economics are compelling: where standard Chinese API markets charge ¥7.3 per dollar, HolySheep operates at ¥1 = $1, representing an 85%+ cost reduction. With support for WeChat and Alipay, latency under 50ms, and free credits on signup, it's become the de facto choice for production AI workflows in 2026.

Architecture Overview

+-------------------+      +---------------------+      +------------------+
|   Cline Agent     | ---> |   HolySheep Gateway | ---> |  Model Router    |
|   (Your Code)     |      |  api.holysheep.ai   |      |  (Quota Aware)   |
+-------------------+      +---------------------+      +--------+---------+
                                                                |
                    +-----------------------------------------+------------------+
                    |                    |                    |                  |
            +-------v-------+    +--------v-------+    +-----v-------+   +----v-------+
            |  GPT-4.1      |    | Claude Sonnet   |    | Gemini 2.5  |   | DeepSeek V3 |
            |  $8/MTok      |    | 4.5 $15/MTok   |    | Flash $2.50 |   | $0.42/MTok  |
            +---------------+    +----------------+    +-------------+   +------------+

Step 1: HolySheep Client Setup

The first thing you need is a properly configured HolySheep client with built-in retry logic and error handling. Here's the production-ready setup I use:

# Python implementation
Requirements: pip install requests httpx aiohttp

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

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

@dataclass
class QuotaBudget:
    """Tracks spending against configured budgets."""
    daily_limit_usd: float = 50.0
    monthly_limit_usd: float = 1000.0
    spent_today: float = 0.0
    spent_month: float = 0.0
    last_reset: datetime = field(default_factory=datetime.utcnow)

    def can_spend(self, amount: float) -> bool:
        if self.spent_today + amount > self.daily_limit_usd:
            return False
        if self.spent_month + amount > self.monthly_limit_usd:
            return False
        return True

    def record_spend(self, amount: float):
        self.spent_today += amount
        self.spent_month += amount

    def check_reset(self):
        now = datetime.utcnow()
        if now.date() > self.last_reset.date():
            self.spent_today = 0.0
            self.last_reset = now

class CircuitBreaker:
    """Prevents cascade failures when models are unavailable or over quota."""

    def __init__(self, failure_threshold: int = 5, recovery_timeout: int = 60):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failures: Dict[str, int] = {}
        self.last_failure_time: Dict[str, datetime] = {}
        self.open_circuits: Dict[str, bool] = {}

    def record_success(self, model: str):
        self.failures[model] = 0
        self.open_circuits[model] = False

    def record_failure(self, model: str):
        self.failures[model] = self.failures.get(model, 0) + 1
        self.last_failure_time[model] = datetime.utcnow()

        if self.failures[model] >= self.failure_threshold:
            self.open_circuits[model] = True

    def is_available(self, model: str) -> bool:
        if not self.open_circuits.get(model, False):
            return True

        # Check if recovery timeout has passed
        last_failure = self.last_failure_time.get(model)
        if last_failure and (datetime.utcnow() - last_failure).seconds >= self.recovery_timeout:
            self.open_circuits[model] = False
            self.failures[model] = 0
            return True

        return False

    def get_status(self, model: str) -> str:
        if self.open_circuits.get(model, False):
            return "OPEN - Circuit breaker active"
        failures = self.failures.get(model, 0)
        if failures > 0:
            return f"DEGRADED - {failures} recent failures"
        return "HEALTHY"

class HolySheepMultiModelClient:
    """
    Production client for HolySheep AI with quota governance and circuit breakers.
    Handles automatic model fallback, cost tracking, and circuit protection.
    """

    def __init__(self, api_key: str, quota_budget: Optional[QuotaBudget] = None):
        self.api_key = api_key
        self.base_url = HOLYSHEEP_BASE_URL
        self.quota = quota_budget or QuotaBudget()
        self.circuit_breaker = CircuitBreaker()
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

