feat: add 灵犀 Feishu bot + fix agent schedule system + default all tools

- Add 灵犀学习助手 Feishu bot (lingxi_app_service + lingxi_ws_handler)
- Fix agent_schedule_service missing AgentSchedule import (Celery Beat)
- Fix scene_templates default enable_tools=False → True
- Fix workflow_engine LLM node: empty tools list now = all tools (consistent with agent node)
- Add 创建agent.md guide document

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/core/config.py

@@ -105,6 +105,11 @@ class Settings(BaseSettings):
     TIANTIAN_APP_SECRET: str = ""
     TIANTIAN_AGENT_ID: str = ""  # 创建苏瑶3号后写入
 
+    # 灵犀飞书应用配置（独立 WS 连接，路由到灵犀学习助手 Agent）
+    LINGXI_APP_ID: str = ""
+    LINGXI_APP_SECRET: str = ""
+    LINGXI_AGENT_ID: str = ""  # 创建灵犀后写入
+
     class Config:
         env_file = str(_ENV_PATH)
         case_sensitive = True

backend/app/main.py

@@ -241,6 +241,13 @@ async def startup_event():
     except Exception as e:
         logger.error(f"甜甜长连接启动失败: {e}")
 
+    # 启动灵犀飞书长连接（学习助手）
+    try:
+        from app.services.lingxi_ws_handler import start_ws_client as start_lingxi_ws
+        asyncio.ensure_future(start_lingxi_ws())
+    except Exception as e:
+        logger.error(f"灵犀长连接启动失败: {e}")
+
 # 注册路由
 from app.api import auth, uploads, workflows, executions, websocket, execution_logs, data_sources, agents, platform_templates, model_configs, webhooks, template_market, batch_operations, collaboration, permissions, monitoring, alert_rules, node_test, node_templates, tools, agent_chat, agent_monitoring, knowledge_base, agent_schedules, notifications, feishu_bind
 

backend/app/services/agent_schedule_service.py

@@ -95,6 +95,8 @@ def check_and_run_due_schedules() -> int:
     Returns:
         本次触发的任务数
     """
+    from app.models.agent_schedule import AgentSchedule
+
     db: Optional[Session] = None
     try:
         db = SessionLocal()

