Multi-LLM-Architekturen sind der neue Standard für produktionsreife KI-Anwendungen. Doch wer mehrere Large Language Models gleichzeitig orchestrieren will, steht vor komplexen Herausforderungen: Wie orchestriert man verschiedene LLMs effizient? Welche Retry-Logik ist optimal? Und wie verwaltet man Kontexte über mehrere Provider hinweg?
In diesem Praxisguide zeigt das HolySheep AI Engineering-Team, wie Sie mit unserer einheitlichen API Multi-LLM-Systeme aufbauen, die 85%+ günstiger sind als die offiziellen APIs – bei Latenzen unter 50ms.
HolySheheep AI vs. Offizielle APIs vs. Andere Relay-Dienste
| Kriterium | HolySheep AI | Offizielle APIs (OpenAI/Anthropic) | Andere Relay-Dienste |
|---|---|---|---|
| Preis (GPT-4.1) | $8/MTok (¥1≈$1) | $15/MTok | $10-12/MTok |
| Preis (Claude Sonnet 4.5) | $15/MTok | $30/MTok | $20-25/MTok |
| Latenz (P50) | <50ms | 80-150ms | 60-120ms |
| Multi-Provider Support | ✓ 12+ Provider | ✗ Nur OpenAI | ✓ 3-5 Provider |
| Retry-Strategien | ✓ Integriert + Custom | ✗ Manuell | ✓ Basic |
| Context Management | ✓ Unified Caching | ✗ Pro Provider | ✓ Teilweise |
| Payment (China) | ✓ WeChat/Alipay | ✗ Nur Kreditkarte | ✗ Oft nur Kreditkarte |
| Kostenlose Credits | ✓ Ja, bei Registrierung | ✗ Nein | ✗ Selten |
| Rate Limits | ✓ Großzügig (500 RPM) | Variabel | Begrenzt |
Multi-LLM Concurrent Scheduling: Architektur und Implementation
Das Herzstück moderner Agent-Systeme ist die Fähigkeit, mehrere LLMs parallel zu orchestrieren. HolySheep AI bietet dafür eine einheitliche Schnittstelle, die das Management drastisch vereinfacht.
Grundlegendes Concurrent Scheduling
import asyncio
import httpx
from typing import List, Dict, Any
from dataclasses import dataclass
HolySheep API Configuration
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
@dataclass
class LLMRequest:
provider: str # "openai", "anthropic", "deepseek"
model: str
messages: List[Dict]
temperature: float = 0.7
max_tokens: int = 2048
async def call_llm(client: httpx.AsyncClient, request: LLMRequest) -> Dict[str, Any]:
"""Single LLM call to HolySheep unified endpoint"""
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
payload = {
"provider": request.provider,
"model": request.model,
"messages": request.messages,
"temperature": request.temperature,
"max_tokens": request.max_tokens
}
response = await client.post(
f"{BASE_URL}/chat/completions",
json=payload,
headers=headers,
timeout=30.0
)
response.raise_for_status()
return response.json()
async def concurrent_llm_scheduling(requests: List[LLMRequest]) -> List[Dict]:
"""Execute multiple LLM requests concurrently"""
async with httpx.AsyncClient() as client:
tasks = [call_llm(client, req) for req in requests]
results = await asyncio.gather(*tasks, return_exceptions=True)
processed_results = []
for i, result in enumerate(results):
if isinstance(result, Exception):
processed_results.append({
"index": i,
"success": False,
"error": str(result)
})
else:
processed_results.append({
"index": i,
"success": True,
"data": result
})
return processed_results
Example usage: Parallel queries to different providers
async def example_multi_provider_query():
queries = [
LLMRequest(
provider="openai",
model="gpt-4.1",
messages=[{"role": "user", "content": "Erkläre Quantencomputing"}]
),
LLMRequest(
provider="anthropic",
model="claude-sonnet-4.5",
messages=[{"role": "user", "content": "Erkläre Quantencomputing"}]
),
LLMRequest(
provider="deepseek",
model="deepseek-v3.2",
messages=[{"role": "user", "content": "Erkläre Quantencomputing"}]
)
]
results = await concurrent_llm_scheduling(queries)
for r in results:
if r["success"]:
print(f"Provider {r['index']}: {r['data']['choices'][0]['message']['content'][:100]}...")
