feat: Phase 5 — AI Research Agent（受控工具白名单 + LLM 编排 + API）

- agent/tools.py：Tool 元数据（JSON Schema）+ 白名单调用（异常转可读反馈，不中断对话）
- agent/tools_impl.py：6 个受控工具 search_stocks / get_market_data / test_factor / run_backtest / get_experiment / compare_experiments —— 全部只读经 Job/Experiment 链路，研究自动归档；无 shell/任意执行/写删数据能力
- agent/llm.py：LLMClient 抽象 + OpenAI 兼容客户端（LLM_API_KEY/LLM_BASE_URL/LLM_MODEL 走 .env，未配置给出引导提示）+ 研究纪律 system prompt（反过拟合/样本外/成本）
- agent/service.py：编排循环（tool/final JSON 决策 → 执行 → 回喂 → 结论），轮次上限兜底，未知工具拒绝
- /api/agent/chat；httpx 移至主依赖；Job 默认工厂抽取（api/agent/executor 复用）
- 测试 6 项（白名单无 shell、完整研究循环产出、未知工具拒绝、轮次兜底），全量 79 passed / ruff clean

backend/app/agent/llm.py

@@ -0,0 +1,77 @@
+"""LLM 客户端抽象（Phase 5）。
+
+实现约定：真实 Key 来自 .env（LLM_API_KEY / LLM_BASE_URL / LLM_MODEL，见 config.yaml 引用）。
+测试注入 FakeLLMClient 走完整编排链路，不触网。
+"""
+
+from __future__ import annotations
+
+from typing import Protocol
+
+import httpx
+
+from app.agent.tools import tools_schema
+from app.agent.tools_impl import build_tools
+from app.core.config import get_settings
+
+SYSTEM_TEMPLATE = """你是个人 A 股量化研究助手（Research Assistant），不是系统管理员。
+
+你可以调用以下工具（每次只能输出一个动作）：
+{tools}
+
+输出规则：只输出一行 JSON，两种形态之一：
+1. 需要调用工具：{{"tool": "<工具名>", "args": {{...}}}}
+2. 给出结论：{{"final": "结论文本"}}
+
+研究纪律（必须遵守）：
+- 先提出假设 → 用工具做因子测试或回测 → 基于实验事实分析，再给结论
+- 不得仅凭单次回测高收益就宣布策略有效；要主动说明样本外、过拟合、
+  look-ahead bias、交易成本、参数敏感性等风险（未做验证的项要明说「未验证」）
+- 全程只读：不得要求删除/修改数据或执行任意命令（你也没有这类工具）
+- 回答使用简体中文
+"""
+
+
+class LLMClient(Protocol):
+    def chat(self, messages: list[dict]) -> str: ...
+
+
+class OpenAICompatibleClient:
+    """OpenAI 兼容 Chat Completions（qwen/dashscope、deepseek、openai 等均适用）。"""
+
+    def __init__(self, *, base_url: str, api_key: str, model: str, timeout: float = 60.0) -> None:
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._model = model
+        self._timeout = timeout
+
+    def chat(self, messages: list[dict]) -> str:
+        with httpx.Client(timeout=self._timeout) as client:
+            resp = client.post(
+                f"{self._base_url}/chat/completions",
+                headers={"Authorization": f"Bearer {self._api_key}"},
+                json={
+                    "model": self._model,
+                    "messages": messages,
+                    "temperature": 0.2,
+                },
+            )
+            resp.raise_for_status()
+            data = resp.json()
+        return data["choices"][0]["message"]["content"]
+
+
+def build_llm_from_settings():
+    """从配置构造 LLM；未配置 Key 时返回 None（调用方给出引导提示）。"""
+    settings = get_settings()
+    if not settings.llm_api_key:
+        return None
+    return OpenAICompatibleClient(
+        base_url=settings.llm_base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1",
+        api_key=settings.llm_api_key,
+        model=settings.llm_model,
+    )
+
+
+def system_prompt() -> str:
+    return SYSTEM_TEMPLATE.format(tools=tools_schema(build_tools()))

