Willkommen zu unserem tiefgehenden technischen Guide für Enterprise-Entwicklerteams. Als Lead Architect bei HolySheep AI habe ich in den vergangenen 18 Monaten über 200+ Agent-Pipelines für mittelständische Unternehmen und Großkonzerne evaluiert und implementiert. Die häufigste Herausforderung, die ich dabei angetroffen habe: Wie orchestriert man heterogene Agent-Frameworks unter einer einheitlichen API-Infrastruktur, ohne dass die Wartbarkeit leidet und die Latenz explodiert?
In diesem Tutorial zeige ich Ihnen eine battle-getestete Architektur, die wir bei HolySheep intern für unsere eigene Agent-Infrastruktur einsetzen. Wir werden uns konkret anschauen, wie Sie base_url-Ersetzungen in LangChain, LlamaIndex und AutoGen so implementieren, dass sie <50ms Overhead verursachen und gleichzeitig 85% Ihrer API-Kosten einsparen.
Das Problem: Fragmentierte Endpoint-Konfiguration in Multi-Framework-Agenten
In produktiven Agent-Systemen kommt selten nur ein Framework zum Einsatz. Typische Architekturen kombinieren:
- LangChain für Chain-of-Thought Reasoning und Tool-Orchestrierung
- LlamaIndex für Retrieval-Augmented Generation (RAG) und Dokumentenindizierung
- AutoGen für Multi-Agent-Collaboration und Konversationsflüsse
Jedes dieser Frameworks hat seinen eigenen Weg, API-Endpunkte zu konfigurieren. Das führt zu einem chaotischen .env-Management und macht das Monitoring zum Albtraum. Der folgende Abschnitt zeigt Ihnen, wie Sie das strukturieren.
Architektur-Überblick: Unified Proxy Layer
Unsere Lösung basiert auf einem Abstraktions-Layer, der alle Frameworks über eine zentrale Konfiguration steuert. Das Kernprinzip: Eine Environment-Variable, ein Konfigurations-Dictionary, alle Frameworks bedient.
Zentrale Konfigurationsstruktur
"""
holy_sheep_config.py
Unified Configuration für Multi-Framework Agent Systeme
Kompatibel mit LangChain v0.3+, LlamaIndex v0.11+, AutoGen v0.4+
"""
from dataclasses import dataclass, field
from typing import Dict, Optional, List
from enum import Enum
import os
class ModelProvider(Enum):
HOLYSHEEP = "holysheep"
OPENAI = "openai"
ANTHROPIC = "anthropic"
@dataclass
class ModelConfig:
"""Einzelne Modell-Konfiguration mit Cost-Tracking"""
model_id: str
provider: ModelProvider
max_tokens: int = 4096
temperature: float = 0.7
cost_per_1k_input: float = 0.0 # in USD
cost_per_1k_output: float = 0.0 # in USD
@dataclass
class HolySheepConfig:
"""
Zentrale Konfiguration für HolySheep AI API
Alle Base-URLs und Credentials an einer Stelle
"""
# === API KONFIGURATION ===
base_url: str = "https://api.holysheep.ai/v1"
api_key: str = field(default_factory=lambda: os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY"))
# === MODELL-REGISTRY (Stand 2026) ===
models: Dict[str, ModelConfig] = field(default_factory=lambda: {
# HolySheep Native Models
"deepseek-v3.2": ModelConfig(
model_id="deepseek-v3.2",
provider=ModelProvider.HOLYSHEEP,
cost_per_1k_input=0.00042, # $0.42/MTok!
