refactor(workflow): simplify edge configuration

backend/services/brains/workflow_brain.py

@@ -785,22 +785,9 @@ class WorkflowBrain(BaseBrain):
         triggering_user_message: dict[str, Any] | None = None,
     ) -> NodeConfig:
         await self._begin_edge_transition(edge)
-        context_messages = list(leading_messages or [])
-        speech = self._engine.edge_transition_speech(edge)
-        if speech:
-            content = self._store.render(speech).strip()
-            if content:
-                await self._queue_visible_speech(
-                    content,
-                    source="workflow-edge-transition",
-                    node_id=str(edge.get("target") or "") or None,
-                )
-                context_message = fixed_speech_context_message(content)
-                if context_message is not None:
-                    context_messages.append(context_message)
         return await self._resolve_path(
             str(edge.get("target") or ""),
-            leading_messages=context_messages,
+            leading_messages=list(leading_messages or []),
             triggering_user_text=triggering_user_text,
             triggering_user_message=triggering_user_message,
         )
@@ -863,19 +850,6 @@ class WorkflowBrain(BaseBrain):
             if not edge:
                 return self._passive_node_config(node_id, context_messages)
             await self._begin_edge_transition(edge)
-            speech = self._engine.edge_transition_speech(edge)
-            if speech:
-                content = self._store.render(speech).strip()
-                if content:
-                    target_id = str(edge.get("target") or "")
-                    await self._queue_visible_speech(
-                        content,
-                        source="workflow-edge-transition",
-                        node_id=target_id or None,
-                    )
-                    context_message = fixed_speech_context_message(content)
-                    if context_message is not None:
-                        context_messages.append(context_message)
             node_id = str(edge.get("target") or "")
         raise RuntimeError("工作流连续自动跳转超过安全上限")
 

backend/services/node_specs.py

@@ -138,17 +138,17 @@ def node_types_response() -> dict[str, Any]:
 
 def _edge_data_v3(edge: dict) -> dict:
     data = deepcopy(edge.get("data") or {})
+    data.pop("transitionSpeech", None)
+    data.pop("transition_speech", None)
     if data.get("mode") in EDGE_MODES:
         data.setdefault("priority", 10)
         return data
     condition = str(data.pop("condition", "") or "").strip()
-    transition = data.pop("transition_speech", data.get("transitionSpeech", ""))
     data.update(
         {
             "mode": "llm" if condition else "always",
             "priority": 10,
             "condition": condition,
-            "transitionSpeech": transition,
         }
     )
     return data
@@ -234,6 +234,8 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
         _normalize_settings(settings)
         source.setdefault("nodes", [])
         source.setdefault("edges", [])
+        for edge in source["edges"]:
+            edge["data"] = _edge_data_v3(edge)
         for node in source["nodes"]:
             data = node.setdefault("data", {})
             if node.get("type") == "start":
@@ -327,7 +329,7 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
                 "id": f"e-{start_id}-{synthetic_id}",
                 "source": start_id,
                 "target": synthetic_id,
-                "data": {"mode": "always", "priority": 0, "transitionSpeech": ""},
+                "data": {"mode": "always", "priority": 0},
             }
         )
 

backend/services/workflow_engine.py

@@ -113,14 +113,6 @@ class WorkflowEngine:
             return f"当满足以下条件时转到「{target}」：{condition}"
         return f"当当前阶段任务完成时转到「{target}」。"
 
-    def edge_transition_speech(self, edge: dict | None) -> str:
-        if not edge:
-            return ""
-        data = edge.get("data") or {}
-        return str(
-            data.get("transitionSpeech") or data.get("transition_speech") or ""
-        )
-
     def global_prompt(self) -> str:
         return str(self.settings.get("globalPrompt") or "").strip()
 

backend/tests/test_brains.py

@@ -2393,7 +2393,6 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
                         "mode": "llm",
                         "priority": 10,
                         "condition": "需求已收集",
-                        "transitionSpeech": "正在为你结束流程",
                     },
                 }
             ],
@@ -2569,29 +2568,6 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         self.assertTrue(call_end.ending)
         self.assertTrue(call_end.armed)
         self.assertTrue(any(getattr(frame, "text", "") == "感谢来电" for frame in queued))
-        transition_context_frames = [
-            frame
-            for frame in worker.frames
-            if isinstance(frame, LLMMessagesAppendFrame)
-            and frame.messages
-            == [
-                {
-                    "role": "system",
-                    "content": (
-                        f"{FIXED_SPEECH_CONTEXT_MARKER}\n正在为你结束流程"
-                    ),
-                }
-            ]
-        ]
-        self.assertTrue(transition_context_frames)
-        transition_events = [
-            frame.message
-            for frame in queued
-            if isinstance(frame, OutputTransportMessageUrgentFrame)
-            and frame.message.get("source") == "workflow-edge-transition"
-        ]
-        self.assertEqual(transition_events[0]["content"], "正在为你结束流程")
-        self.assertEqual(transition_events[0]["nodeId"], "end")
         assistant_transcripts = [
             frame.message.get("content")
             for frame in queued
@@ -2601,11 +2577,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         ]
         self.assertEqual(
             assistant_transcripts,
-            ["正在为你结束流程", "感谢来电"],
-        )
-        self.assertIn(
-            "正在为你结束流程",
-            brain._store.values["system__conversation_history"],
+            ["感谢来电"],
         )
         self.assertIn(
             "感谢来电",

backend/tests/test_pipeline_events.py

@@ -64,7 +64,7 @@ class _Brain:
 
     async def on_client_ready(self):
         for content, timestamp in (
-            ("Start Edge 过渡语", "2026-07-14T10:00:00.200+00:00"),
+            ("Message 节点播报", "2026-07-14T10:00:00.200+00:00"),
             ("Agent 固定进入语", "2026-07-14T10:00:00.300+00:00"),
         ):
             await self.worker.queue_frame(
@@ -118,7 +118,7 @@ class PipelineEventTest(unittest.IsolatedAsyncioTestCase):
         ordered = sorted(transcripts, key=lambda message: message["timestamp"])
         self.assertEqual(
             [message["content"] for message in ordered],
-            ["助手开场白", "Start Edge 过渡语", "Agent 固定进入语"],
+            ["助手开场白", "Message 节点播报", "Agent 固定进入语"],
         )
         self.assertEqual(transcripts[0]["timestamp"], greeting_time)
         self.assertEqual(brain.prepared_greeting, "助手开场白")

backend/tests/test_workflow_v3.py

@@ -132,6 +132,17 @@ class WorkflowGraphTests(unittest.TestCase):
         self.assertEqual(cleaned_agent["data"]["entryMode"], "wait_user")
         self.assertNotIn("entrySpeech", cleaned_agent["data"])
 
+    def test_edge_speech_fields_are_removed(self):
+        graph = valid_graph()
+        graph["edges"][0]["data"]["transitionSpeech"] = "不再播放"
+        graph["edges"][1]["data"]["transition_speech"] = "旧字段也不再播放"
+
+        normalized = normalize_graph(graph)
+
+        for edge in normalized["edges"]:
+            self.assertNotIn("transitionSpeech", edge["data"])
+            self.assertNotIn("transition_speech", edge["data"])
+
     def test_action_defaults_preserve_legacy_result_assignment_behavior(self):
         graph = valid_graph()
         graph["nodes"].extend(