feat: add workflow action runtime policies

backend/services/node_specs.py

@@ -11,6 +11,8 @@ SPEC_VERSION = "3"
 NODE_TYPES = {"start", "agent", "action", "handoff", "end"}
 EDGE_MODES = {"llm", "expression", "always"}
 AGENT_ENTRY_MODES = {"wait_user", "generate", "fixed_speech"}
+ACTION_RESULT_ASSIGNMENT_MODES = {"inherit", "override", "none"}
+ACTION_USER_INPUT_POLICIES = {"queue", "block"}
 AUTOMATIC_NODE_TYPES = {"start", "action", "handoff"}
 EXPRESSION_OPERATORS = {
     "eq",
@@ -154,6 +156,24 @@ def _normalize_agent_data(data: dict[str, Any]) -> None:
         data["inheritGlobalConfig"] = not has_node_overrides
 
 
+def _normalize_action_data(data: dict[str, Any]) -> None:
+    """Add Action defaults while preserving the behavior of saved graphs.
+
+    Older Action nodes always passed an empty mapping when no node-level
+    assignments were configured, so they did *not* inherit the reusable
+    tool's assignments.  Infer ``none`` for that shape and ``override`` for
+    an existing non-empty mapping.  Newly created nodes should persist their
+    intended mode explicitly.
+    """
+    if "resultAssignmentMode" not in data:
+        assignments = data.get("resultAssignments")
+        data["resultAssignmentMode"] = (
+            "override" if isinstance(assignments, dict) and assignments else "none"
+        )
+    data.setdefault("resultAssignments", {})
+    data.setdefault("userInputPolicy", "queue")
+
+
 def _normalize_settings(settings: dict[str, Any], *, global_prompt: str = "") -> None:
     settings.setdefault("globalPrompt", global_prompt)
     settings.setdefault("defaultLlmResourceId", "")
@@ -179,10 +199,11 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
         source.setdefault("nodes", [])
         source.setdefault("edges", [])
         for node in source["nodes"]:
-            if node.get("type") != "agent":
-                continue
             data = node.setdefault("data", {})
-            _normalize_agent_data(data)
+            if node.get("type") == "agent":
+                _normalize_agent_data(data)
+            elif node.get("type") == "action":
+                _normalize_action_data(data)
         return source
 
     nodes = source.get("nodes") or []
@@ -215,6 +236,8 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
             data.setdefault("scope", "session")
         elif new_type == "agent":
             _normalize_agent_data(data)
+        elif new_type == "action":
+            _normalize_action_data(data)
         elif new_type == "start":
             prompt = str(data.pop("prompt", "") or "").strip()
             if prompt:
@@ -326,6 +349,20 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
                 data.get("entrySpeech") or ""
             ).strip():
                 errors.append(f"Agent 节点 {node_id} 的固定进入语不能为空")
+        elif node_type == "action":
+            data = node.get("data") or {}
+            assignment_mode = data.get("resultAssignmentMode")
+            if assignment_mode not in ACTION_RESULT_ASSIGNMENT_MODES:
+                errors.append(
+                    f"Action 节点 {node_id} 的结果变量模式无效:{assignment_mode}"
+                )
+            if not isinstance(data.get("resultAssignments"), dict):
+                errors.append(f"Action 节点 {node_id} 的结果变量映射必须是对象")
+            input_policy = data.get("userInputPolicy")
+            if input_policy not in ACTION_USER_INPUT_POLICIES:
+                errors.append(
+                    f"Action 节点 {node_id} 的用户输入策略无效:{input_policy}"
+                )
 
     if counts["start"] != 1:
         errors.append("工作流必须有且仅有一个 Start 节点")