feat(xingbang): 兴邦金租 AI 评审平台模块(构建于 mini-OpenClaw)

新增兴邦金租评审项目模块, 作为 mini-OpenClaw 之上的完整交付:
- 五智能体流水线(data_curator/risk_scout/case_finder/approval_advisor/risk_monitor)
- evolution 自进化子系统(轨迹采集+奖励计算+模式归纳prompt增强)
- /api/xingbang/* 端点(config/workflow/stream/evolution/trajectories)
- 运行时产物(客户产出情报卡 + 进化轨迹数据)已 gitignore, 不含隐私

遵循兴邦三档分级审批制度, 数据源指向兴邦实验数据包

backend/api/xingbang_api.py

@@ -0,0 +1,145 @@
+"""Xingbang API — 兴邦金租评审工作流 + 自进化模块的 HTTP 接口。
+
+端:
+  POST /api/xingbang/workflow     运行五智能体审批流水线(返回全部产出)
+  POST /api/xingbang/workflow/stream  流式运行(SSE, 逐步展示)
+  GET  /api/xingbang/evolution    查看自进化洞察
+  GET  /api/xingbang/trajectories 列出历史轨迹
+  GET  /api/xingbang/trajectories/{id} 读取单条轨迹
+
+复用 mini-OpenClaw 的 AgentManager.llm 与后端基础设施。
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+from fastapi import APIRouter
+from pydantic import BaseModel
+from sse_starlette.sse import EventSourceResponse
+
+from graph.agent import agent_manager
+from xingbang.workflow import XingbangWorkflow
+from xingbang.config import XINGBANG_CONFIG
+
+router = APIRouter(prefix="/api/xingbang", tags=["xingbang"])
+
+
+# 懒加载单例工作流(复用 agent_manager.llm)
+_workflow: XingbangWorkflow | None = None
+
+
+def _get_workflow() -> XingbangWorkflow:
+    global _workflow
+    if _workflow is None:
+        if agent_manager._llm is None:
+            # 若 agent 未初始化, 手动初始化(需要 BASE_DIR)
+            from pathlib import Path as _P
+            _workflow = XingbangWorkflow(
+                llm=None,
+                base_dir=_P(__file__).resolve().parent.parent,
+                evolution_enabled=True,
+            )
+            raise RuntimeError("Agent 未初始化, 请先启动后端(会自动初始化 agent_manager)")
+        _workflow = XingbangWorkflow(
+            llm=agent_manager._llm,
+            base_dir=Path(__file__).resolve().parent.parent,
+            evolution_enabled=True,
+        )
+    return _workflow
+
+
+class WorkflowRequest(BaseModel):
+    task: str                    # 客户/项目描述
+    customer: str = ""           # 客户名(用于轨迹标注)
+
+
+@router.get("/config")
+async def get_config():
+    """返回兴邦项目配置(数据源、五智能体、档位)。"""
+    return XINGBANG_CONFIG
+
+
+@router.post("/workflow")
+async def run_workflow(req: WorkflowRequest):
+    """运行完整五智能体审批流水线。"""
+    wf = _get_workflow()
+    try:
+        result = await wf.run(req.task, customer=req.customer)
+        return result
+    except RuntimeError as e:
+        from fastapi import HTTPException
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/workflow/stream")
+async def run_workflow_stream(req: WorkflowRequest):
+    """流式运行(SSE), 逐步展示五智能体进度与产出。"""
+    wf = _get_workflow()
+
+    async def gen():
+        async for event in wf.run_stream(req.task, customer=req.customer):
+            yield {
+                "event": event.get("event", "event"),
+                "data": json.dumps(
+                    {k: v for k, v in event.items() if k != "event"},
+                    ensure_ascii=False,
+                ),
+            }
+
+    return EventSourceResponse(gen())
+
+
+class FeedbackRequest(BaseModel):
+    trajectory_id: str
+    feedback: str               # "positive" | "negative"
+
+
+@router.post("/evolution/feedback")
+async def submit_feedback(req: FeedbackRequest):
+    """提交用户反馈(Level 2 奖励信号), 追加进轨迹。"""
+    try:
+        traj_file = (
+            Path(__file__).resolve().parent.parent
+            / "xingbang_evolution" / "trajectories" / f"{req.trajectory_id}.json"
+        )
+        if not traj_file.exists():
+            return {"error": f"轨迹不存在: {req.trajectory_id}"}
+        data = json.loads(traj_file.read_text(encoding="utf-8"))
+        data.setdefault("user_feedback", req.feedback)
+        data["user_feedback"] = req.feedback
+        traj_file.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
+        return {"status": "ok", "trajectory_id": req.trajectory_id, "feedback": req.feedback}
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@router.get("/evolution")
+async def get_evolution():
+    """查看自进化洞察报告。"""
+    wf = _get_workflow()
+    report = wf.evolution.generate_insights()
+    return {"insights": report, "config": XINGBANG_CONFIG}
+
+
+@router.get("/trajectories")
+async def list_trajectories():
+    """列出所有已保存轨迹。"""
+    wf = _get_workflow()
+    paths = wf.recorder.list_trajectories()
+    return {"count": len(paths),
+            "trajectories": [p.stem for p in paths]}
+
+
+@router.get("/trajectories/{trajectory_id}")
+async def get_trajectory(trajectory_id: str):
+    """读取单条轨迹详情。"""
+    wf = _get_workflow()
+    traj = wf.recorder.load(trajectory_id)
+    if not traj:
+        from fastapi import HTTPException
+        raise HTTPException(status_code=404, detail="轨迹不存在")
+    return traj