        # Model routing priority (cost-efficient to expensive)
        self.model_fallback_chain = [
            {"model": "deepseek-v3.2", "cost_per_mtok": 0.42, "context_window": 128000},
            {"model": "gemini-2.5-flash", "cost_per_mtok": 2.50, "context_window": 1000000},
            {"model": "claude-sonnet-4.5", "cost_per_mtok": 15.00, "context_window": 200000},
            {"model": "gpt-4.1", "cost_per_mtok": 8.00, "context_window": 128000},
        ]

    def estimate_cost(self, messages: List[Dict], model: str) -> float:
        """Estimate cost based on input tokens. For production, use token counting."""
        total_chars = sum(len(msg.get("content", "")) for msg in messages)
        estimated_tokens = total_chars / 4  # Rough approximation

        for m in self.model_fallback_chain:
            if m["model"] == model:
                return (estimated_tokens / 1_000_000) * m["cost_per_mtok"]
        return 0.0

    def chat_completion(self,
                       messages: List[Dict[str, str]],
                       task_type: str = "general",
                       max_cost: float = 0.50,
                       force_model: Optional[str] = None) -> Dict[str, Any]:
        """
        Main API call with automatic fallback and circuit breaker protection.

        Args:
            messages: Chat messages in OpenAI-compatible format
            task_type: 'simple', 'reasoning', 'creative' for appropriate routing
            max_cost: Maximum cost allowed for this request (circuit breaker trigger)
            force_model: Override automatic routing
        """

        self.quota.check_reset()

        # Select model chain based on task type
        if force_model:
            model_chain = [m for m in self.model_fallback_chain if m["model"] == force_model]
        elif task_type == "simple":
            model_chain = [self.model_fallback_chain[0]]  # DeepSeek only
        elif task_type == "reasoning":
            model_chain = self.model_fallback_chain[1:3]  # Gemini, Claude
        else:
            model_chain = self.model_fallback_chain

        last_error = None

        for model_config in model_chain:
            model = model_config["model"]

            # Circuit breaker check
            if not self.circuit_breaker.is_available(model):
                print(f"[CircuitBreaker] Skipping {model} - circuit is OPEN")
                continue

            # Budget check
            estimated_cost = self.estimate_cost(messages, model)
            if not self.quota.can_spend(estimated_cost):
                print(f"[QuotaBudget] Would exceed budget. ${estimated_cost:.4f} estimated")
                self.circuit_breaker.record_failure(model)
                continue

            try:
                response = self._make_request(model, messages)

                # Parse actual cost from response
                actual_cost = self._parse_cost(response, model_config["cost_per_mtok"])

                # Verify cost is within limits
                if self.quota.can_spend(actual_cost):
                    self.quota.record_spend(actual_cost)
                    self.circuit_breaker.record_success(model)
                    return response
                else:
                    print(f"[QuotaBudget] Actual cost ${actual_cost:.4f} exceeds remaining budget")
                    self.circuit_breaker.record_failure(model)

            except RateLimitError as e:
                print(f"[RateLimit] {model} rate limited: {e}")
                self.circuit_breaker.record_failure(model)
                continue

            except AuthenticationError as e:
                print(f"[AuthError] Invalid API key: {e}")
                raise  # Don't retry on auth errors

            except Exception as e:
                print(f"[Error] {model} failed: {e}")
                self.circuit_breaker.record_failure(model)
                last_error = e
                continue

        # All models failed
        raise AllModelsExhaustedError(
            f"All models exhausted. Last error: {last_error}. "
            f"Circuit breaker status: {self.circuit_breaker.get_status('deepseek-v3.2')}"
        )

    def _make_request(self, model: str, messages: List[Dict]) -> Dict[str, Any]:
        """Actual HTTP request to HolySheep API."""
        url = f"{self.base_url}/chat/completions"

        payload = {
            "model": model,
            "messages": messages,
            "temperature": 0.7,
            "max_tokens": 4096
        }

        response = requests.post(
            url,
            headers=self.headers,
            json=payload,
            timeout=30
        )

        if response.status_code == 401:
            raise AuthenticationError("Invalid HolySheep API key")
        elif response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 30))
            raise RateLimitError(f"Rate limited. Retry after {retry_after}s", retry_after)
        elif response.status_code != 200:
            raise APIError(f"Request failed with status {response.status_code}: {response.text}")

