feat(workflow): add session context memory support to LLM nodes

api/app/core/workflow/executor.py

@@ -74,6 +74,7 @@ class WorkflowExecutor:
             初始化的工作流状态
         """
         user_message = input_data.get("message") or ""
+        conversation_messages = input_data.get("conv_messages") or []
 
         # 会话变量处理：从配置文件获取变量定义列表，转换为字典（name -> default value）
         config_variables_list = self.workflow_config.get("variables") or []
@@ -114,7 +115,7 @@ class WorkflowExecutor:
         }
 
         return {
-            "messages": [('user', user_message)],
+            "messages": conversation_messages,
             "variables": variables,
             "node_outputs": {},
             "runtime_vars": {},  # 运行时节点变量（简化版，供快速访问）

api/app/core/workflow/nodes/base_node.py

@@ -7,13 +7,13 @@
 import asyncio
 import logging
 from abc import ABC, abstractmethod
-from operator import add
 from typing import Any
 
-from langchain_core.messages import AnyMessage, AIMessage
+from langchain_core.messages import AIMessage
 from langgraph.config import get_stream_writer
 from typing_extensions import TypedDict, Annotated
 
+from app.core.config import settings
 from app.core.workflow.variable_pool import VariablePool
 
 logger = logging.getLogger(__name__)
@@ -25,7 +25,7 @@ class WorkflowState(TypedDict):
     The state object passed between nodes in a workflow, containing messages, variables, node outputs, etc.
     """
     # List of messages (append mode)
-    messages: Annotated[list[tuple[str, str]], add]
+    messages: list[dict[str, str]]
 
     # Set of loop node IDs, used for assigning values in loop nodes
     cycle_nodes: list
@@ -154,7 +154,7 @@ class BaseNode(ABC):
         Returns:
             超时时间
         """
-        return 60
+        return settings.WORKFLOW_NODE_TIMEOUT
         # return self.error_handling.get("timeout", 60)
 
     async def run(self, state: WorkflowState) -> dict[str, Any]:
@@ -203,6 +203,7 @@ class BaseNode(ABC):
             # 返回包装后的输出和运行时变量
             return {
                 **wrapped_output,
+                "messages": state["messages"],
                 "variables": state["variables"],
                 "runtime_vars": {
                     self.node_id: runtime_var
@@ -356,6 +357,7 @@ class BaseNode(ABC):
             # Build complete state update (including node_outputs, runtime_vars, and final streaming buffer)
             state_update = {
                 **final_output,
+                "messages": state["messages"],
                 "variables": state["variables"],
                 "runtime_vars": {
                     self.node_id: runtime_var

api/app/core/workflow/nodes/end/node.py

@@ -6,7 +6,6 @@ End 节点实现
 
 import logging
 import re
-import asyncio
 
 from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
 from app.core.workflow.nodes.enums import NodeType
@@ -38,7 +37,23 @@ class EndNode(BaseNode):
         # 如果配置了输出模板，使用模板渲染；否则使用默认输出
         if output_template:
             output = self._render_template(output_template, state, strict=False)
+            state['messages'].extend([
+                {
+                    "role": "user",
+                    "content": self.get_variable("sys.message", state)
+                },
+                {
+                    "role": "assistant",
+                    "content": output
+                }
+            ])
         else:
+            state['messages'].extend([
+                {
+                    "role": "user",
+                    "content": self.get_variable("sys.message", state)
+                },
+            ])
             output = "工作流已完成"
 
         # 统计信息（用于日志）
@@ -166,6 +181,12 @@ class EndNode(BaseNode):
                 "chunk_index": 1,
                 "is_suffix": False
             })
+            state['messages'].extend([
+                {
+                    "role": "user",
+                    "content": self.get_variable("sys.message", state)
+                }
+            ])
             yield {"__final__": True, "result": output}
             return
 
@@ -176,7 +197,6 @@ class EndNode(BaseNode):
                 source_node_id = edge.get("source")
                 # Check if the source node is an LLM node
                 for node in self.workflow_config.get("nodes", []):
-                    print("="*50)
                     logger.info(f"节点 {self.node_id} 的类型 {node.get("type")}")
                     if node.get("id") == source_node_id and node.get("type") == NodeType.LLM:
                         direct_upstream_llm_nodes.append(source_node_id)
@@ -216,12 +236,24 @@ class EndNode(BaseNode):
             })
             logger.info(f"节点 {self.node_id} 已通过 writer 发送完整内容")
 
+            state['messages'].extend([
+                {
+                    "role": "user",
+                    "content": self.get_variable("sys.message", state)
+                },
+                {
+                    "role": "assistant",
+                    "content": output
+                }
+            ])
+
             # yield completion marker
             yield {"__final__": True, "result": output}
             return
 
         # Has reference to direct upstream LLM node, only output the part after that reference (suffix)
-        logger.info(f"节点 {self.node_id} 检测到直接上游 LLM 节点引用，只输出后缀部分（从索引 {upstream_llm_ref_index + 1} 开始）")
+        logger.info(
+            f"节点 {self.node_id} 检测到直接上游 LLM 节点引用，只输出后缀部分（从索引 {upstream_llm_ref_index + 1} 开始）")
 