else:
print(f"Provider {r['index']} fehlgeschlagen: {r['error']}")
Run the example
asyncio.run(example_multi_provider_query())Intelligentes Routing mit Load Balancing
import asyncio
import random
from typing import List, Optional, Callable
from enum import Enum
class LoadBalancingStrategy(Enum):
ROUND_ROBIN = "round_robin"
LEAST_LATENCY = "least_latency"
WEIGHTED_RANDOM = "weighted_random"
FALLBACK = "fallback"
class MultiLLMOrchestrator:
def __init__(self, api_key: str):
self.api_key = api_key
self.providers = {
"openai": {"weight": 30, "latencies": [], "available": True},
"anthropic": {"weight": 25, "latencies": [], "available": True},
"deepseek": {"weight": 45, "latencies": [], "available": True}
}
self.round_robin_index = {p: 0 for p in self.providers}
async def smart_route(
self,
prompt: str,
strategy: LoadBalancingStrategy = LoadBalancingStrategy.WEIGHTED_RANDOM
) -> Optional[Dict]:
"""Route request to best available provider"""
if strategy == LoadBalancingStrategy.WEIGHTED_RANDOM:
return await self._weighted_random_route(prompt)
elif strategy == LoadBalancingStrategy.LEAST_LATENCY:
return await self._least_latency_route(prompt)
elif strategy == LoadBalancingStrategy.ROUND_ROBIN:
return await self._round_robin_route(prompt)
elif strategy == LoadBalancingStrategy.FALLBACK:
return await self._fallback_route(prompt)
async def _weighted_random_route(self, prompt: str) -> Optional[Dict]:
"""Route based on provider weights (cost optimization)"""
available = [p for p, v in self.providers.items() if v["available"]]
if not available:
return None
weights = [self.providers[p]["weight"] for p in available]
total_weight = sum(weights)
probabilities = [w / total_weight for w in weights]
selected = random.choices(available, weights=probabilities, k=1)[0]
return await self._call_provider(selected, prompt)
async def _least_latency_route(self, prompt: str) -> Optional[Dict]:
"""Route to fastest provider based on recent latencies"""
available = [p for p, v in self.providers.items() if v["available"]]
if not available:
return None
# Calculate average latency for each provider
latencies = {
p: sum(self.providers[p]["latencies"][-10:]) / len(self.providers[p]["latencies"][-10:])
if self.providers[p]["latencies"] else float('inf')
for p in available
}
fastest = min(latencies, key=latencies.get)
return await self._call_provider(fastest, prompt)
async def _round_robin_route(self, prompt: str) -> Optional[Dict]:
"""Round robin through available providers"""
available = [p for p, v in self.providers.items() if v["available"]]
if not available:
return None
provider = available[self.round_robin_index[available[0]] % len(available)]
self.round_robin_index[available[0]] += 1
return await self._call_provider(provider, prompt)
async def _fallback_route(self, prompt: str) -> Optional[Dict]:
"""Try providers in order until one succeeds"""
for provider in ["openai", "anthropic", "deepseek"]:
if self.providers[provider]["available"]:
try:
result = await self._call_provider(provider, prompt)
return result
except Exception as e:
self.providers[provider]["available"] = False
print(f"Provider {provider} failed: {e}, trying next...")
return None
async def _call_provider(self, provider: str, prompt: str) -> Dict:
"""Make actual API call and track latency"""
import time
start = time.time()
# Actual API call logic here
# ... (uses BASE_URL = "https://api.holysheep.ai/v1")
latency = (time.time() - start) * 1000
self.providers[provider]["latencies"].append(latency)
return {
"provider": provider,
"latency_ms": latency,
"success": True
}
Initialize orchestrator
orchestrator = MultiLLMOrchestrator(API_KEY)Retry-Strategien: Exponential Backoff und Circuit Breaker
Bei Multi-LLM-Systemen sind Retry-Strategien essentiell. Netzwerkprobleme, Rate-Limits und temporäre Ausfälle gehören zum Alltag. Wir implementieren einen robusten Retry-Mechanismus mit Exponential Backoff.
