실제 장애 시나리오로 시작하는 문제의 출발점
지난 주, 저는 프로덕션 환경에서 다음과 같은 연속적인 에러를 마주했습니다:
# 첫 번째 에러: Rate Limit 초과
{"error":{"type":"rate_limit_exceeded","message":"Too many requests","retry_after":5}}
두 번째 에러: 연결 타임아웃
ConnectionError: timeout of 30.0 seconds exceeded
세 번째 에러: 인증 실패
401 Unauthorized - Invalid API key or expired token
저는 HolySheep AI 게이트웨이(지금 가입)를 도입하기 전까지 각 AI プロバイダ별로 별도의限流 로직, 重試机制, 모니터링을 구현해야 했고, 이는 유지보수의 지옥이었습니다. 오늘은 HolySheep MCP Agent 게이트웨이를 활용하여 이러한 문제들을 통합적으로 해결하는 방법을 상세히 설명드리겠습니다.
MCP Agent 게이트웨이란 무엇인가
MCP(Model Context Protocol) Agent 게이트웨이는 AI 에이전트가 외부 도구를 호출할 때 발생하는:
- 限流(Rate Limiting): 요청 빈도 제어
- 重試(Retry): 실패 시 자동 재시도
- クォータ治理(Quota Governance): 사용량 할당량 관리
- 调用链监控(Call Chain Monitoring): 추적과 모니터링
을 통합으로 처리하는 게이트웨이입니다.
기본 설정: HolySheep AI 게이트웨이 연결
가장 먼저 HolySheep AI 게이트웨이에 연결하는 기본 설정을 살펴보겠습니다. 다음은 Python 기반의 MCP Agent를 HolySheep 게이트웨이에 연결하는 완전한 예제입니다:
import requests
import time
import json
from typing import Dict, Any, Optional
from datetime import datetime, timedelta
HolySheep AI 게이트웨이 기본 설정
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
class HolySheepMCPGateway:
"""
HolySheep AI MCP Agent 게이트웨이 클라이언트
- 도구 호출限流
- 실패重試
- 쿼터治理
- 调用链监控
"""
def __init__(self, api_key: str, base_url: str = HOLYSHEEP_BASE_URL):
self.api_key = api_key
self.base_url = base_url
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"X-MCP-Client": "python-sdk-v1.0"
})
# 限流 설정
self.max_requests_per_minute = 60
self.request_count = 0
self.window_start = datetime.now()
# 重試 설정
self.max_retries = 3
self.retry_delays = [1, 2, 5] # 초 단위 지연
# 쿼터 설정
self.daily_quota = 100000 # 토큰 단위
self.used_tokens = 0
def _check_rate_limit(self):
"""限流 체크: 1분당 요청 수 제한"""
now = datetime.now()
if (now - self.window_start) > timedelta(minutes=1):
self.request_count = 0
self.window_start = now
if self.request_count >= self.max_requests_per_minute:
sleep_time = 60 - (now - self.window_start).seconds
print(f"[限流] Rate limit reached. Waiting {sleep_time}s...")
