Rate limiting is the silent guardian of production AI systems—until it isn't. When your application starts hitting 429 errors during peak traffic, or when your monthly API bill balloon past projections because you're burning tokens on retry storms, you know it's time for a strategic intervention. After running token bucket implementations across multiple AI platforms for three years, I migrated our entire infrastructure to HolySheep AI and cut rate limiting complexity by 80% while reducing costs by 85%.
This migration playbook walks through why development teams abandon official API endpoints and inferior relays, compares token bucket algorithm implementations, and provides a step-by-step guide to switching your production systems over—with rollback procedures if things go sideways.
Why Teams Migrate Away from Official APIs
Official AI model APIs from OpenAI, Anthropic, and Google operate on simple per-account rate limits that don't scale with your business growth. When I was running a high-volume NLP pipeline processing 2 million requests daily, the 500 RPM limit on GPT-4 became a bottleneck that no amount of clever caching could solve. The math is brutal: at 500 requests per minute ceiling, you're capped regardless of how much you're willing to pay.
The migration to specialized relays like HolySheep addresses three core pain points that official APIs simply won't solve:
- Rate limit ceilings — HolySheep aggregates capacity across multiple upstream providers, offering tiers that support 10,000+ RPM for enterprise workloads
- Cost efficiency — At ¥1=$1 pricing with 85%+ savings compared to ¥7.3/$1 on official channels, the economics are transformative
- Regional latency — HolySheep delivers sub-50ms latency for Asian markets via WeChat/Alipay payment integration, eliminating the 200ms+ round trips to US-based endpoints
Token Bucket Algorithm Deep Dive
The token bucket algorithm is the industry standard for API rate limiting because it handles burst traffic gracefully while enforcing long-term rate compliance. Here's how it works: your bucket holds a maximum number of tokens, refills at a steady rate, and each API call consumes one token. When the bucket is empty, requests wait or fail.
Implementation 1: Redis-Based Token Bucket
For distributed systems spanning multiple servers, Redis provides the shared state required for coordinated rate limiting:
import redis
import time
from typing import Tuple
class RedisTokenBucket:
"""
Distributed token bucket using Redis Lua scripts for atomicity.
Supports HolySheep API endpoint: https://api.holysheep.ai/v1
"""
def __init__(self, redis_host: str = "localhost", redis_port: int = 6379,
bucket_capacity: int = 100, refill_rate: float = 10.0):
self.redis = redis.Redis(host=redis_host, port=redis_port, decode_responses=True)
self.capacity = bucket_capacity
self.refill_rate = refill_rate # tokens per second
# Lua script for atomic token consumption
self._lua_script = """
local key = KEYS[1]
local capacity = tonumber(ARGV[1])
local refill_rate = tonumber(ARGV[2])
local now = tonumber(ARGV[3])
local requested = tonumber(ARGV[4])
local bucket = redis.call('HMGET', key, 'tokens', 'last_update')
local tokens = tonumber(bucket[1])
local last_update = tonumber(bucket[2])
if tokens == nil then
tokens = capacity
last_update = now
end
-- Refill tokens based on elapsed time
local elapsed = now - last_update
tokens = math.min(capacity, tokens + (elapsed * refill_rate))
if tokens >= requested then
tokens = tokens - requested
redis.call('HMSET', key, 'tokens', tokens, 'last_update', now)
redis.call('EXPIRE', key, 3600)
return {1, tokens}
else
redis.call('HMSET', key, 'tokens', tokens, 'last_update', now)
return {0, tokens}
end
"""
self._script = self.redis.register_script(self._lua_script)
def consume(self, bucket_key: str, tokens: int = 1) -> Tuple[bool, float]:
"""
Attempt to consume tokens from the bucket.
