Introduction: Building Your First Multi-Model Agent Pipeline

Have you ever wondered how enterprise companies build AI agents that can seamlessly switch between different language models? In this comprehensive guide, I will walk you through constructing a production-ready agent gateway using LangGraph, integrated with cutting-edge models through HolySheep AI's unified API. Whether you are a developer just starting with AI APIs or an engineer looking to optimize costs, this tutorial will transform abstract concepts into working code.

Throughout my years of building AI infrastructure, I have noticed that most tutorials assume you already understand the ecosystem. Here, we assume nothing. We will build everything from the ground up, explaining each concept as if you have never seen an API call before. By the end, you will have a functioning agent gateway that routes requests intelligently between GPT-5.5 and Claude 4.7, with automatic fallback mechanisms, cost tracking, and streaming support—all running through HolySheep AI's blazing-fast infrastructure with sub-50ms latency.

The best part? HolySheep AI charges just ¥1 = $1 USD, which represents an 85%+ savings compared to standard ¥7.3 rates. With support for WeChat and Alipay payments, plus free credits upon registration, you can start building immediately without financial barriers.

What You Will Build: Architecture Overview

Our enterprise agent gateway will feature a modular architecture designed for scalability and reliability. Here is what we are creating:

Imagine this as building a smart traffic controller for AI requests. When a user sends a prompt, our gateway analyzes it, decides which model is best suited, sends the request, and handles the response—all while keeping track of costs and ensuring the user never experiences downtime.

Prerequisites: What You Need Before Starting

Before diving into the code, let us ensure you have everything required. Think of this as gathering ingredients before cooking—you want everything ready before you start the actual preparation.

No prior experience with LangGraph, OpenAI, or Anthropic APIs is required. We will explain each integration point as we build it.

Environment Setup: Installing Dependencies

First, we need to set up our development environment. Open your terminal and create a new project directory. Name it something memorable—perhaps "agent-gateway" since that is exactly what we are building.

# Create project directory and navigate to it
mkdir agent-gateway
cd agent-gateway


Create a virtual environment (isolates our project dependencies)

python -m venv venv


Activate the virtual environment


On macOS/Linux:

source venv/bin/activate

On Windows:


venv\Scripts\activate



Install required dependencies

pip install langgraph langchain-core langchain-anthropic openai
pip install python-dotenv asyncio aiohttp

After running these commands, you should see "(venv)" appear at the beginning of your terminal line, indicating the virtual environment is active. This isolation ensures our project dependencies do not conflict with other Python projects on your system.

Next, create a file named .env in your project directory—this will store your sensitive API credentials safely:

# .env file - NEVER commit this to version control
HOLYSHEEP_API_KEY=your_holysheep_api_key_here

To get your API key, log into your HolySheep AI dashboard at Sign up here if you have not already. The dashboard displays your API key prominently. Copy it and paste it after the equals sign, replacing "your_holysheep_api_key_here".

Understanding the HolySheep AI Unified API

HolySheep AI provides a unified gateway that aggregates multiple AI providers under a single, consistent API structure. This means you write code once and can switch between models without rewriting your integration logic. The base URL for all API calls is:

https://api.holysheep.ai/v1

This unified approach offers several compelling advantages. First, you get cost efficiency with HolySheep's ¥1=$1 rate, representing massive savings compared to the standard ¥7.3 market rate. Second, you experience blazing performance with sub-50ms latency through optimized infrastructure. Third, you gain payment flexibility with support for WeChat and Alipay alongside traditional methods. Fourth, you receive free starting credits upon registration to experiment without immediate costs.

Here are the current 2026 pricing rates that will inform our cost optimization strategy:

Notice the dramatic price differences between models. DeepSeek V3.2 at $0.42/MTok costs approximately 19 times less than Claude Sonnet 4.5 at $15.00/MTok. Our agent gateway will intelligently route simpler tasks to cheaper models while reserving premium models for complex reasoning tasks.

Building the Model Client: Connecting to HolySheep AI

Now we begin the actual coding. Create a new file called model_client.py. This module will handle all communication with HolySheep AI's API, abstracting away the complexity so our agent logic remains clean and focused.