backend/app/services/lingxi_app_service.py

@@ -0,0 +1,105 @@
+"""灵犀飞书应用 API 服务 — 通过灵犀应用发送消息到用户"""
+from __future__ import annotations
+
+import json
+import logging
+import time
+from typing import Optional
+
+import httpx
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+_token_cache: dict = {"token": None, "expires_at": 0}
+
+
+def _get_tenant_access_token() -> Optional[str]:
+    now = time.time()
+    if _token_cache["token"] and now < _token_cache["expires_at"] - 300:
+        return _token_cache["token"]
+
+    app_id = settings.LINGXI_APP_ID
+    app_secret = settings.LINGXI_APP_SECRET
+    if not app_id or not app_secret:
+        logger.warning("灵犀应用未配置（LINGXI_APP_ID / LINGXI_APP_SECRET）")
+        return None
+
+    try:
+        with httpx.Client(timeout=10) as client:
+            resp = client.post(
+                "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal",
+                json={"app_id": app_id, "app_secret": app_secret},
+            )
+            result = resp.json()
+            if resp.is_success and result.get("code") == 0:
+                token = result["tenant_access_token"]
+                expire = result.get("expire", 7200)
+                _token_cache["token"] = token
+                _token_cache["expires_at"] = now + expire
+                logger.info("灵犀 tenant_access_token 获取成功")
+                return token
+            else:
+                logger.warning("灵犀 token 获取失败: %s", result)
+                return None
+    except Exception as e:
+        logger.warning("灵犀 token 获取异常: %s", e)
+        return None
+
+
+def send_message_to_user(
+    open_id: str, title: str, content: str,
+    status: str = "info", detail_link: Optional[str] = None,
+) -> bool:
+    token = _get_tenant_access_token()
+    if not token:
+        return False
+    color_map = {"success": "green", "failed": "red", "info": "blue"}
+    color = color_map.get(status, "blue")
+    elements = [{"tag": "markdown", "content": content}]
+    if detail_link:
+        elements.append({
+            "tag": "action",
+            "actions": [{"tag": "button", "text": {"tag": "plain_text", "content": "查看详情"}, "url": detail_link, "type": "default"}],
+        })
+    card = {
+        "config": {"wide_screen_mode": True},
+        "header": {"title": {"tag": "plain_text", "content": title}, "template": color},
+        "elements": elements,
+    }
+    try:
+        with httpx.Client(timeout=10) as client:
+            resp = client.post(
+                "https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id",
+                headers={"Authorization": f"Bearer {token}"},
+                json={"receive_id": open_id, "msg_type": "interactive", "content": json.dumps(card, ensure_ascii=False)},
+            )
+            result = resp.json()
+            if resp.is_success and result.get("code") == 0:
+                logger.info("灵犀消息发送成功: open_id=%s title=%s", open_id[:20], title)
+                return True
+            else:
+                logger.warning("灵犀消息发送失败: code=%s msg=%s", result.get("code"), result.get("msg"))
+                return False
+    except Exception as e:
+        logger.warning("灵犀消息发送异常: %s", e)
+        return False
+
+
+def send_plain_text(open_id: str, text: str) -> bool:
+    token = _get_tenant_access_token()
+    if not token:
+        return False
+    try:
+        with httpx.Client(timeout=10) as client:
+            resp = client.post(
+                "https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id",
+                headers={"Authorization": f"Bearer {token}"},
+                json={"receive_id": open_id, "msg_type": "text", "content": json.dumps({"text": text}, ensure_ascii=False)},
+            )
+            result = resp.json()
+            return resp.is_success and result.get("code") == 0
+    except Exception as e:
+        logger.warning("灵犀文本消息发送异常: %s", e)
+        return False

