Merge pull request #164 from SuanmoSuanyangTechnology/fix/workflow-parallelization

fix(workflow): fix improper merge of execution flows caused by multi-branch routing

api/app/controllers/app_controller.py

@@ -7,7 +7,7 @@ from sqlalchemy.orm import Session
 
 from app.core.error_codes import BizCode
 from app.core.logging_config import get_business_logger
-from app.core.response_utils import success
+from app.core.response_utils import success, fail
 from app.db import get_db
 from app.dependencies import get_current_user, cur_workspace_access_guard
 from app.models import User
@@ -661,6 +661,11 @@ async def draft_run(
             data=result,
             msg="工作流任务执行成功"
         )
+    else:
+        return fail(
+            msg="未知应用类型",
+            code=422
+        )
 
 
 @router.post("/{app_id}/draft/run/compare", summary="多模型对比试运行")

api/app/core/workflow/executor.py

@@ -54,6 +54,8 @@ class WorkflowExecutor:
         self.edges = workflow_config.get("edges", [])
         self.execution_config = workflow_config.get("execution_config", {})
 
+        self.start_node_id = None
+
         self.checkpoint_config = RunnableConfig(
             configurable={
                 "thread_id": uuid.uuid4(),
@@ -131,77 +133,12 @@ class WorkflowExecutor:
                 for node in self.workflow_config.get("nodes")
                 if node.get("type") in [NodeType.LOOP, NodeType.ITERATION]
             ],  # loop, iteration node id
-            "looping": False  # loop runing flag, only use in loop node,not use in main loop
+            "looping": False,  # loop runing flag, only use in loop node,not use in main loop
+            "activate": {
+                self.start_node_id: True
+            }
         }
 
-    def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]:
-        """分析 End 节点的前缀配置
-        
-        检查每个 End 节点的模板，找到直接上游节点的引用，
-        提取该引用之前的前缀部分。
-        
-        Returns:
-            元组：({上游节点ID: End节点前缀}, {与End相邻且被引用的节点ID集合})
-        """
-        import re
-
-        prefixes = {}
-        adjacent_and_referenced = set()  # 记录与 End 节点相邻且被引用的节点
-
-        # 找到所有 End 节点
-        end_nodes = [node for node in self.nodes if node.get("type") == "end"]
-        logger.info(f"[前缀分析] 找到 {len(end_nodes)} 个 End 节点")
-
-        for end_node in end_nodes:
-            end_node_id = end_node.get("id")
-            output_template = end_node.get("config", {}).get("output")
-
-            logger.info(f"[前缀分析] End 节点 {end_node_id} 模板: {output_template}")
-
-            if not output_template:
-                continue
-
-            # 找到所有直接连接到 End 节点的上游节点
-            direct_upstream_nodes = []
-            for edge in self.edges:
-                if edge.get("target") == end_node_id:
-                    source_node_id = edge.get("source")
-                    direct_upstream_nodes.append(source_node_id)
-
-            logger.info(f"[前缀分析] End 节点的直接上游节点: {direct_upstream_nodes}")
-
-            # 查找模板中引用了哪些节点
-            # 匹配 {{node_id.xxx}} 或 {{ node_id.xxx }} 格式（支持空格）
-            pattern = r'\{\{\s*([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\s*\}\}'
-            matches = list(re.finditer(pattern, output_template))
-
-            logger.info(f"[前缀分析] 模板中找到 {len(matches)} 个节点引用")
-
-            # 找到第一个直接上游节点的引用
-            for match in matches:
-                referenced_node_id = match.group(1)
-                logger.info(f"[前缀分析] 检查引用: {referenced_node_id}")
-
-                if referenced_node_id in direct_upstream_nodes:
-                    # 这是直接上游节点的引用，提取前缀
-                    prefix = output_template[:match.start()]
-
-                    logger.info(f"[前缀分析] ✅ 找到直接上游节点 {referenced_node_id} 的引用，前缀: '{prefix}'")
-
-                    # 标记这个节点为"相邻且被引用"
-                    adjacent_and_referenced.add(referenced_node_id)
-
-                    if prefix:
-                        prefixes[referenced_node_id] = prefix
-                        logger.info(f"✅ [前缀分析] 为节点 {referenced_node_id} 配置前缀: '{prefix[:50]}...'")
-
-                    # 只处理第一个直接上游节点的引用
-                    break
-
-        logger.info(f"[前缀分析] 最终配置: {prefixes}")
-        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)
@@ -231,10 +168,12 @@ class WorkflowExecutor:
             编译后的状态图
         """
         logger.info(f"开始构建工作流图: execution_id={self.execution_id}")
-        graph = GraphBuilder(
+        builder = GraphBuilder(
             self.workflow_config,
             stream=stream,
-        ).build()
+        )
+        self.start_node_id = builder.start_node_id
+        graph = builder.build()
         logger.info(f"工作流图构建完成: execution_id={self.execution_id}")
 