time.sleep(sleep_time)
self.request_count = 0
self.window_start = datetime.now()
self.request_count += 1
def _check_quota(self, estimated_tokens: int) -> bool:
"""쿼터 체크: 일일 사용량 확인"""
if self.used_tokens + estimated_tokens > self.daily_quota:
print(f"[クォータ] Daily quota exceeded: {self.used_tokens}/{self.daily_quota}")
return False
return True
def call_tool(
self,
tool_name: str,
parameters: Dict[str, Any],
estimated_tokens: int = 1000
) -> Dict[str, Any]:
"""
MCP 도구 호출 메인 메소드
- 限流 적용
- 쿼터 검증
- 실패重試 로직
"""
# 1단계: 限流 체크
self._check_rate_limit()
# 2단계: 쿼터 검증
if not self._check_quota(estimated_tokens):
return {
"status": "quota_exceeded",
"message": "Daily quota limit reached"
}
# 3단계: 重試 로직과 함께 API 호출
last_error = None
for attempt in range(self.max_retries):
try:
response = self.session.post(
f"{self.base_url}/mcp/tools/{tool_name}",
json=parameters,
timeout=30
)
if response.status_code == 200:
result = response.json()
self.used_tokens += estimated_tokens
return {
"status": "success",
"data": result,
"tokens_used": estimated_tokens
}
elif response.status_code == 429:
# Rate limit - 重試
retry_after = int(response.headers.get("Retry-After", 5))
print(f"[重試] Rate limited. Attempt {attempt + 1}/{self.max_retries}")
time.sleep(retry_after)
elif response.status_code == 401:
return {
"status": "auth_error",
"message": "Invalid API key or expired token"
}
elif response.status_code >= 500:
# Server error - 重試
delay = self.retry_delays[min(attempt, len(self.retry_delays) - 1)]
print(f"[重試] Server error. Retrying in {delay}s. Attempt {attempt + 1}")
time.sleep(delay)
else:
return {
"status": "error",
"code": response.status_code,
"message": response.text
}
except requests.exceptions.Timeout:
last_error = "ConnectionError: timeout of 30.0 seconds exceeded"
print(f"[重試] Timeout. Attempt {attempt + 1}/{self.max_retries}")
time.sleep(self.retry_delays[min(attempt, len(self.retry_delays) - 1)])
except requests.exceptions.ConnectionError as e:
last_error = f"ConnectionError: {str(e)}"
print(f"[重試] Connection error. Attempt {attempt + 1}/{self.max_retries}")
time.sleep(self.retry_delays[min(attempt, len(self.retry_delays) - 1)])
except Exception as e:
last_error = str(e)
print(f"[에러] Unexpected error: {last_error}")
break
return {
"status": "failed",
"message": f"All retry attempts failed. Last error: {last_error}"
}
사용 예제
gateway = HolySheepMCPGateway(API_KEY)
result = gateway.call_tool(
tool_name="web-search",
parameters={"query": "latest AI developments", "max_results": 10},
estimated_tokens=500
)
print(json.dumps(result, indent=2))고급 限流: 동적 Rate Limit 설정
기본 限流 외에 HolySheep AI는 각 모델별로異なる rate limit을 지원합니다. 다음은 동적으로限流를 설정하는 고급 예제입니다:
import threading
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import asyncio
@dataclass
class RateLimitConfig:
"""모델별限流 설정"""
model_name: str
requests_per_minute: int
tokens_per_minute: int
concurrent_requests: int = 5
@dataclass
class CallChainSpan:
"""调用链 모니터링용 스팬"""
trace_id: str
span_id: str
parent_span_id: Optional[str]
operation: str
start_time: float
end_time: Optional[float] = None
status: str = "running"
metadata: Dict = field(default_factory=dict)
class AdvancedRateLimiter:
"""
고급 限流管理器
- 모델별限流
- 동적 조정
- 버스트 控制
"""
def __init__(self):
self.configs: Dict[str, RateLimitConfig] = {}
self.request_counters: Dict[str, List[float]] = defaultdict(list)
self.token_counters: Dict[str, List[tuple]] = defaultdict(list)
self.locks: Dict[str, threading.Lock] = defaultdict(threading.Lock)
self.call_chains: Dict[str, List[CallChainSpan]] = {}
# HolySheep AI 모델별 기본限流 설정
self._init_default_configs()
def _init_default_configs(self):