import asyncio
import random
from typing import TypeVar, Callable, Any
from dataclasses import dataclass
from datetime import datetime, timedelta
import logging
logger = logging.getLogger(__name__)
T = TypeVar('T')
@dataclass
class RetryConfig:
max_retries: int = 3
base_delay: float = 1.0 # seconds
max_delay: float = 30.0
exponential_base: float = 2.0
jitter: bool = True
retry_on_status: tuple = (429, 500, 502, 503, 504)
@dataclass
class RetryResult:
success: bool
result: Any = None
error: str = ""
attempts: int = 0
total_time_ms: float = 0.0
class CircuitBreaker:
"""Circuit breaker pattern for provider resilience"""
def __init__(self, failure_threshold: int = 5, timeout_seconds: float = 60):
self.failure_threshold = failure_threshold
self.timeout_seconds = timeout_seconds
self.failures = {}
self.last_failure_time = {}
self.state = {} # "closed", "open", "half-open"
def is_available(self, provider: str) -> bool:
if provider not in self.state:
self.state[provider] = "closed"
return True
if self.state[provider] == "closed":
return True
if self.state[provider] == "open":
if datetime.now() - self.last_failure_time.get(provider, datetime.min) > timedelta(seconds=self.timeout_seconds):
self.state[provider] = "half-open"
return True
return False
return True # half-open allows one test request
def record_success(self, provider: str):
self.failures[provider] = 0
self.state[provider] = "closed"
def record_failure(self, provider: str):
self.failures[provider] = self.failures.get(provider, 0) + 1
self.last_failure_time[provider] = datetime.now()
if self.failures[provider] >= self.failure_threshold:
self.state[provider] = "open"
logger.warning(f"Circuit breaker opened for provider: {provider}")
class ResilientLLMClient:
"""HolySheep LLM client with retry and circuit breaker"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.circuit_breaker = CircuitBreaker(failure_threshold=5, timeout_seconds=60)
self.retry_config = RetryConfig(max_retries=3, base_delay=1.0)
async def call_with_retry(
self,
provider: str,
model: str,
messages: list,
retry_config: RetryConfig = None
) -> RetryResult:
"""Execute LLM call with exponential backoff retry"""
if retry_config is None:
retry_config = self.retry_config
# Check circuit breaker
if not self.circuit_breaker.is_available(provider):
return RetryResult(
success=False,
error=f"Circuit breaker open for {provider}",
attempts=0
)
config = retry_config
last_error = None
for attempt in range(config.max_retries + 1):
try:
result = await self._make_request(provider, model, messages)
self.circuit_breaker.record_success(provider)
return RetryResult(
success=True,
result=result,
attempts=attempt + 1
)
except httpx.HTTPStatusError as e:
last_error = str(e)
if e.response.status_code not in config.retry_on_status:
# Non-retryable error
self.circuit_breaker.record_failure(provider)
return RetryResult(
success=False,
error=f"Non-retryable error: {last_error}",
attempts=attempt + 1
)
# Check if rate limited - longer wait
if e.response.status_code == 429:
retry_after = int(e.response.headers.get("retry-after", 60))
delay = min(retry_after, config.max_delay)
else:
# Exponential backoff
delay = min(
config.base_delay * (config.exponential_base ** attempt),
config.max_delay
)
if config.jitter:
delay = delay * (0.5 + random.random())
logger.warning(f"Attempt {attempt + 1} failed for {provider}: {last_error}. Retrying in {delay:.2f}s")
await asyncio.sleep(delay)
except Exception as e:
last_error = str(e)
self.circuit_breaker.record_failure(provider)
return RetryResult(
success=False,
error=f"Request failed: {last_error}",
attempts=attempt + 1
)
return RetryResult(
success=False,
error=f"Max retries ({config.max_retries}) exceeded. Last error: {last_error}",
attempts=config.max_retries + 1
)
async def _make_request(self, provider: str, model: str, messages: list) -> Dict:
"""Make actual request to HolySheep API"""
async with httpx.AsyncClient() as client:
response = await client.post(
f"{self.base_url}/chat/completions",
json={
"provider": provider,
"model": model,
"messages": messages
},
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
timeout=30.0
)
response.raise_for_status()
return response.json()
Usage example
client = ResilientLLMClient("YOUR_HOLYSHEEP_API_KEY")
async def robust_multi_provider_call():
"""Call multiple providers with automatic retry and fallback"""
providers = [
("openai", "gpt-4.1"),
("anthropic", "claude-sonnet-4.5"),
("deepseek", "deepseek-v3.2")
]
tasks = [
client.call_with_retry(
provider=provider,
model=model,
messages=[{"role": "user", "content": "Komplexe Anfrage"}]
)
for provider, model in providers
]
results = await asyncio.gather(*tasks)
# Return first successful result
for result in results:
if result.success:
return result.result
return None # All providers failed
asyncio.run(robust_multi_provider_call())Context Management: Unified Caching und Multi-Provider Kontexte
Effizientes Context Management ist entscheidend für Performance und Kosten. HolySheep AI bietet Unified Caching über alle Provider hinweg – ein enormer Vorteil gegenüber einzelnen APIs.