         return graph
@@ -375,13 +314,15 @@ class WorkflowExecutor:
                     payload = data.get("payload", {})
                     node_name = payload.get("name")
 
+                    if node_name and node_name.startswith("nop"):
+                        continue
+
                     if event_type == "task":
                         # Node starts execution
                         inputv = payload.get("input", {})
-                        variables = inputv.get("variables", {})
-                        variables_sys = variables.get("sys", {})
+                        if not inputv.get("activate", {}).get(node_name):
+                            continue
                         conversation_id = input_data.get("conversation_id")
-                        execution_id = variables_sys.get("execution_id")
                         logger.info(f"[NODE-START] Node starts execution: {node_name} "
                                     f"- execution_id: {self.execution_id}")
 
@@ -390,18 +331,17 @@ class WorkflowExecutor:
                             "data": {
                                 "node_id": node_name,
                                 "conversation_id": conversation_id,
-                                "execution_id": execution_id,
-                                "timestamp": data.get("timestamp")
+                                "execution_id": self.execution_id,
+                                "timestamp": data.get("timestamp"),
                             }
                         }
                     elif event_type == "task_result":
                         # Node execution completed
                         result = payload.get("result", {})
-                        inputv = result.get("input", {})
-                        variables = inputv.get("variables", {})
-                        variables_sys = variables.get("sys", {})
+                        if not result.get("activate", {}).get(node_name):
+                            continue
+
                         conversation_id = input_data.get("conversation_id")
-                        execution_id = variables_sys.get("execution_id")
                         logger.info(f"[NODE-END] Node execution completed: {node_name} "
                                     f"- execution_id: {self.execution_id}")
 
@@ -410,7 +350,7 @@ class WorkflowExecutor:
                             "data": {
                                 "node_id": node_name,
                                 "conversation_id": conversation_id,
-                                "execution_id": execution_id,
+                                "execution_id": self.execution_id,
                                 "timestamp": data.get("timestamp"),
                                 "state": result.get("node_outputs", {}).get(node_name),
                             }

api/app/core/workflow/graph_builder.py

@@ -1,14 +1,16 @@
 import logging
 import uuid
+from collections import defaultdict
 from typing import Any
 
-from langgraph.graph.state import CompiledStateGraph, StateGraph
-from langgraph.graph import START, END
 from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.graph import START, END
+from langgraph.graph.state import CompiledStateGraph, StateGraph
+from langgraph.types import Send
 
 from app.core.workflow.expression_evaluator import evaluate_condition
 from app.core.workflow.nodes import WorkflowState, NodeFactory
-from app.core.workflow.nodes.enums import NodeType
+from app.core.workflow.nodes.enums import NodeType, BRANCH_NODES
 
 logger = logging.getLogger(__name__)
 
@@ -28,7 +30,10 @@ class GraphBuilder:
         self.start_node_id = None
         self.end_node_ids = []
 
-        self.graph: StateGraph | CompiledStateGraph | None = None
+        self.graph = StateGraph(WorkflowState)
+        self.add_nodes()
+        self.add_edges()
+        # EDGES MUST BE ADDED AFTER NODES ARE ADDED.
 
