Create export.py

core/export.py

@@ -0,0 +1,55 @@
+import io
+import json
+import zipfile
+import pandas as pd
+from typing import Tuple
+from .sim import World
+from .kpi import compute_kpis
+
+def export_run(w: World) -> Tuple[bytes, str]:
+    """
+    Returns (zip_bytes, filename)
+    Contents:
+      - events.jsonl
+      - metrics.csv
+      - run_summary.json
+    """
+    # events
+    events_jsonl = w.events.to_jsonl().encode("utf-8")
+
+    # metrics (single-row snapshot; you can also store per-tick KPI series later)
+    kpi = compute_kpis(w)
+    df = pd.DataFrame([kpi])
+    metrics_csv = df.to_csv(index=False).encode("utf-8")
+
+    # summary
+    summary = {
+        "run_id": w.run_id,
+        "seed": w.seed,
+        "tick": w.step,
+        "minutes_per_tick": w.config.minutes_per_tick,
+        "ledger": {
+            "spend_usd": w.ledger.spend_usd,
+            "llm_calls": w.ledger.llm_calls,
+            "tool_calls": w.ledger.tool_calls,
+            "tokens_prompt": w.ledger.tokens_prompt,
+            "tokens_completion": w.ledger.tokens_completion,
+        },
+        "kpi": kpi,
+        "alerts": w.ledger.alerts[-10:],
+        "tasks_total": len(w.tasks.tasks),
+        "tasks_done": len(w.tasks.done_tasks()),
+        "tasks_overdue": len(w.tasks.overdue_tasks(w.step)),
+    }
+    summary_json = json.dumps(summary, indent=2).encode("utf-8")
+
+    # zip
+    buf = io.BytesIO()
+    with zipfile.ZipFile(buf, mode="w", compression=zipfile.ZIP_DEFLATED) as z:
+        z.writestr("events.jsonl", events_jsonl)
+        z.writestr("metrics.csv", metrics_csv)
+        z.writestr("run_summary.json", summary_json)
+
+    buf.seek(0)
+    fname = f"zen_orchestrator_export__{w.run_id}.zip"
+    return buf.read(), fname