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Zakaria
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"""SDK-native LLM usage aggregation for scan reports."""
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from __future__ import annotations
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import logging
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from typing import Any
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from agents.usage import Usage, deserialize_usage, serialize_usage
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logger = logging.getLogger(__name__)
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class LLMUsageLedger:
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"""Aggregate SDK ``Usage`` objects and attach best-effort cost estimates."""
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def __init__(self) -> None:
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self._total_usage = Usage()
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self._agent_usage: dict[str, Usage] = {}
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self._agent_metadata: dict[str, dict[str, str]] = {}
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self._total_cost = 0.0
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def record(
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self,
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*,
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agent_id: str,
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usage: Usage | None,
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agent_name: str | None = None,
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model: str | None = None,
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) -> bool:
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if usage is None or not _usage_has_activity(usage):
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return False
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normalized_agent_id = str(agent_id or "unknown")
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self._total_usage.add(usage)
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self._agent_usage.setdefault(normalized_agent_id, Usage()).add(usage)
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metadata = self._agent_metadata.setdefault(normalized_agent_id, {})
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if agent_name:
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metadata["agent_name"] = agent_name
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if model:
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metadata["model"] = model
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if not _is_litellm_routed(model):
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estimated = _estimate_litellm_cost(usage, model)
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if estimated:
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self._total_cost += estimated
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return True
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def record_observed_cost(self, cost: float) -> None:
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if isinstance(cost, int | float) and cost > 0:
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self._total_cost += float(cost)
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@property
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def total_cost(self) -> float:
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return _round_cost(self._total_cost)
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def to_record(self) -> dict[str, Any]:
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record = serialize_usage(self._total_usage)
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record["cost"] = _round_cost(self._total_cost)
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record["agents"] = []
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agent_tokens = {aid: _resolve_total_tokens(u) for aid, u in self._agent_usage.items()}
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total_tokens = sum(agent_tokens.values())
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for agent_id in sorted(self._agent_usage):
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usage = self._agent_usage[agent_id]
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metadata = self._agent_metadata.get(agent_id, {})
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agent_cost = (
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self._total_cost * (agent_tokens[agent_id] / total_tokens) if total_tokens else 0.0
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)
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agent_record = serialize_usage(usage)
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agent_record.update(
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{
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"agent_id": agent_id,
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"agent_name": metadata.get("agent_name") or agent_id,
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"model": metadata.get("model"),
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"cost": _round_cost(agent_cost),
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}
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)
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record["agents"].append(agent_record)
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return record
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def hydrate(self, raw_usage: Any) -> None:
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self._total_usage = Usage()
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self._agent_usage.clear()
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self._agent_metadata.clear()
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self._total_cost = 0.0
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if not isinstance(raw_usage, dict):
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return
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try:
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self._total_usage = deserialize_usage(raw_usage)
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except Exception:
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logger.exception("Failed to hydrate aggregate llm_usage from run.json")
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self._total_usage = Usage()
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self._total_cost = _float_or_zero(raw_usage.get("cost"))
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for raw_agent in raw_usage.get("agents") or []:
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if not isinstance(raw_agent, dict):
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continue
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agent_id = str(raw_agent.get("agent_id") or "").strip()
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if not agent_id:
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continue
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try:
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self._agent_usage[agent_id] = deserialize_usage(raw_agent)
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except Exception:
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logger.exception("Failed to hydrate llm_usage for agent %s", agent_id)
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self._agent_usage[agent_id] = Usage()
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metadata: dict[str, str] = {}
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agent_name = raw_agent.get("agent_name")
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model = raw_agent.get("model")
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if isinstance(agent_name, str) and agent_name:
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metadata["agent_name"] = agent_name
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if isinstance(model, str) and model:
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metadata["model"] = model
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self._agent_metadata[agent_id] = metadata
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def _resolve_total_tokens(usage: Usage) -> int:
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total = max(0, int(usage.total_tokens or 0))
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if total > 0:
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return total
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prompt = _int_or_zero(getattr(usage, "input_tokens", 0))
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completion = _int_or_zero(getattr(usage, "output_tokens", 0))
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return prompt + completion
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def _is_litellm_routed(model: str | None) -> bool:
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if not model:
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return False
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name = model.strip().lower()
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if "/" not in name:
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return False
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return not name.startswith("openai/")
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def _usage_has_activity(usage: Usage) -> bool:
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return bool(
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usage.requests
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or usage.input_tokens
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or usage.output_tokens
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or usage.total_tokens
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or usage.request_usage_entries
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)
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def _estimate_litellm_cost(usage: Usage, model: str | None) -> float | None:
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litellm_model = _litellm_model_name(model)
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if not litellm_model:
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return None
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entries = list(usage.request_usage_entries)
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if not entries:
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return _estimate_litellm_entry_cost(usage, litellm_model)
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total = 0.0
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estimated_any = False
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for entry in entries:
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cost = _estimate_litellm_entry_cost(entry, litellm_model)
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if cost is None:
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continue
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total += cost
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estimated_any = True
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return total if estimated_any else None
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def _estimate_litellm_entry_cost(entry: Any, model: str) -> float | None:
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prompt_tokens = _int_or_zero(getattr(entry, "input_tokens", 0))
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completion_tokens = _int_or_zero(getattr(entry, "output_tokens", 0))
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total_tokens = _int_or_zero(getattr(entry, "total_tokens", 0))
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if total_tokens <= 0:
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total_tokens = prompt_tokens + completion_tokens
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if total_tokens <= 0:
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return None
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usage_payload: dict[str, Any] = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": total_tokens,
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}
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prompt_details = _details_to_dict(getattr(entry, "input_tokens_details", None))
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completion_details = _details_to_dict(getattr(entry, "output_tokens_details", None))
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if prompt_details:
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usage_payload["prompt_tokens_details"] = prompt_details
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if completion_details:
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usage_payload["completion_tokens_details"] = completion_details
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from litellm import completion_cost
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candidates = [model]
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if "/" in model:
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candidates.append(model.split("/", 1)[-1])
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cost: Any = None
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for candidate in candidates:
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try:
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cost = completion_cost(
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completion_response={"model": candidate, "usage": usage_payload},
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model=model,
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)
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break
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except Exception: # nosec B112 # noqa: BLE001, S112
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continue
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if cost is None:
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logger.debug("LiteLLM cost estimate unavailable for model %s", model)
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return None
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return cost if isinstance(cost, int | float) and cost >= 0 else None
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def _litellm_model_name(model: str | None) -> str | None:
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if not model:
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return None
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normalized = model.strip()
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for prefix in ("litellm/", "any-llm/", "openai/"):
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if normalized.startswith(prefix):
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normalized = normalized.removeprefix(prefix)
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break
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return normalized or None
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def _details_to_dict(details: Any) -> dict[str, Any]:
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if details is None:
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return {}
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if isinstance(details, list):
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for item in details:
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result = _details_to_dict(item)
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if result:
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return result
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return {}
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if hasattr(details, "model_dump"):
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return _details_to_dict(details.model_dump())
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if not isinstance(details, dict):
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return {}
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return {str(k): v for k, v in details.items() if v is not None}
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def _int_or_zero(value: Any) -> int:
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try:
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return max(0, int(value or 0))
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except (TypeError, ValueError):
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return 0
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def _float_or_zero(value: Any) -> float:
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try:
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result = float(value or 0.0)
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except (TypeError, ValueError):
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return 0.0
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return result if result >= 0 else 0.0
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def _round_cost(cost: float) -> float:
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return round(max(0.0, cost), 10)
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