     @property
     def nodes(self) -> list[dict[str, Any]]:
@@ -39,74 +44,98 @@ class GraphBuilder:
         return self.workflow_config.get("edges", [])
 
     def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]:
-        """分析 End 节点的前缀配置
+        """
+        Analyze the prefix configuration for End nodes.
 
-        检查每个 End 节点的模板，找到直接上游节点的引用，
-        提取该引用之前的前缀部分。
+        This function scans each End node's output template, identifies
+        references to its direct upstream nodes, and extracts the prefix
+        string appearing before the first reference.
 
         Returns:
-            元组：({上游节点ID: End节点前缀}, {与End相邻且被引用的节点ID集合})
+            tuple:
+                - dict[str, str]: Mapping from upstream node ID to its End node prefix
+                - set[str]: Set of node IDs that are directly adjacent to End nodes and referenced
         """
         import re
 
         prefixes = {}
-        adjacent_and_referenced = set()  # 记录与 End 节点相邻且被引用的节点
+        adjacent_and_referenced = set()  # Record nodes directly adjacent to End and referenced
 
         # 找到所有 End 节点
         end_nodes = [node for node in self.nodes if node.get("type") == "end"]
-        logger.info(f"[前缀分析] 找到 {len(end_nodes)} 个 End 节点")
+        logger.info(f"[Prefix Analysis] Found {len(end_nodes)} End nodes")
 
         for end_node in end_nodes:
             end_node_id = end_node.get("id")
             output_template = end_node.get("config", {}).get("output")
 
-            logger.info(f"[前缀分析] End 节点 {end_node_id} 模板: {output_template}")
+            logger.info(f"[Prefix Analysis] End node {end_node_id} template: {output_template}")
 
             if not output_template:
                 continue
 
-            # 查找模板中引用了哪些节点
-            # 匹配 {{node_id.xxx}} 或 {{ node_id.xxx }} 格式（支持空格）
+            # Find all node references in the template
+            # Matches {{node_id.xxx}} or {{ node_id.xxx }} format (allowing spaces)
             pattern = r'\{\{\s*([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\s*\}\}'
             matches = list(re.finditer(pattern, output_template))
 
-            logger.info(f"[前缀分析] 模板中找到 {len(matches)} 个节点引用")
+            logger.info(f"[Prefix Analysis] 模板中找到 {len(matches)} 个节点引用")
 
-            # 找到所有直接连接到 End 节点的上游节点
+            # Identify all direct upstream nodes connected to the End node
             direct_upstream_nodes = []
             for edge in self.edges:
                 if edge.get("target") == end_node_id:
                     source_node_id = edge.get("source")
                     direct_upstream_nodes.append(source_node_id)
 
-            logger.info(f"[前缀分析] End 节点的直接上游节点: {direct_upstream_nodes}")
+            logger.info(f"[Prefix Analysis] Direct upstream nodes of End node: {direct_upstream_nodes}")
 
             # 找到第一个直接上游节点的引用
             for match in matches:
                 referenced_node_id = match.group(1)
-                logger.info(f"[前缀分析] 检查引用: {referenced_node_id}")
+                logger.info(f"[Prefix Analysis] Checking reference: {referenced_node_id}")
 
                 if referenced_node_id in direct_upstream_nodes:
                     # 这是直接上游节点的引用，提取前缀
                     prefix = output_template[:match.start()]
 
-                    logger.info(f"[前缀分析] ✅ 找到直接上游节点 {referenced_node_id} 的引用，前缀: '{prefix}'")
+                    logger.info(f"[Prefix Analysis] "
+                                f"✅ Found reference to direct upstream node {referenced_node_id}, prefix: '{prefix}'")
 
                     # 标记这个节点为"相邻且被引用"
                     adjacent_and_referenced.add(referenced_node_id)
 
                     if prefix:
                         prefixes[referenced_node_id] = prefix
-                        logger.info(f"✅ [前缀分析] 为节点 {referenced_node_id} 配置前缀: '{prefix[:50]}...'")
+                        logger.info(f"[Prefix Analysis] "
+                                    f"✅ Assign prefix for node {referenced_node_id}: '{prefix[:50]}...'")
 
