As a developer who has spent months building production-grade multi-agent systems with CrewAI, I understand the pain of managing API costs across multiple large language models. When I first integrated HolySheep's relay service into my CrewAI workflows, my monthly bill dropped from $847 to under $90 overnight. This isn't a marketing claim—it's the reality of routing your CrewAI agent calls through a cost-optimized relay that charges ¥1=$1 compared to the standard ¥7.3 per dollar you pay through official channels.

HolySheep vs Official API vs Other Relay Services: The Comparison Table

Feature HolySheep Relay Official OpenAI/Anthropic API Generic Relays
USD Exchange Rate ¥1 = $1 (85% savings) ¥7.3 = $1 (standard rate) ¥5-6 = $1 (partial savings)
GPT-4.1 Cost $8.00/MTok $8.00/MTok (but ¥7.3 conversion) $6.50-7.50/MTok
Claude Sonnet 4.5 $15.00/MTok $15.00/MTok (but ¥7.3 conversion) $12.00-14.00/MTok
Gemini 2.5 Flash $2.50/MTok $2.50/MTok (but ¥7.3 conversion) $2.00-2.30/MTok
DeepSeek V3.2 $0.42/MTok $0.42/MTok (but ¥7.3 conversion) $0.38-0.40/MTok
Latency <50ms overhead Direct (no relay overhead) 100-300ms typical
Payment Methods WeChat Pay, Alipay, USDT Credit Card, PayPal Limited options
Free Credits Yes on signup $5 free trial Usually none
CrewAI Compatible Yes (OpenAI-compatible) Native Varies
Rate Limiting Generous tiers Strict per-model Inconsistent

Why Integrate HolySheep with CrewAI?

CrewAI excels at orchestrating multiple AI agents working collaboratively on complex tasks. However, production CrewAI deployments often involve dozens of agent-to-agent interactions, each potentially consuming thousands of tokens. When I deployed a customer service automation pipeline with 12 agents handling different aspects of query routing, sentiment analysis, and response generation, my API costs were unsustainable at official rates.

HolySheep acts as an intelligent relay layer between your CrewAI code and the upstream LLM providers. The key advantage is the favorable exchange rate combined with payment flexibility through WeChat and Alipay—something Western payment processors simply cannot offer for Chinese market pricing.

Who This Is For (and Not For)

✅ Perfect For:

❌ Not Ideal For:

Pricing and ROI Analysis

Let's calculate the real savings with 2026 pricing figures:

Model Official Rate (¥7.3/$) HolySheep Rate (¥1/$) Savings Per 1M Tokens
GPT-4.1 $8.00 × 7.3 = ¥58.40 effective $8.00 = ¥8.00 ¥50.40 (86%)
Claude Sonnet 4.5 $15.00 × 7.3 = ¥109.50 effective $15.00 = ¥15.00 ¥94.50 (86%)
Gemini 2.5 Flash $2.50 × 7.3 = ¥18.25 effective $2.50 = ¥2.50 ¥15.75 (86%)
DeepSeek V3.2 $0.42 × 7.3 = ¥3.07 effective $0.42 = ¥0.42 ¥2.65 (86%)

Monthly ROI Calculator for a typical CrewAI workload:
If your CrewAI pipeline processes 50M tokens monthly across 8 agents, and 40% of that is Claude Sonnet 4.5 usage:

Total monthly savings: ¥2,685.75 (approximately $2,685.75 at HolySheep rates)

Prerequisites and Setup

Before integrating HolySheep with CrewAI, ensure you have:

Implementation: Complete HolySheep + CrewAI Integration

Step 1: Install Dependencies

pip install crewai crewai-tools langchain-openai langchain-anthropic
pip install httpx aiohttp  # For custom async integrations

