feat(workflow): officially support workflow session variables

api/app/controllers/app_controller.py

@@ -60,14 +60,14 @@ def list_apps(
     """
     workspace_id = current_user.current_workspace_id
     service = app_service.AppService(db)
-    
+
     # 当 ids 存在且不为 None 时，根据 ids 获取应用
     if ids is not None:
         app_ids = [id.strip() for id in ids.split(',') if id.strip()]
         items_orm = app_service.get_apps_by_ids(db, app_ids, workspace_id)
         items = [service._convert_to_schema(app, workspace_id) for app in items_orm]
         return success(data=items)
-    
+
     # 正常分页查询
     items_orm, total = app_service.list_apps(
         db,

api/app/core/workflow/executor.py

@@ -3,13 +3,11 @@
 
 基于 LangGraph 的工作流执行引擎。
 """
-
-# import uuid
 import datetime
 import logging
+import uuid
 from typing import Any
 
-from langchain_core.messages import HumanMessage
 from langgraph.graph.state import CompiledStateGraph
 
 from app.core.workflow.graph_builder import GraphBuilder
@@ -55,6 +53,12 @@ class WorkflowExecutor:
         self.edges = workflow_config.get("edges", [])
         self.execution_config = workflow_config.get("execution_config", {})
 
+        self.checkpoint_config = {
+            "configurable": {
+                "thread_id": uuid.uuid4(),
+            }
+        }
+
     def _prepare_initial_state(self, input_data: dict[str, Any]) -> WorkflowState:
         """准备初始状态（注入系统变量和会话变量）
 
@@ -95,7 +99,7 @@ class WorkflowExecutor:
                             case VariableType.ARRAY_NUMBER | VariableType.ARRAY_OBJECT | VariableType.ARRAY_BOOLEAN | VariableType.ARRAY_STRING:
                                 conversation_vars[var_name] = []
         input_variables = input_data.get("variables") or {}  # Start 节点的自定义变量
-
+        conversation_vars = conversation_vars | input_data.get("conv", {})
         # 构建分层的变量结构
         variables = {
             "sys": {
@@ -110,7 +114,7 @@ class WorkflowExecutor:
         }
 
         return {
-            "messages": [HumanMessage(content=user_message)],
+            "messages": [('user', user_message)],
             "variables": variables,
             "node_outputs": {},
             "runtime_vars": {},  # 运行时节点变量（简化版，供快速访问）
@@ -196,6 +200,28 @@ class WorkflowExecutor:
         logger.info(f"[前缀分析] 与 End 相邻且被引用的节点: {adjacent_and_referenced}")
         return prefixes, adjacent_and_referenced
 
+    def _build_final_output(self, result, elapsed_time):
+        node_outputs = result.get("node_outputs", {})
+        final_output = self._extract_final_output(node_outputs)
+        token_usage = self._aggregate_token_usage(node_outputs)
+        conversation_id = None
+        for node_id, node_output in node_outputs.items():
+            if node_output.get("node_type") == "start":
+                conversation_id = node_output.get("output", {}).get("conversation_id")
+                break
+
+        return {
+            "status": "completed",
+            "output": final_output,
+            "node_outputs": node_outputs,
+            "messages": result.get("messages", []),
+            "conversation_id": conversation_id,
+            "elapsed_time": elapsed_time,
+            "token_usage": token_usage,
+            "error": result.get("error"),
+            "variables": result.get("variables", {}),
+        }
+
     def build_graph(self, stream=False) -> CompiledStateGraph:
         """构建 LangGraph
 
@@ -236,40 +262,16 @@ class WorkflowExecutor:
 
         # 3. 执行工作流
         try:
-            result = await graph.ainvoke(initial_state)
+
+            result = await graph.ainvoke(initial_state, config=self.checkpoint_config)
 
             # 计算耗时
             end_time = datetime.datetime.now()
             elapsed_time = (end_time - start_time).total_seconds()
 