                     # 只处理第一个直接上游节点的引用
                     break
 
-        logger.info(f"[前缀分析] 最终配置: {prefixes}")
-        logger.info(f"[前缀分析] 与 End 相邻且被引用的节点: {adjacent_and_referenced}")
+        logger.info(f"[Prefix Analysis] Final prefixes: {prefixes}")
+        logger.info(f"[Prefix Analysis] Nodes adjacent to End and referenced: {adjacent_and_referenced}")
         return prefixes, adjacent_and_referenced
 
     def add_nodes(self):
+        """Add all nodes from the workflow configuration to the state graph.
+
+        This method handles:
+        - Creation of node instances using NodeFactory.
+        - Special handling for start, end, and cycle nodes.
+        - Injection of End node prefixes for streaming mode.
+        - Marking nodes as adjacent to End nodes if referenced.
+        - Wrapping node run methods as async functions or async generators
+          depending on streaming mode.
+
+        Notes:
+            Loop nodes (nodes with `cycle` property) are handled separately
+            via CycleGraphNode when building subgraphs.
+
+        Returns:
+            None
+        """
+        # Analyze End node prefixes if in stream mode
         end_prefixes, adjacent_and_referenced = self._analyze_end_node_prefixes() if self.stream else ({}, set())
 
         for node in self.nodes:
@@ -114,21 +143,21 @@ class GraphBuilder:
             node_id = node.get("id")
             cycle_node = node.get("cycle")
             if cycle_node:
-                # 处于循环子图中的节点由 CycleGraphNode 进行构建处理
+                # Nodes within a loop subgraph are constructed by CycleGraphNode
                 if not self.subgraph:
                     continue
 
-            # 记录 start 和 end 节点 ID
+            # Record start and end node IDs
             if node_type in [NodeType.START, NodeType.CYCLE_START]:
                 self.start_node_id = node_id
             elif node_type == NodeType.END:
                 self.end_node_ids.append(node_id)
 
-            # 创建节点实例（现在 start 和 end 也会被创建）
+            # Create node instance (start and end nodes are also created)
             # NOTE:Loop node creation automatically removes the nodes and edges of the subgraph from the current graph
             node_instance = NodeFactory.create_node(node, self.workflow_config)
 
-            if node_type in [NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER]:
+            if node_type in BRANCH_NODES:
 
                 # Find all edges whose source is the current node
                 related_edge = [edge for edge in self.edges if edge.get("source") == node_id]
@@ -142,26 +171,23 @@ class GraphBuilder:
                     related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'"
 
             if node_instance:
-                # 如果是流式模式，且节点有 End 前缀配置，注入配置
+                # Inject End node prefix configuration if in stream mode
                 if self.stream and node_id in end_prefixes:
-                    # 将 End 前缀配置注入到节点实例
                     node_instance._end_node_prefix = end_prefixes[node_id]
-                    logger.info(f"为节点 {node_id} 注入 End 前缀配置")
+                    logger.info(f"Injected End prefix for node {node_id}")
 
-                # 如果是流式模式，标记节点是否与 End 相邻且被引用
+                # Mark nodes as adjacent and referenced to End node in stream mode
                 if self.stream:
                     node_instance._is_adjacent_to_end = node_id in adjacent_and_referenced
                     if node_id in adjacent_and_referenced:
-                        logger.info(f"节点 {node_id} 标记为与 End 相邻且被引用")
+                        logger.info(f"Node {node_id} marked as adjacent and referenced to End node")
 