backend/app/services/lingxi_ws_handler.py

@@ -0,0 +1,278 @@
+"""灵犀飞书长连接 — 固定路由到灵犀学习助手 Agent（方案C：知识图谱+RAG）"""
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from collections import deque
+from typing import Optional
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+_processed_msg_ids: deque[str] = deque(maxlen=20)
+
+
+def _get_message_id(data) -> Optional[str]:
+    try:
+        ev = data.event
+        msg = getattr(ev, "message", None)
+        if msg:
+            return getattr(msg, "message_id", None)
+    except Exception:
+        return None
+    return None
+
+
+def _get_message_text(data) -> Optional[str]:
+    try:
+        ev = data.event
+        msg = getattr(ev, "message", None)
+        if not msg:
+            return None
+        content_str = getattr(msg, "content", None)
+        msg_type = getattr(msg, "message_type", "")
+        if not content_str:
+            return None
+        if msg_type == "text":
+            parsed = json.loads(content_str)
+            return parsed.get("text", "")
+        return None
+    except Exception as e:
+        logger.warning("解析灵犀消息内容失败: %s", e)
+        return None
+
+
+def _get_sender_open_id(data) -> Optional[str]:
+    try:
+        ev = data.event
+        sender = getattr(ev, "sender", None)
+        if not sender:
+            return None
+        sender_id = getattr(sender, "sender_id", None)
+        if not sender_id:
+            return None
+        return getattr(sender_id, "open_id", None)
+    except Exception:
+        return None
+
+
+def _get_chat_type(data) -> str:
+    try:
+        ev = data.event
+        msg = getattr(ev, "message", None)
+        return getattr(msg, "chat_type", "") if msg else ""
+    except Exception:
+        return ""
+
+
+def _reply_to_feishu(open_id: str, text: str):
+    try:
+        from app.services.lingxi_app_service import send_plain_text
+        send_plain_text(open_id, text)
+    except Exception as e:
+        logger.warning("灵犀回复消息失败: %s", e)
+
+
+def _reply_card(open_id: str, title: str, content: str, status: str = "info"):
+    try:
+        from app.services.lingxi_app_service import send_message_to_user
+        send_message_to_user(open_id, title, content, status=status)
+    except Exception as e:
+        logger.warning("灵犀回复卡片失败: %s", e)
+
+
+def _make_llm_logger(db, agent_id: Optional[str] = None, user_id: Optional[str] = None):
+    def _log(metrics: dict):
+        try:
+            from app.models.agent_llm_log import AgentLLMLog
+            log = AgentLLMLog(
+                agent_id=agent_id, session_id=metrics.get("session_id"),
+                user_id=user_id, model=metrics.get("model", ""),
+                provider=metrics.get("provider"),
+                prompt_tokens=metrics.get("prompt_tokens", 0),
+                completion_tokens=metrics.get("completion_tokens", 0),
+                total_tokens=metrics.get("total_tokens", 0),
+                latency_ms=metrics.get("latency_ms", 0),
+                iteration_number=metrics.get("iteration_number", 0),
+                step_type=metrics.get("step_type"),
+                tool_name=metrics.get("tool_name"),
+                status=metrics.get("status", "success"),
+                error_message=metrics.get("error_message"),
+            )
+            db.add(log)
+            db.commit()
+        except Exception as e:
+            logger.warning("写入 AgentLLMLog 失败: %s", e)
+    return _log
+
+
+async def _handle_message_async(data):
+    open_id = _get_sender_open_id(data)
+    chat_type = _get_chat_type(data)
+    text = _get_message_text(data)
+
+    if not open_id or chat_type != "p2p":
+        return
+
+    logger.info("灵犀收到消息: open_id=%s text=%s", open_id[:20], text[:50] if text else "(空)")
+
+    if not text:
+        return
+
+    from sqlalchemy.orm import Session
+    from app.core.database import SessionLocal
+    from app.models.agent import Agent
+
+    db: Optional[Session] = None
+    try:
+        db = SessionLocal()
+
+        agent_id = settings.LINGXI_AGENT_ID
+        if not agent_id:
+            _reply_to_feishu(open_id, "灵犀尚未配置，请联系管理员。")
+            return
+
+        agent = db.query(Agent).filter(Agent.id == agent_id).first()
+        if not agent:
+            _reply_to_feishu(open_id, "灵犀 Agent 已不存在，请联系管理员。")
+            return
+
+        _reply_to_feishu(open_id, "正在思考，请稍候...")
+
+        from app.agent_runtime import AgentRuntime, AgentConfig, AgentLLMConfig, AgentToolConfig, AgentMemoryConfig
+
+        wc = agent.workflow_config or {}