# model_client.py
import os
import json
from typing import Optional, Dict, Any, AsyncIterator
from openai import AsyncOpenAI
from dotenv import load_dotenv

load_dotenv()

class HolySheepModelClient:
    """
    Unified client for accessing multiple AI models through HolySheep AI.
    Supports GPT-5.5, Claude 4.7, and other models via a single interface.
    """
    
    def __init__(self, api_key: Optional[str] = None):
        self.api_key = api_key or os.getenv("HOLYSHEEP_API_KEY")
        if not self.api_key:
            raise ValueError("HolySheep API key must be provided or set in HOLYSHEEP_API_KEY environment variable")
        
        # Initialize async client with HolySheep AI base URL
        self.client = AsyncOpenAI(
            api_key=self.api_key,
            base_url="https://api.holysheep.ai/v1",  # HolySheep unified API endpoint
            timeout=60.0,
            max_retries=3
        )
        
        # Model mappings for HolySheep AI
        self.model_configs = {
            "gpt-5.5": {
                "model_id": "gpt-4.1",  # Maps to closest available model
                "cost_per_mtok": 8.00,
                "strengths": ["reasoning", "code", "analysis"],
                "max_tokens": 128000
            },
            "claude-4.7": {
                "model_id": "claude-sonnet-4.5",
                "cost_per_mtok": 15.00,
                "strengths": ["writing", "long-context", "safety"],
                "max_tokens": 200000
            },
            "deepseek-v3.2": {
                "model_id": "deepseek-v3.2",
                "cost_per_mtok": 0.42,
                "strengths": ["cost-efficiency", "code", "reasoning"],
                "max_tokens": 64000
            },
            "gemini-flash": {
                "model_id": "gemini-2.5-flash",
                "cost_per_mtok": 2.50,
                "strengths": ["speed", "multimodal", "low-latency"],
                "max_tokens": 1000000
            }
        }
        
        # Usage tracking
        self.total_tokens_used = 0
        self.total_cost_usd = 0.0
    
    async def complete(
        self,
        model: str,
        messages: list,
        temperature: float = 0.7,
        max_tokens: Optional[int] = None,
        stream: bool = False
    ) -> Dict[str, Any]:
        """
        Send a completion request to the specified model.
        
        Args:
            model: Model identifier (e.g., 'gpt-5.5', 'claude-4.7')
            messages: List of message dictionaries with 'role' and 'content'
            temperature: Sampling temperature (0.0 to 2.0)
            max_tokens: Maximum tokens in response
            stream: Whether to stream the response
            
        Returns:
            Response dictionary with content and usage metadata
        """
        if model not in self.model_configs:
            raise ValueError(f"Unknown model: {model}. Available: {list(self.model_configs.keys())}")
        
        config = self.model_configs[model]
        
        try:
            response = await self.client.chat.completions.create(
                model=config["model_id"],
                messages=messages,
                temperature=temperature,
                max_tokens=max_tokens or config["max_tokens"] // 4,
                stream=stream
            )
            
            if stream:
                return response  # Return iterator for streaming
            
            # Calculate costs based on actual usage
            usage = response.usage
            output_tokens = usage.completion_tokens
            input_tokens = usage.prompt_tokens
            cost = (output_tokens / 1_000_000) * config["cost_per_mtok"]
            
            # Update tracking
            self.total_tokens_used += output_tokens
            self.total_cost_usd += cost
            
            return {
                "content": response.choices[0].message.content,
                "model": model,
                "usage": {
                    "input_tokens": input_tokens,
                    "output_tokens": output_tokens,
                    "cost_usd": round(cost, 4)
                },
                "latency_ms": response.response_ms if hasattr(response, 'response_ms') else None
            }
            
        except Exception as e:
            return {
                "error": str(e),
                "model": model,
                "fallback_recommended": True
            }
    
    async def complete_with_fallback(
        self,
        primary_model: str,
        fallback_models: list,
        messages: list,
        **kwargs
    ) -> Dict[str, Any]:
        """
        Attempt completion with primary model, fall back to alternatives on failure.
        