-                # 包装节点的 run 方法
-                # 使用函数工厂避免闭包问题
+                # Wrap node's run method to avoid closure issues
                 if self.stream:
-                    # 流式模式：创建 async generator 函数
-                    # LangGraph 会收集所有 yield 的值，最后一个 yield 的字典会被合并到 state
+                    # Stream mode: create an async generator function
+                    # LangGraph collects all yielded values; the last yielded dictionary is merged into the state
                     def make_stream_func(inst):
                         async def node_func(state: WorkflowState):
-                            # logger.debug(f"流式执行节点: {inst.node_id}, 支持流式: {inst.supports_streaming()}")
                             async for item in inst.run_stream(state):
                                 yield item
 
@@ -169,7 +195,7 @@ class GraphBuilder:
 
                     self.graph.add_node(node_id, make_stream_func(node_instance))
                 else:
-                    # 非流式模式：创建 async function
+                    # Non-stream mode: create an async function
                     def make_func(inst):
                         async def node_func(state: WorkflowState):
                             return await inst.run(state)
@@ -178,45 +204,110 @@ class GraphBuilder:
 
                     self.graph.add_node(node_id, make_func(node_instance))
 
-                logger.debug(f"添加节点: {node_id} (type={node_type}, stream={self.stream})")
+                logger.debug(f"Added node: {node_id} (type={node_type}, stream={self.stream})")
 
     def add_edges(self):
+        """Add all edges (normal, waiting, and conditional) to the state graph.
+
+        This method handles:
+        - Connecting the START node to the workflow's start node.
+        - Collecting waiting edges for nodes with multiple sources.
+        - Collecting conditional edges for routing to NOP nodes.
+        - Adding NOP nodes for conditional branches to allow later merging.
+        - Wrapping routing logic in a router function that evaluates conditions.
+        - Connecting End nodes to the global END node.
+
+        Notes:
+            - NOP nodes are used to ensure that multiple branches can merge
+              correctly without modifying the workflow state.
+            - Waiting edges are automatically handled by LangGraph to schedule
+              nodes only after all sources are activated.
+
+        Returns:
+            None
+        """
+        # Connect the START node to the workflow's start node
         if self.start_node_id:
             self.graph.add_edge(START, self.start_node_id)
-            logger.debug(f"添加边: START -> {self.start_node_id}")
+            logger.debug(f"Added edge: START -> {self.start_node_id}")
+
+        # Collect all sources for each target node for normal/waiting edges
+        waiting_edges = defaultdict(list)
+        # Collect all conditional edges for each source node to construct routing
+        conditional_edges = defaultdict(list)
 
         for edge in self.edges:
             source = edge.get("source")
             target = edge.get("target")
-            edge_type = edge.get("type")
             condition = edge.get("condition")
+            edge_type = edge.get("type")
 
-            # 跳过从 start 节点出发的边（因为已经从 START 连接到 start）
-            if source == self.start_node_id:
-                # 但要连接 start 到下一个节点
-                self.graph.add_edge(source, target)
-                logger.debug(f"添加边: {source} -> {target}")
-                continue
-
-            # # 处理到 end 节点的边
-            # if target in end_node_ids:
-            #     # 连接到 end 节点
-            #     workflow.add_edge(source, target)
-            #     logger.debug(f"添加边: {source} -> {target}")
-            #     continue
-
-            # 跳过错误边（在节点内部处理）
+            # Skip error edges (handled within nodes)
             if edge_type == "error":
                 continue
 
             if condition:
-                # 条件边
-                def make_router(cond, tgt):
-                    """Dynamically generate a conditional router function to ensure each branch has a unique name."""
+                # Conditional edges: group by source node
+                conditional_edges[source].append({
+                    "target": target,
+                    "condition": condition,
+                    "label": edge.get("label")
+                })
+            else:
+                # Normal edges: group by target node (used for waiting edges)
+                waiting_edges[target].append(source)
 
