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Typed execution errors and results.
Provides explicit error typing instead of plain strings,
structured results, and handling policies.
"""
import builtins
from datetime import UTC, datetime
from enum import Enum
from typing import Any
from pydantic import BaseModel, ConfigDict
__all__ = [
"AgentNotFoundError",
"BudgetExceededError",
"ErrorAction",
"ErrorPolicy",
"ExecutionError",
"ExecutionMetrics",
"RetryExhaustedError",
"StepExecutionResult",
"TimeoutError",
"ValidationError",
]
class ExecutionError(Exception):
"""Base execution error for a step/agent with metadata."""
def __init__(
self,
message: str,
agent_id: str | None = None,
step_index: int | None = None,
cause: Exception | None = None,
recoverable: bool = True,
):
super().__init__(message)
self.message = message
self.agent_id = agent_id
self.step_index = step_index
self.cause = cause
self.recoverable = recoverable
self.timestamp = datetime.now(UTC)
def to_dict(self) -> dict[str, Any]:
"""Serialize the error to a dictionary for logging or response."""
return {
"type": self.__class__.__name__,
"message": self.message,
"agent_id": self.agent_id,
"step_index": self.step_index,
"recoverable": self.recoverable,
"timestamp": self.timestamp.isoformat(),
"cause": str(self.cause) if self.cause else None,
}
class TimeoutError(ExecutionError, builtins.TimeoutError): # noqa: A001
"""Agent timeout error."""
def __init__(
self,
agent_id: str,
timeout_seconds: float,
step_index: int | None = None,
):
super().__init__(
f"Agent '{agent_id}' timed out after {timeout_seconds}s",
agent_id=agent_id,
step_index=step_index,
recoverable=True,
)
self.timeout_seconds = timeout_seconds
class RetryExhaustedError(ExecutionError):
"""Error raised when all retry attempts are exhausted."""
def __init__(
self,
agent_id: str,
attempts: int,
last_error: Exception | None = None,
step_index: int | None = None,
):
super().__init__(
f"Agent '{agent_id}' failed after {attempts} attempts",
agent_id=agent_id,
step_index=step_index,
cause=last_error,
recoverable=False,
)
self.attempts = attempts
self.last_error = last_error
class BudgetExceededError(ExecutionError):
"""Error raised when a budget limit is exceeded."""
def __init__(
self,
budget_type: str,
limit: float,
used: float,
agent_id: str | None = None,
):
super().__init__(
f"{budget_type.capitalize()} budget exceeded: {used}/{limit}",
agent_id=agent_id,
recoverable=False,
)
self.budget_type = budget_type
self.limit = limit
self.used = used
class AgentNotFoundError(ExecutionError):
"""Error raised when an agent is not found in the graph."""
def __init__(self, agent_id: str):
super().__init__(
f"Agent '{agent_id}' not found in graph",
agent_id=agent_id,
recoverable=False,
)
class ValidationError(ExecutionError):
"""Error raised when input data or parameters fail validation."""
def __init__(
self,
message: str,
field: str | None = None,
value: Any = None,
):
super().__init__(message, recoverable=False)
self.field = field
self.value = value
class ErrorAction(str, Enum):
SKIP = "skip"
RETRY = "retry"
PRUNE = "prune"
FALLBACK = "fallback"
ROLLBACK = "rollback"
ABORT = "abort"
class ErrorPolicy(BaseModel):
"""Policy that maps error types to handling actions."""
on_timeout: ErrorAction = ErrorAction.RETRY
on_retry_exhausted: ErrorAction = ErrorAction.PRUNE
on_budget_exceeded: ErrorAction = ErrorAction.ABORT
on_agent_not_found: ErrorAction = ErrorAction.SKIP
on_validation_error: ErrorAction = ErrorAction.ABORT
on_unknown_error: ErrorAction = ErrorAction.SKIP
max_skipped_agents: int = 5
abort_on_critical_path: bool = True
def get_action(self, error: "ExecutionError") -> ErrorAction:
"""Return the action corresponding to the error type."""
if isinstance(error, TimeoutError):
return self.on_timeout
if isinstance(error, RetryExhaustedError):
return self.on_retry_exhausted
if isinstance(error, BudgetExceededError):
return self.on_budget_exceeded
if isinstance(error, AgentNotFoundError):
return self.on_agent_not_found
if isinstance(error, ValidationError):
return self.on_validation_error
return self.on_unknown_error
class ExecutionMetrics(BaseModel):
"""Aggregated metrics for tokens, requests, agents, and timing."""
prompt_tokens: int = 0
completion_tokens: int = 0
total_tokens: int = 0
start_time: datetime | None = None
end_time: datetime | None = None
latency_ms: float = 0.0
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
retried_requests: int = 0
total_agents: int = 0
executed_agents: int = 0
skipped_agents: int = 0
failed_agents: int = 0
@property
def success_rate(self) -> float:
"""Fraction of successful requests."""
if self.total_requests == 0:
return 0.0
return self.successful_requests / self.total_requests
@property
def duration_seconds(self) -> float:
"""Total execution duration in seconds."""
if self.start_time and self.end_time:
return (self.end_time - self.start_time).total_seconds()
return 0.0
def add_request(
self,
prompt_tokens: int,
completion_tokens: int,
success: bool,
latency_ms: float,
retried: bool = False,
) -> None:
"""Add metrics for a single request or step."""
self.prompt_tokens += prompt_tokens
self.completion_tokens += completion_tokens
self.total_tokens += prompt_tokens + completion_tokens
self.total_requests += 1
self.latency_ms += latency_ms
if success:
self.successful_requests += 1
else:
self.failed_requests += 1
if retried:
self.retried_requests += 1
def to_dict(self) -> dict[str, Any]:
"""Serialize metrics to a dictionary."""
return {
"tokens": {
"prompt": self.prompt_tokens,
"completion": self.completion_tokens,
"total": self.total_tokens,
},
"requests": {
"total": self.total_requests,
"successful": self.successful_requests,
"failed": self.failed_requests,
"retried": self.retried_requests,
"success_rate": self.success_rate,
},
"agents": {
"total": self.total_agents,
"executed": self.executed_agents,
"skipped": self.skipped_agents,
"failed": self.failed_agents,
},
"timing": {
"duration_seconds": self.duration_seconds,
"total_latency_ms": self.latency_ms,
"avg_latency_ms": self.latency_ms / max(1, self.total_requests),
},
}
class StepExecutionResult(BaseModel):
"""Result of a step/agent execution with metrics and status flags."""
model_config = ConfigDict(arbitrary_types_allowed=True)
agent_id: str
success: bool
response: str | None = None
error: "ExecutionError | None" = None
prompt_tokens: int = 0
completion_tokens: int = 0
latency_ms: float = 0.0
attempts: int = 1
quality_score: float = 1.0
skipped: bool = False
fallback_used: bool = False
@property
def tokens_used(self) -> int:
"""Total tokens consumed by this step."""
return self.prompt_tokens + self.completion_tokens
def to_dict(self) -> dict[str, Any]:
"""Serialize the step result to a dictionary."""
return {
"agent_id": self.agent_id,
"success": self.success,
"response_length": len(self.response) if self.response else 0,
"error": self.error.to_dict() if self.error else None,
"metrics": {
"prompt_tokens": self.prompt_tokens,
"completion_tokens": self.completion_tokens,
"latency_ms": self.latency_ms,
"attempts": self.attempts,
"quality_score": self.quality_score,
},
"status": {
"skipped": self.skipped,
"fallback_used": self.fallback_used,
},
}
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