-            # 提取节点输出（现在包含 start 和 end 节点）
-            node_outputs = result.get("node_outputs", {})
-
-            # 提取最终输出（从最后一个非 start/end 节点）
-            final_output = self._extract_final_output(node_outputs)
-
-            # 聚合 token 使用情况
-            token_usage = self._aggregate_token_usage(node_outputs)
-
-            # 提取 conversation_id（从 start 节点输出）
-            conversation_id = None
-            for node_id, node_output in node_outputs.items():
-                if node_output.get("node_type") == "start":
-                    conversation_id = node_output.get("output", {}).get("conversation_id")
-                    break
-
             logger.info(f"工作流执行完成: execution_id={self.execution_id}, elapsed_time={elapsed_time:.2f}s")
 
-            return {
-                "status": "completed",
-                "output": final_output,
-                "node_outputs": node_outputs,
-                "messages": result.get("messages", []),
-                "conversation_id": conversation_id,
-                "elapsed_time": elapsed_time,
-                "token_usage": token_usage,
-                "error": result.get("error")
-            }
+            return self._build_final_output(result, elapsed_time)
 
         except Exception as e:
             # 计算耗时（即使失败也记录）
@@ -331,11 +333,11 @@ class WorkflowExecutor:
         # 3. Execute workflow
         try:
             chunk_count = 0
-            final_state = None
 
             async for event in graph.astream(
                     initial_state,
                     stream_mode=["updates", "debug", "custom"],  # Use updates + debug + custom mode
+                    config=self.checkpoint_config
             ):
                 # event should be a tuple: (mode, data)
                 # But let's handle both cases
@@ -411,12 +413,11 @@ class WorkflowExecutor:
                 elif mode == "updates":
                     # Handle state updates - store final state
                     logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())}")
-                    final_state = data
 
             # 计算耗时
             end_time = datetime.datetime.now()
             elapsed_time = (end_time - start_time).total_seconds()
-
+            result = graph.get_state(self.checkpoint_config).values
             logger.info(
                 f"Workflow execution completed (streaming), "
                 f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s"
@@ -425,12 +426,7 @@ class WorkflowExecutor:
             # 发送 workflow_end 事件
             yield {
                 "event": "workflow_end",
-                "data": {
-                    "execution_id": self.execution_id,
-                    "status": "completed",
-                    "elapsed_time": elapsed_time,
-                    "timestamp": end_time.isoformat()
-                }
+                "data": self._build_final_output(result, elapsed_time)
             }
 
         except Exception as e:

api/app/core/workflow/graph_builder.py

@@ -4,6 +4,7 @@ from typing import Any
 
 from langgraph.graph.state import CompiledStateGraph, StateGraph
 from langgraph.graph import START, END
+from langgraph.checkpoint.memory import InMemorySaver
 
 from app.core.workflow.expression_evaluator import evaluate_condition
 from app.core.workflow.nodes import WorkflowState, NodeFactory
@@ -249,4 +250,5 @@ class GraphBuilder:
         self.graph = StateGraph(WorkflowState)
         self.add_nodes()
         self.add_edges()  # 添加边必须在添加节点之后
-        return self.graph.compile()
+        checkpointer = InMemorySaver()
+        return self.graph.compile(checkpointer=checkpointer)

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

@@ -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[AnyMessage], add]
+    messages: Annotated[list[tuple[str, str]], add]
 
     # Set of loop node IDs, used for assigning values in loop nodes
     cycle_nodes: list
@@ -203,6 +203,7 @@ class BaseNode(ABC):
             # 返回包装后的输出和运行时变量
             return {
                 **wrapped_output,
+                "variables": state["variables"],
                 "runtime_vars": {
                     self.node_id: runtime_var
                 },
@@ -355,6 +356,7 @@ class BaseNode(ABC):
             # Build complete state update (including node_outputs, runtime_vars, and final streaming buffer)
             state_update = {
                 **final_output,
+                "variables": state["variables"],
                 "runtime_vars": {
                     self.node_id: runtime_var
                 },