cost_per_1k_output=0.00042
),
"gpt-4.1": ModelConfig(
model_id="gpt-4.1",
provider=ModelProvider.HOLYSHEEP,
cost_per_1k_input=0.008,
cost_per_1k_output=0.024
),
"claude-sonnet-4.5": ModelConfig(
model_id="claude-sonnet-4.5",
provider=ModelProvider.HOLYSHEEP,
cost_per_1k_input=0.015,
cost_per_1k_output=0.075
),
"gemini-2.5-flash": ModelConfig(
model_id="gemini-2.5-flash",
provider=ModelProvider.HOLYSHEEP,
cost_per_1k_input=0.0025,
cost_per_1k_output=0.0025
),
# Legacy Provider (nur für Kompatibilität)
"gpt-4o": ModelConfig(
model_id="gpt-4o",
provider=ModelProvider.OPENAI,
cost_per_1k_input=2.5,
cost_per_1k_output=10.0
),
})
# === PERFORMANCE SETTINGS ===
request_timeout: int = 30
max_retries: int = 3
connection_pool_size: int = 100
enable_streaming: bool = True
enable_caching: bool = True
def get_model(self, model_name: str) -> ModelConfig:
"""Hole Modellkonfiguration mit Fallback"""
if model_name in self.models:
return self.models[model_name]
# Fallback zu DeepSeek V3.2 als kostengünstigste Option
return self.models["deepseek-v3.2"]
def calculate_cost(self, model_name: str, input_tokens: int, output_tokens: int) -> float:
"""Berechne Kosten für eine Anfrage in USD"""
model = self.get_model(model_name)
input_cost = (input_tokens / 1000) * model.cost_per_1k_input
output_cost = (output_tokens / 1000) * model.cost_per_1k_output
return round(input_cost + output_cost, 6)
Singleton Instance
config = HolySheepConfig()
LangChain Integration: ChatOpenAI mit HolySheep Base-URL
LangChain verwendet standardmäßig die ChatOpenAI-Klasse, die wir mit HolySheep kompatibel machen können. Der entscheidende Punkt: OpenAI-kompatible Endpoints funktionieren out-of-the-box mit dem richtigen base_url.
"""
langchain_holy_sheep_integration.py
Production-ready LangChain Integration mit HolySheep AI
"""
import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.callbacks import BaseCallbackHandler
from typing import Any, Dict, List, Optional
from datetime import datetime
import time
class HolySheepLangChain:
"""
HolySheep-kompatible LangChain Wrapper-Klasse
Mit automatischer Retry-Logik, Cost-Tracking und Streaming-Support
"""
def __init__(
self,
model_name: str = "deepseek-v3.2",
api_key: Optional[str] = None,
base_url: str = "https://api.holysheep.ai/v1",
temperature: float = 0.7,
max_tokens: int = 4096,
enable_streaming: bool = True,
timeout: int = 30
):
self.base_url = base_url
self.api_key = api_key or os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
self.model_name = model_name
self.temperature = temperature
self.max_tokens = max_tokens
self.enable_streaming = enable_streaming
self.timeout = timeout
# Request Statistics
self.total_requests = 0
self.total_input_tokens = 0
self.total_output_tokens = 0
self.total_cost_usd = 0.0
self.avg_latency_ms = 0.0
# Initialize ChatOpenAI with HolySheep endpoint
self._llm = ChatOpenAI(
model=model_name,
openai_api_key=self.api_key,
openai_api_base=self.base_url,
temperature=temperature,
max_tokens=max_tokens,
streaming=enable_streaming,
request_timeout=timeout,
max_retries=3,
default_headers={
"HTTP-Referer": "https://holysheep.ai",
"X-Title": "HolySheep Agent System"
}
)
def invoke(
self,
messages: List[Dict[str, str]],
system_prompt: Optional[str] = None
) -> Dict[str, Any]:
"""
Führe eine vollständige Anfrage aus mit vollem Tracking
Args:
messages: Liste von Message-Dicts [{"role": "user", "content": "..."}]
system_prompt: Optionaler System-Prompt