backend/app/agent/service.py

@@ -0,0 +1,98 @@
+"""Agent 编排：自然语言 → 受控工具调用循环 → 结论（AGENT.md §29 假设-实验-分析循环）。"""
+
+from __future__ import annotations
+
+import json
+import re
+from typing import Any
+
+from app.agent.llm import SYSTEM_TEMPLATE, LLMClient
+from app.agent.tools import Tool, tools_schema
+from app.agent.tools_impl import build_tools
+
+MAX_TOOL_ROUNDS = 5
+_DECISION_PATTERN = re.compile(r"\{.*\}", re.DOTALL)
+
+
+def _parse_decision(text: str) -> dict[str, Any]:
+    """容忍 LLM 输出中的代码块/前后缀，提取首个 JSON 对象。"""
+    match = _DECISION_PATTERN.search(text)
+    if not match:
+        raise ValueError(f"无法从模型输出中解析动作 JSON：{text[:200]}")
+    try:
+        return json.loads(match.group(0))
+    except json.JSONDecodeError as exc:
+        raise ValueError(f"模型输出的 JSON 不合法：{text[:200]}") from exc
+
+
+class AgentService:
+    """受控研究 Agent：每轮让 LLM 决策 tool/final，执行工具并把结果回喂，直至 final。"""
+
+    def __init__(self, llm: LLMClient, tools: list[Tool] | None = None) -> None:
+        self._llm = llm
+        self._tools = {t.name: t for t in (tools or build_tools())}
+
+    def _by_name(self, name: str) -> Tool:
+        tool = self._tools.get(name)
+        if tool is None:
+            raise ValueError(f"工具不存在：{name}（可用 {sorted(self._tools)}）")
+        return tool
+
+    def chat(self, message: str, max_rounds: int = MAX_TOOL_ROUNDS) -> dict:
+        messages: list[dict] = [
+            {
+                "role": "system",
+                "content": SYSTEM_TEMPLATE.format(tools=tools_schema(list(self._tools.values()))),
+            },
+            {"role": "user", "content": message},
+        ]
+        actions: list[dict] = []
+
+        for _round in range(max_rounds):
+            try:
+                decision = _parse_decision(self._llm.chat(messages))
+            except ValueError as exc:
+                # LLM 回复格式异常：回传错误要求重试一次结构化输出
+                messages.append(
+                    {
+                        "role": "user",
+                        "content": f"输出格式错误，请只输出一行 JSON（tool 或 final）：{exc}",
+                    }
+                )
+                continue
+            if "final" in decision:
+                return {"reply": str(decision["final"]), "actions": actions}
+            tool_name = str(decision.get("tool", ""))
+            args = decision.get("args") or {}
+            if not isinstance(args, dict):
+                args = {}
+            tool = self._by_name(tool_name)  # 白名单之外的调用直接报错
+            output = tool.invoke(args)
+            actions.append({"tool": tool_name, "args": args, "output": output[:1000]})
+            messages.append(
+                {
+                    "role": "assistant",
+                    "content": f'{{"tool": "{tool_name}", "args": {json.dumps(args, ensure_ascii=False)}}}',
+                }
+            )
+            messages.append(
+                {
+                    "role": "user",
+                    "content": f"工具 {tool_name} 返回：\n{output}\n请继续（如需再调用输出 tool，否则输出 final）。",
+                }
+            )
+
+        # 轮次耗尽：强制收尾
+        messages.append(
+            {
+                "role": "user",
+                "content": "已达工具调用轮次上限，请直接给出基于已有事实的最终结论（只输出 final JSON）。",
+            }
+        )
+        try:
+            decision = _parse_decision(self._llm.chat(messages))
+        except ValueError:
+            decision = {
+                "final": "研究轮次耗尽且模型未给出结构化结论，请人工查看 actions 中的实验输出。"
+            }
+        return {"reply": str(decision.get("final", decision)), "actions": actions}

backend/app/agent/tools.py

@@ -0,0 +1,45 @@
+"""AI Research Agent（Phase 5）。
+
+Agent 是 Research Assistant（AGENT.md §28/§29）：
+- 只能调用本目录 tools 提供的**白名单受控工具**（只读研究能力）
+- 禁止：执行任意 shell / 修改删除数据 / 修改配置与凭证
+- 研究行为：提出假设 → 建立实验 → 运行测试 → 分析 → 下一步，禁止以单次高收益宣告策略有效
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+TOOL_CALL_TAG = "__tool__"
+
+
+@dataclass(frozen=True)
+class Tool:
+    """受控工具元数据（LLM 可见的 JSON Schema + 调用实现）。"""
+
+    name: str
+    description: str
+    parameters: dict[str, Any]
+    handler: Callable[[dict[str, Any]], str] = field(repr=False)
+
+    def schema(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "description": self.description,
+            "parameters": self.parameters,
+        }
+
+    def invoke(self, args: dict[str, Any]) -> str:
+        """执行工具；任何异常都转为可读错误串（不向上抛，避免中断整轮对话）。"""
+        try:
+            result = self.handler(args)
+            return result if isinstance(result, str) else json.dumps(result, ensure_ascii=False)
+        except Exception as exc:  # noqa: BLE001 —— 工具异常反馈给 LLM 而非崩溃
+            return json.dumps({"error": f"{type(exc).__name__}: {exc}"}, ensure_ascii=False)
+
+
+def tools_schema(tools: list[Tool]) -> str:
+    return json.dumps([t.schema() for t in tools], ensure_ascii=False, indent=1)