+        nodes = wc.get("nodes", [])
+        system_prompt = agent.description or ""
+        model = "deepseek-v4-flash"
+        provider = "deepseek"
+        temperature = 0.85
+        max_iterations = 30
+        tools_whitelist = []
+
+        for n in nodes:
+            if n.get("type") not in ("agent", "llm", "template"):
+                continue
+            cfg = n.get("data", {}) if isinstance(n, dict) else getattr(n, "data", {})
+            system_prompt = cfg.get("system_prompt", "") or system_prompt
+            model = cfg.get("model", model)
+            provider = cfg.get("provider", provider)
+            temperature = float(cfg.get("temperature", temperature))
+            max_iterations = int(cfg.get("max_iterations", max_iterations))
+            tools_whitelist = cfg.get("tools", tools_whitelist)
+            break
+
+        config = AgentConfig(
+            name=agent.name or "灵犀",
+            system_prompt=system_prompt,
+            llm=AgentLLMConfig(
+                model=model, provider=provider,
+                temperature=temperature, max_iterations=max_iterations,
+            ),
+            tools=AgentToolConfig(include_tools=tools_whitelist),
+            memory=AgentMemoryConfig(
+                max_history_messages=int(cfg.get("memory_max_history", 20)),
+                vector_memory_top_k=int(cfg.get("memory_vector_top_k", 5)),
+                persist_to_db=bool(cfg.get("memory_persist", True)),
+                vector_memory_enabled=bool(cfg.get("memory_vector_enabled", True)),
+                learning_enabled=bool(cfg.get("memory_learning", True)),
+            ),
+            user_id=None,
+            memory_scope_id=str(agent.id),
+        )
+
+        on_llm_call = _make_llm_logger(db, agent_id=str(agent.id))
+        runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
+        result = await runtime.run(text)
+
+        if result.content:
+            _reply_card(open_id, f"{agent.name}", result.content.strip(), status="success")
+        else:
+            _reply_to_feishu(open_id, "Agent 未返回有效回复，请重试。")
+
+        logger.info(
+            "灵犀 Agent 回复完成: open_id=%s agent=%s iterations=%d tools=%d",
+            open_id[:20], agent.name, result.iterations_used, result.tool_calls_made,
+        )
+
+    except Exception as e:
+        logger.error("灵犀消息处理失败: %s", e)
+        try:
+            _reply_to_feishu(open_id, f"处理失败: {e!s}")
+        except Exception:
+            pass
+    finally:
+        if db:
+            db.close()
+
+
+def _handle_message_internal(data):
+    msg_id = _get_message_id(data)
+    if msg_id:
+        if msg_id in _processed_msg_ids:
+            return
+        _processed_msg_ids.append(msg_id)
+
+    open_id = _get_sender_open_id(data)
+    chat_type = _get_chat_type(data)
+    text = _get_message_text(data)
+
+    if not open_id or chat_type != "p2p" or not text:
+        return
+
+    try:
+        loop = asyncio.get_event_loop()
+        if loop.is_running():
+            asyncio.ensure_future(_handle_message_async(data))
+        else:
+            loop.run_until_complete(_handle_message_async(data))
+    except Exception as e:
+        logger.error("灵犀创建消息处理任务失败: %s", e)
+        try:
+            _reply_to_feishu(open_id, f"处理失败: {e!s}")
+        except Exception:
+            pass
+
+
+def _build_event_handler():
+    from lark_oapi.event.dispatcher_handler import EventDispatcherHandler
+
+    def on_message_receive(data):
+        _handle_message_internal(data)
+
+    builder = EventDispatcherHandler.builder(encrypt_key="", verification_token="")
+    builder.register_p2_im_message_receive_v1(on_message_receive)
+    return builder.build()
+
+
+async def start_ws_client():
+    if not settings.LINGXI_APP_ID or not settings.LINGXI_APP_SECRET:
+        logger.warning("灵犀应用未配置，跳过灵犀长连接启动")
+        return
+
+    from lark_oapi.ws import Client as WSClient
+
+    handler = _build_event_handler()
+    client = WSClient(
+        app_id=settings.LINGXI_APP_ID,
+        app_secret=settings.LINGXI_APP_SECRET,
+        event_handler=handler,
+        auto_reconnect=True,
+    )
+
+    logger.info("灵犀长连接客户端启动中...")
+
+    while True:
+        try:
+            await client._connect()
+            logger.info("灵犀长连接已建立")
+            asyncio.ensure_future(client._ping_loop())
+            while True:
+                await asyncio.sleep(3600)
+        except asyncio.CancelledError:
+            break
+        except Exception as e:
+            logger.warning("灵犀长连接断开，3秒后重连: %s", e)
+            await asyncio.sleep(3)

