feat(intelligent-eval): backfill decision logs for completed evals

COMPLETED 状态的评估（历史/异常路径）可能完全没有决策日志，
导致旧报告决策过程为空。扩展 _supplement_decision_logs 支持
COMPLETED：按时段补 execute_session + 补 start_analysis（历史回填），
scan loop 每分钟自动回填，无需一次性脚本。幂等，只补缺失类型。

backend/agenteval/web/app.py

@@ -47,9 +47,14 @@ def _supplement_decision_logs(session) -> int:
 
     方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
     （如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
-    状态推导决策并补录：欠账时补 execute_session，所有会话完成后补
-    start_analysis。只补"该类型缺失"的，不重复；且只记录状态，不改变 agent
-    的实际执行。
+    状态推导决策并补录：
+
+    - EXECUTING：欠账（completed < estimated）补 execute_session，所有会话
+      完成后补 start_analysis。
+    - COMPLETED：历史评估/异常路径可能完全没有决策日志，回填 execute_session
+      （按 plan 时段逐条）+ start_analysis，让旧报告也有决策过程可看。
+
+    只补"该类型缺失"的，不重复；且只记录状态，不改变 agent 的实际执行。
 
     Returns:
         补录的决策日志条数。
@@ -61,39 +66,90 @@ def _supplement_decision_logs(session) -> int:
     from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalDecisionLogDB, IntelligentEvalSessionDB
 
     evals = session.exec(
-        select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.EXECUTING.value)
+        select(IntelligentEvalDB).where(
+            IntelligentEvalDB.status.in_(
+                [
+                    IntelligentEvalStatus.EXECUTING.value,
+                    IntelligentEvalStatus.COMPLETED.value,
+                ]
+            )
+        )
     ).all()
 
     added = 0
     for ev in evals:
         plan = ev.get_plan() if ev.plan else {}
         estimated = plan.get("estimated_sessions", 0)
-        sessions = session.exec(
-            select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)
-        ).all()
+        sessions = session.exec(select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)).all()
         completed = sum(1 for s in sessions if s.status == "completed")
         types = {
-            l.decision_type
-            for l in session.exec(
-                select(IntelligentEvalDecisionLogDB).where(
-                    IntelligentEvalDecisionLogDB.eval_id == ev.id
-                )
+            x.decision_type
+            for x in session.exec(
+                select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
             ).all()
         }
-        if "execute_session" not in types and completed < estimated:
-            create_decision_log(
-                ev.id, "execute_session", "平台兜底：时段欠账需执行会话", "platform",
-                {"platform_supplemented": True, "completed": completed, "estimated": estimated},
-                session,
-            )
-            added += 1
-        elif "start_analysis" not in types and sessions and completed >= estimated:
-            create_decision_log(
-                ev.id, "start_analysis", "平台兜底：所有会话已完成开始分析", "platform",
-                {"platform_supplemented": True, "completed": completed, "estimated": estimated},
-                session,
-            )
-            added += 1
+        if ev.status == IntelligentEvalStatus.EXECUTING.value:
+            if "execute_session" not in types and completed < estimated:
+                create_decision_log(
+                    ev.id,
+                    "execute_session",
+                    "平台兜底：时段欠账需执行会话",
+                    "platform",
+                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                    session,
+                )
+                added += 1
+            elif "start_analysis" not in types and sessions and completed >= estimated:
+                create_decision_log(
+                    ev.id,
+                    "start_analysis",
+                    "平台兜底：所有会话已完成开始分析",
+                    "platform",
+                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                    session,
+                )
+                added += 1
+        elif ev.status == IntelligentEvalStatus.COMPLETED.value:
+            # 历史回填：completed 评估决策日志全缺失时，按时段补 execute_session
+            if "execute_session" not in types:
+                slots = plan.get("time_distribution") or []
+                if slots:
+                    for slot in slots:
+                        create_decision_log(
+                            ev.id,
+                            "execute_session",
+                            f"平台兜底：时段{slot.get('time_slot', '')}执行会话（历史回填）",
+                            "platform",
+                            {
+                                "platform_supplemented": True,
+                                "time_slot": slot.get("time_slot"),
+                                "sessions": slot.get("sessions"),
+                                "completed": completed,
+                                "estimated": estimated,
+                            },
+                            session,
+                        )
+                        added += 1
+                else:
+                    create_decision_log(
+                        ev.id,
+                        "execute_session",
+                        "平台兜底：执行会话（历史回填）",
+                        "platform",
+                        {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                        session,
+                    )
+                    added += 1
+            if "start_analysis" not in types and sessions:
+                create_decision_log(
+                    ev.id,
+                    "start_analysis",
+                    "平台兜底：所有会话已完成开始分析（历史回填）",
+                    "platform",
+                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                    session,
+                )
+                added += 1
     return added
 