"""기본 모델별限流 설정"""
self.configs = {
"gpt-4.1": RateLimitConfig(
model_name="gpt-4.1",
requests_per_minute=500,
tokens_per_minute=150000,
concurrent_requests=10
),
"claude-sonnet-4.5": RateLimitConfig(
model_name="claude-sonnet-4.5",
requests_per_minute=400,
tokens_per_minute=120000,
concurrent_requests=8
),
"gemini-2.5-flash": RateLimitConfig(
model_name="gemini-2.5-flash",
requests_per_minute=1000,
tokens_per_minute=500000,
concurrent_requests=20
),
"deepseek-v3.2": RateLimitConfig(
model_name="deepseek-v3.2",
requests_per_minute=800,
tokens_per_minute=300000,
concurrent_requests=15
)
}
def _cleanup_old_entries(self, counter: List[float], window_seconds: int = 60):
"""시간 창 밖의 오래된 항목 정리"""
current_time = time.time()
return [t for t in counter if current_time - t < window_seconds]
def _acquire(
self,
model: str,
estimated_tokens: int,
trace_id: str = None
) -> tuple[bool, Optional[float]]:
"""
限流 토큰 획득
Returns: (성공여부, 대기시간)
"""
if model not in self.configs:
model = "deepseek-v3.2" # 기본값
config = self.configs[model]
lock = self.locks[model]
with lock:
current_time = time.time()
# 요청 수 체크
self.request_counters[model] = self._cleanup_old_entries(
self.request_counters[model]
)
if len(self.request_counters[model]) >= config.requests_per_minute:
oldest = min(self.request_counters[model])
wait_time = 60 - (current_time - oldest)
return False, wait_time
# 토큰 수 체크
self.token_counters[model] = [
(t, tok) for t, tok in self.token_counters[model]
if current_time - t < 60
]
total_tokens = sum(tok for _, tok in self.token_counters[model])
if total_tokens + estimated_tokens > config.tokens_per_minute:
oldest = min(t for t, _ in self.token_counters[model])
wait_time = 60 - (current_time - oldest)
return False, wait_time
# 성공
self.request_counters[model].append(current_time)
self.token_counters[model].append((current_time, estimated_tokens))
return True, None
def start_trace(self, trace_id: str, parent_span_id: str = None) -> str:
"""호출 체인 추적 시작"""
span_id = f"span-{len(self.call_chains.get(trace_id, []))}"
span = CallChainSpan(
trace_id=trace_id,
span_id=span_id,
parent_span_id=parent_span_id,
operation="gateway_call",
start_time=time.time()
)
self.call_chains[trace_id].append(span)
return span_id
def end_trace(
self,
trace_id: str,
span_id: str,
status: str = "success",
metadata: Dict = None
):
"""호출 체인 추적 종료"""
if trace_id in self.call_chains:
for span in self.call_chains[trace_id]:
if span.span_id == span_id:
span.end_time = time.time()
span.status = status
span.metadata = metadata or {}
break
def get_metrics(self, model: str) -> Dict:
"""현재 메트릭스 조회"""
if model not in self.configs:
return {}
config = self.configs[model]
current_time = time.time()
active_requests = len([
t for t in self.request_counters[model]
if current_time - t < 60
])
active_tokens = sum(
tok for t, tok in self.token_counters[model]
if current_time - t < 60
)
return {
"model": model,
"requests_per_minute": {
"limit": config.requests_per_minute,
"used": active_requests,
"remaining": config.requests_per_minute - active_requests
},
"tokens_per_minute": {
"limit": config.tokens_per_minute,
"used": active_tokens,
"remaining": config.tokens_per_minute - active_tokens
}
}
사용 예제
limiter = AdvancedRateLimiter()
async def agent_workflow():
"""에이전트 워크플로우 예제"""
import uuid
trace_id = str(uuid.uuid4())
# 트레이스 시작
span_id = limiter.start_trace(trace_id)
# GPT-4.1으로 분석 요청
can_proceed, wait_time = limiter._acquire("gpt-4.1", estimated_tokens=2000)
if not can_proceed:
print(f"[限流] GPT-4.1 limited. Wait {wait_time:.1f}s")
time.sleep(wait_time)
can_proceed, _ = limiter._acquire("gpt-4.1", estimated_tokens=2000)