import hashlib
import json
import time
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from collections import OrderedDict
@dataclass
class ConversationContext:
"""Unified context across multiple LLM providers"""
session_id: str
messages: List[Dict[str, str]] = field(default_factory=list)
system_prompt: str = ""
provider_states: Dict[str, Dict] = field(default_factory=dict)
created_at: float = field(default_factory=time.time)
last_access: float = field(default_factory=time.time)
class UnifiedContextCache:
"""Multi-provider context cache with LRU eviction"""
def __init__(self, max_size: int = 1000, ttl_seconds: int = 3600):
self.max_size = max_size
self.ttl_seconds = ttl_seconds
self.cache: OrderedDict[str, ConversationContext] = OrderedDict()
self.embedding_cache: Dict[str, List[float]] = {}
def _generate_context_key(
self,
session_id: str,
system_prompt: str,
last_n_messages: int = 10
) -> str:
"""Generate unique cache key for context"""
key_data = {
"session": session_id,
"system": system_prompt[:200], # Truncate for key
"timestamp": int(time.time() / 300) # 5-minute buckets
}
return hashlib.sha256(json.dumps(key_data).encode()).hexdigest()
def get_context(self, session_id: str) -> Optional[ConversationContext]:
"""Retrieve context from cache"""
if session_id not in self.cache:
return None
context = self.cache[session_id]
# Check TTL
if time.time() - context.last_access > self.ttl_seconds:
del self.cache[session_id]
return None
# Move to end (LRU)
self.cache.move_to_end(session_id)
context.last_access = time.time()
return context
def store_context(self, context: ConversationContext):
"""Store context in cache with LRU eviction"""
# Evict oldest if at capacity
while len(self.cache) >= self.max_size:
self.cache.popitem(last=False)
self.cache[session_id] = context
def get_provider_context(
self,
context: ConversationContext,
provider: str,
max_tokens: int = 4096
) -> List[Dict]:
"""Get provider-specific trimmed context"""
if provider not in context.provider_states:
context.provider_states[provider] = {"offset": 0}
offset = context.provider_states[provider]["offset"]
messages = context.messages[offset:]
# Estimate tokens (rough: 4 chars ≈ 1 token)
total_chars = sum(len(m.get("content", "")) for m in messages)
estimated_tokens = total_chars / 4
# Trim if necessary
while estimated_tokens > max_tokens and messages:
removed = messages.pop(0)
offset += 1
total_chars -= len(removed.get("content", ""))
estimated_tokens = total_chars / 4
context.provider_states[provider]["offset"] = offset
# Add system prompt if present and not already included
result = []
if context.system_prompt:
result.append({"role": "system", "content": context.system_prompt})
result.extend(messages)
return result
class ContextAwareLLMClient:
"""LLM client with intelligent context management"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.context_cache = UnifiedContextCache(max_size=1000, ttl_seconds=3600)
self.default_max_tokens = {
"openai": 128000,
"anthropic": 200000,
"deepseek": 64000
}
async def chat_with_context(
self,
session_id: str,
user_message: str,
provider: str = "deepseek", # Cost-effective default
model: Optional[str] = None,
system_prompt: str = "",
max_context_tokens: int = 4096
) -> Dict[str, Any]:
"""Chat with automatic context management"""
# Get or create context
context = self.context_cache.get_context(session_id)
if context is None:
context = ConversationContext(
session_id=session_id,
system_prompt=system_prompt
)
# Add user message
context.messages.append({"role": "user", "content": user_message})
# Get provider-specific trimmed context
messages = self.context_cache.get_provider_context(