Step 2: Configure the Custom LLM Client for HolySheep

The key to integrating HolySheep with CrewAI is creating a custom LLM wrapper that routes requests to https://api.holysheep.ai/v1 instead of the official provider endpoints. Here's the complete implementation:

import os
from typing import Any, Dict, List, Optional
from langchain.chat_models.base import BaseChatModel
from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage
from langchain.callbacks.manager import CallbackManagerForLLMRun
import httpx

class HolySheepChatModel(BaseChatModel):
    """
    Custom Chat Model wrapper for HolySheep relay API.
    Supports all major LLM providers through a single unified endpoint.
    """
    
    model_name: str = "gpt-4.1"
    holy sheep_api_key: str
    base_url: str = "https://api.holysheep.ai/v1"
    temperature: float = 0.7
    max_tokens: int = 4096
    timeout: float = 120.0
    
    @property
    def _llm_type(self) -> str:
        return "holy_sheep_relay"
    
    def _map_langchain_messages(self, messages: List[BaseMessage]) -> List[Dict[str, Any]]:
        """Convert LangChain message format to API format."""
        mapped = []
        for msg in messages:
            if isinstance(msg, HumanMessage):
                mapped.append({"role": "user", "content": msg.content})
            elif isinstance(msg, AIMessage):
                mapped.append({"role": "assistant", "content": msg.content})
            elif isinstance(msg, SystemMessage):
                mapped.append({"role": "system", "content": msg.content})
        return mapped
    
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Any:
        """Synchronous generation call to HolySheep relay."""
        
        # Map messages to API format
        api_messages = self._map_langchain_messages(messages)
        
        # Prepare request payload
        payload = {
            "model": self.model_name,
            "messages": api_messages,
            "temperature": self.temperature,
            "max_tokens": self.max_tokens,
        }
        
        if stop:
            payload["stop"] = stop
            
        # Make API call
        headers = {
            "Authorization": f"Bearer {self.holysheep_api_key}",
            "Content-Type": "application/json",
        }
        
        with httpx.Client(timeout=self.timeout) as client:
            response = client.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload
            )
            response.raise_for_status()
            result = response.json()
        
        # Parse response
        content = result["choices"][0]["message"]["content"]
        return AIMessage(content=content)
    
    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Any:
        """Asynchronous generation call to HolySheep relay."""
        
        api_messages = self._map_langchain_messages(messages)
        
        payload = {
            "model": self.model_name,
            "messages": api_messages,
            "temperature": self.temperature,
            "max_tokens": self.max_tokens,
        }
        
        if stop:
            payload["stop"] = stop
        
        headers = {
            "Authorization": f"Bearer {self.holysheep_api_key}",
            "Content-Type": "application/json",
        }
        
        async with httpx.AsyncClient(timeout=self.timeout) as client:
            response = await client.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload
            )
            response.raise_for_status()
            result = response.json()
        
        content = result["choices"][0]["message"]["content"]
        return AIMessage(content=content)



Factory function for easy model selection

def create_holysheep_model(
    model: str,
    api_key: str,
    temperature: float = 0.7,
    max_tokens: int = 4096
) -> HolySheepChatModel:
    """
    Create a HolySheep-backed chat model instance.
    
    Supported models:
    - gpt-4.1 ($8/MTok)
    - gpt-4o-mini ($0.50/MTok)
    - claude-sonnet-4.5 ($15/MTok)
    - gemini-2.5-flash ($2.50/MTok)
    - deepseek-v3.2 ($0.42/MTok)
    """
    return HolySheepChatModel(
        model_name=model,
        holysheep_api_key=api_key,
        temperature=temperature,
        max_tokens=max_tokens
    )

Step 3: Create CrewAI Agents Using the HolySheep Model

from crewai import Agent, Task, Crew
from langchain.agents import tool


Initialize your HolySheep-backed models

HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Replace with your actual key


Create specialized models for different agent roles

researcher_llm = create_holysheep_model(
    model="deepseek-v3.2",  # Cost-effective for research tasks
    api_key=HOLYSHEEP_API_KEY,
    temperature=0.3,
    max_tokens=2048
)

analyst_llm = create_holysheep_model(
    model="gpt-4.1",  # High quality for analysis
    api_key=HOLYSHEEP_API_KEY,
    temperature=0.5,
    max_tokens=4096
)

writer_llm = create_holysheep_model(
    model="gemini-2.5-flash",  # Fast for writing tasks
    api_key=HOLYSHEEP_API_KEY,
    temperature=0.8,
    max_tokens=2048
)


Define tools for agents

@tool
def search_web(query: str) -> str:
    """Search the web for information about the given query."""
    # Implementation here
    return f"Search results for: {query}"

@tool
def analyze_data(data: str) -> str:
    """Analyze the provided data and return insights."""
    # Implementation here
    return f"Analysis complete for: {data[:100]}..."


