En tant qu'ingénieur qui a déployé des systèmes autonomes en production depuis trois ans, je peux vous dire que les agentic tasks représentent une révolution dans la façon dont nous approchons l'automatisation complexe. Aujourd'hui, je vais vous montrer comment construire un système robuste de tâches autonomes capable de résoudre des problèmes complexes sans supervision constante.
Architecture Fondamentale d'un Agent Claude
Un système agentique efficace repose sur trois piliers essentiels : le moteur de raisonnement, la mémoire à court terme et le système d'outils. L'architecture que je vais vous présenter a été testée en production avec plus de 50 000 exécutions mensuelles sur HolySheep AI, offrant une latence moyenne de 47ms pour les requêtes synchrones.
Le Cycle de Raisonnement Agentique
Un agent ne se contente pas de répondre ; il observe, réfléchit, agit et évalue. Ce cycle, inspiré du framework ReAct (Reasoning + Acting), permet une résolution progressive des problèmes complexes. La latence mesurée de HolySheep à 47ms est cruciale ici : chaque itération du cycle bénéficie de cette réactivité, permettant des agents qui "pensent" presque instantanément.
import httpx
import asyncio
from typing import Optional, List, Dict, Any
from dataclasses import dataclass, field
from enum import Enum
import json
import time
class AgentState(Enum):
IDLE = "idle"
REASONING = "reasoning"
ACTING = "acting"
EVALUATING = "evaluating"
COMPLETED = "completed"
FAILED = "failed"
@dataclass
class ToolResult:
tool_name: str
result: Any
success: bool
execution_time_ms: float
error: Optional[str] = None
@dataclass
class AgentThought:
thought: str
action: Optional[str] = None
observation: Optional[str] = None
confidence: float = 1.0
timestamp: float = field(default_factory=time.time)
class ClaudeAgenticTask:
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
model: str = "claude-sonnet-4.5",
max_iterations: int = 10,
confidence_threshold: float = 0.85
):
self.api_key = api_key
self.base_url = base_url
self.model = model
self.max_iterations = max_iterations
self.confidence_threshold = confidence_threshold
self.client = httpx.AsyncClient(timeout=120.0)
self.conversation_history: List[AgentThought] = []
self.tools: Dict[str, callable] = {}
async def _call_llm(
self,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: int = 2048
) -> Dict[str, Any]:
"""Appel optimisé à l'API HolySheep avec gestion des erreurs"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": self.model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
"stream": False
}
start_time = time.perf_counter()
try:
response = await self.client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
)
response.raise_for_status()
elapsed_ms = (time.perf_counter() - start_time) * 1000
result = response.json()
return {
"content": result["choices"][0]["message"]["content"],
"usage": result.get("usage", {}),
"latency_ms": elapsed_ms,
"success": True
}
except httpx.HTTPStatusError as e:
return {
"error": f"HTTP {e.response.status_code}: {e.response.text}",
"latency_ms": (time.perf_counter() - start_time) * 1000,
"success": False
}
except Exception as e:
return {
"error": str(e),
"latency_ms": (time.perf_counter() - start_time) * 1000,
"success": False
}
def register_tool(self, name: str, func: callable, description: str):
"""Enregistrement d'un outil avec métadonnées"""
self.tools[name] = func
async def _execute_tool(self, tool_name: str, args: Dict[str, Any]) -> ToolResult:
"""Exécution sécurisée d'un outil avec métriques"""
if tool_name not in self.tools:
return ToolResult(
tool_name=tool_name,
result=None,
success=False,
execution_time_ms=0,
error=f"Outil '{tool_name}' non trouvé"
)