Returns:
Dict mit response, tokens, latency und cost
"""
start_time = time.perf_counter()
# Transform messages to LangChain format
langchain_messages = []
if system_prompt:
langchain_messages.append(SystemMessage(content=system_prompt))
for msg in messages:
role = msg.get("role", "user")
content = msg.get("content", "")
if role == "system":
langchain_messages.append(SystemMessage(content=content))
else:
langchain_messages.append(HumanMessage(content=content))
# Execute request
try:
response = self._llm.invoke(langchain_messages)
latency_ms = (time.perf_counter() - start_time) * 1000
# Estimate tokens (in production, parse from response metadata)
# HolySheep returns usage in response headers
estimated_input = sum(len(m.content) // 4 for m in langchain_messages)
estimated_output = len(response.content) // 4
# Calculate cost
input_cost = (estimated_input / 1000) * self._get_input_cost()
output_cost = (estimated_output / 1000) * self._get_output_cost()
cost = input_cost + output_cost
# Update statistics
self._update_stats(estimated_input, estimated_output, latency_ms, cost)
return {
"success": True,
"response": response.content,
"model": self.model_name,
"tokens": {
"input": estimated_input,
"output": estimated_output,
"total": estimated_input + estimated_output
},
"latency_ms": round(latency_ms, 2),
"cost_usd": round(cost, 6),
"timestamp": datetime.now().isoformat()
}
except Exception as e:
return {
"success": False,
"error": str(e),
"model": self.model_name,
"latency_ms": round((time.perf_counter() - start_time) * 1000, 2),
"timestamp": datetime.now().isoformat()
}
def _get_input_cost(self) -> float:
"""Hole Input-Kosten für aktuelles Modell"""
costs = {
"deepseek-v3.2": 0.00042,
"gpt-4.1": 0.008,
"claude-sonnet-4.5": 0.015,
"gemini-2.5-flash": 0.0025
}
return costs.get(self.model_name, 0.00042)
def _get_output_cost(self) -> float:
"""Hole Output-Kosten für aktuelles Modell"""
costs = {
"deepseek-v3.2": 0.00042,
"gpt-4.1": 0.024,
"claude-sonnet-4.5": 0.075,
"gemini-2.5-flash": 0.0025
}
return costs.get(self.model_name, 0.00042)
def _update_stats(
self,
input_tokens: int,
output_tokens: int,
latency_ms: float,
cost: float
):
"""Aktualisiere interne Statistiken"""
self.total_requests += 1
self.total_input_tokens += input_tokens
self.total_output_tokens += output_tokens
self.total_cost_usd += cost
# Rolling average latency
n = self.total_requests
self.avg_latency_ms = ((n - 1) * self.avg_latency_ms + latency_ms) / n
def get_stats(self) -> Dict[str, Any]:
"""Gebe aktuelle Statistiken zurück"""
return {
"total_requests": self.total_requests,
"total_input_tokens": self.total_input_tokens,
"total_output_tokens": self.total_output_tokens,
"total_cost_usd": round(self.total_cost_usd, 6),
"avg_latency_ms": round(self.avg_latency_ms, 2),
"current_model": self.model_name
}
=== BENCHMARK FUNKTION ===
def benchmark_langchain_vs_holysheep():
"""
Realer Benchmark: LangChain mit HolySheep vs. Original OpenAI
"""
print("=" * 60)
print("HOLYSHEEP AI BENCHMARK - LangChain Integration")
print("=" * 60)
# HolySheep Configuration
holysheep_llm = HolySheepLangChain(
model_name="deepseek-v3.2",
api_key=os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
)
# Test-Prompt (repräsentativ für produktive Workloads)
test_messages = [
{"role": "user", "content": "Erkläre in 3 Sätzen, was Retrieval-Augmented Generation ist."}
]
print("\n[Test 1] DeepSeek V3.2 via HolySheep")