backend/app/agent/tools/__init__.py

@@ -1 +0,0 @@
-"""Agent 受控工具集：每个 Tool 是后端服务的只读 / 沙箱化入口。"""

backend/app/agent/tools_impl.py

@@ -0,0 +1,248 @@
+"""受控工具集实现（AGENT.md §28）：Agent 只能调用这里的白名单工具。
+
+全部工具经 Job/Experiment 链路或只读查询执行：
+- 不提供 shell / 任意代码执行 / 修改配置与凭证 / 删除数据
+- 任何研究都会产出 Experiment 归档（可复现）
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import date
+
+from app.agent.tools import Tool
+from app.application.services.job_executor import default_factories, submit_and_run
+from app.domain.entities.research import (
+    BacktestResult,
+    FactorTestReport,
+    ResearchSpec,
+)
+
+
+def _day(text: str) -> date:
+    return date.fromisoformat(text)
+
+
+def _pick(mapping: dict, key: str, default=None):
+    val = mapping.get(key, default)
+    if isinstance(val, str):
+        val = val.strip()
+        if val == "":
+            return default
+    return val
+
+
+def build_tools(factories: dict | None = None) -> list[Tool]:
+    facts = factories or default_factories()
+    session_factory = facts["session_factory"]
+    stock_repo_f = facts["stock_repo_factory"]
+    daily_repo_f = facts["daily_repo_factory"]
+    exp_repo_f = facts["experiment_repo_factory"]
+
+    def search_stocks(args: dict) -> str:
+        q = str(_pick(args, "q", "") or "").upper()
+        with session_factory() as session:
+            stocks = stock_repo_f(session).list()
+        rows = [
+            s for s in stocks if (not q) or q in s.symbol.upper() or q in (s.name or "").upper()
+        ][:15]
+        if not rows:
+            return "未找到匹配股票"
+        return "\n".join(
+            f"{s.symbol} {s.name} 行业={s.industry or '-'} 上市={s.list_date}" for s in rows
+        )
+
+    def get_market_data(args: dict) -> str:
+        symbol = str(_pick(args, "symbol", "")).upper()
+        start = _day(str(_pick(args, "start", "2024-01-01")))
+        end = _day(str(_pick(args, "end", date.today().isoformat())))
+        with session_factory() as session:
+            bars = daily_repo_f(session).get_range(symbol, start, end)
+        if not bars:
+            return f"{symbol} 在 {start}~{end} 无日线数据（可能未同步）"
+        head, tail = bars[0], bars[-1]
+        last = "\n".join(f"{b.trade_date} close={b.close}" for b in bars[-8:])
+        change = float(tail.close) / float(head.close) - 1 if head.close and tail.close else None
+        return (
+            f"{symbol} {start}~{end} 共 {len(bars)} 根日线；"
+            f"区间 {head.trade_date}→{tail.trade_date} 收盘 {head.close}→{tail.close}"
+            f"（涨跌 {change * 100:.2f}% 若数据完整）；最近 8 根：\n{last}"
+        )
+
+    def _run_spec(spec: ResearchSpec, desc: str) -> str:
+        job = submit_and_run(spec, factories=facts)
+        if job.status != "success":
+            return f"{desc} 执行失败：{job.error}"
+        if spec.type == "backtest":
+            result = BacktestResult.model_validate_json(job.result_json or "{}")
+            s = result.summary
+            return (
+                f"回测完成（Experiment {job.experiment_id}，代码版本 {_code_version(job, exp_repo_f)}）。"
+                f"总收益 {s.total_return_pct:.2f}%，年化 {s.annual_return_pct:.2f}%，"
+                f"Sharpe {s.sharpe:.2f}，最大回撤 {s.max_drawdown_pct:.2f}%，"
+                f"交易 {s.total_trades} 笔，平均换手 {s.avg_turnover_pct:.1f}%。"
+                f"未建模约束 {len(result.unimplemented)} 项（成本/涨跌停近似见实验详情）。"
+            )
+        report = FactorTestReport.model_validate_json(job.result_json or "{}")
+        qs = ", ".join(f"Q{q.quantile + 1}: {q.return_pct:.2f}%" for q in report.quantile_returns)
+        return (
+            f"因子测试完成（Experiment {job.experiment_id}）。IC {report.ic_mean:.4f}，"
+            f"RankIC {report.rank_ic_mean:.4f}，ICIR {report.icir:.2f}，正收益占比 "
+            f"{report.positive_ratio_pct:.1f}%，样本 {report.sample_days} 日；分层未来收益 {qs}。"
+            f"注意：单因子测试不代表策略有效，需结合稳健性分析。"
+        )
+
+    def test_factor(args: dict) -> str:
+        name = str(_pick(args, "name", ""))
+        start = _day(str(_pick(args, "start", "2024-01-01")))
+        end = _day(str(_pick(args, "end", "2024-12-31")))
+        spec = ResearchSpec(
+            type="factor_test",
+            universe={"exclude_st": True, "min_listing_days": 0},
+            factors=[{"name": name, "weight": 1.0}],
+            selection={"top_n": 10},
+            rebalance="monthly",
+            period=(start, end),
+        )
+        return _run_spec(spec, f"因子 {name} 测试")
+
+    def run_backtest(args: dict) -> str:
+        factor_names = [f.strip() for f in str(_pick(args, "factors", "momentum_60")).split(",")]
+        top_n = int(_pick(args, "top_n", 5) or 5)
+        rebalance = str(_pick(args, "rebalance", "monthly"))
+        exclude_st = bool(_pick(args, "exclude_st", True))
+        start = _day(str(_pick(args, "start", "2024-01-01")))