        return response.json()

    def _parse_cost(self, response: Dict, cost_per_mtok: float) -> float:
        """Parse actual token usage from response and calculate cost."""
        usage = response.get("usage", {})
        prompt_tokens = usage.get("prompt_tokens", 0)
        completion_tokens = usage.get("completion_tokens", 0)
        total_tokens = prompt_tokens + completion_tokens

        return (total_tokens / 1_000_000) * cost_per_mtok

    def get_health_report(self) -> Dict[str, Any]:
        """Get detailed health report for monitoring dashboards."""
        self.quota.check_reset()
        return {
            "quota": {
                "daily_limit": self.quota.daily_limit_usd,
                "daily_spent": self.quota.spent_today,
                "daily_remaining": self.quota.daily_limit_usd - self.quota.spent_today,
                "monthly_limit": self.quota.monthly_limit_usd,
                "monthly_spent": self.quota.spent_month,
                "monthly_remaining": self.quota.monthly_limit_usd - self.quota.spent_month,
            },
            "circuits": {
                model: self.circuit_breaker.get_status(model)
                for model in [m["model"] for m in self.model_fallback_chain]
            }
        }


Custom Exceptions
class APIError(Exception):
    pass

class RateLimitError(APIError):
    def __init__(self, message, retry_after=30):
        super().__init__(message)
        self.retry_after = retry_after

class AuthenticationError(APIError):
    pass

class AllModelsExhaustedError(APIError):
    pass

Step 2: Cline Agent Integration

Now let's integrate this with a Cline-style agent that automatically selects the right model based on task complexity:

# Complete Cline Agent with HolySheep Multi-Model Routing
This is the production configuration I run in our data pipeline

import asyncio
from typing import List, Dict, Optional, Callable
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ClineAgent:
    """
    Cline-style agent that routes tasks through HolySheep with:
    - Automatic complexity-based model selection
    - Budget enforcement per task category
    - Circuit breaker protection
    - Detailed cost tracking
    """

    def __init__(self, api_key: str, config: Optional[Dict] = None):
        self.config = config or self._default_config()
        self.client = HolySheepMultiModelClient(
            api_key=api_key,
            quota_budget=QuotaBudget(
                daily_limit_usd=self.config["daily_budget"],
                monthly_limit_usd=self.config["monthly_budget"]
            )
        )

        # Task routing rules
        self.task_routes = {
            "code_generation": {
                "simple": "deepseek-v3.2",      # Function templates, boilerplate
                "moderate": "gemini-2.5-flash",  # Algorithm implementation
                "complex": "claude-sonnet-4.5",  # System design, architecture
                "max_budget": 2.50
            },
            "data_analysis": {
                "simple": "deepseek-v3.2",       # SQL queries, basic aggregations
                "moderate": "gemini-2.5-flash",   # Statistical analysis
                "complex": "claude-sonnet-4.5",   # ML model selection, complex transformations
                "max_budget": 5.00
            },
            "writing": {
                "simple": "deepseek-v3.2",        # Summaries, format conversion
                "moderate": "gemini-2.5-flash",   # Blog posts, documentation
                "complex": "claude-sonnet-4.5",   # Long-form content, creative writing
                "max_budget": 3.00
            },
            "reasoning": {
                "simple": "gemini-2.5-flash",     # Quick calculations
                "moderate": "claude-sonnet-4.5", # Multi-step reasoning
                "complex": "gpt-4.1",            # Complex problem solving
                "max_budget": 8.00
            },
            "fallback": {
                "simple": "deepseek-v3.2",
                "moderate": "gemini-2.5-flash",
                "complex": "claude-sonnet-4.5",
                "max_budget": 1.00
            }
        }

    def _default_config(self) -> Dict:
        return {
            "daily_budget": 50.00,
            "monthly_budget": 1000.00,
            "enable_circuit_breaker": True,
            "max_retries": 3,
            "timeout_seconds": 30,
            "auto_fallback": True
        }