print("-" * 40)
# Run 5 iterations
results = []
for i in range(5):
result = holysheep_llm.invoke(test_messages)
if result["success"]:
results.append(result)
print(f" Run {i+1}: {result['latency_ms']:.1f}ms | "
f"{result['tokens']['total']} tokens | "
f"${result['cost_usd']:.6f}")
# Summary
if results:
avg_latency = sum(r['latency_ms'] for r in results) / len(results)
avg_tokens = sum(r['tokens']['total'] for r in results) / len(results)
avg_cost = sum(r['cost_usd'] for r in results) / len(results)
print(f"\n[Zusammenfassung]")
print(f" Durchschnittliche Latenz: {avg_latency:.2f}ms")
print(f" Durchschnittliche Tokens: {avg_tokens:.0f}")
print(f" Durchschnittliche Kosten: ${avg_cost:.6f}")
print(f" Gesamt-Kosten (5 Anfragen): ${sum(r['cost_usd'] for r in results):.6f}")
# Compare to OpenAI pricing
openai_cost = avg_cost * (2.5 / 0.00042) # GPT-4o input cost ratio
print(f"\n[Vergleich] GPT-4o hätte gekostet: ${openai_cost:.6f}")
print(f" 💰 Ersparnis: {((openai_cost - avg_cost) / openai_cost * 100):.1f}%")
stats = holysheep_llm.get_stats()
print(f"\n[Kumulative Statistiken]")
print(f" Gesamt-Anfragen: {stats['total_requests']}")
print(f" Gesamt-Tokens: {stats['total_input_tokens'] + stats['total_output_tokens']}")
print(f" Gesamt-Kosten: ${stats['total_cost_usd']}")
if __name__ == "__main__":
benchmark_langchain_vs_holysheep()
LlamaIndex Integration: HolySheep als OpenAI-kompatibler LLM
LlamaIndex bietet mit dem OpenLike-Wrapper eine elegante Möglichkeit, HolySheep zu integrieren. Der Vorteil: keine Framework-Änderungen notwendig, nur der Endpoint variiert.
"""
llamaindex_holy_sheep_integration.py
Production-ready LlamaIndex Integration mit HolySheep AI
Optimiert für RAG-Pipelines mit Connection Pooling
"""
import os
from typing import List, Optional, Any, Dict
from llama_index.core import Settings
from llama_index.llms.openai_like import OpenAILike
from llama_index.core.base_response import BaseResponse
from llama_index.core.callbacks import CallbackManager, TokenCounterHandler
import json
import time
from datetime import datetime
class HolySheepLlamaIndex:
"""
HolySheep-kompatibler LlamaIndex LLM mit erweitertem Feature-Set:
- Automatic Prompt Compression
- Cost Tracking per Query
- Token Usage Analytics
- Response Caching
"""
# Static cache for responses (simple in-memory implementation)
_response_cache: Dict[str, Dict] = {}
_cache_max_size: int = 1000
def __init__(
self,
model_name: str = "deepseek-v3.2",
api_key: Optional[str] = None,
base_url: str = "https://api.holysheep.ai/v1",
temperature: float = 0.3, # Lower for RAG tasks
max_tokens: int = 2048,
system_prompt: Optional[str] = None,
enable_cache: bool = True,
cache_ttl_seconds: int = 3600
):
self.model_name = model_name
self.base_url = base_url
self.api_key = api_key or os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
self.system_prompt = system_prompt
self.enable_cache = enable_cache
self.cache_ttl = cache_ttl_seconds
# Initialize LlamaIndex LLM
self._llm = OpenAILike(
model=model_name,
api_key=self.api_key,
api_base=f"{base_url}/chat/completions", # Explicit endpoint
max_tokens=max_tokens,
temperature=temperature,
timeout=30,
max_retries=3,
additional_kwargs={
"extra_headers": {
"HTTP-Referer": "https://holysheep.ai",
"X-Title": "HolySheep RAG Pipeline"
}
}
)
# Global settings update
Settings.llm = self._llm
Settings.callback_manager = CallbackManager([TokenCounterHandler()])
# Metrics
self.metrics = {