+        end = _day(str(_pick(args, "end", "2024-12-31")))
+        spec = ResearchSpec(
+            type="backtest",
+            universe={"exclude_st": exclude_st, "min_listing_days": 0},
+            factors=[{"name": n, "weight": 1.0} for n in factor_names],
+            selection={"top_n": top_n},
+            rebalance=rebalance,
+            period=(start, end),
+        )
+        return _run_spec(spec, "回测")
+
+    def get_experiment(args: dict) -> str:
+        exp_id = str(_pick(args, "experiment_id", "")).upper()
+        with session_factory() as session:
+            exp = exp_repo_f(session).get(exp_id)
+        if exp is None:
+            return f"Experiment {exp_id} 不存在（可用列表：GET /api/experiments）"
+        spec = json.loads(exp.spec_json)
+        return (
+            f"Experiment {exp.id} [{exp.kind}] 因子={[f['name'] for f in spec.get('factors', [])]} "
+            f"区间={spec.get('period')} 调仓={spec.get('rebalance')}；摘要：{exp.summary_text or '-'} "
+            f"代码版本={exp.code_version or '-'} 创建={exp.created_at}"
+        )
+
+    def compare_experiments(args: dict) -> str:
+        ids = [
+            x.strip().upper()
+            for x in str(_pick(args, "experiment_ids", "")).split(",")
+            if x.strip()
+        ]
+        if not ids:
+            return "请提供 experiment_ids（逗号分隔）"
+        with session_factory() as session:
+            repo = exp_repo_f(session)
+            rows = [(i, repo.get(i)) for i in ids]
+        out = []
+        for exp_id, exp in rows:
+            if exp is None:
+                out.append(f"{exp_id}: 不存在")
+            else:
+                spec = json.loads(exp.spec_json)
+                out.append(
+                    f"{exp.id}: 因子={[f['name'] for f in spec.get('factors', [])]} "
+                    f"区间={spec.get('period')} → {exp.summary_text or '-'}"
+                )
+        return "\n".join(out)
+
+    return [
+        Tool(
+            "search_stocks",
+            "按代码或名称搜索股票，返回基础信息（只读）",
+            {
+                "type": "object",
+                "properties": {"q": {"type": "string", "description": "代码或名称关键字"}},
+            },
+            search_stocks,
+        ),
+        Tool(
+            "get_market_data",
+            "读取一只股票一段区间的日线行情摘要（只读，不复权）",
+            {
+                "type": "object",
+                "properties": {
+                    "symbol": {"type": "string", "description": "如 600519.SH"},
+                    "start": {"type": "string", "description": "YYYY-MM-DD"},
+                    "end": {"type": "string", "description": "YYYY-MM-DD"},
+                },
+                "required": ["symbol"],
+            },
+            get_market_data,
+        ),
+        Tool(
+            "test_factor",
+            "对单个因子做 IC/RankIC/分层测试并归档 Experiment",
+            {
+                "type": "object",
+                "properties": {
+                    "name": {
+                        "type": "string",
+                        "description": "因子名（momentum_60 / volatility_20 等）",
+                    },
+                    "start": {"type": "string"},
+                    "end": {"type": "string"},
+                },
+                "required": ["name"],
+            },
+            test_factor,
+        ),
+        Tool(
+            "run_backtest",
+            "运行 TopK 低频回测并归档 Experiment（成本/涨跌停近似建模）",
+            {
+                "type": "object",
+                "properties": {
+                    "factors": {"type": "string", "description": "逗号分隔的因子名"},
+                    "top_n": {"type": "integer"},
+                    "rebalance": {"type": "string", "enum": ["monthly", "weekly"]},
+                    "exclude_st": {"type": "boolean"},
+                    "start": {"type": "string"},
+                    "end": {"type": "string"},
+                },
+            },
+            run_backtest,
+        ),
+        Tool(
+            "get_experiment",
+            "读取已归档实验的摘要",
+            {
+                "type": "object",
+                "properties": {"experiment_id": {"type": "string"}},
+                "required": ["experiment_id"],
+            },
+            get_experiment,
+        ),
+        Tool(
+            "compare_experiments",
+            "对比多个实验（因子/区间/收益摘要）",
+            {
+                "type": "object",
+                "properties": {"experiment_ids": {"type": "string"}},
+                "required": ["experiment_ids"],
+            },
+            compare_experiments,
+        ),
+    ]
+
+
+def _code_version(job, exp_repo_f) -> str:
+    try:
+        with default_factories()["session_factory"]() as session:
+            exp = exp_repo_f(session).get(job.experiment_id or "")
+            return exp.code_version or "-" if exp else "-"
+    except Exception:  # noqa: BLE001
+        return "-"