# 실제 API 호출 시뮬레이션
# ... API 호출 로직 ...
# 트레이스 종료
limiter.end_trace(trace_id, span_id, "success", {
"model": "gpt-4.1",
"tokens": 2000,
"latency_ms": 250
})
# 메트릭스 확인
metrics = limiter.get_metrics("gpt-4.1")
print(f"[메트릭스] {metrics}")
동기 실행
asyncio.run(agent_workflow())실패 重試: 고급 지数 백오프 전략
HolySheep AI 게이트웨이에서 실패 重試를 구현할 때 중요한 것은 적절한 지数 백오프(Exponential Backoff)와 지터(Jitter)를 적용하는 것입니다:
import random
from typing import Callable, Any, TypeVar, Generic
from functools import wraps
import logging
T = TypeVar('T')
logger = logging.getLogger(__name__)
class RetryStrategy:
"""重試 전략 클래스"""
@staticmethod
def exponential_backoff(
attempt: int,
base_delay: float = 1.0,
max_delay: float = 60.0,
multiplier: float = 2.0
) -> float:
"""지수 백오프 계산"""
delay = base_delay * (multiplier ** attempt)
return min(delay, max_delay)
@staticmethod
def exponential_backoff_with_jitter(
attempt: int,
base_delay: float = 1.0,
max_delay: float = 60.0,
jitter_range: float = 0.3
) -> float:
"""지수 백오프 + 지터"""
base = RetryStrategy.exponential_backoff(
attempt, base_delay, max_delay
)
jitter = base * random.uniform(-jitter_range, jitter_range)
return max(0.1, base + jitter)
@staticmethod
def decorrelated_jitter(
attempt: int,
prev_delay: float = 1.0,
base_delay: float = 1.0,
max_delay: float = 60.0
) -> float:
"""상관관계가 없는 지터 (AWS 권장 방식)"""
if prev_delay > base_delay:
delay = min(prev_delay * 3, max_delay)
else:
delay = base_delay
return delay * (1 + random.random())
def holy_sheep_retry(
max_attempts: int = 5,
strategy: str = "exponential_jitter",
retryable_errors: tuple = (
"timeout",
"rate_limit_exceeded",
"ConnectionError",
"ServiceUnavailable"
),
timeout: float = 30.0
):
"""
HolySheep AI 게이트웨이용 重試 데코레이터
Args:
max_attempts: 최대 시도 횟수
strategy: 백오프 전략 ('exponential', 'exponential_jitter', 'decorrelated')
retryable_errors: 重試 가능한 에러 타입
timeout: 요청 타임아웃 (초)
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
@wraps(func)
def wrapper(*args, **kwargs) -> T:
prev_delay = 0
for attempt in range(max_attempts):
try:
result = func(*args, **kwargs)
# 응답 체크
if isinstance(result, dict):
error_type = result.get("error", {}).get("type", "")
if error_type == "rate_limit_exceeded":
raise RateLimitError(
result["error"].get("message", "Rate limited"),
retry_after=result["error"].get("retry_after", 5)
)
elif error_type and error_type in retryable_errors:
raise RetryableError(error_type)
return result
except RateLimitError as e:
if attempt == max_attempts - 1:
logger.error(f"[重試실패] Rate limit exceeded after {max_attempts} attempts")
raise
wait_time = e.retry_after or 5
logger.warning(
f"[限流重試] Attempt {attempt + 1}/{max_attempts}, "
f"waiting {wait_time}s per server recommendation"
)
time.sleep(wait_time)
prev_delay = wait_time
except RetryableError as e:
if attempt == max_attempts - 1:
logger.error(f"[重試실패] {e} after {max_attempts} attempts")
raise
if strategy == "exponential":
delay = RetryStrategy.exponential_backoff(attempt)
elif strategy == "exponential_jitter":
delay = RetryStrategy.exponential_backoff_with_jitter(attempt)
else: # decorrelated
delay = RetryStrategy.decorrelated_jitter(
attempt, prev_delay
)
logger.warning(
f"[重試] {e}. Attempt {attempt + 1}/{max_attempts}, "
f"waiting {delay:.2f}s"
)
time.sleep(delay)
prev_delay = delay
except ConnectionError as e:
if attempt == max_attempts - 1:
logger.error(f"[接続에러] {e} after {max_attempts} attempts")
raise
delay = RetryStrategy.exponential_backoff_with_jitter(attempt)
logger.warning(
f"[接続重試] {e}. Attempt {attempt + 1}/{max_attempts}, "
f"waiting {delay:.2f}s"
)
time.sleep(delay)
prev_delay = delay
raise MaxRetriesExceededError(f"Max retries ({max_attempts}) exceeded")
return wrapper
return decorator
class RateLimitError(Exception):
"""Rate Limit 에러"""
def __init__(self, message: str, retry_after: int = 5):
super().__init__(message)
self.retry_after = retry_after
class RetryableError(Exception):
"""재시도 가능한 에러"""
pass
class MaxRetriesExceededError(Exception):
"""최대 重試 횟수 초과"""
pass
실제 사용 예제
@holy_sheep_retry(
max_attempts=4,
strategy="exponential_jitter",
timeout=30.0
)
def call_holy_sheep_model(
prompt: str,
model: str = "gpt-4.1",
temperature: float = 0.7
) -> dict:
"""HolySheep AI 모델 호출 (重試 데코레이터 적용)"""
response = requests.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": model,
"messages": [{"role": "user", "content": prompt}],
"temperature": temperature
},
timeout=30
)
if response.status_code == 429:
error_data = response.json()
raise RateLimitError(
"Rate limit exceeded",
retry_after=int(response.headers.get("Retry-After", 5))
)
if response.status_code >= 500:
raise RetryableError(f"Server error: {response.status_code}")
if response.status_code != 200:
raise Exception(f"API error: {response.status_code} - {response.text}")
return response.json()
호출 예제
try:
result = call_holy_sheep_model(
prompt="한국의 AI 산업 동향에 대해 설명해주세요.",
model="claude-sonnet-4.5",
temperature=0.7
)
print(f"Success: {result['choices'][0]['message']['content'][:100]}...")
except MaxRetriesExceededError as e:
print(f"[실패] 모든 重試 시도 실패: {e}")
except Exception as e:
print(f"[에러] 예기치 않은 에러: {e}")쿼터治理: 다단계 할당량 관리
기업 환경에서는 조직、部门、프로젝트별로 쿼터를 세분화하여 관리해야 합니다. HolySheep AI의 쿼터治理 기능을 활용한 구현 예제입니다:
from enum import Enum
from dataclasses import dataclass
from typing import Optional, Dict, List
import hashlib
class QuotaType(Enum):
"""쿼터 유형"""
DAILY = "daily"
MONTHLY = "monthly"
TIERED = "tiered" # 단계별
PAY_AS_YOU_GO = "pay_as_you_go"
class QuotaExceededAction(Enum):
"""쿼터 초과 시 액션"""
BLOCK = "block"
QUEUE = "queue"
UPGRADE_PROMPT = "upgrade_prompt"
FALLBACK_MODEL = "fallback_model"
@dataclass
class QuotaTier:
"""쿼터 티어"""
name: str
daily_limit: int # 토큰
monthly_limit: int
rate_limit_rpm: int
rate_limit_tpm: int
allowed_models: List[str]
overage_allowed: bool = False
overage_cost_per_1k: float = 0.0
@dataclass
class QuotaAllocation:
"""쿼터 할당"""
quota_id: str
quota_type: QuotaType
tier: QuotaTier
used_daily: int = 0
used_monthly: int = 0
last_reset_daily: datetime = None
last_reset_monthly: datetime = None
def check_and_consume(self, tokens: int) -> tuple[bool, Optional[str]]:
"""토큰 사용 가능 여부 확인 및 소비"""
now = datetime.now()
# 일일 리셋 체크
if (not self.last_reset_daily or
(now - self.last_reset_daily).days >= 1):
self.used_daily = 0
self.last_reset_daily = now
# 월간 리셋 체크
if (not self.last_reset_monthly or
(now - self.last_reset_monthly).days >= 30):
self.used_monthly = 0
self.last_reset_monthly = now
# 일일 쿼터 체크
if self.used_daily + tokens > self.tier.daily_limit:
return False, f"Daily quota exceeded ({self.used_daily}/{self.tier.daily_limit})"
# 월간 쿼터 체크
if self.used_monthly + tokens > self.tier.monthly_limit:
return False, f"Monthly quota exceeded ({self.used_monthly}/{self.tier.monthly_limit})"
# 소진
self.used_daily += tokens
self.used_monthly += tokens
return True, None
def get_remaining(self) -> Dict:
"""잔여 쿼터 조회"""
return {
"daily_remaining": self.tier.daily_limit - self.used_daily,
"monthly_remaining": self.tier.monthly_limit - self.used_monthly,
"daily_used_percent": (self.used_daily / self.tier.daily_limit * 100),
"monthly_used_percent": (self.used_monthly / self.tier.monthly_limit * 100)
}
class QuotaManager:
"""
HolySheep AI 쿼터治理マネージャー
- 다단계 쿼터 관리
- 조직/부서/프로젝트별 할당
- 초과 사용 제어
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.quotas: Dict[str, QuotaAllocation] = {}
self.fallback_models: Dict[str, str] = {
"gpt-4.1": "deepseek-v3.2",
"claude-sonnet-4.5": "gemini-2.5-flash"
}
self._init_tiers()
def _init_tiers(self):
"""기본 티어 설정"""
self.tiers = {
"free": QuotaTier(
name="Free",
daily_limit=100000,
monthly_limit=1000000,
rate_limit_rpm=60,
rate_limit_tpm=60000,
allowed_models=["deepseek-v3.2", "gemini-2.5-flash"],
overage_allowed=False
),
"starter": QuotaTier(
name="Starter",
daily_limit=1000000,
monthly_limit=10000000,
rate_limit_rpm=500,
rate_limit_tpm=150000,
allowed_models=["gpt-4.1", "claude-sonnet-4.5",
"gemini-2.5-flash", "deepseek-v3.2"],
overage_allowed=True,
overage_cost_per_1k=0.003
),
"pro": QuotaTier(
name="Pro",
daily_limit=10000000,
monthly_limit=100000000,
rate_limit_rpm=2000,
rate_limit_tpm=500000,
allowed_models=["gpt-4.1", "claude-sonnet-4.5",
"gemini-2.5-flash", "deepseek-v3.2"],
overage_allowed=True,
overage_cost_per_1k=0.002
)
}
def create_quota(
self,
org_id: str,
team_id: str,
project_id: str,
tier_name: str = "starter"
) -> str:
"""새 쿼터 생성"""
quota_id = hashlib.sha256(
f"{org_id}:{team_id}:{project_id}".encode()
).hexdigest()[:16]
self.quotas[quota_id] = QuotaAllocation(
quota_id=quota_id,
quota_type=QuotaType.DAILY,
tier=self.tiers.get(tier_name, self.tiers["starter"])
)
return quota_id
def check_model_access(
self,
quota_id: str,
model: str
) -> tuple[bool, Optional[str]]:
"""모델 접근 권한 확인"""
if quota_id not in self.quotas:
return False, "Quota not found"
quota = self.quotas[quota_id]
if model not in quota.tier.allowed_models:
return False, f"Model {model} not allowed in {quota.tier.name} tier"
return True, None
def request_tokens(
self,
quota_id: str,
tokens: int,
model: str,
action_on_exceed: QuotaExceededAction = QuotaExceededAction.BLOCK
) -> dict:
"""토큰 사용 요청 처리"""
# 모델 접근 권한 체크
can_access, error = self.check_model_access(quota_id, model)
if not can_access:
return {
"status": "denied",
"reason": error,
"action": "block"
}
# 쿼터 체크
quota = self.quotas.get(quota_id)
if not quota:
return {
"status": "error",
"reason": "Quota not found"
}
can_use, reason = quota.check_and_consume(tokens)
if not can_use:
if action_on_exceed == QuotaExceededAction.BLOCK:
return {
"status": "blocked",
"reason": reason,
"remaining": quota.get_remaining(),
"action": "block"
}
elif action_on_exceed == QuotaExceededAction.FALLBACK_MODEL:
fallback = self.fallback_models.get(model, "deepseek-v3.2")
return {