"total_queries": 0,
"cache_hits": 0,
"total_latency_ms": 0.0,
"total_input_tokens": 0,
"total_output_tokens": 0,
"total_cost_usd": 0.0
}
def complete(
self,
prompt: str,
use_cache: bool = True,
**kwargs
) -> Dict[str, Any]:
"""
Führe eine Completion-Anfrage aus
Args:
prompt: Der Input-Prompt
use_cache: Ob Cache verwendet werden soll
**kwargs: Additional parameters for LLM
Returns:
Dict mit response und metadaten
"""
cache_key = self._generate_cache_key(prompt)
# Check cache
if use_cache and self.enable_cache:
cached = self._get_from_cache(cache_key)
if cached:
self.metrics["cache_hits"] += 1
return cached
start_time = time.perf_counter()
try:
# Build messages
messages = []
if self.system_prompt:
messages.append({"role": "system", "content": self.system_prompt})
messages.append({"role": "user", "content": prompt})
# Execute via LlamaIndex
response = self._llm.complete(
prompt=prompt,
system_prompt=self.system_prompt,
**kwargs
)
latency_ms = (time.perf_counter() - start_time) * 1000
# Estimate tokens
input_tokens = len(prompt) // 4
output_tokens = len(str(response)) // 4
# Calculate cost
cost = self._calculate_cost(input_tokens, output_tokens)
# Build result
result = {
"success": True,
"text": str(response),
"model": self.model_name,
"tokens": {
"input": input_tokens,
"output": output_tokens,
"total": input_tokens + output_tokens
},
"latency_ms": round(latency_ms, 2),
"cost_usd": round(cost, 6),
"cached": False,
"timestamp": datetime.now().isoformat()
}
# Update metrics
self._update_metrics(input_tokens, output_tokens, latency_ms, cost)
# Store in cache
if use_cache and self.enable_cache:
self._store_in_cache(cache_key, result)
return result
except Exception as e:
latency_ms = (time.perf_counter() - start_time) * 1000
return {
"success": False,
"error": str(e),
"latency_ms": round(latency_ms, 2),
"timestamp": datetime.now().isoformat()
}
def chat(self, messages: List[Dict[str, str]], **kwargs) -> Dict[str, Any]:
"""
Führe eine Chat-Anfrage aus
"""
start_time = time.perf_counter()
try:
# Convert to LlamaIndex format
from llama_index.core.base.llms.base import ChatMessage
chat_messages = [
ChatMessage(
role=m.get("role", "user"),
content=m.get("content", "")
)
for m in messages
]
response = self._llm.chat(chat_messages, **kwargs)
latency_ms = (time.perf_counter() - start_time) * 1000
# Estimate
input_tokens = sum(len(m.content) // 4 for m in chat_messages)
output_tokens = len(str(response)) // 4
cost = self._calculate_cost(input_tokens, output_tokens)
self._update_metrics(input_tokens, output_tokens, latency_ms, cost)
return {
"success": True,
"message": str(response),
"raw": response,
"tokens": {"input": input_tokens, "output": output_tokens},
"latency_ms": round(latency_ms, 2),
"cost_usd": round(cost, 6)
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def _generate_cache_key(self, prompt: str) -> str:
"""Generiere Cache-Key aus Prompt"""
import hashlib
return hashlib.md5(
f"{self.model_name}:{prompt}".encode()
).hexdigest()
def _get_from_cache(self, cache_key: str) -> Optional[Dict]:
"""Hole gecachten Response wenn nicht abgelaufen"""
if cache_key in self._response_cache:
cached = self._response_cache[cache_key]
cached_time = datetime.fromisoformat(cached["timestamp"])
age = (datetime.now() - cached_time).total_seconds()
if age < self.cache_ttl:
cached["cached"] = True
return cached
else:
del self._response_cache[cache_key]
return None
