ในบทความนี้ผมจะพาทุกท่านไปทำความรู้จักกับการ deploy AutoGen Multi-Agent System ในประเทศจีนโดยใช้ OpenAI compatible API gateway พร้อมทั้งออกแบบ rate limiting ที่เหมาะสมสำหรับ production environment จากประสบการณ์ตรงในการ setup infrastructure ให้กับ enterprise client หลายราย

ทำไมต้องใช้ API Gateway สำหรับ Multi-Agent

เมื่อเรามี AutoGen agents หลายตัวที่ต้องการ communicate กัน แต่ละตัวอาจต้องการ access ไปยัง LLM API ที่ต่างกัน ไม่ว่าจะเป็น GPT-4.1, Claude Sonnet 4.5 หรือ Gemini 2.5 Flash การมี central API gateway จะช่วยให้เราสามารถ:

เปรียบเทียบต้นทุน LLM Providers ปี 2026

ก่อนจะเริ่มต้น implementation มาดูต้นทุนของแต่ละ provider กัน เพื่อให้เราสามารถ optimize cost ได้อย่างเหมาะสม

Model Output Price ($/MTok) 10M Tokens/เดือน
GPT-4.1 $8.00 $80
Claude Sonnet 4.5 $15.00 $150
Gemini 2.5 Flash $2.50 $25
DeepSeek V3.2 $0.42 $4.20

จากตารางจะเห็นได้ว่า DeepSeek V3.2 มีราคาถูกมากเพียง $0.42/MTok ซึ่งถูกกว่า GPT-4.1 ถึง 19 เท่า อย่างไรก็ตาม สำหรับงานที่ต้องการคุณภาพสูง เราอาจต้องใช้ model ที่แพงกว่า ในการนี้ สมัครที่นี่ เพื่อรับ unified API ที่รองรับทุก provider ในราคาพิเศษ อัตราแลกเปลี่ยนเพียง ¥1=$1 ประหยัดได้ถึง 85%+

AutoGen Multi-Agent Architecture พร้อม Gateway

ในส่วนนี้เราจะมาดู architecture ของ AutoGen multi-agent system ที่เชื่อมต่อผ่าน OpenAI compatible API gateway โดยใช้ HolySheep AI เป็น unified gateway

"""
AutoGen Multi-Agent System with OpenAI Compatible Gateway
Supports GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2
"""

import os
import json
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import httpx


=== Configuration ===

HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"


Model configurations with pricing (2026 rates)

MODEL_CONFIGS = {
    "gpt-4.1": {
        "provider": "openai",
        "cost_per_mtok": 8.00,
        "rate_limit_rpm": 500,
        "use_case": "complex_reasoning"
    },
    "claude-sonnet-4.5": {
        "provider": "anthropic", 
        "cost_per_mtok": 15.00,
        "rate_limit_rpm": 400,
        "use_case": "long_context"
    },
    "gemini-2.5-flash": {
        "provider": "google",
        "cost_per_mtok": 2.50,
        "rate_limit_rpm": 1000,
        "use_case": "fast_inference"
    },
    "deepseek-v3.2": {
        "provider": "deepseek",
        "cost_per_mtok": 0.42,
        "rate_limit_rpm": 2000,
        "use_case": "cost_effective"
    }
}

@dataclass
class TokenUsage:
    """Track token usage per agent"""
    agent_id: str
    model: str
    total_tokens: int = 0
    request_count: int = 0
    total_cost: float = 0.0
    last_reset: datetime = field(default_factory=datetime.now)

    def add_usage(self, tokens: int, model: str):
        self.total_tokens += tokens
        self.request_count += 1
        self.total_cost += (tokens / 1_000_000) * MODEL_CONFIGS[model]["cost_per_mtok"]

    def get_cost_report(self) -> Dict[str, Any]:
        return {
            "agent_id": self.agent_id,
            "model": self.model,
            "total_tokens": self.total_tokens,
            "request_count": self.request_count,
            "total_cost_usd": round(self.total_cost, 4),
            "last_reset": self.last_reset.isoformat()
        }

class RateLimiter:
    """Token bucket rate limiter with sliding window"""
    
    def __init__(self, rpm: int, monthly_budget: float):
        self.rpm = rpm
        self.monthly_budget = monthly_budget
        self.requests = []
        self.monthly_spent = 0.0
        self.window_size = 60  # 1 minute window
        
    def is_allowed(self, estimated_tokens: int, model: str) -> bool:
        now = datetime.now()
        