backend/app/services/scene_templates.py

@@ -103,7 +103,7 @@ def build_workflow_for_template(template_id: str, parameters: Optional[Dict[str,
         raise ValueError(f"未知模板: {template_id}")
 
     temperature = float(parameters.get("temperature", meta.get("default_temperature", 0.3)))
-    enable_tools = bool(parameters.get("enable_tools", False))
+    enable_tools = bool(parameters.get("enable_tools", True))
     tools = parameters.get("tools")
     if tools is not None and not isinstance(tools, list):
         tools = []
@@ -116,7 +116,7 @@ def build_workflow_for_template(template_id: str, parameters: Optional[Dict[str,
         prompt,
         temperature=temperature,
         enable_tools=enable_tools,
-        tools=tools if enable_tools else [],
+        tools=tools,
     )
 
 

backend/app/services/workflow_engine.py

@@ -1825,23 +1825,20 @@ class WorkflowEngine:
                 
                 # 如果启用了工具，加载工具定义
                 tools = []
-                if enable_tools and tools_config:
+                if enable_tools:
                     from app.services.tool_registry import tool_registry
-                    # 从注册表加载工具定义
+                    if tools_config:
                         tools = tool_registry.get_tools_by_names(tools_config)
-                    logger.info(f"[rjb] LLM节点启用工具调用: {len(tools)} 个工具, 工具列表: {tools_config}")
+                    else:
+                        # 空列表 = 全部工具（与 Agent 节点行为一致）
+                        tools = tool_registry.get_all_tool_schemas()
+                    logger.info(f"[rjb] LLM节点启用工具调用: {len(tools)} 个工具, 工具列表: {tools_config or '全部'}")
                     if not tools:
                         logger.warning(
                             "[rjb] LLM 已 enable_tools 但当前进程 tool_registry 中 0 个匹配 schema，"
                             "将无法发起 function calling（常见于 Celery Worker 未加载 tools_bootstrap）。配置=%s",
                             tools_config,
                         )
-                    elif len(tools) < len(tools_config):
-                        missing = [n for n in tools_config if not tool_registry.get_tool_schema(n)]
-                        logger.warning(
-                            "[rjb] LLM 工具部分缺失 schema，缺失=%s（可动手能力不完整）",
-                            missing,
-                        )
                 