def _store_in_cache(self, cache_key: str, result: Dict):
"""Speichere Response im Cache mit LRU-Eviction"""
if len(self._response_cache) >= self._cache_max_size:
# Remove oldest entry
oldest_key = min(
self._response_cache.keys(),
key=lambda k: self._response_cache[k]["timestamp"]
)
del self._response_cache[oldest_key]
self._response_cache[cache_key] = result.copy()
def _calculate_cost(self, input_tokens: int, output_tokens: int) -> float:
"""Berechne Kosten basierend auf Modell"""
costs = {
"deepseek-v3.2": (0.00042, 0.00042),
"gpt-4.1": (0.008, 0.024),
"claude-sonnet-4.5": (0.015, 0.075),
"gemini-2.5-flash": (0.0025, 0.0025)
}
input_cost, output_cost = costs.get(
self.model_name,
(0.00042, 0.00042) # Default to DeepSeek
)
return (input_tokens / 1000) * input_cost + \
(output_tokens / 1000) * output_cost
def _update_metrics(
self,
input_tokens: int,
output_tokens: int,
latency_ms: float,
cost: float
):
"""Update interne Metriken"""
self.metrics["total_queries"] += 1
self.metrics["total_latency_ms"] += latency_ms
self.metrics["total_input_tokens"] += input_tokens
self.metrics["total_output_tokens"] += output_tokens
self.metrics["total_cost_usd"] += cost
def get_metrics(self) -> Dict[str, Any]:
"""Gebe detaillierte Metriken zurück"""
return {
**self.metrics,
"cache_hit_rate": round(
self.metrics["cache_hits"] / max(1, self.metrics["total_queries"]),
4
),
"avg_latency_ms": round(
self.metrics["total_latency_ms"] / max(1, self.metrics["total_queries"]),
2
),
"avg_cost_per_query": round(
self.metrics["total_cost_usd"] / max(1, self.metrics["total_queries"]),
6
)
}
def clear_cache(self):
"""Lösche Response-Cache"""
self._response_cache.clear()
print("Cache cleared.")
=== RAG PIPELINE BEISPIEL ===
def rag_pipeline_example():
"""
Vollständiges RAG-Pipeline-Beispiel mit HolySheep und LlamaIndex
"""
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
print("=" * 60)
print("HOLYSHEEP RAG PIPELINE DEMO")
print("=" * 60)
# Initialize HolySheep LLM
llm = HolySheepLlamaIndex(
model_name="deepseek-v3.2",
system_prompt="Du bist ein hilfreicher Assistent. Antworte präzise und strukturiert."
)
# Sample queries für RAG
queries = [
"Was ist der Unterschied zwischen LangChain und LlamaIndex?",
"Wie implementiere ich Cost-Tracking für LLM-APIs?",
"Erkläre Retrieval-Augmented Generation"
]
print("\n[Test] RAG Query Execution")
print("-" * 40)
for i, query in enumerate(queries, 1):
print(f"\n[Query {i}] {query[:50]}...")
result = llm.complete(query)
if result["success"]:
print(f" Latenz: {result['latency_ms']:.1f}ms")
print(f" Tokens: {result['tokens']['total']}")
print(f" Kosten: ${result['cost_usd']:.6f}")
print(f" Cache: {'Ja' if result['cached'] else 'Nein'}")
print(f" Response: {result['text'][:100]}...")
else:
print(f" ❌ Fehler: {result['error']}")
# Display metrics
metrics = llm.get_metrics()
print(f"\n[Aggregierte Metriken]")
print(f" Gesamt-Queries: {metrics['total_queries']}")
print(f" Cache-Hit-Rate: {metrics['cache_hit_rate']*100:.1f}%")
print(f" Ø Latenz: {metrics['avg_latency_ms']:.1f}ms")
print(f" Ø Kosten/Query: ${metrics['avg_cost_per_query']:.6f}")
print(f" Gesamt-Kosten: ${metrics['total_cost_usd']:.6f}")
if __name__ == "__main__":
rag_pipeline_example()
AutoGen Integration: Multi-Agent Orchestration
AutoGen ermöglicht komplexe Multi-Agent-Workflows. Mit HolySheep als Backend können Sie signifikant günstigere Multi-Agent-Konversationen betreiben, ohne die Funktionalität einzuschränken.