         # Collect suffix parts
         suffix_parts = []
@@ -258,6 +290,17 @@ class EndNode(BaseNode):
         # 构建完整输出（用于返回，包含前缀 + 动态内容 + 后缀）
         full_output = self._render_template(output_template, state, strict=False)
 
+        state['messages'].extend([
+            {
+                "role": "user",
+                "content": self.get_variable("sys.message", state)
+            },
+            {
+                "role": "assistant",
+                "content": full_output
+            }
+        ])
+
         logger.info(f"[后缀调试] 节点 {self.node_id} 后缀部分数量: {len(suffix_parts)}")
         logger.info(f"[后缀调试] 后缀内容: '{suffix}'")
         logger.info(f"[后缀调试] 后缀长度: {len(suffix)}")
@@ -280,7 +323,8 @@ class EndNode(BaseNode):
             })
             logger.info(f"节点 {self.node_id} 已通过 writer 发送后缀，full_content 长度: {len(full_output)}")
         else:
-            logger.warning(f"[后缀调试] 节点 {self.node_id} 后缀为空，不发送！upstream_llm_ref_index={upstream_llm_ref_index}, parts数量={len(parts)}")
+            logger.warning(f"[后缀调试] 节点 {self.node_id} 后缀为空，不发送！"
+                           f"upstream_llm_ref_index={upstream_llm_ref_index}, parts数量={len(parts)}")
 
         # 统计信息
         node_outputs = state.get("node_outputs", {})

api/app/core/workflow/nodes/llm/config.py

@@ -11,12 +11,12 @@ class MessageConfig(BaseModel):
     """消息配置"""
 
     role: str = Field(
-        ...,
+        default='user',
         description="消息角色：system, user, assistant"
     )
 
     content: str = Field(
-        ...,
+        default="",
         description="消息内容，支持模板变量，如：{{ sys.message }}"
     )
 
@@ -30,6 +30,23 @@ class MessageConfig(BaseModel):
         return v.lower()
 
 
+class MemoryWindowSetting(BaseModel):
+    enable: bool = Field(
+        default=False,
+        description="启用记忆"
+    )
+
+    enable_window: bool = Field(
+        default=False,
+        description="启用记忆窗口"
+    )
+
+    window_size: int = Field(
+        default=20,
+        description="记忆窗口大小"
+    )
+
+
 class LLMNodeConfig(BaseNodeConfig):
     """LLM 节点配置
     
@@ -48,6 +65,11 @@ class LLMNodeConfig(BaseNodeConfig):
         description="上下文"
     )
 
+    memory: MemoryWindowSetting = Field(
+        ...,
+        description="对话上下文窗口"
+    )
+
     # 简单模式
     prompt: str | None = Field(
         default=None,

api/app/core/workflow/nodes/llm/node.py

@@ -85,28 +85,31 @@ class LLMNode(BaseNode):
         """
 
         # 1. 处理消息格式（优先使用 messages）
-        messages_config = self.config.get("messages")
+        messages_config = self.typed_config.messages
 
         if messages_config:
             # 使用 LangChain 消息格式
             messages = []
             for msg_config in messages_config:
-                role = msg_config.get("role", "user").lower()
-                content_template = msg_config.get("content", "")
+                role = msg_config.role.lower()
+                content_template = msg_config.content
                 content_template = self._render_context(content_template, state)
                 content = self._render_template(content_template, state)
 
                 # 根据角色创建对应的消息对象
                 if role == "system":
-                    messages.append(SystemMessage(content=content))
+                    messages.append({"role": "system", "content": content})
                 elif role in ["user", "human"]:
-                    messages.append(HumanMessage(content=content))
+                    messages.append({"role": "user", "content": content})
                 elif role in ["ai", "assistant"]:
-                    messages.append(AIMessage(content=content))
+                    messages.append({"role": "user", "content": content})
                 else:
                     logger.warning(f"未知的消息角色: {role}，默认使用 user")
-                    messages.append(HumanMessage(content=content))
+                    messages.append({"role": "user", "content": content})
 
+            if self.typed_config.memory.enable:
+                # if self.typed_config.memory.enable_window:
+                messages = messages[:-1] + state["messages"][-self.typed_config.memory.window_size:] + messages[-1:]
             prompt_or_messages = messages
         else:
             # 使用简单的 prompt 格式（向后兼容）
@@ -189,7 +192,7 @@ class LLMNode(BaseNode):
         return {
             "prompt": prompt_or_messages if isinstance(prompt_or_messages, str) else None,
             "messages": [
-                {"role": msg.__class__.__name__.replace("Message", "").lower(), "content": msg.content}
+                {"role": msg.get("role"), "content": msg.get("content", "")}
                 for msg in prompt_or_messages
             ] if isinstance(prompt_or_messages, list) else None,
             "config": {