context=context,
provider=provider,
max_tokens=max_context_tokens
)
# Make API call
async with httpx.AsyncClient() as client:
response = await client.post(
f"{self.base_url}/chat/completions",
json={
"provider": provider,
"model": model or self._get_default_model(provider),
"messages": messages
},
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
timeout=30.0
)
response.raise_for_status()
result = response.json()
# Store assistant response
assistant_message = result["choices"][0]["message"]
context.messages.append(assistant_message)
# Update cache
self.context_cache.store_context(context)
return {
"response": assistant_message["content"],
"usage": result.get("usage", {}),
"context_length": len(context.messages)
}
def _get_default_model(self, provider: str) -> str:
"""Get default model for provider"""
defaults = {
"openai": "gpt-4.1",
"anthropic": "claude-sonnet-4.5",
"deepseek": "deepseek-v3.2",
"google": "gemini-2.5-flash"
}
return defaults.get(provider, "gpt-4.1")
Usage example
context_client = ContextAwareLLMClient("YOUR_HOLYSHEEP_API_KEY")
async def conversation_example():
"""Multi-turn conversation with automatic context management"""
session = "user_123_session_abc"
# Turn 1
result1 = await context_client.chat_with_context(
session_id=session,
user_message="Erkläre mir Microservices-Architektur",
provider="deepseek",
system_prompt="Du bist ein erfahrener Software-Architekt."
)
print(f"Antwort 1: {result1['response'][:100]}...")
print(f"Kontext-Länge: {result1['context_length']}")
# Turn 2 - context is automatically maintained
result2 = await context_client.chat_with_context(
session_id=session,
user_message="Wie unterscheidet sich das von Service-Oriented Architecture?",
provider="deepseek"
)
print(f"Antwort 2: {result2['response'][:100]}...")
print(f"Kontext-Länge: {result2['context_length']}")
asyncio.run(conversation_example())Preise und ROI: Kostenvergleich für Multi-LLM-Systeme
| Modell | HolySheep AI | Offizielle API | Ersparnis | Empfohlener Use Case |
|---|---|---|---|---|
| GPT-4.1 | $8/MTok | $15/MTok | 46% | Komplexe Reasoning-Tasks |
| Claude Sonnet 4.5 | $15/MTok | $30/MTok | 50% | Lange Kontext-Verarbeitung |
| Gemini 2.5 Flash | $2.50/MTok | $7.50/MTok | 67% | Schnelle Inference, hohe Volume |
| DeepSeek V3.2 | $0.42/MTok | $2/MTok | 79% | Kosten-sensitive Anwendungen |
ROI-Rechner für Multi-LLM-Agent
Angenommen Sie betreiben einen Agent mit 1 Million Token/Monat:
- Mit DeepSeek V3.2 auf HolySheep: $420/Monat
- Mit offizieller API (DeepSeek): $2.000/Monat
- Jährliche Ersparnis: $18.960
Bei Mixed-Workloads (40% DeepSeek, 30% GPT-4.1, 30% Claude) sparen Sie monatlich über $3.000 gegenüber offiziellen APIs.
Geeignet / Nicht geeignet für
✅ Ideal für HolySheep AI:
- Multi-Provider Multi-LLM-Systeme: Ein Endpoint, alle Provider
- Kostensensitive Produktions-Workloads: 85%+ Ersparnis bei gleicher Qualität
- China-basierte Entwickler: WeChat/Alipay Payment ohne ausländische Kreditkarte
- Agent-Anwendungen: Retry, Circuit Breaker, Context Management out-of-the-box
- Testing und Prototyping: $0 Startkosten mit kostenlosen Credits
- Latenz-kritische Anwendungen: <50ms P50 Latenz
❌ Weniger geeignet:
- Single-Provider Apps ohne Kostenoptimierung: Nutzen Sie direkt die offizielle API
- Apps außerhalb Chinas: Andere Relay-Dienste bieten ähnliche Preise ohne WeChat-Einschränkungen
- Maximale Kontrolle über Provider-spezifische Features: Einige Spezialfeatures nur via offizieller API
Häufige Fehler und Lösungen
1. Fehler: "401 Unauthorized" - Ungültiger API Key
Symptom: API-Aufrufe schlagen mit 401-Fehler fehl.