        # Clean old requests outside window
        cutoff = now - timedelta(seconds=self.window_size)
        self.requests = [req_time for req_time in self.requests if req_time > cutoff]
        
        # Check RPM limit
        if len(self.requests) >= self.rpm:
            return False
            
        # Check monthly budget
        estimated_cost = (estimated_tokens / 1_000_000) * MODEL_CONFIGS[model]["cost_per_mtok"]
        if self.monthly_spent + estimated_cost > self.monthly_budget:
            return False
            
        self.requests.append(now)
        return True
    
    def record_usage(self, tokens: int, model: str):
        cost = (tokens / 1_000_000) * MODEL_CONFIGS[model]["cost_per_mtok"]
        self.monthly_spent += cost

class UnifiedAPIGateway:
    """OpenAI-compatible API gateway for AutoGen"""
    
    def __init__(self, api_key: str, base_url: str = HOLYSHEEP_BASE_URL):
        self.api_key = api_key
        self.base_url = base_url
        self.client = httpx.AsyncClient(timeout=120.0)
        self.usage_trackers: Dict[str, TokenUsage] = {}
        self.rate_limiters: Dict[str, RateLimiter] = {}
        
    async def chat_completion(
        self,
        messages: List[Dict],
        model: str = "deepseek-v3.2",
        agent_id: str = "default",
        max_tokens: int = 4096,
        temperature: float = 0.7
    ) -> Dict[str, Any]:
        """Send chat completion request through unified gateway"""
        
        # Initialize trackers if needed
        if agent_id not in self.usage_trackers:
            self.usage_trackers[agent_id] = TokenUsage(agent_id, model)
            rpm = MODEL_CONFIGS[model]["rate_limit_rpm"]
            self.rate_limiters[agent_id] = RateLimiter(rpm, monthly_budget=1000.0)
            
        # Check rate limit
        limiter = self.rate_limiters[agent_id]
        if not limiter.is_allowed(max_tokens, model):
            raise Exception(f"Rate limit exceeded for agent {agent_id}")
            
        # Prepare request
        payload = {
            "model": model,
            "messages": messages,
            "max_tokens": max_tokens,
            "temperature": temperature
        }
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        # Make request
        response = await self.client.post(
            f"{self.base_url}/chat/completions",
            json=payload,
            headers=headers
        )
        
        if response.status_code != 200:
            raise Exception(f"API Error: {response.status_code} - {response.text}")
            
        result = response.json()
        
        # Track usage
        usage = result.get("usage", {})
        total_tokens = usage.get("total_tokens", 0)
        self.usage_trackers[agent_id].add_usage(total_tokens, model)
        limiter.record_usage(total_tokens, model)
        
        return result
        
    def get_all_usage_reports(self) -> List[Dict[str, Any]]:
        """Get usage reports for all agents"""
        return [tracker.get_cost_report() for tracker in self.usage_trackers.values()]
        
    async def close(self):
        await self.client.aclose()


=== Example: Multi-Agent System ===


async def main():
    gateway = UnifiedAPIGateway(api_key=HOLYSHEEP_API_KEY)
    
    # Define agents with different models
    agents = {
        "planner": {"model": "deepseek-v3.2", "role": "Task planning"},
        "researcher": {"model": "gemini-2.5-flash", "role": "Information gathering"},
        "writer": {"model": "gpt-4.1", "role": "Content generation"},
        "reviewer": {"model": "claude-sonnet-4.5", "role": "Quality assurance"}
    }
    