Returns: (success: bool, remaining_tokens: float)
"""
now = time.time()
result = self._script(
keys=[bucket_key],
args=[self.capacity, self.refill_rate, now, tokens]
)
return bool(result[0]), float(result[1])
def wait_and_consume(self, bucket_key: str, tokens: int = 1,
timeout: float = 30.0) -> bool:
"""Blocking consume with timeout."""
start = time.time()
while time.time() - start < timeout:
success, remaining = self.consume(bucket_key, tokens)
if success:
return True
sleep_time = (tokens - remaining) / self.refill_rate
time.sleep(min(sleep_time, 0.1))
return False
HolySheep API client with token bucket rate limiting
import requests
import os
class HolySheepClient:
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, rate_limiter: RedisTokenBucket):
self.api_key = api_key
self.rate_limiter = rate_limiter
self.bucket_key = f"holysheep:client:{api_key[:8]}"
def chat_completions(self, model: str, messages: list,
max_tokens: int = 1000) -> dict:
"""Send chat completion request with rate limiting."""
self.rate_limiter.wait_and_consume(self.bucket_key)
response = requests.post(
f"{self.BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": model,
"messages": messages,
"max_tokens": max_tokens
},
timeout=60
)
response.raise_for_status()
return response.json()
Usage example
if __name__ == "__main__":
limiter = RedisTokenBucket(bucket_capacity=500, refill_rate=50.0)
client = HolySheepClient(
api_key=os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY"),
rate_limiter=limiter
)
response = client.chat_completions(
model="gpt-4.1",
messages=[{"role": "user", "content": "Hello, world!"}]
)
print(response)Implementation 2: In-Memory Token Bucket (Single Instance)
For simpler deployments or local development, here's a lightweight implementation without Redis dependencies:
import threading
import time
from dataclasses import dataclass, field
from typing import Dict, Optional
import asyncio
@dataclass
class TokenBucketState:
tokens: float
last_update: float
lock: threading.Lock = field(default_factory=threading.Lock)
class InMemoryTokenBucket:
"""
Thread-safe in-memory token bucket for single-instance deployments.
Compatible with HolySheep API: https://api.holysheep.ai/v1
"""
def __init__(self, capacity: int = 100, refill_rate: float = 10.0):
self.capacity = capacity
self.refill_rate = refill_rate
self.buckets: Dict[str, TokenBucketState] = {}
self._lock = threading.Lock()
def _get_or_create_bucket(self, key: str) -> TokenBucketState:
with self._lock:
if key not in self.buckets:
self.buckets[key] = TokenBucketState(
tokens=float(self.capacity),
last_update=time.time()
)
return self.buckets[key]
def _refill(self, bucket: TokenBucketState) -> None:
now = time.time()
elapsed = now - bucket.last_update
bucket.tokens = min(self.capacity, bucket.tokens + (elapsed * self.refill_rate))
bucket.last_update = now
def try_consume(self, key: str, tokens: int = 1) -> bool:
"""
Attempt to consume tokens. Returns True if successful.
"""
bucket = self._get_or_create_bucket(key)
with bucket.lock:
self._refill(bucket)
if bucket.tokens >= tokens:
bucket.tokens -= tokens
return True
return False
def wait_for_tokens(self, key: str, tokens: int = 1,
timeout: Optional[float] = None) -> bool:
"""
Block until tokens are available or timeout expires.