backend/app/api/agent.py

@@ -0,0 +1,40 @@
+"""AI Research Agent API（Phase 5）。
+
+POST /api/agent/chat {message} → {reply, actions:[{tool,args,output}]}
+未配置 LLM Key 时返回 400 引导（不会崩溃）。
+"""
+
+from __future__ import annotations
+
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel, Field
+
+from app.agent.llm import LLMClient, build_llm_from_settings
+from app.agent.service import AgentService
+
+router = APIRouter(prefix="/agent", tags=["agent"])
+
+
+class AgentChatRequest(BaseModel):
+    message: str = Field(min_length=1, max_length=2000)
+
+
+def _llm_or_raise() -> LLMClient:
+    llm = build_llm_from_settings()
+    if llm is None:
+        raise HTTPException(
+            status_code=400,
+            detail="未配置 LLM：请在根目录 .env 中设置 LLM_API_KEY（可选 LLM_BASE_URL / LLM_MODEL），"
+            "参考 .env.example 与 AGENT.md §33",
+        )
+    return llm
+
+
+@router.post("/chat", summary="与 AI 研究助手对话（受控工具）")
+def agent_chat(
+    body: AgentChatRequest,
+    llm: Annotated[LLMClient, Depends(_llm_or_raise)],
+) -> dict:
+    return AgentService(llm).chat(body.message)

backend/app/api/jobs.py

@@ -14,7 +14,7 @@ from datetime import datetime
 from fastapi import APIRouter, BackgroundTasks, HTTPException
 from fastapi.responses import StreamingResponse
 
-from app.api.deps import DbSession, JobRepoDep, _engine_factory
+from app.api.deps import DbSession, JobRepoDep
 from app.application.services.job_executor import execute_job, new_id
 from app.domain.entities.research import (
     BacktestResult,
@@ -24,27 +24,17 @@ from app.domain.entities.research import (
     ResearchSpec,
 )
 from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import (
-    SqlAlchemyExperimentRepository,
     SqlAlchemyJobRepository,
 )
-from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
-    SqlAlchemyDailyBarRepository,
-    SqlAlchemyStockRepository,
-)
 from app.infrastructure.persistence.sqlalchemy.session import SessionLocal
 
 router = APIRouter(prefix="/jobs", tags=["jobs"])
 
 
 def _bg_factories():
-    return {
-        "session_factory": SessionLocal,
-        "job_repo_factory": lambda s: SqlAlchemyJobRepository(s),
-        "experiment_repo_factory": lambda s: SqlAlchemyExperimentRepository(s),
-        "stock_repo_factory": lambda s: SqlAlchemyStockRepository(s),
-        "daily_repo_factory": lambda s: SqlAlchemyDailyBarRepository(s),
-        "engine": _engine_factory(),
-    }
+    from app.application.services.job_executor import default_factories
+
+    return default_factories()
 
 
 def _decode_result(kind: str, result_json: str | None):

backend/app/api/router.py

@@ -8,7 +8,7 @@ from __future__ import annotations
 
 from fastapi import APIRouter
 
-from app.api import experiments, factors, health, jobs, research, stocks
+from app.api import agent, experiments, factors, health, jobs, research, stocks
 
 api_router = APIRouter()
 api_router.include_router(health.router)
@@ -17,3 +17,4 @@ api_router.include_router(factors.router)
 api_router.include_router(research.router)
 api_router.include_router(jobs.router)
 api_router.include_router(experiments.router)
+api_router.include_router(agent.router)

backend/app/application/services/job_executor.py

@@ -18,6 +18,7 @@ from app.domain.entities.research import (
     BacktestResult,
     ExperimentRecord,
     FactorTestReport,
+    JobRecord,
     JobStatus,
     ResearchSpec,
 )
@@ -144,3 +145,56 @@ def execute_job(
                     session.commit()
         except Exception:  # noqa: BLE001
             pass
+
+
+def default_factories() -> dict:
+    """后台执行所需的独立 Session / Repository / 引擎装配（跨请求生命周期）。"""
+    from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import (
+        SqlAlchemyExperimentRepository,
+        SqlAlchemyJobRepository,
+    )
+    from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
+        SqlAlchemyDailyBarRepository,
+        SqlAlchemyStockRepository,
+    )
+    from app.infrastructure.persistence.sqlalchemy.session import SessionLocal
+    from app.quant.engine import LocalEngine
+
+    return {
+        "session_factory": SessionLocal,
+        "job_repo_factory": lambda s: SqlAlchemyJobRepository(s),
+        "experiment_repo_factory": lambda s: SqlAlchemyExperimentRepository(s),
+        "stock_repo_factory": lambda s: SqlAlchemyStockRepository(s),
+        "daily_repo_factory": lambda s: SqlAlchemyDailyBarRepository(s),
+        "engine": LocalEngine(),
+    }
+
+
+def submit_and_run(spec: ResearchSpec, *, factories: dict | None = None) -> JobRecord:
+    """同步创建并执行一个 Job（复用 Job 状态机与 Experiment 归档），返回终态 Job。"""
+    facts = factories or default_factories()
+    session_factory = facts["session_factory"]
+    job_repo = facts["job_repo_factory"]
+    job = JobRecord(
+        id=new_id("JOB"),
+        kind=spec.type,
+        spec_json=json.dumps(spec.model_dump(mode="json"), ensure_ascii=False),
+        status=JobStatus.QUEUED,
+        created_at=datetime.now(),
+    )
+    with session_factory() as session:
+        job_repo(session).create(job)
+        session.commit()
+    execute_job(
+        job.id,
+        session_factory=session_factory,
+        job_repo_factory=job_repo,
+        experiment_repo_factory=facts["experiment_repo_factory"],
+        stock_repo_factory=facts["stock_repo_factory"],
+        daily_repo_factory=facts["daily_repo_factory"],
+        engine=facts["engine"],
+    )
+    with session_factory() as session:
+        done = job_repo(session).get(job.id)
+    assert done is not None
+    return done

backend/app/core/config.py

@@ -68,6 +68,9 @@ class Settings:
     data_source_primary: str
     data_source_fallback: str
     tushare_token: str
+    llm_api_key: str
+    llm_base_url: str | None
+    llm_model: str
     storage: dict[str, Path]
     config_path: Path = CONFIG_PATH
     env_path: Path = ENV_PATH
@@ -102,6 +105,9 @@ def get_settings() -> Settings:
     secret_env = _deep(cfg, "app.secret_key_env") or "APP_SECRET_KEY"
     url_env = _deep(cfg, "database.url_env") or "DATABASE_URL"
     token_env = _deep(cfg, "data_source.tushare_token_env") or "TUSHARE_TOKEN"
+    llm_key_env = _deep(cfg, "agent.llm_key_env") or "LLM_API_KEY"
+    llm_url_env = _deep(cfg, "agent.llm_base_url_env") or "LLM_BASE_URL"
+    llm_model_env = _deep(cfg, "agent.llm_model_env") or "LLM_MODEL"
 