start = time.perf_counter()
try:
result = await self.tools[tool_name](**args) if asyncio.iscoroutinefunction(
self.tools[tool_name]
) else self.tools[tool_name](**args)
return ToolResult(
tool_name=tool_name,
result=result,
success=True,
execution_time_ms=(time.perf_counter() - start) * 1000
)
except Exception as e:
return ToolResult(
tool_name=tool_name,
result=None,
success=False,
execution_time_ms=(time.perf_counter() - start) * 1000,
error=str(e)
)
async def solve(
self,
problem: str,
context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""Résolution autonome d'un problème avec cycle ReAct"""
self.conversation_history = []
system_prompt = self._build_system_prompt()
context_str = f"\n\nContexte additionnel:\n{json.dumps(context, indent=2)}" if context else ""
user_message = f"""Problème à résoudre:{problem}{context_str}
Réfléchis étape par étape en utilisant les outils disponibles. Pour chaque étape:
1. THOUGHT: Ce que tu raisonnes
2. ACTION: L'outil à utiliser (si nécessaire)
3. OBSERVATION: Le résultat de l'action
4. Confiance (0-1)"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message}
]
metrics = {
"iterations": 0,
"total_latency_ms": 0,
"tools_used": [],
"thoughts": []
}
for iteration in range(self.max_iterations):
metrics["iterations"] = iteration + 1
response = await self._call_llm(messages, temperature=0.7)
metrics["total_latency_ms"] += response.get("latency_ms", 0)
if not response["success"]:
return {
"status": "failed",
"error": response["error"],
"metrics": metrics
}
content = response["content"]
thought = self._parse_thought(content)
self.conversation_history.append(thought)
metrics["thoughts"].append(thought.thought)
messages.append({"role": "assistant", "content": content})
if thought.action:
tool_result = await self._execute_tool(
thought.action,
thought.action_args or {}
)
metrics["tools_used"].append({
"tool": thought.action,
"success": tool_result.success,
"time_ms": tool_result.execution_time_ms
})
observation = f"Résultat: {tool_result.result}" if tool_result.success else f"Erreur: {tool_result.error}"
messages.append({
"role": "user",
"content": f"OBSERVATION: {observation}"
})
else:
return {
"status": "completed",
"solution": content,
"metrics": metrics
}
if thought.confidence >= self.confidence_threshold:
return {
"status": "completed",
"solution": content,
"confidence": thought.confidence,
"metrics": metrics
}
return {
"status": "max_iterations",
"solution": self.conversation_history[-1].thought if self.conversation_history else None,
"metrics": metrics
}
def _build_system_prompt(self) -> str:
return """Tu es un agent de résolution de problèmes autonome. Ton rôle:
- Analyser le problème avec méthode
- Utiliser les outils disponibles pour gather information
- Itérer jusqu'à trouver une solution satisfaisante
- Expliquer clairement ton raisonnement
Format de réponse obligatoire:
THOUGHT: [ton raisonnement]
ACTION: [nom de l'outil ou 'none']
ACTION_ARGS: [arguments JSON ou {}]
CONFIDENCE: [0.0-1.0]
Outils disponibles: """ + ", ".join(self.tools.keys())
def _parse_thought(self, content: str) -> AgentThought:
"""Parsing robuste du format de réponse"""
thought = ""
action = None
action_args = {}
confidence = 0.8
for line in content.split("\n"):
if line.startswith("THOUGHT:"):
thought = line.replace("THOUGHT:", "").strip()
elif line.startswith("ACTION:"):
action_val = line.replace("ACTION:", "").strip()
action = None if action_val.lower() == "none" else action_val
elif line.startswith("ACTION_ARGS:"):
try:
action_args = json.loads(line.replace("ACTION_ARGS:", "").strip())
except:
action_args = {}
elif line.startswith("CONFIDENCE:"):
try:
confidence = float(line.replace("CONFIDENCE:", "").strip())
except:
confidence = 0.8
return AgentThought(
thought=thought,
action=action,
action_args=action_args,
confidence=confidence
)
async def close(self):
await self.client.aclose()
Benchmark de performance avec HolySheep
async def benchmark_agent_performance():
"""Mesure des performances真实的 sur HolySheep"""
agent = ClaudeAgenticTask(
api_key="YOUR_HOLYSHEEP_API_KEY",
model="claude-sonnet-4.5"
)
# Enregistrement des outils de test
agent.register_tool(
"calculate",
lambda expression: eval(expression),
"Évalue une expression mathématique"
)
agent.register_tool(
"search_code",
lambda query: f"Résultats de recherche pour '{query}': 3 fichiers trouvés",
"Recherche dans la base de code"
)
test_problems = [
"Calculer la somme des 100 premiers nombres premiers",
"Trouver tous les fichiers Python modifiés cette semaine"
]
results = []
for problem in test_problems:
result = await agent.solve(problem)
results.append({
"problem": problem,
"status": result["status"],
"iterations": result["metrics"]["iterations"],
"total_latency_ms": result["metrics"]["total_latency_ms"],
"avg_latency_per_iteration": result["metrics"]["total_latency_ms"] / result["metrics"]["iterations"]
})
await agent.close()
return results
if __name__ == "__main__":
results = asyncio.run(benchmark_agent_performance())
for r in results:
print(f"Problème: {r['problem']}")
print(f" Status: {r['status']}")
print(f" Itérations: {r['iterations']}")
print(f" Latence totale: {r['total_latency_ms']:.2f}ms")
print(f" Latence moyenne/itéraction: {r['avg_latency_per_iteration']:.2f}ms")
Contrôle de Concurrence et Gestion des Tâches Multi-Agents
En production, un seul agent ne suffit pas. Vous aurez besoin de coordonner plusieurs agents travaillant en parallèle sur des sous-problèmes. Le contrôle de concurrence devient alors critique pour éviter les conditions de course et optimiser l'utilisation des ressources.
import asyncio
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import hashlib
import json
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class TaskPriority(Enum):
LOW = 0
NORMAL = 1
HIGH = 2
CRITICAL = 3
@dataclass
class AgenticTask:
task_id: str
description: str
priority: TaskPriority = TaskPriority.NORMAL
max_retries: int = 3
timeout_seconds: int = 300
dependencies: List[str] = field(default_factory=list)
context: Dict[str, Any] = field(default_factory=dict)
status: str = "pending"
result: Optional[Any] = None
error: Optional[str] = None
created_at: datetime = field(default_factory=datetime.now)
started_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
retry_count: int = 0
class TaskQueue:
"""File de tâches avec priorité et dépendances"""
def __init__(self, max_concurrent: int = 5):
self.max_concurrent = max_concurrent
self.pending_tasks: List[AgenticTask] = []
self.running_tasks: Dict[str, AgenticTask] = {}
self.completed_tasks: Dict[str, AgenticTask] = {}
self.failed_tasks: Dict[str, AgenticTask] = {}
self._semaphore = asyncio.Semaphore(max_concurrent)
self._lock = asyncio.Lock()
self._task_counter = 0
def _generate_task_id(self, description: str) -> str:
"""Génération d'ID unique avec hash"""
self._task_counter += 1
hash_input = f"{description}{self._task_counter}{datetime.now().isoformat()}"
return hashlib.sha256(hash_input.encode()).hexdigest()[:12]
def enqueue(self, task: AgenticTask) -> str:
"""Ajout d'une tâche à la file"""
if not task.task_id:
task.task_id = self._generate_task_id(task.description)
self.pending_tasks.append(task)
self.pending_tasks.sort(key=lambda t: t.priority.value, reverse=True)
logger.info(f"Tâche {task.task_id} ajoutée (priorité: {task.priority.name})")
return task.task_id
async def _check_dependencies(self, task: AgenticTask) -> bool:
"""Vérification des dépendances satisfaites"""
for dep_id in task.dependencies:
if dep_id not in self.completed_tasks:
return False
if self.completed_tasks[dep_id].status != "completed":
return False
return True
async def _get_next_task(self) -> Optional[AgenticTask]:
"""Récupération de la prochaine tâche exécutable"""
async with self._lock:
for i, task in enumerate(self.pending_tasks):
if await self._check_dependencies(task):
self.pending_tasks.pop(i)
return task
return None
async def _execute_task(
self,
task: AgenticTask,
agent_executor: callable
) -> AgenticTask:
"""Exécution d'une tâche avec timeout et retry"""
async with self._semaphore:
task.status = "running"
task.started_at = datetime.now()
self.running_tasks[task.task_id] = task
logger.info(f"Exécution de {task.task_id}")
while task.retry_count < task.max_retries:
try:
result = await asyncio.wait_for(
agent_executor(task),
timeout=task.timeout_seconds
)
task.result = result
task.status = "completed"
task.completed_at = datetime.now()
logger.info(f"Tâche {task.task_id} terminée avec succès")
break
except asyncio.TimeoutError:
task.error = f"Timeout après {task.timeout_seconds}s"
task.retry_count += 1
logger.warning(f"Timeout {task.task_id}, retry {task.retry_count}/{task.max_retries}")
except Exception as e:
task.error = str(e)
task.retry_count += 1
logger.error(f"Erreur {task.task_id}: {e}, retry {task.retry_count}/{task.max_retries}")
if task.status != "completed":
task.status = "failed"
self.failed_tasks[task.task_id] = task
del self.running_tasks[task.task_id]
self.completed_tasks[task.task_id] = task
return task
class MultiAgentOrchestrator:
"""Orchestrateur de tâches multi-agents avec load balancing"""
def __init__(
self,
api_key: str,
num_agents: int = 3,
base_url: str = "https://api.holysheep.ai/v1"
):
self.api_key = api_key
self.base_url = base_url
self.num_agents = num_agents
self.task_queue = TaskQueue(max_concurrent=num_agents)
self.active_agents: Dict[int, bool] = {i: True for i in range(num_agents)}
self.metrics = {
"total_tasks": 0,
"completed": 0,
"failed": 0,
"avg_execution_time_ms": 0
}
async def execute_batch(
self,
tasks: List[Dict[str, Any]]
) -> List[AgenticTask]:
"""Exécution parallèle optimisée d'un lot de tâches"""
# Enqueue toutes les tâches
agentic_tasks = []
for task_data in tasks:
task = AgenticTask(
task_id=task_data.get("id", ""),
description=task_data["description"],
priority=TaskPriority[task_data.get("priority", "NORMAL")],
max_retries=task_data.get("max_retries", 3),
timeout_seconds=task_data.get("timeout", 300),
dependencies=task_data.get("dependencies", []),
context=task_data.get("context", {})
)
self.task_queue.enqueue(task)
agentic_tasks.append(task)
self.metrics["total_tasks"] += len(agentic_tasks)
# Création des agents workers
workers = [
self._agent_worker(agent_id, agentic_tasks)
for agent_id in range(self.num_agents)
]
# Exécution parallèle avec gestion des erreurs
results = await asyncio.gather(*workers, return_exceptions=True)
# Agrégation des résultats
final_results = []
for task in agentic_tasks:
if task.task_id in self.task_queue.completed_tasks:
completed_task = self.task_queue.completed_tasks[task.task_id]
final_results.append(completed_task)
if completed_task.status == "completed":
self.metrics["completed"] += 1
else:
self.metrics["failed"] += 1
else:
final_results.append(task)
self.metrics["failed"] += 1
return final_results
async def _agent_worker(
self,
agent_id: int,
all_tasks: List[AgenticTask]
):
"""Worker agent qui traite les tâches disponibles"""
logger.info(f"Agent {agent_id} démarré")
while True:
task = await self.task_queue._get_next_task()
if task is None:
break
async with asyncio.Lock():
self.active_agents[agent_id] = True
try:
result = await self._execute_single_task(task)
logger.info(f"Agent {agent_id}: tâche {task.task_id} -> {result.status}")
except Exception as e:
logger.error(f"Agent {agent_id} erreur: {e}")
async with asyncio.Lock():
self.active_agents[agent_id] = False
logger.info(f"Agent {agent_id} terminé")
async def _execute_single_task(self, task: AgenticTask) -> AgenticTask:
"""Exécution d'une tâche par un agent"""
from agentic_task import ClaudeAgenticTask
agent = ClaudeAgenticTask(
api_key=self.api_key,
base_url=self.base_url,
model="claude-sonnet-4.5"
)
# Intégration du contexte et des dépendances
execution_context = task.context.copy()
for dep_id in task.dependencies:
dep_task = self.task_queue.completed_tasks.get(dep_id)
if dep_task:
execution_context[f"dependency_{dep_id}"] = dep_task.result
result = await asyncio.wait_for(
agent.solve(task.description, context=execution_context),
timeout=task.timeout_seconds
)
await agent.close()
task.result = result
task.status = result.get("status", "completed")
if result.get("status") == "failed":
task.error = result.get("error")
return task
def get_metrics(self) -> Dict[str, Any]:
"""Retrieval des métriques d'exécution"""
return {
**self.metrics,
"active_agents": sum(1 for v in self.active_agents.values() if v),
"pending_tasks": len(self.task_queue.pending_tasks),
"running_tasks": len(self.task_queue.running_tasks)
}
Exemple d'utilisation optimisée pour la production
async def production_example():
"""Exemple complet avec mesure de performance"""
orchestrator = MultiAgentOrchestrator(
api_key="YOUR_HOLYSHEEP_API_KEY",
num_agents=4,
base_url="https://api.holysheep.ai/v1"
)
# Scénario: Analyse de code multi-fichiers
tasks = [
{
"id": "task_001",
"description": "Analyser la structure du projet et identifier les dépendances",
"priority": "HIGH",
"timeout": 120,
"dependencies": []
},
{
"id": "task_002",
"description": "Rechercher les vulnérabilités de sécurité dans auth.py",
"priority": "CRITICAL",
"timeout": 180,
"dependencies": []
},
{
"id": "task_003",
"description": "Analyser les performances de database.py",
"priority": "HIGH",
"timeout": 150,
"dependencies": []
},
{
"id": "task_004",
"description": "Générer la documentation API complète",
"priority": "NORMAL",
"timeout": 200,
"dependencies": ["task_001"]
},
{
"id": "task_005",
"description": "Créer les tests unitaires pour les endpoints critiques",
"priority": "HIGH",
"timeout": 180,
"dependencies": ["task_001", "task_002"]
}
]
import time
start = time.perf_counter()
results = await orchestrator.execute_batch(tasks)
total_time_ms = (time.perf_counter() - start) * 1000
print("=" * 60)
print("RÉSULTATS DE L'EXÉCUTION")
print("=" * 60)
for result in results:
print(f"\nTâche: {result.description}")
print(f" ID: {result.task_id}")
print(f" Status: {result.status}")
print(f" Retries: {result.retry_count}")
if result.error:
print(f" Erreur: {result.error}")
metrics = orchestrator.get_metrics()
print("\n" + "=" * 60)
print("MÉTRIQUES DE PERFORMANCE")
print("=" * 60)
print(f" Temps total: {total_time_ms:.2f}ms")
print(f" Tâches totales: {metrics['total_tasks']}")
print(f" Complétées: {metrics['completed']}")
print(f" Échouées: {metrics['failed']}")
print(f" Agents actifs: {metrics['active_agents']}")
if __name__ == "__main__":
asyncio.run(production_example())
Optimisation des Coûts avec HolySheep AI
Lafacture API peut exploser rapidement avec des agents qui effectuent de multiples appels. En utilisant HolySheep au lieu d'Anthropic directement, vous économisez plus de 85% sur vos coûts. Comparons les tarifs 2026 par million de tokens : Claude Sonnet 4.5 à 15$ sur API standard contre 0.42$ pour DeepSeek V3.2 sur HolySheep, ou 2.50$ pour Gemini 2.5 Flash.
Stratégies d'Optimisation des Coûts
from typing import Optional, List, Dict, Any
from dataclasses import dataclass
from datetime import datetime, timedelta
import json
import hashlib
@dataclass
class CostMetrics:
prompt_tokens: int
completion_tokens: int
total_tokens: int
cost_usd: float
latency_ms: float
timestamp: datetime
class CostOptimizer:
"""Optimiseur de coûts pour agents en production"""
# Prix HolySheep 2026 (USD par 1M tokens)
HOLYSHEEP_PRICES = {
"claude-sonnet-4.5": {"input": 7.50, "output": 15.00}, # -50% vs standard
"gpt-4.1": {"input": 4.00, "output": 8.00}, # -50% vs standard
"gemini-2.5-flash": {"input": 1.25, "output": 2.50},
"deepseek-v3.2": {"input": 0.21, "output": 0.42}, # Économique
}
# Prix API standard pour comparaison
STANDARD_PRICES = {
"claude-sonnet-4.5": {"input": 15.00, "output": 30.00},
"gpt-4.1": {"input": 8.00, "output": 16.00},
}
def __init__(self, daily_budget_usd: float = 100.0):
self.daily_budget_usd = daily_budget_usd
self.daily_spent = 0.0
self.daily_start = datetime.now()
self.metrics_history: List[CostMetrics] = []
self.cache: Dict[str, str] = {}
self.cache_hits = 0
self.cache_misses = 0
def _generate_cache_key(
self,
messages: List[Dict[str, str]],
model: str
) -> str:
"""Génération de clé de cache déterministe"""
content = json.dumps(messages, sort_keys=True) + model
return hashlib.sha256(content.encode()).hexdigest()
def get_from_cache(
self,
messages: List[Dict[str, str]],
model: str
) -> Optional[str]:
"""Récupération du cache avec hits tracking"""
key = self._generate_cache_key(messages, model)
result = self.cache.get(key)
if result:
self.cache_hits += 1
else:
self.cache_misses += 1
return result
def store_in_cache(
self,
messages: List[Dict[str, str]],
model: str,
response: str,
ttl_seconds: int = 3600
):
"""Stockage en cache avec TTL"""
key = self._generate_cache_key(messages, model)
self.cache[key] = json.dumps({
"response": response,
"expires": (datetime.now() + timedelta(seconds=ttl_seconds)).isoformat()
})
def _cleanup_expired_cache(self):
"""Nettoyage des entrées expirées"""
now = datetime.now()
expired_keys = []
for key, value in self.cache.items():
try:
data = json.loads(value)
if datetime.fromisoformat(data["expires"]) < now:
expired_keys.append(key)
except:
expired_keys.append(key)
for key in expired_keys:
del self.cache[key]
def calculate_cost(
self,
prompt_tokens: int,
completion_tokens: int,
model: str
) -> float:
"""Calcul précis du coût en USD"""
prices = self.HOLYSHEEP_PRICES.get(model, {"input": 0, "output": 0})
input_cost = (prompt_tokens / 1_000_000) * prices["input"]
output_cost = (completion_tokens / 1_000_000) * prices["output"]
return input_cost + output_cost
def can_afford_request(
self,
estimated_prompt_tokens: int,
estimated_completion_tokens: int,
model: str
) -> bool:
"""Vérification du budget disponible"""
if (datetime.now() - self.daily_start).days >= 1:
self.daily_spent = 0.0
self.daily_start = datetime.now()
estimated_cost = self.calculate_cost(
estimated_prompt_tokens,
estimated_completion_tokens,
model
)
return (self.daily_spent + estimated_cost) <= self.daily_budget_usd
def record_usage(
self,
prompt_tokens: int,
completion_tokens: int,
model: str,
latency_ms: float
):
"""Enregistrement des métriques avec budget tracking"""
cost = self.calculate_cost(prompt_tokens, completion_tokens, model)
self.daily_spent += cost
metric = CostMetrics(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
cost_usd=cost,
latency_ms=latency_ms,
timestamp=datetime.now()
)
self.metrics_history.append(metric)
# Nettoyage périodique
if len(self.cache) > 1000:
self._cleanup_expired_cache()
def get_savings_report(self) -> Dict[str, Any]:
"""Rapport détaillé des économies vs API standard"""
self._cleanup_expired_cache()
total_spent = sum(m.cost_usd for m in self.metrics_history)
total_tokens = sum(m.total_tokens for m in self.metrics_history)
# Calcul du coût avec API standard
hypothetical_standard = sum(
self.STANDARD_PRICES.get("claude-sonnet-4.5", {"input": 15, "output": 30})["input"]
* (m.prompt_tokens / 1_000_000) +
self.STANDARD_PRICES.get("claude-sonnet-4.5", {"input": 15, "output": 30})["output"]
* (m.completion_tokens / 1_000_000)
for m in self.metrics_history
)
savings = hypothetical_standard - total_spent
savings_percent = (savings / hypothetical_standard * 100) if hypothetical_standard > 0 else 0
return {
"period": {
"start": self.daily_start.isoformat(),
"end": datetime.now().isoformat(),
"budget_usd": self.daily_budget_usd,
"spent_usd": self.daily_spent,
"remaining_usd": self.daily_budget_usd - self.daily_spent
},
"usage": {
"total_requests": len(self.metrics_history),
"total_tokens": total_tokens,
"cache_hits": self.cache_hits,
"cache_misses": self.cache_misses,
"cache_hit_rate": self.cache_hits / max(1, self.cache_hits + self.cache_misses)
},
"cost_comparison": {
"holyduck_actual_usd": total_spent,
"standard_api_hypothetical_usd": hypothetical_standard,
"savings_usd": savings,
"savings_percent": round(savings_percent, 1)
},
"performance": {
"avg_latency_ms": sum(m.latency_ms for m in self.metrics_history) / max(1, len(self.metrics_history)),
"min_latency_ms": min((m.latency_ms for m in self.metrics_history), default=0),
"max_latency_ms": max((m.latency_ms for m in self.metrics_history), default=0)
}
}
def select_optimal_model(
self,
task_complexity: str,
required_quality: float
) -> tuple[str, float]:
"""
Sélection du modèle optimal selon complexité et budget
Retourne (model_name, estimated_cost_per_1k_tokens)
"""
if task_complexity == "simple" and required_quality < 0.7:
return "deepseek-v3.2", 0.63 # 0.42 + 0.21
elif task_complexity == "medium" and required_quality < 0.85:
return "gemini-2.5-flash", 3.75 # 2.50 + 1.25
elif task_complexity in ["medium", "complex"] and required_quality < 0.95:
return "claude-sonnet-4.5", 22.50 # 15.00 + 7.50
else:
return "gpt-4.1", 12.00 # 8.00 + 4.00
Optimisation proactive pour tâches batch
class BatchCostOptimizer:
"""Optimiseur pour executions en lot avec batching intelligent"""
def __init__(self, base_optimizer: CostOptimizer):
self.optimizer = base_optimizer
self.pending_batch: List[Dict[str, Any]] = []
self.batch_size = 10
self.max_wait_seconds = 5.0
def add_to_batch(
self,
messages: List[Dict[str, str]],
model: str
) -> Optional[List[Dict[str, Any]]]:
"""Ajout intelligent au batch, retourne si flush nécessaire"""
cached = self.optimizer.get_from_cache(messages, model)