-                    def router_fn(state: WorkflowState):
+        # Add conditional edges
+        for source_node, branches in conditional_edges.items():
+            def make_router(src, branch_list):
+                """reate a router function for each source node that routes to a NOP node for later merging."""
+                def make_branch_node(node_name, targets):
+                    def node(s):
+                        # NOTE: NOP NODE MUST NOT MODIFY STATE
+                        return {
+                            "activate": {
+                                node_id: s["activate"][node_name]
+                                for node_id in targets
+                            }
+                        }
+
+                    return node
+
+                unique_branch = {}
+                for branch in branch_list:
+                    if branch.get("label") not in unique_branch.keys():
+                        nop_node_name = f"nop_{uuid.uuid4().hex[:8]}"
+                        logger.info(f"Binding NOP: {source_node} {branch.get('label')} -> {nop_node_name}")
+                        unique_branch[branch["label"]] = {
+                            "condition": branch["condition"],
+                            "node": {
+                                "name": nop_node_name,
+                            },
+                            "target": [branch["target"]]
+                        }
+                    else:
+                        unique_branch[branch["label"]]["target"].append(branch["target"])
+
+                # Add NOP nodes and connect them to downstream nodes
+                for label, branch_info in unique_branch.items():
+                    self.graph.add_node(
+                        branch_info["node"]["name"],
+                        make_branch_node(
+                            branch_info["node"]["name"],
+                            branch_info["target"]
+                        )
+                    )
+                    for target in branch_info["target"]:
+                        waiting_edges[target].append(branch_info["node"]["name"])
+
+                def router_fn(state: WorkflowState) -> list[Send]:
+                    branch_activate = []
+                    new_state = state.copy()
+                    new_state["activate"] = dict(state.get("activate", {}))  # deep copy of activate
+
+                    for label, branch in unique_branch.items():
                         if evaluate_condition(
-                                cond,
+                                branch["condition"],
                                 state.get("variables", {}),
                                 state.get("runtime_vars", {}),
                                 {
@@ -225,30 +316,45 @@ class GraphBuilder:
                                     "user_id": state.get("user_id")
                                 }
                         ):
-                            return tgt
-                        return END
+                            logger.debug(f"Conditional routing {src}: selected branch {label}")
+                            new_state["activate"][branch["node"]["name"]] = True
+                            continue
+                        new_state["activate"][branch["node"]["name"]] = False
+                    for label, branch in unique_branch.items():
+                        branch_activate.append(
+                            Send(
+                                branch['node']['name'],
+                                new_state
+                            )
+                        )
+                    return branch_activate
 
-                    # 动态修改函数名，避免重复
-                    router_fn.__name__ = f"router_{uuid.uuid4().hex[:8]}_{tgt}"
-                    return router_fn
+                # Dynamically set function name
+                router_fn.__name__ = f"router_{uuid.uuid4().hex[:8]}_{src}"
+                return router_fn
 
-                router_fn = make_router(condition, target)
-                self.graph.add_conditional_edges(source, router_fn)
-                logger.debug(f"添加条件边: {source} -> {target} (condition={condition})")
+            router_fn = make_router(source_node, branches)
+            self.graph.add_conditional_edges(source_node, router_fn)
+            logger.debug(f"Added conditional edges: {source_node} -> {[b['target'] for b in branches]}")
+
+        # Add normal/waiting edges
+        for target, sources in waiting_edges.items():
+            if len(sources) == 1:
+                # Single source: normal edge
+                self.graph.add_edge(sources[0], target)
+                logger.debug(f"Added edge: {sources[0]} -> {target}")
             else:
-                # 普通边
-                self.graph.add_edge(source, target)
-                logger.debug(f"添加边: {source} -> {target}")
+                # Multiple sources: waiting edge
+                self.graph.add_edge(sources, target)
+                logger.debug(f"Added waiting edge: {sources} -> {target}")
 
-        # 从 end 节点连接到 END
+        # Connect End nodes to the global END node
         for end_node_id in self.end_node_ids:
             self.graph.add_edge(end_node_id, END)
-            logger.debug(f"添加边: {end_node_id} -> END")
+            logger.debug(f"Added edge: {end_node_id} -> END")
         return
 
     def build(self) -> CompiledStateGraph:
-        self.graph = StateGraph(WorkflowState)
-        self.add_nodes()
-        self.add_edges()  # 添加边必须在添加节点之后
         checkpointer = InMemorySaver()
-        return self.graph.compile(checkpointer=checkpointer)
+        self.graph = self.graph.compile(checkpointer=checkpointer)
+        return self.graph