    def estimate_task_complexity(self, task: str) -> str:
        """
        Heuristic for estimating task complexity.
        In production, this could use a separate classification model.
        """
        complex_keywords = [
            "architecture", "design", "optimize", "refactor",
            "multiple", "complex", "enterprise", "distributed",
            "scalable", "concurrent", "parallel", "debug"
        ]
        simple_keywords = [
            "simple", "basic", "list", "sum", "count", "get",
            "format", "convert", "quick", "short"
        ]

        task_lower = task.lower()
        complex_score = sum(1 for kw in complex_keywords if kw in task_lower)
        simple_score = sum(1 for kw in simple_keywords if kw in task_lower)

        if complex_score > simple_score:
            return "complex"
        elif simple_score > complex_score:
            return "simple"
        return "moderate"

    def classify_task(self, task: str) -> str:
        """Classify task type based on content."""
        task_lower = task.lower()

        if any(x in task_lower for x in ["code", "function", "class", "implement", "sql", "query"]):
            return "code_generation"
        elif any(x in task_lower for x in ["analyze", "data", "statistics", "chart", "plot"]):
            return "data_analysis"
        elif any(x in task_lower for x in ["write", "document", "article", "blog", "explain"]):
            return "writing"
        elif any(x in task_lower for x in ["reason", "think", "solve", "calculate", "determine"]):
            return "reasoning"
        return "fallback"

    async def execute_task(self, task: str, context: Optional[Dict] = None) -> Dict[str, Any]:
        """
        Main entry point for executing a Cline agent task.
        Returns the response with full metadata including cost and model used.
        """
        context = context or {}

        # Classify and estimate
        task_type = context.get("override_type") or self.classify_task(task)
        complexity = context.get("override_complexity") or self.estimate_task_complexity(task)
        route = self.task_routes.get(task_type, self.task_routes["fallback"])

        # Check budget for this task
        task_budget = context.get("max_budget", route["max_budget"])
        health = self.client.get_health_report()

        if health["quota"]["daily_remaining"] < task_budget:
            logger.warning(
                f"Task budget ${task_budget:.2f} exceeds daily remaining "
                f"${health['quota']['daily_remaining']:.2f}"
            )
            task_budget = health["quota"]["daily_remaining"] * 0.5  # Cap at 50% of remaining

        # Build messages
        system_prompt = self._build_system_prompt(task_type, context)
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": task}
        ]

        # Execute with selected model
        start_time = asyncio.get_event_loop().time()

        try:
            response = self.client.chat_completion(
                messages=messages,
                task_type=task_type,
                max_cost=task_budget,
                force_model=route.get(complexity)
            )

            execution_time = asyncio.get_event_loop().time() - start_time

            return {
                "success": True,
                "response": response["choices"][0]["message"]["content"],
                "model": response.get("model", route.get(complexity)),
                "usage": response.get("usage", {}),
                "execution_time_ms": round(execution_time * 1000),
                "task_type": task_type,
                "complexity": complexity,
                "health_report": self.client.get_health_report()
            }

        except AllModelsExhaustedError as e:
            logger.error(f"All models exhausted for task: {e}")
            return {
                "success": False,
                "error": str(e),
                "task_type": task_type,
                "complexity": complexity,
                "health_report": health,
                "recommendation": "Reduce daily budget or wait for quota reset"
            }

    def _build_system_prompt(self, task_type: str, context: Dict) -> str:
        """Build system prompt based on task type."""
        base_prompts = {
            "code_generation": "You are an expert programmer. Write clean, efficient, well-documented code. Always include error handling.",
            "data_analysis": "You are a data scientist. Provide accurate analysis with appropriate statistical methods. Show your methodology.",
            "writing": "You are a professional writer. Create clear, engaging content appropriate for the intended audience.",
            "reasoning": "You are a logical problem solver. Think step by step and explain your reasoning clearly."
        }
        return base_prompts.get(task_type, "You are a helpful AI assistant.")

    def get_health_dashboard(self) -> Dict[str, Any]:
        """Get full health dashboard for monitoring."""
        return self.client.get_health_report()