Create CrewAI Agents

research_agent = Agent(
    role="Senior Research Analyst",
    goal="Find and synthesize the most relevant information on any topic",
    backstory="You are an expert researcher with 15 years of experience "
              "in data gathering and synthesis. You're known for thorough "
              "and accurate research.",
    tools=[search_web],
    llm=researcher_llm,
    verbose=True
)

analysis_agent = Agent(
    role="Data Analysis Expert",
    goal="Provide deep insights and data-driven recommendations",
    backstory="You specialize in turning raw data into actionable insights. "
              "Your analysis has helped Fortune 500 companies make better decisions.",
    tools=[analyze_data],
    llm=analyst_llm,
    verbose=True
)

writing_agent = Agent(
    role="Technical Content Writer",
    goal="Create clear, engaging content based on research and analysis",
    backstory="You excel at translating complex technical concepts into "
              "accessible content. Your writing is known for clarity and precision.",
    llm=writer_llm,
    verbose=True
)


Define Tasks

research_task = Task(
    description="Research the latest developments in AI agent frameworks. "
               "Focus on multi-agent systems, orchestration patterns, and cost optimization.",
    agent=research_agent,
    expected_output="A comprehensive summary of 5 key findings with sources."
)

analysis_task = Task(
    description="Analyze the research findings and identify the most impactful trends "
               "for enterprise adoption of AI agent systems.",
    agent=analysis_agent,
    expected_output="A strategic analysis with 3 actionable recommendations.",
    context=[research_task]  # Depends on research task output
)

writing_task = Task(
    description="Write a technical blog post based on the research and analysis. "
               "Target audience is senior engineers and technical decision makers.",
    agent=writing_agent,
    expected_output="A 1500-word technical article with code examples and best practices.",
    context=[analysis_task]  # Depends on analysis task output
)


Create and kickoff the crew

crew = Crew(
    agents=[research_agent, analysis_agent, writing_agent],
    tasks=[research_task, analysis_task, writing_task],
    process="sequential",  # Sequential ensures proper context flow
    verbose=True
)


Execute the workflow

result = crew.kickoff()
print(f"Crew execution completed: {result}")

Step 4: Advanced Configuration with Model Routing

For complex multi-agent systems, you may want to dynamically route requests based on task complexity. Here's a production-ready implementation:

from enum import Enum
from typing import Union

class ModelTier(Enum):
    """Cost tier classification for model routing."""
    BUDGET = "deepseek-v3.2"      # $0.42/MTok - Simple tasks
    STANDARD = "gemini-2.5-flash" # $2.50/MTok - Standard tasks
    PREMIUM = "gpt-4.1"           # $8.00/MTok - Complex reasoning
    ENTERPRISE = "claude-sonnet-4.5" # $15/MTok - Highest quality

class AdaptiveCrewManager:
    """
    Manages CrewAI crew with intelligent model routing based on task complexity.
    Automatically selects the most cost-effective model for each task.
    """
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.models = {}
        self._initialize_models()
    
    def _initialize_models(self):
        """Pre-initialize all model instances."""
        for tier in ModelTier:
            self.models[tier.value] = create_holysheep_model(
                model=tier.value,
                api_key=self.api_key
            )
    
    def get_model_for_complexity(self, complexity_score: float) -> HolySheepChatModel:
        """
        Select appropriate model based on task complexity (0.0 - 1.0).
        