        This method implements circuit breaker logic—tries the primary model first,
        and if it fails, systematically attempts each fallback model until one succeeds.
        """
        models_to_try = [primary_model] + fallback_models
        
        for model in models_to_try:
            result = await self.complete(model, messages, **kwargs)
            
            if "error" not in result:
                result["model_used"] = model
                result["was_fallback"] = model != primary_model
                return result
            
            print(f"Model {model} failed: {result['error']}, trying next...")
        
        return {
            "error": "All models failed",
            "models_attempted": models_to_try
        }
    
    def get_cost_summary(self) -> Dict[str, Any]:
        """Return current session's cost and usage summary."""
        return {
            "total_tokens": self.total_tokens_used,
            "total_cost_usd": round(self.total_cost_usd, 4),
            "model_configs": {
                name: {"cost_per_mtok": cfg["cost_per_mtok"]}
                for name, cfg in self.model_configs.items()
            }
        }



Factory function for easy instantiation

def create_model_client() -> HolySheepModelClient:
    """Create and return a new HolySheepModelClient instance."""
    return HolySheepModelClient()

This client class handles the heavy lifting of API communication. Notice how we map friendly model names like "gpt-5.5" to actual model identifiers supported by HolySheep AI. The complete_with_fallback method implements resilient error handling—if the primary model fails, we automatically try alternatives, ensuring your agent never leaves users hanging.

Creating the LangGraph Agent with Intelligent Routing

LangGraph is a powerful framework for building stateful, multi-agent workflows. Think of it as a flowchart where each node is a function that processes data and passes results to the next node. We will create a graph with decision points that intelligently route requests to the optimal model.

Create a file named agent_graph.py:

# agent_graph.py
from typing import TypedDict, Annotated, Sequence
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
import json

from model_client import create_model_client


Initialize our HolySheep AI client

model_client = create_model_client()

class AgentState(TypedDict):
    """Defines the data that flows through our agent graph."""
    messages: Sequence[HumanMessage | AIMessage | SystemMessage]
    current_task: str
    selected_model: str
    response: str
    cost_usd: float
    routing_reason: str

def analyze_task_complexity(state: AgentState) -> AgentState:
    """
    Analyzes the incoming task and determines which model is optimal.
    This is the 'brain' of our routing system.
    """
    messages = state["messages"]
    user_message = messages[-1].content if messages else ""
    
    # Simple heuristic-based routing (in production, use a classifier model)
    task_lower = user_message.lower()
    
    # Code-heavy tasks benefit from DeepSeek's cost efficiency
    if any(keyword in task_lower for keyword in ["code", "function", "debug", "python", "javascript", "api"]):
        selected_model = "deepseek-v3.2"
        routing_reason = "Code task detected - routing to cost-efficient DeepSeek V3.2 ($0.42/MTok)"
    
    # Long-form writing and analysis gets Claude's superior reasoning
    elif any(keyword in task_lower for keyword in ["write", "essay", "story", "analyze", "explain", "compare"]):
        if len(user_message) > 1000:
            selected_model = "claude-4.7"
            routing_reason = "Long writing/analysis task - routing to Claude 4.7 for superior reasoning"
        else:
            selected_model = "deepseek-v3.2"
            routing_reason = "Short writing task - using cost-efficient DeepSeek V3.2"
    
    # Fast responses and simple queries get Gemini Flash
    elif any(keyword in task_lower for keyword in ["quick", "simple", "what is", "define", "when", "where"]):
        selected_model = "gemini-flash"
        routing_reason = "Simple query - routing to fast Gemini 2.5 Flash for low-latency response"
    
    # Complex reasoning defaults to GPT-5.5
    else:
        selected_model = "gpt-5.5"
        routing_reason = "General task - routing to GPT-4.1 (via GPT-5.5 interface) for balanced performance"
    
    state["selected_model"] = selected_model
    state["routing_reason"] = routing_reason
    return state

def execute_model_call(state: AgentState) -> AgentState:
    """
    Executes the actual API call to the selected model.
    Includes automatic fallback to ensure reliability.
    """
    model = state["selected_model"]
    messages = state["messages"]
    
    # Define fallback chain for each primary model
    fallback_chains = {
        "gpt-5.5": ["claude-4.7", "deepseek-v3.2"],
        "claude-4.7": ["gpt-5.5", "deepseek-v3.2"],
        "deepseek-v3.2": ["gpt-5.5", "gemini-flash"],
        "gemini-flash": ["gpt-5.5", "deepseek-v3.2"]
    }
    
    fallbacks = fallback_chains.get(model, ["deepseek-v3.2"])
    
    # Format messages for API call
    api_messages = [
        {"role": "system", "content": "You are a helpful AI assistant. Provide clear, concise, and accurate responses."}
    ]
    for msg in messages:
        if isinstance(msg, HumanMessage):
            api_messages.append({"role": "user", "content": msg.content})
        elif isinstance(msg, AIMessage):
            api_messages.append({"role": "assistant", "content": msg.content})
    
    # Make the call with fallback support
    result = model_client.complete_with_fallback(
        primary_model=model,
        fallback_models=fallbacks,
        messages=api_messages,
        temperature=0.7
    )
    
    if "error" in result:
        state["response"] = f"I apologize, but I encountered an error: {result['error']}"
        state["cost_usd"] = 0.0
    else:
        state["response"] = result["content"]
        state["cost_usd"] = result["usage"]["cost_usd"]
        if result.get("was_fallback"):
            state["routing_reason"] += f" (Note: Fell back to {result['model_used']} due to primary model availability)"
    
    return state

def should_continue(state: AgentState) -> str:
    """Determines if the graph should continue processing or end."""
    # For this basic implementation, we always end after the first response
    return END


Build the LangGraph

def create_agent_graph():
    """
    Constructs and returns the configured agent workflow graph.
    """
    workflow = StateGraph(AgentState)
    
    # Add nodes - each node is a function that processes state
    workflow.add_node("analyzer", analyze_task_complexity)
    workflow.add_node("model_executor", execute_model_call)
    
    # Define the flow
    workflow.set_entry_point("analyzer")
    workflow.add_edge("analyzer", "model_executor")
    workflow.add_edge("model_executor", END)
    
    return workflow.compile()


Create a singleton instance

agent_graph = create_agent_graph()

async def run_agent(user_message: str) -> dict:
    """
    Main entry point for running the agent.
    
    Args:
        user_message: The user's input text
        
    Returns:
        Dictionary containing response, model used, cost, and routing reason
    """
    initial_state = AgentState(
        messages=[HumanMessage(content=user_message)],
        current_task=user_message,
        selected_model="",
        response="",
        cost_usd=0.0,
        routing_reason=""
    )
    
    final_state = await agent_graph.ainvoke(initial_state)
    
    return {
        "response": final_state["response"],
        "model_used": final_state["selected_model"],
        "cost_usd": final_state["cost_usd"],
        "routing_reason": final_state["routing_reason"],
        "total_session_cost": model_client.get_cost_summary()["total_cost_usd"]
    }

This LangGraph implementation is where the magic happens. The analyze_task_complexity function acts as our routing brain, examining the user's message and deciding which model will best serve their needs. The execute_model_call node handles the actual API communication, including the critical fallback logic that ensures your agent remains functional even when individual models experience issues.

Building the Web Interface: Making It User-Friendly

Now let us create a simple web interface to interact with our agent. We will use FastAPI, which is Python's modern framework for building APIs. Create a file named main.py:

# main.py
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from typing import Optional, List
import uvicorn
import asyncio

from agent_graph import run_agent
from model_client import create_model_client, HolySheepModelClient


Initialize FastAPI app

app = FastAPI(
    title="HolySheep AI Agent Gateway",
    description="Enterprise-grade multi-model AI agent with intelligent routing",
    version="1.0.0"
)


Configure CORS for web interface access

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


Global model client for cost tracking

model_client: Optional[HolySheepModelClient] = None

class ChatRequest(BaseModel):
    """Request model for chat endpoint."""
    message: str = Field(..., min_length=1, max_length=50000, description="User message")
    system_prompt: Optional[str] = Field(None, description="Optional system prompt override")
    temperature: Optional[float] = Field(0.7, ge=0.0, le=2.0, description="Sampling temperature")

class ChatResponse(BaseModel):
    """Response model for chat endpoint."""
    response: str
    model_used: str
    cost_usd: float
    routing_reason: str
    total_session_cost: float
    session_id: str

class HealthResponse(BaseModel):
    """Health check response."""
    status: str
    latency_ms: float
    models_available: List[str]
    current_session_cost: float


In-memory session storage (use Redis in production)

sessions = {}
session_counter = 0

@app.on_event("startup")
async def startup_event():
    """Initialize the model client on server startup."""
    global model_client
    model_client = create_model_client()
    print("HolySheep AI Agent Gateway initialized successfully!")

@app.get("/health", response_model=HealthResponse)
async def health_check():
    """Health check endpoint with latency measurement."""
    import time
    start = time.time()
    
    # Quick connectivity check
    await model_client.complete(
        model="deepseek-v3.2",
        messages=[{"role": "user", "content": "ping"}],
        max_tokens=5
    )
    
    latency_ms = round((time.time() - start) * 1000, 2)
    
    return HealthResponse(
        status="healthy",
        latency_ms=latency_ms,
        models_available=list(model_client.model_configs.keys()),
        current_session_cost=model_client.get_cost_summary()["total_cost_usd"]
    )

@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    """Main chat endpoint for interacting with the agent."""
    global session_counter
    
    try:
        result = await run_agent(request.message)
        
        session_counter += 1
        session_id = f"session_{session_counter}"
        
        return ChatResponse(
            response=result["response"],
            model_used=result["model_used"],
            cost_usd=result["cost_usd"],
            routing_reason=result["routing_reason"],
            total_session_cost=result["total_session_cost"],
            session_id=session_id
        )
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Agent error: {str(e)}")

@app.get("/stats")
async def get_stats():
    """Get current session statistics and cost breakdown."""
    if not model_client:
        raise HTTPException(status_code=500, detail="Model client not initialized")
    
    summary = model_client.get_cost_summary()
    return {
        "total_tokens_used": summary["total_tokens"],
        "total_cost_usd": summary["total_cost_usd"],
        "pricing_reference": summary["model_configs"],
        "savings_note": "HolySheep AI rate: ¥1=$1 (85%+ savings vs standard ¥7.3 rate)"
    }

@app.get("/")
async def root():
    """Root endpoint with API documentation."""
    return {
        "message": "Welcome to HolySheep AI Agent Gateway",
        "version": "1.0.0",
        "docs": "/docs",
        "health": "/health",
        "chat": "/chat (POST)",
        "stats": "/stats",
        "features": [
            "Intelligent model routing based on task complexity",
            "Automatic fallback to ensure reliability",
            "Real-time cost tracking",
            "Sub-50ms latency through HolySheep AI infrastructure"
        ]
    }

if __name__ == "__main__":
    print("Starting HolySheep AI Agent Gateway on http://0.0.0.0:8000")
    uvicorn.run(app, host="0.0.0.0", port=8000)

This FastAPI application exposes our agent through clean HTTP endpoints. The /chat endpoint accepts user messages and returns structured responses including the model used, costs incurred, and the routing reasoning. The /health endpoint provides latency measurements so you can verify HolySheep AI's promised sub-50ms performance in real-time.

Testing Your Agent Gateway

Let us verify everything works correctly. First, install the additional FastAPI dependencies, then start the server:

# Install FastAPI and uvicorn
pip install fastapi uvicorn pydantic


Start the server

python main.py

You should see output indicating the server is starting. Once running, open your browser to http://localhost:8000/docs to access the interactive API documentation. FastAPI automatically generates a Swagger UI interface where you can test endpoints directly.

Try the following test scenarios to verify intelligent routing:

Check the /stats endpoint to see accumulated costs. You will notice the significant savings from routing appropriately—code tasks routed to DeepSeek V3.2 at $0.42/MTok cost dramatically less than routing everything to Claude Sonnet 4.5 at $15.00/MTok.

Monitoring and Observability

In production environments, visibility into your agent's behavior is crucial. Let me share what I learned deploying similar systems: logging everything is never enough, but logging the right things makes debugging trivial.

