feat: Agent 运行时、对话 API、作业助手与引擎修复及前端执行超时

- agent_runtime 模块与 agent_chat API，前端 AgentChat 视图与路由对接
- workflow_engine: code 节点命名空间与 json 引用修复
- llm_service: 工具调用 extra_body（如 DeepSeek）
- create_homework_manager_agent / _3 脚本与测试脚本扩展
- frontend: WORKFLOW_EXECUTION_HTTP_TIMEOUT_MS、AgentChatPreview/MainLayout 等
- 文档：架构说明与自主 Agent 改造完成情况

Made-with: Cursor

backend/app/agent_runtime/memory.py

@@ -0,0 +1,135 @@
+"""
+Agent 记忆管理：包装已有 persistent_memory_service，提供会话级和长期记忆。
+"""
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Dict, List, Optional
+from sqlalchemy.orm import Session
+
+from app.core.database import SessionLocal
+from app.services.persistent_memory_service import (
+    load_persistent_memory,
+    save_persistent_memory,
+    persist_enabled,
+)
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+class AgentMemory:
+    """
+    分层记忆管理器：
+
+    - 工作记忆：当前会话消息列表（由 AgentRuntime 直接管理）
+    - 长期记忆：从 MySQL 加载/保存的用户画像和关键事实
+    - 上下文压缩：对话过长时自动裁剪或总结
+    """
+
+    def __init__(
+        self,
+        scope_kind: str = "agent",
+        scope_id: Optional[str] = None,
+        session_key: Optional[str] = None,
+        persist: bool = True,
+        max_history: int = 20,
+    ):
+        self.scope_kind = scope_kind
+        self.scope_id = scope_id or "default"
+        self.session_key = session_key or "default_session"
+        self.persist = persist and persist_enabled()
+        self.max_history = max_history
+        # 从长期记忆加载的上下文（启动时加载）
+        self._long_term_context: Dict[str, Any] = {}
+
+    async def initialize(self) -> str:
+        """
+        初始化记忆：从 DB/Redis 加载长期记忆，构造初始上下文文本。
+        返回注入 system prompt 的记忆文本块。
+        """
+        if not self.persist or not self.scope_id:
+            return ""
+
+        db: Optional[Session] = None
+        try:
+            db = SessionLocal()
+            payload = load_persistent_memory(
+                db, self.scope_kind, self.scope_id, self.session_key
+            )
+            if payload and isinstance(payload, dict):
+                self._long_term_context = payload
+                # 构建注入 system prompt 的记忆文本
+                parts = []
+                profile = payload.get("user_profile")
+                if profile and isinstance(profile, dict):
+                    profile_text = json.dumps(profile, ensure_ascii=False)
+                    parts.append(f"## 用户画像\n{profile_text}")
+
+                context = payload.get("context")
+                if context and isinstance(context, dict):
+                    ctx_text = json.dumps(context, ensure_ascii=False)
+                    parts.append(f"## 上下文\n{ctx_text}")
+
+                history = payload.get("conversation_history")
+                if history and isinstance(history, list) and len(history) > 0:
+                    summary = self._summarize_history(history)
+                    parts.append(f"## 历史对话摘要\n{summary}")
+
+                if parts:
+                    return "\n\n".join(parts)
+        except Exception as e:
+            logger.warning("加载长期记忆失败: %s", e)
+        finally:
+            if db:
+                db.close()
+        return ""
+
+    async def save_context(
+        self, user_message: str, assistant_reply: str
+    ) -> None:
+        """将单轮对话保存到长期记忆。"""
+        if not self.persist or not self.scope_id:
+            return
+
+        # 更新上下文
+        ctx = self._long_term_context.get("context", {})
+        ctx["last_user_message"] = user_message[:500]
+        ctx["last_assistant_reply"] = assistant_reply[:500]
+        self._long_term_context["context"] = ctx
+
+        db: Optional[Session] = None
+        try:
+            db = SessionLocal()
+            save_persistent_memory(
+                db, self.scope_kind, self.scope_id,
+                self.session_key, self._long_term_context,
+            )
+        except Exception as e:
+            logger.warning("保存长期记忆失败: %s", e)
+        finally:
+            if db:
+                db.close()
+
+    def trim_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """
+        裁剪消息列表：保留最近的 N 条，但始终保留第一条 system 消息。
+        """
+        if len(messages) <= self.max_history:
+            return messages
+
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+
+        trimmed = other_msgs[-(self.max_history - len(system_msgs)):]
+        return system_msgs + trimmed
+
+    @staticmethod
+    def _summarize_history(history: List[Dict[str, Any]]) -> str:
+        """简单汇总历史对话（不做 LLM 压缩，仅计数）。"""
+        turns = 0
+        for m in history:
+            if m.get("role") == "user":
+                turns += 1
+        return f"共 {turns} 轮历史对话（详情已存入长期记忆）"