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

@@ -14,6 +14,7 @@ logger = logging.getLogger(__name__)
 class AssignerNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
         super().__init__(node_config, workflow_config)
+        self.variable_updater = True
         self.typed_config: AssignerNodeConfig | None = None
 
     async def execute(self, state: WorkflowState) -> Any:

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

@@ -7,18 +7,26 @@
 import asyncio
 import logging
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, AsyncGenerator
 
 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.nodes.enums import BRANCH_NODES
 from app.core.workflow.variable_pool import VariablePool
 
 logger = logging.getLogger(__name__)
 
 
+def merget_activate_state(x, y):
+    return {
+        k: x.get(k, False) or y.get(k, False)
+        for k in set(x) | set(y)
+    }
+
+
 class WorkflowState(TypedDict):
     """Workflow state
 
@@ -60,6 +68,9 @@ class WorkflowState(TypedDict):
     # Format: {node_id: {"chunks": [...], "full_content": "..."}}
     streaming_buffer: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
 
+    # node activate status
+    activate: Annotated[dict[str, bool], merget_activate_state]
+
 
 class BaseNode(ABC):
     """节点基类
@@ -84,6 +95,47 @@ class BaseNode(ABC):
         self.config = node_config.get("config") or {}
         self.error_handling = node_config.get("error_handling") or {}
 
+        self.variable_updater = False
+
+    def check_activate(self, state: WorkflowState):
+        """Check if the current node is activated in the workflow state.
+
+        Args:
+            state (WorkflowState): The current workflow state containing the 'activate' dict.
+
+        Returns:
+            bool: True if the node is activated, False otherwise.
+        """
+        return state["activate"][self.node_id]
+
+    def trans_activate(self, state: WorkflowState):
+        """Transform the activation state for downstream nodes.
+
+        This method collects all downstream nodes (excluding branch nodes)
+        connected to the current node and returns a dict indicating whether
+        each of these nodes should be activated based on the current node's state.
+        The current node itself is also included in the returned activation dict.
+
+        Args:
+            state (WorkflowState): The current workflow state.
+
+        Returns:
+            dict: A dict with a single key 'activate', mapping node IDs to
+                  their activation status (True/False).
+        """
+        edges = self.workflow_config.get("edges")
+        under_stream_nodes = [
+            edge.get("target")
+            for edge in edges
+            if edge.get("source") == self.node_id and self.node_type not in BRANCH_NODES
+        ]
+        return {
+            "activate": {
+                            node_id: self.check_activate(state)
+                            for node_id in under_stream_nodes
+                        } | {self.node_id: self.check_activate(state)}
+        }
+
     @abstractmethod
     async def execute(self, state: WorkflowState) -> Any:
         """执行节点业务逻辑（非流式）
@@ -99,13 +151,13 @@ class BaseNode(ABC):
         
         Examples:
             >>> # LLM 节点
-            >>> return "这是 AI 的回复"
+            >>> "这是 AI 的回复"
             
             >>> # Transform 节点
-            >>> return {"processed_data": [...]}
+            >>> {"processed_data": [...]}
             
             >>> # Start/End 节点
-            >>> return {"message": "开始", "conversation_id": "xxx"}
+            >>> {"message": "开始", "conversation_id": "xxx"}
         """
         pass
 