    # Simulate multi-agent conversation
    for agent_name, config in agents.items():
        messages = [
            {"role": "system", "content": f"You are a {config['role']} agent."},
            {"role": "user", "content": f"Process task for {agent_name}"}
        ]
        
        try:
            response = await gateway.chat_completion(
                messages=messages,
                model=config["model"],
                agent_id=agent_name
            )
            print(f"{agent_name}: {response['choices'][0]['message']['content'][:100]}")
        except Exception as e:
            print(f"Error for {agent_name}: {e}")
    
    # Print cost report
    print("\n=== Cost Report ===")
    for report in gateway.get_all_usage_reports():
        print(f"{report['agent_id']}: {report['total_tokens']} tokens, ${report['total_cost_usd']}")
        
    await gateway.close()

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

Rate Limiting Strategy สำหรับ Multi-Agent

การออกแบบ rate limiting ที่ดีต้องคำนึงถึงหลายปัจจัย ไม่ว่าจะเป็น requests per minute (RPM), tokens per minute (TPM), และ monthly budget constraints ในส่วนนี้เราจะมาดู advanced rate limiting strategy ที่ implement ได้จริง

"""
Advanced Rate Limiting System for AutoGen Multi-Agent
Implements Token Bucket + Sliding Window + Priority Queue
"""

import asyncio
import time
from typing import Dict, Tuple
from collections import defaultdict
from dataclasses import dataclass, field
from enum import Enum
import heapq

class Priority(Enum):
    HIGH = 1    # Critical agents (planner, coordinator)
    MEDIUM = 2  # Standard agents (researcher, analyzer)
    LOW = 3     # Background agents (logger, monitor)

@dataclass(order=True)
class QueuedRequest:
    priority: Tuple[int, float] = field(compare=True)
    agent_id: str = field(compare=False)
    model: str = field(compare=False)
    estimated_tokens: int = field(compare=False)
    future: asyncio.Future = field(compare=False)
    enqueue_time: float = field(compare=False)

class AdvancedRateLimiter:
    """Rate limiter with priority queue and fair scheduling"""
    
    def __init__(
        self,
        rpm_limits: Dict[str, int],
        tpm_limits: Dict[str, int],
        model_costs: Dict[str, float]
    ):
        self.rpm_limits = rpm_limits
        self.tpm_limits = tpm_limits
        self.model_costs = model_costs
        
        # Token bucket state
        self.rpm_buckets: Dict[str, float] = defaultdict(lambda: time.time())
        self.tpm_buckets: Dict[str, float] = defaultdict(lambda: time.time())
        
        # Sliding window counters
        self.rpm_window: Dict[str, list] = defaultdict(list)
        self.tpm_window: Dict[str, list] = defaultdict(list)
        
        # Priority queue for requests
        self.request_queue: List[QueuedRequest] = []
        self.processing_lock = asyncio.Lock()
        
        # Budget tracking
        self.daily_budget = 100.0
        self.daily_spent = 0.0
        self.budget_reset_time = self._get_next_reset()
        
    def _get_next_reset(self) -> float:
        """Get next daily reset timestamp (midnight UTC)"""
        now = time.time()
        return int(now / 86400) * 86400 + 86400
        
    def _refill_bucket(self, bucket_name: str, limit: int, window: int) -> float:
        """Refill token bucket based on elapsed time"""
        now = time.time()
        elapsed = now - getattr(self, f"{bucket_name}_buckets")[bucket_name]
        refill = (elapsed / window) * limit
        return min(refill, limit)
        
    def _check_rpm_limit(self, agent_id: str) -> bool:
        """Check requests per minute limit using sliding window"""
        now = time.time()
        cutoff = now - 60
        
        # Remove old requests
        self.rpm_window[agent_id] = [
            t for t in self.rpm_window[agent_id] if t > cutoff
        ]
        
        rpm_limit = self.rpm_limits.get(agent_id, 60)
        return len(self.rpm_window[agent_id]) < rpm_limit
        
    def _check_tpm_limit(self, agent_id: str, tokens: int) -> bool:
        """Check tokens per minute limit"""
        now = time.time()
        cutoff = now - 60
        