 
@@ -127,8 +183,16 @@ async def _trigger_intelligent_worker() -> bool:
         proc = await asyncio.to_thread(
             subprocess.run,
             [
-                "docker", "exec", "openclaw-eval", "openclaw", "agent",
-                "--agent", "main", "-m", worker_msg, "--json",
+                "docker",
+                "exec",
+                "openclaw-eval",
+                "openclaw",
+                "agent",
+                "--agent",
+                "main",
+                "-m",
+                worker_msg,
+                "--json",
             ],
             capture_output=True,
             text=True,

tests/integration/test_intelligent_eval_scan_scheduler.py

@@ -5,6 +5,7 @@ wakes every minute but could never pull a task. The lifespan now starts an
 asyncio background task that scans executing evals every 60s. This test
 verifies that on application startup the scan is actually invoked.
 """
+
 from unittest.mock import MagicMock
 
 from fastapi.testclient import TestClient
@@ -117,8 +118,10 @@ def test_supplement_execute_session_log(monkeypatch, db_session):
     from agenteval.storage.db import IntelligentEvalDB
 
     ev = IntelligentEvalDB(
-        name="supp-eval", target_id="t1",
-        status=IntelligentEvalStatus.EXECUTING.value, started_at=__import__("agenteval.storage.db", fromlist=["utc_now"]).utc_now(),
+        name="supp-eval",
+        target_id="t1",
+        status=IntelligentEvalStatus.EXECUTING.value,
+        started_at=__import__("agenteval.storage.db", fromlist=["utc_now"]).utc_now(),
     )
     ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
     db_session.add(ev)
@@ -136,8 +139,10 @@ def test_supplement_start_analysis_log(monkeypatch, db_session):
     from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
 
     ev = IntelligentEvalDB(
-        name="supp-eval2", target_id="t1",
-        status=IntelligentEvalStatus.EXECUTING.value, started_at=utc_now(),
+        name="supp-eval2",
+        target_id="t1",
+        status=IntelligentEvalStatus.EXECUTING.value,
+        started_at=utc_now(),
     )
     ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
     db_session.add(ev)
@@ -147,3 +152,79 @@ def test_supplement_start_analysis_log(monkeypatch, db_session):
     db_session.commit()
 
     assert app_mod._supplement_decision_logs(db_session) == 1
+
+
+def test_supplement_completed_backfill(monkeypatch, db_session):
+    """Completed eval with no decision logs → backfill execute_session + start_analysis."""
+    import agenteval.web.app as app_mod
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import (
+        IntelligentEvalDB,
+        IntelligentEvalDecisionLogDB,
+        IntelligentEvalSessionDB,
+        utc_now,
+    )
+    from sqlmodel import select
+
+    ev = IntelligentEvalDB(
+        name="supp-completed",
+        target_id="t1",
+        status=IntelligentEvalStatus.COMPLETED.value,
+        started_at=utc_now(),
+    )
+    ev.set_plan(
+        {
+            "time_distribution": [
+                {"time_slot": "0-1h", "sessions": 1},
+                {"time_slot": "1-2h", "sessions": 1},
+            ],
+            "estimated_sessions": 2,
+        }
+    )
+    db_session.add(ev)
+    db_session.commit()
+    for _ in range(2):
+        s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
+        db_session.add(s)
+    db_session.commit()
+
+    # 首轮：2 时段 → 2 条 execute_session + 1 条 start_analysis
+    assert app_mod._supplement_decision_logs(db_session) == 3
+    # 幂等：二次调用不再补
+    assert app_mod._supplement_decision_logs(db_session) == 0
+
+    logs = db_session.exec(
+        select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
+    ).all()
+    types = sorted(x.decision_type for x in logs)
+    assert types == ["execute_session", "execute_session", "start_analysis"]
+    assert all(x.cron_id == "platform" for x in logs)
+    assert all(x.get_context().get("platform_supplemented") for x in logs)
+
+
+def test_supplement_completed_skips_when_already_logged(monkeypatch, db_session):
+    """Completed eval that already has worker-reported logs is not duplicated."""
+    import agenteval.web.app as app_mod
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
+
+    ev = IntelligentEvalDB(
+        name="supp-completed2",
+        target_id="t1",
+        status=IntelligentEvalStatus.COMPLETED.value,
+        started_at=utc_now(),
+    )
+    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
+    db_session.add(ev)
+    db_session.commit()
+    s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
+    db_session.add(s)
+    db_session.commit()
+
+    from agenteval.intelligent_eval.decision_logs import create_decision_log
+
+    # 已有 worker 正常上报的两条日志
+    create_decision_log(ev.id, "execute_session", "时段0-1h欠账1个会话，需要执行会话", "manual-run-1", {}, db_session)
+    create_decision_log(ev.id, "start_analysis", "所有会话已完成，开始分析", "manual-run-1", {}, db_session)
+
+    assert app_mod._supplement_decision_logs(db_session) == 0