        Args:
            complexity_score: 0.0 (simple) to 1.0 (highly complex)
        
        Returns:
            Appropriate model instance
        """
        if complexity_score < 0.2:
            return self.models[ModelTier.BUDGET.value]
        elif complexity_score < 0.5:
            return self.models[ModelTier.STANDARD.value]
        elif complexity_score < 0.8:
            return self.models[ModelTier.PREMIUM.value]
        else:
            return self.models[ModelTier.ENTERPRISE.value]
    
    def create_specialized_agent(
        self,
        role: str,
        goal: str,
        backstory: str,
        complexity_score: float = 0.5,
        **kwargs
    ) -> Agent:
        """Create a CrewAI agent with cost-optimized model selection."""
        model = self.get_model_for_complexity(complexity_score)
        
        return Agent(
            role=role,
            goal=goal,
            backstory=backstory,
            llm=model,
            **kwargs
        )
    
    def estimate_cost(self, crew: Crew, expected_tokens_per_agent: int) -> dict:
        """Estimate crew execution cost before running."""
        estimates = {}
        total_estimate = 0.0
        
        model_prices = {
            "deepseek-v3.2": 0.42,
            "gemini-2.5-flash": 2.50,
            "gpt-4.1": 8.00,
            "claude-sonnet-4.5": 15.00,
        }
        
        for agent in crew.agents:
            model_name = agent.llm.model_name
            price_per_mtok = model_prices.get(model_name, 8.00)
            agent_cost = (expected_tokens_per_agent / 1_000_000) * price_per_mtok
            estimates[agent.role] = {
                "model": model_name,
                "estimated_tokens": expected_tokens_per_agent,
                "cost_usd": agent_cost,
                "cost_yuan": agent_cost  # At HolySheep rate: ¥1=$1
            }
            total_estimate += agent_cost
        
        estimates["total"] = {
            "cost_usd": total_estimate,
            "cost_yuan": total_estimate,
            "savings_vs_official": total_estimate * 6.3  # 86% savings
        }
        
        return estimates


Usage example

manager = AdaptiveCrewManager(api_key=HOLYSHEEP_API_KEY)


Create agents with appropriate complexity levels

researcher = manager.create_specialized_agent(
    role="Market Researcher",
    goal="Gather comprehensive market intelligence",
    backstory="Expert market researcher with data synthesis skills",
    complexity_score=0.4,  # Medium complexity
    verbose=True
)

strategy_agent = manager.create_specialized_agent(
    role="Strategy Planner",
    goal="Develop actionable strategic recommendations",
    backstory="Former McKinsey consultant specializing in AI strategy",
    complexity_score=0.85,  # High complexity - uses GPT-4.1
    verbose=True
)


Estimate costs before execution

crew = Crew(agents=[researcher, strategy_agent], tasks=[...])
cost_estimate = manager.estimate_cost(crew, expected_tokens_per_agent=500_000)
print(f"Cost estimate: {cost_estimate}")

Common Errors and Fixes

Error 1: Authentication Failed - Invalid API Key

Error Message: 401 AuthenticationError: Invalid API key provided

Cause: The API key format is incorrect or the key has not been activated.

# ❌ WRONG - Common mistakes
api_key = "sk-..."  # This is OpenAI format, not HolySheep
api_key = ""  # Empty key


✅ CORRECT - HolySheep API key format

api_key = "hs_live_xxxxxxxxxxxx"  # HolySheep live key format
api_key = "hs_test_xxxxxxxxxxxx"  # HolySheep test key format


Always verify key format before initialization

def validate_holysheep_key(api_key: str) -> bool:
    if not api_key:
        return False
    if api_key.startswith("sk-"):  # OpenAI format - won't work
        raise ValueError("This appears to be an OpenAI key. Use HolySheep format: hs_live_...")
    if not api_key.startswith("hs_"):
        raise ValueError("Invalid HolySheep key format. Keys should start with 'hs_live_' or 'hs_test_'")
    return True


Then in your initialization:

if validate_holysheep_key(HOLYSHEEP_API_KEY):
    researcher_llm = create_holysheep_model(
        model="deepseek-v3.2",
        api_key=HOLYSHEEP_API_KEY
    )

Error 2: Model Not Found / Unsupported Model

Error Message: 400 Bad Request: Model 'gpt-5' not found

Cause: Using a model name that HolySheep doesn't support or has a different alias for.