        # Calculate token usage in window
        tokens_in_window = sum(
            t for t in self.tpm_window[agent_id] if t > cutoff
        )
        
        tpm_limit = self.tpm_limits.get(agent_id, 90000)
        return (tokens_in_window + tokens) <= tpm_limit
        
    def _check_budget(self, tokens: int, model: str) -> bool:
        """Check daily budget"""
        now = time.time()
        
        # Reset budget if new day
        if now >= self.budget_reset_time:
            self.daily_spent = 0.0
            self.budget_reset_time = self._get_next_reset()
            
        cost = (tokens / 1_000_000) * self.model_costs[model]
        return (self.daily_spent + cost) <= self.daily_budget
        
    async def acquire(
        self,
        agent_id: str,
        model: str,
        estimated_tokens: int,
        priority: Priority = Priority.MEDIUM
    ) -> asyncio.Future:
        """Acquire rate limit permission, queue if necessary"""
        future = asyncio.Future()
        
        queued_request = QueuedRequest(
            priority=(priority.value, time.time()),
            agent_id=agent_id,
            model=model,
            estimated_tokens=estimated_tokens,
            future=future,
            enqueue_time=time.time()
        )
        
        async with self.processing_lock:
            heapq.heappush(self.request_queue, queued_request)
            
        # Start processing
        asyncio.create_task(self._process_queue())
        
        return await future
        
    async def _process_queue(self):
        """Process queued requests in priority order"""
        async with self.processing_lock:
            if not self.request_queue:
                return
                
            request = heapq.heappop(self.request_queue)
            
            # Check all limits
            can_proceed = (
                self._check_rpm_limit(request.agent_id) and
                self._check_tpm_limit(request.agent_id, request.estimated_tokens) and
                self._check_budget(request.estimated_tokens, request.model)
            )
            
            if can_proceed:
                # Record usage
                now = time.time()
                self.rpm_window[request.agent_id].append(now)
                self.tpm_window[request.agent_id].append(request.estimated_tokens)
                
                cost = (request.estimated_tokens / 1_000_000) * self.model_costs[request.model]
                self.daily_spent += cost
                
                request.future.set_result(True)
            else:
                # Re-queue with delay
                await asyncio.sleep(0.5)
                heapq.heappush(self.request_queue, request)
                
    def get_stats(self) -> Dict:
        """Get current rate limiter statistics"""
        return {
            "daily_budget_remaining": self.daily_budget - self.daily_spent,
            "queue_length": len(self.request_queue),
            "rpm_usage": {
                agent: len(requests) 
                for agent, requests in self.rpm_window.items()
            },
            "budget_reset_in": self.budget_reset_time - time.time()
        }


=== Usage Example ===


async def example_usage():
    # Initialize rate limiter with model costs
    model_costs = {
        "gpt-4.1": 8.00,
        "claude-sonnet-4.5": 15.00,
        "gemini-2.5-flash": 2.50,
        "deepseek-v3.2": 0.42
    }
    
    limiter = AdvancedRateLimiter(
        rpm_limits={
            "planner": 60,
            "researcher": 120,
            "writer": 90,
            "reviewer": 60
        },
        tpm_limits={
            "planner": 150000,
            "researcher": 200000,
            "writer": 180000,
            "reviewer": 120000
        },
        model_costs=model_costs
    )
    
    # Simulate agent requests
    tasks = []
    for i in range(10):
        agent_id = ["planner", "researcher", "writer", "reviewer"][i % 4]
        model = ["deepseek-v3.2", "gemini-2.5-flash", "gpt-4.1", "claude-sonnet-4.5"][i % 4]
        priority = [Priority.HIGH, Priority.MEDIUM, Priority.MEDIUM, Priority.LOW][i % 4]
        
        task = limiter.acquire(
            agent_id=agent_id,
            model=model,
            estimated_tokens=2000,
            priority=priority
        )
        tasks.append((agent_id, task))
    
    # Wait for all permissions
    results = await asyncio.gather(*[t for _, t in tasks])
    print(f"Granted: {sum(results)}/{len(results)} requests")
    print(f"Stats: {limiter.get_stats()}")