     default_url = "sqlite:///./data/quant.db"
     database_url = os.environ.get(url_env) or default_url
@@ -122,5 +128,8 @@ def get_settings() -> Settings:
         data_source_primary=str(_deep(cfg, "data_source.primary") or "tushare"),
         data_source_fallback=str(_deep(cfg, "data_source.fallback") or "sina"),
         tushare_token=os.environ.get(token_env, ""),
+        llm_api_key=os.environ.get(llm_key_env, ""),
+        llm_base_url=os.environ.get(llm_url_env) or None,
+        llm_model=os.environ.get(llm_model_env) or "qwen-plus",
         storage=_resolve_storage_dirs(cfg),
     )

backend/pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "sqlalchemy>=2.0",
     "alembic>=1.13",
     "pyyaml>=6.0",
+    "httpx>=0.28.1",
 ]
 
 [project.optional-dependencies]

backend/tests/test_agent.py

@@ -0,0 +1,135 @@
+"""Phase 5 Agent 测试：受控工具白名单 / 编排循环 / 异常输入（用 FakeLLM，不触网）。"""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+from app.agent.service import AgentService
+from app.agent.tools_impl import build_tools
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+
+class FakeLLM:
+    """按顺序返回预设决策的假 LLM。"""
+
+    def __init__(self, decisions: list[dict]) -> None:
+        self._decisions = decisions
+        self.calls = 0
+
+    def chat(self, messages: list[dict]) -> str:
+        self.calls += 1
+        return json.dumps(self._decisions.pop(0), ensure_ascii=False)
+
+
+@pytest.fixture()
+def tmp_factories(tmp_path) -> dict:
+    """注入 tmp SQLite 的默认工厂（隔离本机开发库）。"""
+    engine = create_engine(f"sqlite:///{tmp_path / 'agent.db'}", future=True)
+    Base.metadata.create_all(engine)
+    session_factory = lambda: Session(engine)  # noqa: E731
+
+    from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import (
+        SqlAlchemyExperimentRepository,
+        SqlAlchemyJobRepository,
+    )
+    from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
+        SqlAlchemyDailyBarRepository,
+        SqlAlchemyStockRepository,
+    )
+    from app.quant.engine import LocalEngine
+
+    return {
+        "session_factory": session_factory,
+        "job_repo_factory": lambda s: SqlAlchemyJobRepository(s),
+        "experiment_repo_factory": lambda s: SqlAlchemyExperimentRepository(s),
+        "stock_repo_factory": lambda s: SqlAlchemyStockRepository(s),
+        "daily_repo_factory": lambda s: SqlAlchemyDailyBarRepository(s),
+        "engine": LocalEngine(),
+    }
+
+
+class TestToolCatalog:
+    def test_whitelist_and_metadata(self) -> None:
+        tools = build_tools()
+        names = {t.name for t in tools}
+        assert {
+            "search_stocks",
+            "get_market_data",
+            "test_factor",
+            "run_backtest",
+            "get_experiment",
+            "compare_experiments",
+        } <= names
+        for t in tools:
+            assert t.description
+            assert t.parameters["type"] == "object"
+
+    def test_no_arbitrary_shell_tool(self) -> None:
+        names = {t.name for t in build_tools()}
+        assert "shell" not in names
+        assert "execute" not in names
+
+
+class TestAgentOrchestration:
+    def test_full_research_loop(self, tmp_factories) -> None:
+        llm = FakeLLM(
+            [
+                {
+                    "tool": "test_factor",
+                    "args": {"name": "momentum_60", "start": "2024-03-01", "end": "2024-10-31"},
+                },
+                {
+                    "tool": "run_backtest",
+                    "args": {
+                        "factors": "momentum_60",
+                        "top_n": 3,
+                        "start": "2024-03-01",
+                        "end": "2024-10-31",
+                    },
+                },
+                {"final": "结论：单次样本内回测较好，但需要样本外验证，暂不判定策略有效。"},
+            ]
+        )
+        svc = AgentService(llm, build_tools(factories=tmp_factories))
+        out = svc.chat("请测试 momentum_60 因子并跑一次回测，再给结论。")
+
+        assert [a["tool"] for a in out["actions"]] == ["test_factor", "run_backtest"]
+        # 工具输出真实来自引擎/归档链路（空库也返回结构化文本）
+        assert all("output" in a and a["output"] for a in out["actions"])
+        assert "样本外" in out["reply"]
+
+    def test_tool_error_fed_back_without_abort(self, tmp_factories) -> None:
+        llm = FakeLLM(
+            [
+                {"tool": "search_stocks", "args": {"q": "茅台"}},
+                {"final": "完成"},
+            ]
+        )
+        svc = AgentService(llm, build_tools(factories=tmp_factories))
+        out = svc.chat("查一下茅台")
+        assert out["actions"][0]["tool"] == "search_stocks"
+
+    def test_unknown_tool_is_rejected(self) -> None:
+        class RigidLLM:
+            def chat(self, messages):  # noqa: ARG002
+                return json.dumps({"tool": "delete_database", "args": {}}, ensure_ascii=False)
+
+        svc = AgentService(RigidLLM(), build_tools())
+        with pytest.raises(ValueError, match="工具不存在"):
+            svc.chat("恶意调用")
+
+    def test_round_limit_forces_final(self, tmp_factories) -> None:
+        # LLM 一直要求调工具 → 轮次耗尽强制 final
+        class LoopLLM:
+            def chat(self, messages):  # noqa: ARG002
+                return json.dumps(
+                    {"tool": "search_stocks", "args": {"q": "600519"}}, ensure_ascii=False
+                )
+
+        svc = AgentService(LoopLLM(), build_tools(factories=tmp_factories))
+        out = svc.chat("一直搜索")
+        assert "actions" in out
+        assert len(out["actions"]) <= 6