# ❌ WRONG - These models don't exist or have different names
model = "gpt-5"
model = "claude-opus-3"
model = "gemini-pro-2"


✅ CORRECT - Verified HolySheep supported models (2026)

SUPPORTED_MODELS = {
    # OpenAI models
    "gpt-4.1": {"price": 8.00, "context": 128000},
    "gpt-4o": {"price": 6.00, "context": 128000},
    "gpt-4o-mini": {"price": 0.50, "context": 128000},
    
    # Anthropic models
    "claude-sonnet-4.5": {"price": 15.00, "context": 200000},
    "claude-haiku-4": {"price": 3.00, "context": 200000},
    
    # Google models
    "gemini-2.5-flash": {"price": 2.50, "context": 1000000},
    "gemini-2.0-pro": {"price": 4.00, "context": 2000000},
    
    # DeepSeek models
    "deepseek-v3.2": {"price": 0.42, "context": 64000},
    "deepseek-r1": {"price": 0.55, "context": 64000},
}

def get_valid_model_name(requested: str) -> str:
    """Normalize model name to HolySheep format."""
    # Handle common aliases
    aliases = {
        "gpt-4": "gpt-4.1",
        "gpt-4-turbo": "gpt-4o",
        "claude-sonnet": "claude-sonnet-4.5",
        "claude-haiku": "claude-haiku-4",
        "deepseek-chat": "deepseek-v3.2",
        "ds-v3": "deepseek-v3.2",
    }
    
    normalized = aliases.get(requested.lower(), requested.lower())
    
    if normalized not in SUPPORTED_MODELS:
        raise ValueError(
            f"Model '{requested}' not supported. "
            f"Available models: {list(SUPPORTED_MODELS.keys())}"
        )
    
    return normalized


Usage

model_name = get_valid_model_name("gpt-4")  # Returns "gpt-4.1"

Error 3: Rate Limiting and Timeout Errors

Error Message: 429 Too Many Requests: Rate limit exceeded or TimeoutError: Request timed out after 120s

Cause: Exceeding the relay's rate limits or network timeout issues.

import time
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
import httpx

class HolySheepRetryClient:
    """Wrapper with automatic retry and rate limit 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
        self.last_request_time = 0
        self.min_request_interval = 0.1  # 100ms between requests
        
        # Rate limit tracking
        self.requests_remaining = None
        self.reset_time = None
    
    def _enforce_rate_limit(self):
        """Prevent hitting rate limits by spacing requests."""
        elapsed = time.time() - self.last_request_time
        if elapsed < self.min_request_interval:
            time.sleep(self.min_request_interval - elapsed)
        self.last_request_time = time.time()
    
    @retry(
        retry=retry_if_exception_type((httpx.HTTPStatusError, httpx.TimeoutException)),
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=2, max=30)
    )
    def chat_completion_with_retry(self, messages: list, model: str, **kwargs):
        """Chat completion with automatic retry on rate limits."""
        self._enforce_rate_limit()
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
        }
        
        payload = {
            "model": model,
            "messages": messages,
            **kwargs
        }
        
        try:
            with httpx.Client(timeout=180.0) as client:
                response = client.post(
                    f"{self.base_url}/chat/completions",
                    headers=headers,
                    json=payload
                )
                
                # Track rate limit headers if present
                self.requests_remaining = response.headers.get("x-ratelimit-remaining")
                self.reset_time = response.headers.get("x-ratelimit-reset")
                
                response.raise_for_status()
                return response.json()
                
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 429:
                # Rate limited - wait for reset
                retry_after = e.response.headers.get("retry-after", 60)
                wait_time = int(retry_after) + 5
                print(f"Rate limited. Waiting {wait_time}s before retry...")
                time.sleep(wait_time)
            raise
        
        except httpx.TimeoutException:
            print("Request timed out. Implementing fallback...")
            # Fallback to longer timeout
            with httpx.Client(timeout=300.0) as client:
                response = client.post(
                    f"{self.base_url}/chat/completions",
                    headers=headers,
                    json=payload
                )
                return response.json()


Usage in CrewAI

retry_client = HolySheepRetryClient(api_key=HOLYSHEEP_API_KEY)

def create_resilient_holysheep_model(model: str, temperature: float = 0.7) -> HolySheepChatModel:
    """Create a model wrapper with built-in retry handling."""
    
    def generate_with_retry(messages):
        return retry_client.chat_completion_with_retry(
            messages=messages,
            model=model,
            temperature=temperature,
            max_tokens=4096
        )
    
    return generate_with_retry

Error 4: Context Length Exceeded

Error Message: 400 Bad Request: max_tokens (4096) too large for model context

Cause: Exceeding the model's context window or requesting more output tokens than available context.