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

Deployment Guide สำหรับ Production

สำหรับการ deploy ขึ้น production จริง ผมแนะนำให้ใช้ Docker compose ร่วมกับ Redis สำหรับ distributed rate limiting และ monitoring ด้วย Prometheus

# docker-compose.yml for AutoGen Multi-Agent Production Deployment

version: '3.8'

services:
  # AutoGen Multi-Agent Application
  autogen-app:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: autogen-multi-agent
    ports:
      - "8000:8000"
    environment:
      - HOLYSHEEP_API_KEY=${HOLYSHEEP_API_KEY}
      - HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
      - REDIS_URL=redis://redis:6379
      - LOG_LEVEL=INFO
      - RATE_LIMIT_RPM=1000
      - RATE_LIMIT_TPM=500000
    depends_on:
      - redis
    restart: unless-stopped
    networks:
      - autogen-network
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  # Redis for Distributed Rate Limiting
  redis:
    image: redis:7-alpine
    container_name: autogen-redis
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data
    command: redis-server --appendonly yes --maxmemory 256mb --maxmemory-policy allkeys-lru
    restart: unless-stopped
    networks:
      - autogen-network
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 3

  # Prometheus for Monitoring
  prometheus:
    image: prom/prometheus:latest
    container_name: autogen-prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus-data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--storage.tsdb.retention.time=30d'
    restart: unless-stopped
    networks:
      - autogen-network

  # Grafana for Visualization
  grafana:
    image: grafana/grafana:latest
    container_name: autogen-grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD}
    volumes:
      - grafana-data:/var/lib/grafana
    depends_on:
      - prometheus
    restart: unless-stopped
    networks:
      - autogen-network

networks:
  autogen-network:
    driver: bridge

volumes:
  redis-data:
  prometheus-data:
  grafana-data:
# FastAPI Application with AutoGen Integration

Save as app.py


from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
import asyncio
import os
from datetime import datetime
import httpx


Import our custom modules

from gateway import UnifiedAPIGateway, MODEL_CONFIGS
from rate_limiter import AdvancedRateLimiter, Priority

app = FastAPI(
    title="AutoGen Multi-Agent API",
    description="Production-ready AutoGen Multi-Agent System with Unified Gateway",
    version="1.0.0"
)


CORS middleware

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


Initialize gateway and rate limiter

api_key = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
gateway = UnifiedAPIGateway(
    api_key=api_key,
    base_url="https://api.holysheep.ai/v1"
)

rate_limiter = AdvancedRateLimiter(
    rpm_limits={"default": 500},
    tpm_limits={"default": 200000},
    model_costs={k: v["cost_per_mtok"] for k, v in MODEL_CONFIGS.items()}
)


Request/Response Models

class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    messages: List[Message]
    model: str = "deepseek-v3.2"
    agent_id: str = "default"
    max_tokens: int = 4096
    temperature: float = 0.7
    priority: str = "MEDIUM"

class AgentConfig(BaseModel):
    agent_id: str
    model: str
    system_prompt: str
    max_tokens: int = 4096


Global agent registry

agents_registry: Dict[str, AgentConfig] = {}

@app.on_event("startup")
async def startup():
    print("AutoGen Multi-Agent API starting up...")
    print(f"Gateway URL: {gateway.base_url}")
    print(f"Available models: {list(MODEL_CONFIGS.keys())}")

@app.on_event("shutdown")
async def shutdown():
    await gateway.close()

@app.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "timestamp": datetime.now().isoformat(),
        "gateway": gateway.base_url,
        "models_available": len(MODEL_CONFIGS)
    }

@app.post("/chat/completions")
async def chat_completions(request: ChatRequest):
    """Main endpoint for chat completions through unified gateway"""
    
    # Check rate limit
    priority_map = {"HIGH": Priority.HIGH, "MEDIUM": Priority.MEDIUM, "LOW": Priority.LOW}
    priority = priority_map.get(request.priority, Priority.MEDIUM)
    
    try:
        # Acquire rate limit permission
        await rate_limiter.acquire(
            agent_id=request.agent_id,
            model=request.model,
            estimated_tokens=request.max_tokens,
            priority=priority
        )
        