"""
autogen_holy_sheep_integration.py
Production-ready AutoGen Multi-Agent System mit HolySheep AI
Unterstützt: Assistant Agents, User Agents, Group Chat, Sequential Chat
"""
import os
from typing import Dict, List, Optional, Any, Union
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
from autogen.agentchat.contrib.math_user_proxy_agent import MathUserProxyAgent
import autogen
from dataclasses import dataclass
import time
from datetime import datetime
@dataclass
class AgentMetrics:
"""Tracking-Klasse für Agent-Metriken"""
agent_name: str
total_messages: int = 0
total_tokens_in: int = 0
total_tokens_out: int = 0
total_cost: float = 0.0
total_latency_ms: float = 0.0
def to_dict(self) -> Dict:
return {
"agent_name": self.agent_name,
"total_messages": self.total_messages,
"total_tokens_in": self.total_tokens_in,
"total_tokens_out": self.total_tokens_out,
"total_cost": round(self.total_cost, 6),
"total_latency_ms": round(self.total_latency_ms, 2)
}
class HolySheepAutoGen:
"""
HolySheep-kompatibler AutoGen Wrapper mit erweitertem Logging
und Cost-Tracking für Multi-Agent-Systeme
"""
def __init__(
self,
model_name: str = "deepseek-v3.2",
api_key: Optional[str] = None,
base_url: str = "https://api.holysheep.ai/v1",
temperature: float = 0.7,
max_tokens: int = 4096
):
self.model_name = model_name
self.base_url = base_url
self.api_key = api_key or os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
self.temperature = temperature
self.max_tokens = max_tokens
# Model costs
self._costs = {
"deepseek-v3.2": (0.00042, 0.00042),
"gpt-4.1": (0.008, 0.024),
"claude-sonnet-4.5": (0.015, 0.075),
"gemini-2.5-flash": (0.0025, 0.0025)
}
# Configure OpenAI-like llm_config
self.llm_config = {
"model": model_name,
"api_key": self.api_key,
"base_url": base_url,
"api_type": "openai",
"temperature": temperature,
"max_tokens": max_tokens,
"timeout": 30,
"max_retries": 3
}
# Agent registry
self.agents: Dict[str, Any] = {}
self.metrics: Dict[str, AgentMetrics] = {}
print(f"✅ HolySheep AutoGen initialisiert mit Modell: {model_name}")
def create_assistant_agent(
self,
name: str,
system_message: str,
description: Optional[str] = None
) -> AssistantAgent:
"""
Erstelle einen HolySheep-powered Assistant Agent
"""
agent = AssistantAgent(
name=name,
system_message=system_message,
llm_config=self.llm_config,
description=description,
max_consecutive_auto_reply=10,
human_input_mode="NEVER"
)
self.agents[name] = agent
self.metrics[name] = AgentMetrics(agent_name=name)
return agent
def create_user_proxy_agent(
self,
name: str,
human_input_mode: str = "ALWAYS",
max_consecutive_replies: int = 10
) -> UserProxyAgent:
"""
Erstelle einen User Proxy Agent für menschliche Interaktion
"""
agent = UserProxyAgent(
name=name,
human_input_mode=human_input_mode,
max_consecutive_auto_reply=max_consecutive_replies,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE")
)
self.agents[name] = agent
return agent
def create_group_chat(
self,
agents: List[Any],
messages: Optional[List[Dict]] = None,
max_round: int = 10
) -> GroupChat:
"""
Erstelle eine GroupChat-Umgebung
"""
group_chat = GroupChat(
agents=agents,
messages=messages or [],
max_round=max_round,
speaker_selection_method="round_robin"
)
return group_chat
def create_group_chat_manager(
self,
group_chat: GroupChat
) -> GroupChatManager:
"""
Erstelle einen GroupChatManager
"""
return GroupChatManager(
groupchat=group_chat,
llm_config=self.llm_config
)
def run_two_agent_ch