backend/uv.lock

@@ -911,6 +911,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "alembic" },
     { name = "fastapi" },
+    { name = "httpx" },
     { name = "pyyaml" },
     { name = "sqlalchemy" },
     { name = "uvicorn", extra = ["standard"] },
@@ -932,6 +933,7 @@ dev = [
 requires-dist = [
     { name = "alembic", specifier = ">=1.13" },
     { name = "fastapi", specifier = ">=0.115" },
+    { name = "httpx", specifier = ">=0.28.1" },
     { name = "pyyaml", specifier = ">=6.0" },
     { name = "sqlalchemy", specifier = ">=2.0" },
     { name = "tushare", marker = "extra == 'datasource-tushare'", specifier = ">=1.4" },

docs/ROADMAP.md

@@ -8,14 +8,14 @@
 
 ## 0. 总览与里程碑
 
-| 里程碑 | 内容 | 验收口径 |
+| 里程碑 | 内容 | 状态（2026-09 已实施） |
 |---|---|---|
-| M0 ✅ | 工程初始化：uv + FastAPI 骨架、分层包、SQLAlchemy/Alembic、CI 前检查（pytest/ruff） | `/api/health` 可用、测试通过、已推送远端 |
-| M1 | Phase 1 数据层：Tushare 拉取 → 标准化 → SQLite/Parquet | 命令行可全量/增量同步，来源可追溯 |
-| M2 | Phase 2 Qlib：Qlib Dataset 构建 + 因子 + LightGBM + 回测（Adapter 封装） | Research Specification 能驱动一次完整回测，产出标准结果 |
-| M3 | Phase 3 Web：股票池 / 因子 / 选股 / 回测 / 结果可视化 | 浏览器完成「选池→算因子→回测→看图」闭环 |
-| M4 | Phase 4 Experiment：研究全量可复现归档 + Job/SSE 异步化 | 任一历史实验可一键复跑 |
-| M5 | Phase 5 AI Agent：自然语言 → Research Plan → 受控 Tool → Experiment | Agent 能独立完成一次「因子假设→测试→结论」并留档 |
+| M0 ✅ | 工程初始化 | commit 2a52ee5 |
+| M1 ✅ | Phase 1 数据层（Tushare→SQLite/Parquet、Failover 审计、防未来函数） | commit 2da2342 · 38 tests |
+| M2 ✅ | Phase 2 研究引擎（Spec→因子→评估→低频回测→标准结果；Qlib 桥接占位见 §2 备注） | commit e9f59d3 · 60 tests |
+| M3 ✅ | Phase 3 Web（业务 API + Next.js 前端闭环） | commit 92627f5 · 68 tests |
+| M4 ✅ | Phase 4 Experiment 归档 + Job/SSE 异步（一键复跑） | commit 0ea229d · 73 tests |
+| M5 ✅ | Phase 5 AI Agent（受控 Tool 白名单 + LLM 编排；Key 配置见 .env） | commit（本轮）· 79 tests |
 
 每阶段结束时同步更新：README / AGENT.md 相关清单 / 文档；**禁止跨阶段提前堆量**（AGENT.md §38）。