feat(learning): add learning runtime with pattern mining

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>

backend/app/agents/learning/retrospector.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.agents.schemas.learning import SessionRetrospective
+
+
+def _classify_task_type(query_text: str) -> str:
+    normalized = (query_text or "").lower()
+    if any(token in normalized for token in ("总结", "分析", "对比", "report", "analyze")):
+        return "analysis"
+    if any(token in normalized for token in ("安排", "提醒", "日程", "todo", "task")):
+        return "planning_or_execution"
+    if any(token in normalized for token in ("文档", "资料", "年报", "search", "查")):
+        return "retrieval"
+    return "general"
+
+
+def build_session_retrospective(
+    *,
+    request_id: str,
+    session_id: str,
+    user_query: str,
+    state: dict[str, Any] | None,
+    runtime_context: dict[str, Any] | None = None,
+) -> SessionRetrospective:
+    state = state or {}
+    if hasattr(runtime_context, "model_dump"):
+        runtime_context = runtime_context.model_dump(mode="json")
+    runtime_context = runtime_context or {}
+    skill_shortlist = state.get("skill_shortlist") or []
+    used_skill_names = [
+        item.get("skill_name")
+        for item in skill_shortlist
+        if isinstance(item, dict) and item.get("skill_name")
+    ]
+
+    task_refs = []
+    for task in (state.get("completed_tasks") or [])[:4]:
+        if isinstance(task, dict):
+            task_refs.append(
+                {
+                    "task_id": task.get("task_id"),
+                    "title": task.get("title"),
+                    "status": task.get("status"),
+                }
+            )
+
+    event_refs = []
+    for event in (state.get("event_trace") or [])[:8]:
+        if isinstance(event, dict):
+            event_refs.append(
+                {
+                    "event_type": event.get("event_type"),
+                    "task_id": event.get("task_id"),
+                    "agent_id": event.get("agent_id"),
+                }
+            )
+
+    verification_evidence = []
+    for evidence in (state.get("verification_evidence") or [])[:6]:
+        if isinstance(evidence, dict):
+            verification_evidence.append(evidence)
+
+    verification_status = state.get("verification_status")
+    execution_mode = state.get("execution_mode")
+    primary_agent = state.get("current_agent") or "master"
+    retrospective_shortlist = state.get("retrospective_shortlist") or []
+
+    summary_parts = [
+        f"本轮请求按 {execution_mode or 'unknown'} 模式处理",
+        f"主要负责 agent 为 {primary_agent}",
+    ]
+    if verification_status:
+        summary_parts.append(f"验证结果为 {verification_status}")
+    if used_skill_names:
+        summary_parts.append(f"命中技能候选 {', '.join(used_skill_names[:3])}")
+    if retrospective_shortlist:
+        summary_parts.append(f"参考了 {len(retrospective_shortlist)} 条历史复盘")
+
+    final_response = state.get("final_response")
+    outcome = "completed" if final_response else "failed"
+    if not final_response and verification_status == "passed":
+        outcome = "completed"
+    if final_response and verification_status == "skipped":
+        outcome = "partial"
+
+    return SessionRetrospective(
+        retrospective_id=request_id,
+        user_id=str(runtime_context.get("user_id") or ""),
+        conversation_id=session_id,
+        response_message_id=request_id,
+        query_text=user_query,
+        final_response=final_response,
+        summary="；".join(summary_parts) + "。",
+        task_type=_classify_task_type(user_query),
+        execution_mode=execution_mode,
+        primary_agent=primary_agent,
+        verification_status=verification_status,
+        verification_summary=state.get("verification_summary"),
+        used_skill_names=used_skill_names,
+        evidence_refs=verification_evidence,
+        task_refs=task_refs,
+        event_refs=event_refs,
+        context_snapshot={
+            "runtime_request_context": runtime_context,
+            "recommended_runtime_mode": runtime_context.get("recommended_runtime_mode"),
+            "parallel_worthiness": state.get("parallel_worthiness"),
+            "retrospective_shortlist_count": len(retrospective_shortlist),
+            "scheduled_subtask_count": len(state.get("scheduled_subtasks") or []),
+            "merge_report": dict(state.get("merge_report") or {}),
+            "verification_report": dict(state.get("verification_report") or {}),
+        },
+        outcome=outcome,
+    )