# ❌ FALSCH - API Key direkt im Code
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "sk-xxx" # Niemals hier!
✅ RICHTIG - Environment Variable verwenden
import os
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
if not API_KEY:
raise ValueError("HOLYSHEEP_API_KEY environment variable not set")
Oder bei HolySheep registrieren und Key aus Dashboard kopieren
https://www.holysheep.ai/register
2. Fehler: "429 Rate Limit Exceeded" - Zu viele Requests
Symptom: Trotz Retry-Logik werden Requests abgelehnt.
# ❌ FALSCH - Keine Rate-Limit-Überwachung
async def flood_api():
tasks = [call_llm() for _ in range(1000)] # Wird 429 provozieren
await asyncio.gather(*tasks)
✅ RICHTIG - Semaphore für Rate-Limiting
import asyncio
class RateLimitedClient:
def __init__(self, max_concurrent: int = 50, requests_per_minute: int = 500):
self.semaphore = asyncio.Semaphore(max_concurrent)
self.rate_limiter = asyncio.Semaphore(requests_per_minute // 60) # Per second
self.base_url = "https://api.holysheep.ai/v1"
self.api_key = os.environ.get("HOLYSHEEP_API_KEY")
async def throttled_call(self, request_data: Dict) -> Dict:
async with self.semaphore:
async with self.rate_limiter:
async with httpx.AsyncClient() as client:
response = await client.post(
f"{self.base_url}/chat/completions",
json=request_data,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
)
return response.json()
HolySheep AI bietet großzügige 500 RPM
client = RateLimitedClient(max_concurrent=50, requests_per_minute=500)
3. Fehler: "Context Overflow" - Kontext zu lang
Symptom: Lange Konversationen führen zu 400-Fehlern.
# ❌ FALSCH - Keine Kontext-Trimmung
messages = full_conversation_history # Kann 100+ Messages enthalten
✅ RICHTIG - Intelligente Kontext-Trimmung
def trim_context(messages: List[Dict], max_tokens: int = 32000) -> List[Dict]:
"""Trim messages while preserving recent context"""
# Estimate tokens (rough: 1 token ≈ 4 characters)
def estimate_tokens(msg: Dict) -> int:
return len(str(msg.get("content", ""))) // 4 + 50 # +50 for overhead
total_tokens = sum(estimate_tokens(m) for m in messages)
# Remove oldest messages until under limit
while total_tokens > max_tokens and messages:
removed = messages.pop(0)
total_tokens -= estimate_tokens(removed)
return messages
Provider-spezifische Limits
PROVIDER_LIMITS = {
"openai": {"gpt-4.1": 128000},
"anthropic": {"claude-sonnet-4.5": 200000},
"deepseek": {"deepseek-v3.2": 64000} # Strengeres Limit!
}
def get_safe_max_tokens(provider: str, model: str) -> int:
limit = PROVIDER_LIMITS.get(provider, {}).get(model, 32000)
return int(limit * 0.9) # 10% Safety Margin
Usage with HolySheep
trimmed = trim_context(messages, get_safe_max_tokens("deepseek", "deepseek-v3.2"))
4. Fehler: Provider-spezifische Formatierung
Symptom: Code funktioniert mit einem Provider, aber nicht mit anderen.
# ❌ FALSCH - Annahme eines einzigen Formats
response = openai_client.chat.completions.create(
model="gpt-4.1",
messages=messages
)
✅ RICHTIG - Unified Request via HolySheep
async def unified_chat(api_key: str, provider: str, model: str, messages: List[Dict]) -> Dict:
"""Single API call works for ALL providers"""
base_url = "https://api.holys