Enhance your main.py with structured logging and metrics:

# Add to main.py imports
import logging
from datetime import datetime
from collections import defaultdict


Configure structured logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger("agent_gateway")


Add metrics tracking

metrics = defaultdict(int)
metrics_by_model = defaultdict(lambda: {"requests": 0, "total_cost": 0.0, "total_latency": 0.0})


Update the chat endpoint with metrics

@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    global session_counter, metrics, metrics_by_model
    
    import time
    start_time = time.time()
    
    try:
        result = await run_agent(request.message)
        
        # Track metrics
        metrics["total_requests"] += 1
        metrics["total_cost_usd"] = result["total_session_cost"]
        
        model = result["model_used"]
        metrics_by_model[model]["requests"] += 1
        metrics_by_model[model]["total_cost"] += result["cost_usd"]
        metrics_by_model[model]["total_latency"] += (time.time() - start_time) * 1000
        
        # Log structured data
        logger.info(
            f"Request completed | "
            f"model={model} | "
            f"cost=${result['cost_usd']:.4f} | "
            f"latency={round((time.time() - start_time) * 1000, 2)}ms | "
            f"routing={result['routing_reason'][:50]}..."
        )
        
        session_counter += 1
        return ChatResponse(
            response=result["response"],
            model_used=model,
            cost_usd=result["cost_usd"],
            routing_reason=result["routing_reason"],
            total_session_cost=result["total_session_cost"],
            session_id=f"session_{session_counter}"
        )
    
    except Exception as e:
        logger.error(f"Request failed: {str(e)}")
        metrics["failed_requests"] += 1
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/metrics")
async def get_metrics():
    """Prometheus-compatible metrics endpoint."""
    return {
        "total_requests": metrics["total_requests"],
        "failed_requests": metrics.get("failed_requests", 0),
        "total_cost_usd": round(metrics["total_cost_usd"], 4),
        "by_model": {
            model: {
                "requests": data["requests"],
                "total_cost_usd": round(data["total_cost_usd"], 4),
                "avg_latency_ms": round(data["total_latency"] / data["requests"], 2) if data["requests"] > 0 else 0
            }
            for model, data in metrics_by_model.items()
        }
    }

This metrics integration provides the observability you need for production monitoring. Each request logs its model, cost, latency, and routing decision—essential data for optimizing both performance and budget.

Production Deployment Considerations

When moving from development to production, several factors require attention. First, implement proper authentication—currently our API has no access control. Consider adding API key authentication or integrating with your existing identity provider. Second, set up rate limiting to prevent abuse—FastAPI integrates well with libraries like slowapi. Third, use Redis for session storage instead of in-memory dictionaries, which lose data on server restarts. Fourth, deploy multiple instances behind a load balancer for high availability.

For cost management, set up budget alerts through HolySheep AI's dashboard. Given the dramatic price differences between models—DeepSeek V3.2 at $0.42/MTok versus Claude Sonnet 4.5 at $15.00/MTok—aggressive routing to cheaper models for appropriate tasks can reduce costs by 90% or more without sacrificing quality for suitable use cases.

HolySheep AI's ¥1=$1 rate compared to the standard ¥7.3 means your budget stretches approximately 7.3 times further. For an enterprise processing 10 million tokens daily, this difference represents thousands of dollars in monthly savings.

Common Errors and Fixes

Throughout my experience building AI integrations, I have encountered numerous issues that caused frustration. Here are the most common problems with their solutions, so you can avoid the debugging headaches I endured.

Error 1: "Authentication Error - Invalid API Key"

Symptom: The API returns a 401 status code with "Authentication failed" or "Invalid API key" message.

Cause: The HolySheep API key is missing, incorrect, or not loaded properly from the environment.

Solution: Verify your .env file exists and contains the correct key. Also ensure load_dotenv() is called before accessing environment variables:

# Fix: Ensure .env file is properly loaded
from dotenv import load_dotenv
import os


Load .env file explicitly

load_dotenv()


Verify the key is loaded

api_key = os.getenv("HOLYSHEEP_API_KEY")
if not api_key:
    raise ValueError(
        "HOLYSHEEP_API_KEY not found. "
        "Create a .env file with HOLYSHEEP_API_KEY=your_key_here"
    )


Test the connection

client = AsyncOpenAI(
    api_key=api_key,
    base_url="https://api.holysheep.ai/v1"
)


Verify by making a simple test call

async def verify_connection():
    try:
        response = await client.chat.completions.create(
            model="deepseek-v3.2",
            messages=[{"role": "user", "content": "test"}],
            max_tokens=10

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