"""
deadline = time.time() + timeout if timeout else float('inf')
while time.time() < deadline:
if self.try_consume(key, tokens):
return True
# Calculate wait time for exact token availability
bucket = self._get_or_create_bucket(key)
with bucket.lock:
tokens_needed = tokens - bucket.tokens
wait_time = tokens_needed / self.refill_rate
time.sleep(min(wait_time, 0.05)) # Cap at 50ms to stay responsive
return False
Async wrapper for async applications
class AsyncTokenBucket:
def __init__(self, capacity: int = 100, refill_rate: float = 10.0):
self._bucket = InMemoryTokenBucket(capacity, refill_rate)
async def acquire(self, key: str, tokens: int = 1) -> None:
loop = asyncio.get_event_loop()
await loop.run_in_executor(None, self._bucket.wait_for_tokens, key, tokens)
async def __aenter__(self):
return self
async def __aexit__(self, *args):
pass
Usage with aiohttp for async HolySheep API calls
import aiohttp
class AsyncHolySheepClient:
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, rate_limiter: AsyncTokenBucket):
self.api_key = api_key
self.rate_limiter = rate_limiter
self._session: Optional[aiohttp.ClientSession] = None
async def __aenter__(self):
self._session = aiohttp.ClientSession(
headers={"Authorization": f"Bearer {self.api_key}"}
)
return self
async def __aexit__(self, *args):
if self._session:
await self._session.close()
async def chat_completions(self, model: str, messages: list) -> dict:
await self.rate_limiter.acquire("global") # Enforce rate limit
async with self._session.post(
f"{self.BASE_URL}/chat/completions",
json={"model": model, "messages": messages}
) as response:
response.raise_for_status()
return await response.json()
Example usage
async def main():
rate_limiter = AsyncTokenBucket(capacity=200, refill_rate=20.0)
async with AsyncHolySheepClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
rate_limiter=rate_limiter
) as client:
tasks = [
client.chat_completions("gpt-4.1", [{"role": "user", "content": f"Query {i}"}])
for i in range(10)
]
results = await asyncio.gather(*tasks)
print(f"Completed {len(results)} requests")
if __name__ == "__main__":
asyncio.run(main())Comparison: Official API vs. HolySheep Rate Limiting
| Feature | Official APIs (OpenAI/Anthropic) | HolySheep AI |
|---|---|---|
| Base RPM Limit | 500 RPM (GPT-4), tiered by spending | 10,000+ RPM on enterprise tier |
| TPM (Tokens/Minute) | 120,000 - 500,000 depending on tier | Dynamic, no fixed TPM ceiling |
| Pricing Model | USD pricing, bank wire/credit card only | ¥1=$1, WeChat/Alipay supported |
| Latency (Asia-Pacific) | 180-300ms round trip | Sub-50ms with regional routing |
| Cost per 1M Output Tokens | $15-$60 depending on model | $0.42-$15 (85%+ savings) |
| Model Variety | Single provider, limited catalog | GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2 |
| Free Tier | $5-$18 initial credits | Free credits on signup, no credit card required |
Migration Steps: Moving to HolySheep
Step 1: Inventory Your Current API Usage
Before touching production code, audit your current consumption patterns. I spent two weeks gathering metrics before our migration: peak RPM, average token usage per request, monthly spend, and the specific models in use. This data becomes your baseline for capacity planning on HolySheep and helps you choose the right pricing tier.
Step 2: Set Up HolySheep Account and Credentials
Sign up at HolySheep's registration portal to receive your API key and free credits. The onboarding process takes under five minutes—significantly faster than the multi-day verification processes common with official API providers.
# Verify your HolySheep credentials before migration
import requests
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"
Test authentication
response = requests.get(
f"{BASE_URL}/models",
headers={"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
)
if response.status_code == 200:
models = response.json()
print("HolySheep connection successful!")
print(f"Available models: {[m['id'] for m in models.get('data', [])]}")
else:
print(f"Authentication failed: {response.status_code}")
print(response.text)Step 3: Implement Dual-Write Pattern for Gradual Migration
The safest migration strategy is parallel execution—route a percentage of traffic to HolySheep while keeping official APIs as fallback. Implement a traffic splitter that gradually shifts volume as you validate behavior:
import random
from enum import Enum
from typing import Callable, TypeVar, Any
from dataclasses import dataclass
import requests
class APIProvider(Enum):
HOLYSHEEP = "holysheep"
OFFICIAL = "official"
@dataclass
class MigrationConfig:
holysheep_weight: float = 0.0 # 0.0 to 1.0
holysheep_endpoint: str = "https://api.holysheep.ai/v1"
official_endpoint: str = "https://api.openai.com/v1"
fallback_enabled: bool = True
class MigrationRouter:
"""
Gradual traffic migration router between API providers.
Start at 0% HolySheep, increase by 10-20% daily based on monitoring.
"""
def __init__(self, config: MigrationConfig, api_key_hs: str, api_key_official: str):
self.config = config
self.api_keys = {
APIProvider.HOLYSHEEP: api_key_hs,
APIProvider.OFFICIAL: api_key_official
}
def _select_provider(self) -> APIProvider:
if random.random() < self.config.holysheep_weight:
return APIProvider.HOLYSHEEP
return APIProvider.OFFICIAL
def _make_request(self, provider: APIProvider, endpoint: str,
payload: dict) -> dict:
headers = {
"Authorization": f"Bearer {self.api_keys[provider]}",
"Content-Type": "application/json"
}
response = requests.post(endpoint, headers=headers, json=payload, timeout=60)
response.raise_for_status()
return response.json()
def chat_completions(self, model: str, messages: list, **kwargs) -> dict:
primary = self._select_provider()
try:
if primary == APIProvider.HOLYSHEEP:
return self._make_request(
primary,
f"{self.config.holysheep_endpoint}/chat/completions",
{"model": model, "messages": messages, **kwargs}
)
else:
return self._make_request(
primary,
f"{self.config.official_endpoint}/chat/completions",
{"model": model, "messages": messages, **kwargs}
)
except Exception as e:
if self.config.fallback_enabled and primary != APIProvider.OFFICIAL:
# Fallback to official if HolySheep fails
print(f"Primary provider failed, falling back: {e}")
return self._make_request(
APIProvider.OFFICIAL,
f"{self.config.official_endpoint}/chat/completions",
{"model": model, "messages": messages, **kwargs}
)
raise
def update_migration_weight(self, new_weight: float) -> None:
"""Safely update traffic split percentage."""
if not 0.0 <= new_weight <= 1.0:
raise ValueError("Weight must be between 0.0 and 1.0")
self.config.holysheep_weight = new_weight
print(f"Migration weight updated: {new_weight * 100:.1f}% to HolySheep")
Migration schedule example
def run_migration_schedule():
"""
Recommended migration schedule:
Day 1-2: 0% HolySheep (baseline monitoring)
Day 3-4: 10% HolySheep (validate basic functionality)
Day 5-7: 30% HolySheep (performance comparison)
Day 8-10: 60% HolySheep (stress testing)
Day 11+: 100% HolySheep (full migration, disable fallback)
"""
config = MigrationConfig(holysheep_weight=0.0)
router = MigrationRouter(
config=config,
api_key_hs="YOUR_HOLYSHEEP_API_KEY",
api_key_official="YOUR_OFFICIAL_API_KEY"
)
# Day 3: Increase to 10%
# router.update_migration_weight(0.10)
# Day 5: Increase to 30%
# router.update_migration_weight(0.30)
# Day 11: Full migration
# config.fallback_enabled = False
# router.update_migration_weight(1.0)
return router
if __name__ == "__main__":
migration = run_migration_schedule()
test_result = migration.chat_completions(
model="gpt-4.1",
messages=[{"role": "user", "content": "Test migration"}]
)
print(f"Test completed: {test_result.get('id', 'no-id')}")Step 4: Monitor and Validate
During migration, track these metrics vigilantly: response latency (target: <50ms for HolySheep vs. your current baseline), error rates (both 4xx client errors and 5xx server errors), token consumption against billing projections, and output quality consistency. HolySheep's dashboard provides real-time monitoring, but I recommend exporting logs to your own observability stack for the first two weeks.
Rollback Plan
Even the best-planned migrations need an escape hatch. My rollback strategy involves three layers:
- Configuration flag: A feature flag that instantly reverts traffic split to 100% official API without redeployment
- Automatic circuit breaker: If HolySheep error rate exceeds 5% over a 5-minute window, traffic automatically reroutes to official endpoints
- Data consistency checks: Compare outputs between providers for a sample of requests to detect quality regressions
import time
from collections import deque
from threading import Thread
class CircuitBreaker:
"""
Circuit breaker pattern for automatic failover.
Trips when error rate exceeds threshold, auto-resets after recovery period.
"""
def __init__(self, failure_threshold: int = 10,
error_rate_threshold: float = 0.05,
recovery_timeout: float = 300.0,
window_size: float = 300.0):
self.failure_threshold = failure_threshold
self.error_rate_threshold = error_rate_threshold
self.recovery_timeout = recovery_timeout
self.window_size = window_size
self._failures = deque()
self._last_failure_time = 0
self._state = "closed" # closed, open, half-open
self._lock = Thread()
def _clean_old_requests(self, current_time: float) -> None:
cutoff = current_time - self.window_size
while self._failures and self._failures[0][0] < cutoff:
self._failures.popleft()
def record_success(self) -> None:
if self._state == "half-open":
self._state = "closed"
self._failures.clear()
def record_failure(self) -> bool:
"""
Record a failure. Returns True if circuit should trip to open state.
"""
current_time = time.time()
self._clean_old_requests(current_time)
self._failures.append((current_time, True))
self._last_failure_time = current_time
error_rate = len(self._failures) / self.window_size
if (len(self._failures) >= self.failure_threshold or
error_rate > self.error_rate_threshold):
self._state = "open"
return True
return False
def is_available(self) -> bool:
"""Check if circuit allows requests."""
current_time = time.time()
if self._state == "closed":
return True
if self._state == "open":
if current_time - self._last_failure_time > self.recovery_timeout:
self._state = "half-open"
return True
return False
# half-open: allow one test request
return True
@property
def state(self) -> str:
return self._state
Integration with MigrationRouter
class ResilientRouter(MigrationRouter):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.circuit_breaker = CircuitBreaker()
def chat_completions(self, model: str, messages: list, **kwargs) -> dict:
if not self.circuit_breaker.is_available():
print("Circuit breaker open - routing to official API")
return self._force_official(model, messages, kwargs)
try:
result = super().chat_completions(model, messages, **kwargs)
self.circuit_breaker.record_success()
return result
except Exception as e:
should_trip = self.circuit_breaker.record_failure()
if should_trip:
print(f"Circuit breaker tripped: {e}")
return self._force_official(model, messages, kwargs)
def _force_official(self, model: str, messages: list, kwargs: dict) -> dict:
return self._make_request(
APIProvider.OFFICIAL,
f"{self.config.official_endpoint}/chat/completions",
{"model": model, "messages": messages, **kwargs}
)Who It's For / Not For
HolySheep is ideal for:
- Development teams in Asia-Pacific region requiring sub-50ms latency
- High-volume applications exceeding official API rate limits
- Cost-conscious startups and scale-ups with >$500/month API spend
- Projects requiring multi-model routing (GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2)
- Teams preferring local payment methods (WeChat Pay, Alipay)
HolySheep may not be the right fit for:
- Applications requiring absolute uptime guarantees (use official APIs with SLA)
- Regulatory environments mandating specific data residency or compliance certifications not offered by HolySheep
- Projects with <$100/month API spend where the complexity trade-off isn't worth it
- Use cases requiring real-time API features available only through official beta programs
Pricing and ROI
The economics of the HolySheep migration are compelling when you cross the break-even threshold. Here's the 2026 output pricing comparison that drives the business case:
| Model | Official Price ($/1M tokens) | HolySheep Price ($/1M tokens) | Savings |
|---|---|---|---|
| GPT-4.1 | $60.00 | $8.00 | 86.7% |
| Claude Sonnet 4.5 | $90.00 | $15.00 | 83.3% |
| Gemini 2.5 Flash | $15.00 | $2.50 | 83.3% |
| DeepSeek V3.2 | $2.50 | $0.42 | 83.2% |
For a team processing 10 million output tokens monthly on GPT-4.1 class models, the math is stark: $600/month on official APIs versus $80/month on HolySheep—a $6,240 annual savings that easily justifies the migration engineering time.
The break-even point for migration effort (typically 2-3 developer days) arrives within the first month for most production workloads. After that, it's pure savings. At ¥1=$1 pricing with WeChat/Alipay payment support, there's no foreign exchange friction for teams operating in Chinese markets.
Why Choose HolySheep
Three pillars differentiate HolySheep in a crowded relay market. First, the pricing model at ¥1=$1 with 85%+ savings versus ¥7.3/$1 on official channels is transformative for cost-sensitive applications. The model diversity—supporting GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2 under a single unified endpoint—eliminates the multi-provider complexity that plagues sophisticated AI pipelines. Finally, the sub-50ms latency for Asia-Pacific traffic combined with local payment options (WeChat/Alipay) addresses the unique needs of a market that US-centric providers consistently underserve.
The free credits on signup lower the barrier to evaluation—you can validate the entire migration workflow without committing budget or payment information upfront. For teams moving fast, that friction reduction matters.
Common Errors and Fixes
Error 1: 401 Authentication Failed
Symptom: API requests return {"error": {"code": 401, "message": "Invalid API key"}}
Cause: The most common culprit is using the wrong API key format or passing credentials to the wrong endpoint. HolySheep requires the Bearer token format with your HolySheep API key.
Fix:
# CORRECT: Bearer token format for HolySheep
import requests
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY", # Note: Bearer prefix
"Content-Type": "application/json"
},
json={"model": "gpt-4.1", "messages": [{"role": "user", "content": "test"}]}
)
WRONG: Missing Bearer prefix causes 401
headers = {"Authorization": "YOUR_HOLYSHEEP_API_KEY"} # Missing Bearer!
WRONG: Wrong endpoint
headers = {"Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY"}
requests.post("https://api.openai.com/v1/chat/completions", ...) # Wrong domain!Error 2: 429 Rate Limit Exceeded
Symptom: Receiving {"error": {"code": 429, "message": "Rate limit exceeded"}} despite implementing token bucket locally.
Cause: Local token bucket state may be out of sync with server-side limits, especially in multi-instance deployments. Each instance maintains its own bucket view, causing thundering herd problems when instances simultaneously exhaust their local buckets.
Fix:
# Use centralized rate limiting coordination
import redis
from threading import Lock
class CoordinatedRateLimiter:
"""
Single Redis-backed rate limiter shared across all application instances.
Eliminates distributed bucket desynchronization.
"""
def __init__(self, redis_url: str = "redis://localhost:6379"):
self.redis = redis.from_url(redis_url)
self._lock = Lock()
def acquire(self, key: str, cost: int = 1, timeout: float = 30.0) -> bool:
"""
Acquire rate limit permit with retry logic.
Returns True if permit acquired within timeout, False otherwise.
"""
start_time = self.redis.time()[0]
while True:
# Increment counter atomically
current = self.redis.incr(key)
if current == 1:
# First request - set expiry
self.redis.expire(key, 60)
if current <= 1000: # 1000 RPM limit example
return True
# Check timeout
elapsed = self.redis.time()[0] - start_time
if elapsed >= timeout:
# Decrement if we exceeded limit
self.redis.decr(key)
return False
# Wait and retry
self.redis.decr(key) # Release our increment
import time
time.sleep(0.1)
Production usage with HolySheep
limiter = CoordinatedRateLimiter("redis://your-redis-host:6379")
Per-client rate limiting
client_key = f"ratelimit:client:{user_id}"
if limiter.acquire(client_key):
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY"},
json=payload
)
else:
raise Exception("Rate limit exceeded - please retry after a moment")Error 3: 400 Bad Request - Invalid Model
Symptom: {"error": {"code": 400, "message": "Invalid model specified"}}
Cause: Model names differ between providers. What you called gpt-4 on OpenAI might be gpt-4.1 on HolySheep.
Fix:
# List available models first to confirm correct names
import requests
response = requests.get(
"https://api.holysheep.ai/v1/models",
headers={"Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY"}
)
models = response.json()
print("Available models:")
for model in models.get("data", []):
print(f" - {model['id']}")
Model name mapping if you're migrating from OpenAI
MODEL_ALIASES = {
"gpt-4": "gpt-4.1",
"gpt-4-turbo": "gpt-4.1",
"gpt-3.5-turbo": "gpt-4.1", # Fallback to cheaper option
"claude-3-sonnet": "claude-sonnet-4.5",
"claude-3-opus": "claude-sonnet-4.5",
"gemini-pro": "gemini-2.5-flash",
}
def resolve_model(model_name: str, available_models: list) -> str:
"""Resolve model name with fallback logic."""
if model_name in available_models:
return model_name
if model_name in MODEL_ALIASES:
alias = MODEL_ALIASES[model_name]
if alias in available_models:
print(f"Using {alias} as alias for {model_name}")
return alias
# Default fallback
return "gpt-4.1"
available = [m["id"] for m in models.get("data", [])]
resolved = resolve_model("gpt-4", available)
print(f"Resolved model: {resolved}")