"status": "fallback",
"original_model": model,
"fallback_model": fallback,
"action": "fallback"
}
return {
"status": "approved",
"tokens_used": tokens,
"remaining": quota.get_remaining(),
"cost_estimate": tokens / 1000 * quota.tier.overage_cost_per_1k
}
def get_quota_status(self, quota_id: str) -> Optional[Dict]:
"""쿼터 상태 조회"""
quota = self.quotas.get(quota_id)
if not quota:
return None
return {
"quota_id": quota_id,
"tier": quota.tier.name,
**quota.get_remaining(),
"allowed_models": quota.tier.allowed_models,
"overage_enabled": quota.tier.overage_allowed
}
사용 예제
quota_manager = QuotaManager(API_KEY)
쿼터 생성
quota_id = quota_manager.create_quota(
org_id="acme-corp",
team_id="ai-research",
project_id="chatbot-v2",
tier_name="starter"
)
print(f"[쿼터생성] Quota ID: {quota_id}")
토큰 사용 요청
result = quota_manager.request_tokens(
quota_id=quota_id,
tokens=50000,
model="gpt-4.1"
)
print(f"[토큰요청] {result}")
쿼터 상태 확인
status = quota_manager.get_quota_status(quota_id)
print(f"[상태] {status}")调用链监控: 분산 트레이싱 구현
복잡한 AI Agent 워크플로우에서는 여러 모델과 도구가 연쇄적으로 호출됩니다. HolySheep AI의 분산 트레이싱 기능을 활용한 모니터링 구현입니다:
import uuid
import json
from dataclasses import dataclass, asdict
from datetime import datetime
from typing import List, Dict, Any, Optional
import queue
import threading
@dataclass
class TraceEvent:
"""트레이스 이벤트"""
timestamp: str
trace_id: str
span_id: str
parent_span_id: Optional[str]
event_type: str # llm_call, tool_call, agent_thought, etc.
duration_ms: float
status: str
metadata: Dict[str, Any]
model: Optional[str] = None
tokens_used: Optional[int] = None
error: Optional[str] = None
class DistributedTracer:
"""
분산 트레이싱 시스템
- Multi-agent 调用链 추적
- 성능 모니터링
- 비용 추적
"""
def __init__(self, buffer_size: int = 1000):
self.trace_id = str(uuid.uuid4())
self.spans: Dict[str, Dict] = {}
self.events: List[TraceEvent] = []
self.buffer_size = buffer_size
self.event_queue: queue.Queue = queue.Queue(maxsize=buffer_size)
self._lock = threading.Lock()
self.span_stack: List[str] = [] # 현재 활성 스팬 스택
def start_span(
self,
name: str,
parent_span_id: Optional[str] = None,
metadata: Dict = None
) -> str:
"""새 스팬 시작"""
span_id = f"span-{uuid.uuid4().hex[:8]}"
with self._lock:
self.spans[span_id] = {
"name": name,
"span_id": span_id,
"parent_span_id": parent_span_id or (
self.span_stack[-1] if self.span_stack else None
),
"trace_id": self.trace_id,
"start_time": datetime.now().isoformat(),
"end_time": None,
"duration_ms": None,
"status": "running",
"metadata": metadata or {},
"children": []
}
if self.spans[span_id]["parent_span_id"]:
parent = self.spans[self.spans[span_id]["parent_span_id"]]
parent["children"].append(span_id)
self.span_stack.append(span_id)
return span_id
def end_span(
self,
span_id: str,
status: str = "success",
metadata: Dict = None
):
"""스팬 종료"""
with self._lock:
if span_id in self.spans:
span = self.spans[span_id]
start = datetime.fromisoformat(span["start_time"])
end = datetime.now()
span["end_time"] = end.isoformat()
span["duration_ms"] = (end - start).total_seconds() * 1000
span["status"] = status
if metadata:
span["metadata"].update(metadata)
# 스택에서 제거
if span_id in self.span_stack:
self.span_stack.remove(span_id)
def record_llm_call(
self,
model: str,
prompt_tokens: int,
completion_tokens