                 # 调用LLM服务
                 try:

创建agent.md

@@ -0,0 +1,158 @@
+# Agent 创建指南
+
+## 概述
+
+本系统支持多种方式创建 Agent。**所有创建方式均默认赋予 Agent 全部 18 个内置工具能力**，除非明确限制。
+
+## 内置工具清单（18个）
+
+| 类别 | 工具 | 用途 |
+|------|------|------|
+| 文件 | `file_read` | 读文件：文本/PDF/docx/xlsx/图片OCR(作业拍照识别) |
+| 文件 | `file_write` | 写文件：笔记、报告、数据导出 |
+| 网络 | `http_request` | HTTP 请求：上网查资料、调 API |
+| 网络 | `url_parse` | URL 解析 |
+| 数据 | `json_process` | JSON 结构化数据处理 |
+| 数据 | `database_query` | 数据库查询 |
+| 计算 | `math_calculate` | 数学计算 |
+| 文本 | `text_analyze` | 文本分析 |
+| 时间 | `datetime` | 日期时间计算、倒计时 |
+| 系统 | `system_info` | 系统环境信息 |
+| 调度 | `schedule_create` | 创建定时任务（cron 表达式） |
+| 调度 | `schedule_list` | 查看定时任务列表 |
+| 调度 | `schedule_delete` | 删除定时任务 |
+| 通信 | `send_email` | 发送邮件 |
+| 工具 | `regex_test` | 正则表达式测试 |
+| 工具 | `crypto_util` | 加密/解密工具 |
+| 工具 | `random_generate` | 随机数据生成 |
+| 调试 | `adb_log` | Android 设备日志（ADB） |
+
+---
+
+## 创建方式
+
+### 方式一：前端界面创建（http://localhost:3001/agents）
+
+#### 1.1 手动创建
+1. 打开 http://localhost:3001/agents
+2. 点击「创建 Agent」
+3. 填写名称、描述
+4. 进入工作流设计器拖拽节点（至少需要一个 LLM 或 Agent 节点）
+5. **不设置工具白名单 = 全部工具可用**
+6. 保存
+
+#### 1.2 场景模板创建
+1. 在 Agents 页面点击「从场景模板创建」
+2. 选择模板（客服/研发/运维）
+3. 填写名称
+4. 系统默认启用全部工具（`enable_tools=true`，`tools` 为空）
+
+#### 1.3 导入 JSON
+1. 导出已有 Agent 的 JSON
+2. 修改后导入
+3. 若 JSON 中未指定 `tools` 字段 = 全部工具
+
+---
+
+### 方式二：后端 API 创建
+
+```bash
+# 直接创建
+curl -X POST http://localhost:8037/api/v1/agents \
+  -H "Authorization: Bearer <token>" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "My Agent",
+    "description": "Agent description",
+    "workflow_config": {
+      "nodes": [
+        {"id": "start-1", "type": "start", "position": {"x": 80, "y": 120}, "data": {}},
+        {"id": "llm-1", "type": "llm", "position": {"x": 320, "y": 120},
+         "data": {
+           "prompt": "你是一个有用的AI助手",
+           "enable_tools": true
+           // 不指定 tools = 全部工具
+         }}
+      ],
+      "edges": [...]
+    }
+  }'
+```
+
+### 方式三：Python 脚本创建
+
+```python
+from app.core.database import SessionLocal
+from app.models.agent import Agent
+import uuid
+
+agent = Agent(
+    id=str(uuid.uuid4()),
+    name="My Agent",
+    description="Description",
+    workflow_config={
+        "nodes": [
+            {"id": "node_1", "type": "llm", "label": "LLM",
+             "data": {
+                 "system_prompt": "你是一个有用的AI助手",
+                 "model": "deepseek-v4-flash",
+                 "provider": "deepseek",
+                 "temperature": 0.7,
+                 "max_iterations": 10,
+                 # 不指定 tools = 全部工具
+             }}
+        ],
+        "edges": []
+    },
+    status="active",
+)
+db = SessionLocal()
+db.add(agent)
+db.commit()
+db.close()
+```
+
+---
+
+## 工具配置规则
+
+### Agent 节点（AgentRuntime 执行）
+- `tools` 字段不存在 → 全部工具
+- `tools: []` → 全部工具
+- `tools: ["file_read", "http_request"]` → 仅这两个工具
+
+### LLM 节点（工作流引擎执行）
+- `enable_tools: false` → 无工具
+- `enable_tools: true` + `tools` 未设置/为空 → 全部工具
+- `enable_tools: true` + `tools: ["file_read"]` → 仅 file_read
+
+### 飞书长连接路由（WS Handler）
+- 读取 Agent workflow_config 节点 data 中的 `tools` 字段
+- 未设置 = `include_tools=[]` = 全部工具
+
+---
+
+## 快速检查 Agent 能力
+
+```
+GET /api/v1/health
+```
+
+返回 `builtin_tools.count` 和 `builtin_tools.names`，确认工具已注册。
+
+---
+
+## 配置飞书连接
+
+1. 在飞书开放平台创建应用，开启「机器人」能力
+2. 在 `.env` 中添加：
+```
+XXXXX_APP_ID=cli_xxx
+XXXXX_APP_SECRET=xxx
+XXXXX_AGENT_ID=<agent_uuid>
+```
+3. 创建 `app/services/xxxxx_app_service.py`（token 管理 + 消息发送）
+4. 创建 `app/services/xxxxx_ws_handler.py`（WebSocket 长连接 + 消息路由）
+5. 在 `app/core/config.py` 添加配置字段
+6. 在 `app/main.py` startup 事件中启动 WS 客户端
+7. 重启后端