Example usage with asyncio
async def main():
    # Initialize with your API key from https://www.holysheep.ai/register
    agent = ClineAgent(
        api_key="YOUR_HOLYSHEEP_API_KEY",
        config={
            "daily_budget": 50.00,
            "monthly_budget": 1000.00
        }
    )

    # Example tasks
    tasks = [
        ("Write a Python function to calculate fibonacci numbers recursively", "simple"),
        ("Design a microservices architecture for a real-time chat application", "complex"),
        ("Analyze this sales data and identify trends", "moderate"),
    ]

    for task, complexity_override in tasks:
        print(f"\n{'='*60}")
        print(f"Task: {task[:50]}...")
        print(f"Complexity: {complexity_override}")

        result = await agent.execute_task(
            task,
            context={"override_complexity": complexity_override}
        )

        if result["success"]:
            print(f"✅ Model: {result['model']}")
            print(f"⏱️  Execution time: {result['execution_time_ms']}ms")
            print(f"💰 Usage: {result['usage']}")
            print(f"\nResponse preview: {result['response'][:200]}...")
        else:
            print(f"❌ Error: {result['error']}")
            print(f"💡 Recommendation: {result.get('recommendation', 'N/A')}")

        # Show updated health
        print(f"\n📊 Health: {agent.get_health_dashboard()}")

if __name__ == "__main__":
    asyncio.run(main())

Step 3: Real-Time Monitoring Dashboard

For production deployments, I monitor everything through a simple dashboard that tracks quota usage and circuit breaker status:

# Monitoring endpoint for health checks and alerting
Deploy this as a FastAPI endpoint

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn

app = FastAPI(title="HolySheep Agent Monitor")

Shared client instance (use dependency injection in production)
agent_client = None

class AgentConfig(BaseModel):
    api_key: str
    daily_budget: float = 50.0
    monthly_budget: float = 1000.0

@app.post("/initialize")
def initialize_agent(config: AgentConfig):
    """Initialize or update the agent configuration."""
    global agent_client
    agent_client = ClineAgent(
        api_key=config.api_key,
        config={
            "daily_budget": config.daily_budget,
            "monthly_budget": config.monthly_budget
        }
    )
    return {"status": "initialized", "config": config.dict()}

@app.get("/health")
def health_check():
    """Health check endpoint for load balancers and monitoring."""
    if not agent_client:
        raise HTTPException(status_code=503, detail="Agent not initialized")

    health = agent_client.get_health_dashboard()

    # Determine status
    daily_pct = health["quota"]["daily_spent"] / health["quota"]["daily_limit"] * 100

    if daily_pct >= 90:
        status = "CRITICAL"
    elif daily_pct >= 75:
        status = "WARNING"
    elif daily_pct >= 50:
        status = "DEGRADED"
    else:
        status = "HEALTHY"

    return {
        "status": status,
        "daily_budget_used_pct": round(daily_pct, 2),
        "circuits": health["circuits"],
        "monthly_budget_used_pct": round(
            health["quota"]["monthly_spent"] / health["quota"]["monthly_limit"] * 100, 2
        )
    }

@app.get("/circuit-status")
def circuit_status():
    """Get detailed circuit breaker status for all models."""
    if not agent_client:
        raise HTTPException(status_code=503, detail="Agent not initialized")

    return agent_client.get_health_dashboard()["circuits"]

@app.post("/execute")
def execute_task(task: str, context: dict = None):
    """Execute a task through the agent."""
    if not agent_client:
        raise HTTPException(status_code=503, detail="Agent not initialized")

    import asyncio
    result = asyncio.run(agent_client.execute_task(task, context))
    return result

Run with: uvicorn monitoring_server:app --host 0.0.0.0 --port 8000

Cost Optimization Results

After implementing these changes in our production environment, here's what I observed over a 30-day period:

Model Pricing Reference (2026)

Who This Is For

This Guide Is For:

• Development teams running Cline agents or similar AI-powered automation pipelines
• Engineering managers who need predictable API budgets without sacrificing capability
• Startups building AI products that need enterprise-grade reliability at startup costs
• Enterprise teams migrating from fragmented API providers to unified management
• DevOps engineers responsible for monitoring and cost control of AI infrastructure

This Guide Is NOT For:

• Single-user applications with no budget constraints
• Teams already satisfied with their current API spending and reliability
• Projects requiring models not currently supported by HolySheep (though the roadmap is aggressive)
• Organizations with existing mature circuit breaker implementations that would require significant refactoring

Common Errors and Fixes

Error 1: "401 Unauthorized - Invalid API Key"

Symptom: Every request fails immediately with 401 error, even though you copied the key correctly.

# WRONG - Common mistake with whitespace or encoding issues
headers = {
    "Authorization": f"Bearer {api_key} ",  # Trailing space!
}

CORRECT - Clean key without extra characters
headers = {
    "Authorization": f"Bearer {api_key.strip()}",
}

Also check: Make sure you're using the v1 endpoint
BASE_URL = "https://api.holysheep.ai/v1"  # NOT api.holysheep.ai/chat/completions directly

Error 2: "429 Too Many Requests - Retry-After Not Honored"

Symptom: Getting rate limited despite implementing retry logic. The retries seem to make it worse.

# WRONG - Aggressive retry that bypasses rate limits
for attempt in range(10):
    try:
        response = requests.post(url, json=payload, headers=headers)
        if response.status_code == 429:
            time.sleep(1)  # Too fast!
            continue
    except Exception:
        time.sleep(1)

CORRECT - Honor Retry-After header and implement exponential backoff
def robust_request_with_backoff(url, payload, headers, max_retries=5):
    for attempt in range(max_retries):
        try:
            response = requests.post(url, json=payload, headers=headers, timeout=30)

            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 60))
                # Use circuit breaker to track failures
                circuit_breaker.record_failure(current_model)
                print(f"Rate limited. Waiting {retry_after}s before retry {attempt + 1}")
                time.sleep(retry_after)
                continue

            return response

        except requests.exceptions.Timeout:
            wait_time = 2 ** attempt + random.uniform(0, 1)
            print(f"Timeout. Waiting {wait_time:.2f}s before retry {attempt + 1}")
            time.sleep(wait_time)

    raise RateLimitError("All retries exhausted")

Error 3: "Circuit Breaker Not Preventing Cascade Failures"

Symptom: Circuit breaker opens but the fallback model also gets overwhelmed, causing total system failure.

# WRONG - Sequential circuit breakers that can all open at once
breaker_deepseek = CircuitBreaker(failure_threshold=5)
breaker_gemini = CircuitBreaker(failure_threshold=5)
breaker_claude = CircuitBreaker(failure_threshold=5)

CORRECT - Shared state circuit breaker with group protection
class HierarchicalCircuitBreaker:
    """
    Implements tiered circuit breaking:
    1. Individual model breakers
    2. Global budget breaker
    3. Tier-based fallback protection
    """

    def __init__(self):
        self.model_breakers = {}
        self.tier_limits = {
            "tier1_budget": 10.00,   # DeepSeek max per hour
            "tier2_budget": 25.00,   # Gemini max per hour
            "tier3_budget": 15.00,   # Claude/GPT max per hour
        }
        self.tier_spent = {"tier1": 0, "tier2": 0, "tier3": 0}
        self.tier_reset_time = {}

    def check_and_record(self, model: str, cost: float) -> bool:
        """Returns True if request is allowed, False if blocked."""

        # Determine tier
        if model == "deepseek-v3.2":
            tier = "tier1"
        elif model == "gemini-2.5-flash":
            tier = "tier2"
        else:
            tier = "tier3"

        # Check tier budget
        if self.tier_spent[tier] + cost > self.tier_limits[f"{tier}_budget"]:
            print(f"[Breaker] Tier {tier} budget exhausted (${self.tier_spent[tier]:.2f}/${self.tier_limits[f'{tier}_budget']:.2f})")
            return False

        # Check individual circuit breaker
        if model in self.model_breakers and not self
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