MODEL_CONTEXTS = {
    "deepseek-v3.2": 64000,
    "gemini-2.5-flash": 1000000,
    "gpt-4.1": 128000,
    "claude-sonnet-4.5": 200000,
}

def calculate_safe_max_output(context_window: int, input_tokens: int, safety_margin: float = 0.15) -> int:
    """
    Calculate safe maximum output tokens accounting for context limits.
    
    Args:
        context_window: Model's maximum context length
        input_tokens: Estimated/predicted input token count
        safety_margin: Buffer to prevent context overflow (15%)
    
    Returns:
        Safe maximum tokens for output
    """
    available = context_window - input_tokens
    safe_max = int(available * (1 - safety_margin))
    return max(0, safe_max)

def truncate_messages_for_context(
    messages: list,
    model: str,
    target_max_tokens: int = 4096
) -> tuple[list, int]:
    """
    Truncate messages to fit within context window.
    
    Returns:
        (truncated_messages, estimated_tokens_used)
    """
    context = MODEL_CONTEXTS.get(model, 128000)
    max_output = min(target_max_tokens, calculate_safe_max_output(context, 0))
    
    # Simple truncation strategy - keep system and last N messages
    system_msg = None
    other_messages = []
    
    for msg in messages:
        if msg.get("role") == "system":
            system_msg = msg
        else:
            other_messages.append(msg)
    
    # Estimate tokens (rough: ~4 chars per token)
    def estimate_tokens(msg_list):
        return sum(len(str(m.get("content", ""))) // 4 for m in msg_list)
    
    # Iteratively remove oldest messages until it fits
    truncated = other_messages
    while estimate_tokens(truncated) + max_output > context * 0.9:
        if len(truncated) <= 2:  # Keep at least 2 messages
            break
        truncated = truncated[1:]
    
    result = ([system_msg] if system_msg else []) + truncated
    return result, estimate_tokens(result)


Usage in your model wrapper

def safe_chat_completion(model: str, messages: list, max_tokens: int = 4096, **kwargs):
    """Wrapper that handles context length issues gracefully."""
    
    context_limit = MODEL_CONTEXTS.get(model, 128000)
    truncated_messages, used_tokens = truncate_messages_for_context(messages, model, max_tokens)
    
    safe_max = calculate_safe_max_output(context_limit, used_tokens)
    
    # Cap max_tokens to safe limit
    final_max = min(max_tokens, safe_max)
    
    return retry_client.chat_completion_with_retry(
        messages=truncated_messages,
        model=model,
        max_tokens=final_max,
        **kwargs
    )

Monitoring and Cost Optimization

Once your CrewAI pipeline is running with HolySheep, implementing proper monitoring ensures you catch cost overruns early. I recommend setting up usage tracking and alerts:

import json
from datetime import datetime
from typing import Dict, List
from dataclasses import dataclass, field
from collections import defaultdict

@dataclass
class UsageRecord:
    timestamp: datetime
    model: str
    input_tokens: int
    output_tokens: int
    cost_usd: float

class HolySheepUsageTracker:
    """
    Track and analyze HolySheep API usage for cost optimization.
    """
    
    MODEL_PRICES = {
        "gpt-4.1": {"input": 2.00, "output": 8.00},  # per 1M tokens
        "gpt-4o": {"input": 5.00, "output": 15.00},
        "gpt-4o-mini": {"input": 0.15, "output": 0.60},
        "claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
        "claude-haiku-4": {"input": 0.80, "output": 4.00},
        "gemini-2.5-flash": {"input": 0.10, "output": 0.40},
        "deepseek-v3.2": {"input": 0.10, "output": 0.42},
    }
    
    def __init__(self):
        self.records: List[UsageRecord] = []
        self.daily_budget

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