        # Make request through gateway
        response = await gateway.chat_completion(
            messages=[msg.dict() for msg in request.messages],
            model=request.model,
            agent_id=request.agent_id,
            max_tokens=request.max_tokens,
            temperature=request.temperature
        )
        
        return response
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/agents/register")
async def register_agent(config: AgentConfig):
    """Register a new agent configuration"""
    agents_registry[config.agent_id] = config
    return {
        "status": "registered",
        "agent_id": config.agent_id,
        "model": config.model
    }

@app.get("/agents")
async def list_agents():
    """List all registered agents"""
    return {
        "agents": [
            {
                "agent_id": agent.agent_id,
                "model": agent.model,
                "system_prompt": agent.system_prompt[:50] + "..."
            }
            for agent in agents_registry.values()
        ]
    }

@app.get("/usage")
async def get_usage_report():
    """Get usage report for all agents"""
    reports = gateway.get_all_usage_reports()
    rate_stats = rate_limiter.get_stats()
    
    # Calculate totals
    total_tokens = sum(r["total_tokens"] for r in reports)
    total_cost = sum(r["total_cost_usd"] for r in reports)
    
    return {
        "agent_reports": reports,
        "totals": {
            "total_tokens": total_tokens,
            "total_cost_usd": round(total_cost, 4)
        },
        "rate_limiter_stats": rate_stats
    }

@app.get("/models/pricing")
async def get_model_pricing():
    """Get current model pricing (2026 rates)"""
    return {
        "models": [
            {
                "name": name,
                "provider": config["provider"],
                "price_per_mtok": config["cost_per_mtok"],
                "rate_limit_rpm": config["rate_limit_rpm"],
                "use_case": config["use_case"]
            }
            for name, config in MODEL_CONFIGS.items()
        ],
        "currency": "USD",
        "last_updated": "2026-01-01"
    }

@app.post("/multi-agent/process")
async def multi_agent_process(
    task: str,
    agent_chain: List[str],
    background_tasks: BackgroundTasks
):
    """Process task through a chain of agents"""
    
    if len(agent_chain) == 0:
        raise HTTPException(status_code=400, detail="Agent chain cannot be empty")
    
    results = []
    current_messages = [{"role": "user", "content": task}]
    
    for agent_id in agent_chain:
        if agent_id not in agents_registry:
            raise HTTPException(status_code=404, detail=f"Agent {agent_id} not found")
            
        agent = agents_registry[agent_id]
        
        # Add system prompt
        full_messages = [
            {"role": "system", "content": agent.system_prompt}
        ] + current_messages
        
        try:
            response = await gateway.chat_completion(
                messages=full_messages,
                model=agent.model,
                agent_id=agent_id,
                max_tokens=agent.max_tokens
            )
            
            content = response["choices"][0]["message"]["content"]
            results.append({
                "agent_id": agent_id,
                "model": agent.model,
                "output": content,
                "usage": response.get("usage", {})
            })
            
            # Pass output to next agent
            current_messages = [{"role": "assistant", "content": content}]
            
        except Exception as e:
            results.append({
                "agent_id": agent_id,
                "error": str(e)
            })
            
    return {
        "task": task,
        "agent_chain": agent_chain,
        "results": results,
        "total_agents": len(results)
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

ข้อผิดพลาดที่พบบ่อยและวิธีแก้ไข

1. Error 401 Unauthorized - Invalid API Key

ข้อผิดพลาดนี้เกิดขึ้นเมื่อ API key ไม่ถูกต้องหรือหมดอายุ วิธีแก้ไขคือตรวจสอบว่า API key ถูกตั้งค่าอย่างถูกต้องและมีสิทธิ์เข้าถึง model ที่ต้องการ

# ❌ วิธีที่ผิด - ใช้ API key โดยตรงในโค้ด
api_key = "sk-xxxxx-xxxxx-xxxxx"  # ไม

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