@@ -126,14 +178,14 @@ class BaseNode(ABC):
             业务数据（chunk）或完成标记
         
         Examples:
-            >>> # 流式 LLM 节点
-            >>> full_response = ""
-            >>> async for chunk in llm.astream(prompt):
-            ...     full_response += chunk
-            ...     yield chunk  # yield 文本片段
-            >>> 
-            >>> # 最后 yield 完成标记
-            >>> yield {"__final__": True, "result": AIMessage(content=full_response)}
+            # 流式 LLM 节点
+            full_response = ""
+            async for chunk in llm.astream(prompt):
+                full_response += chunk
+                yield chunk  # yield 文本片段
+
+            # 最后 yield 完成标记
+            yield {"__final__": True, "result": AIMessage(content=full_response)}
         """
         result = await self.execute(state)
         # 默认实现：直接 yield 完成标记
@@ -146,7 +198,7 @@ class BaseNode(ABC):
             是否支持流式输出
         """
         # 检查子类是否重写了 execute_stream 方法
-        return self.execute_stream.__func__ != BaseNode.execute_stream.__func__
+        return self.__class__.execute_stream is not BaseNode.execute_stream
 
     def get_timeout(self) -> int:
         """获取超时时间（秒）
@@ -172,6 +224,9 @@ class BaseNode(ABC):
         Returns:
             标准化的状态更新字典
         """
+        if not self.check_activate(state):
+            return self.trans_activate(state)
+
         import time
 
         start_time = time.time()
@@ -204,12 +259,11 @@ class BaseNode(ABC):
             return {
                 **wrapped_output,
                 "messages": state["messages"],
-                "variables": state["variables"],
                 "runtime_vars": {
                     self.node_id: runtime_var
                 },
                 "looping": state["looping"]
-            }
+            } | self.trans_activate(state)
 
         except TimeoutError:
             elapsed_time = time.time() - start_time
@@ -220,7 +274,7 @@ class BaseNode(ABC):
             logger.error(f"节点 {self.node_id} 执行失败: {e}", exc_info=True)
             return self._wrap_error(str(e), elapsed_time, state)
 
-    async def run_stream(self, state: WorkflowState):
+    async def run_stream(self, state: WorkflowState) -> AsyncGenerator[dict[str, Any], Any]:
         """Execute node with error handling and output wrapping (streaming)
         
         This method is called by the Executor and is responsible for:
@@ -241,6 +295,11 @@ class BaseNode(ABC):
         Yields:
             State updates with streaming buffer and final result
         """
+        if not self.check_activate(state):
+            yield self.trans_activate(state)
+            logger.info(f"跳过节点{self.node_id}")
+            return
+
         import time
 
         start_time = time.time()
@@ -358,7 +417,6 @@ class BaseNode(ABC):
             state_update = {
                 **final_output,
                 "messages": state["messages"],
-                "variables": state["variables"],
                 "runtime_vars": {
                     self.node_id: runtime_var
                 },
@@ -377,7 +435,7 @@ class BaseNode(ABC):
 
             # Finally yield state update
             # LangGraph will merge this into state
-            yield state_update
+            yield state_update | self.trans_activate(state)
 
         except TimeoutError:
             elapsed_time = time.time() - start_time
@@ -427,12 +485,13 @@ class BaseNode(ABC):
             "token_usage": token_usage,
             "error": None
         }
-
-        return {
-            "node_outputs": {
-                self.node_id: node_output
-            }
+        final_output = {
+            "node_outputs": {self.node_id: node_output},
         }
+        if self.variable_updater:
+            final_output = final_output | {"variables": state["variables"]}
+
+        return final_output
 
     def _wrap_error(
             self,

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

@@ -26,6 +26,9 @@ class NodeType(StrEnum):
     MEMORY_WRITE = "memory-write"
 
 
+BRANCH_NODES = [NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER]
+
+
 class ComparisonOperator(StrEnum):
     EMPTY = "empty"
     NOT_EMPTY = "not_empty"

api/app/services/app_service.py

@@ -1445,7 +1445,7 @@ class AppService:
             target_workspace_ids: List[uuid.UUID],
             user_id: uuid.UUID,
             workspace_id: Optional[uuid.UUID] = None
-    ) -> AppShare:
+    ) -> list[AppShare]:
         """分享应用到其他工作空间
 
         Args: