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5da61d4 05fc799 5da61d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from __future__ import annotations
from dataclasses import dataclass
from typing import Dict, Any, List
@dataclass
class KPIReport:
steps: int
simulated_seconds: float
wall_seconds: float
total_cost_usd: float
total_tokens_in: int
total_tokens_out: int
total_actions: int
per_agent: Dict[str, Dict[str, Any]]
def compute_kpis(run_log: List[Dict[str, Any]]) -> KPIReport:
if not run_log:
return KPIReport(
steps=0,
simulated_seconds=0.0,
wall_seconds=0.0,
total_cost_usd=0.0,
total_tokens_in=0,
total_tokens_out=0,
total_actions=0,
per_agent={},
)
steps = int(max(e.get("t", 0) for e in run_log))
simulated_seconds = float(sum(e.get("sim_seconds", 0.0) for e in run_log))
wall_seconds = float(sum(e.get("wall_seconds", 0.0) for e in run_log))
total_cost_usd = float(sum(e.get("cost_usd", 0.0) for e in run_log))
total_tokens_in = int(sum(e.get("tokens_in", 0) for e in run_log))
total_tokens_out = int(sum(e.get("tokens_out", 0) for e in run_log))
total_actions = int(sum(1 for e in run_log if e.get("action")))
per_agent: Dict[str, Dict[str, Any]] = {}
for e in run_log:
agent = e.get("agent")
if not agent:
continue
d = per_agent.setdefault(
agent,
{"actions": 0, "cost_usd": 0.0, "tokens_in": 0, "tokens_out": 0, "wall_seconds": 0.0, "sim_seconds": 0.0},
)
if e.get("action"):
d["actions"] += 1
d["cost_usd"] += float(e.get("cost_usd", 0.0))
d["tokens_in"] += int(e.get("tokens_in", 0))
d["tokens_out"] += int(e.get("tokens_out", 0))
d["wall_seconds"] += float(e.get("wall_seconds", 0.0))
d["sim_seconds"] += float(e.get("sim_seconds", 0.0))
return KPIReport(
steps=steps,
simulated_seconds=simulated_seconds,
wall_seconds=wall_seconds,
total_cost_usd=total_cost_usd,
total_tokens_in=total_tokens_in,
total_tokens_out=total_tokens_out,
total_actions=total_actions,
per_agent=per_agent,
)
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