feat(examples): use Chinese ecommerce agent names

examples/ecommerce-customer-service/README.md

@@ -18,10 +18,12 @@ Chinese customer question
 ```
 
 The router, specialist prompts, synthetic data, tool results, CLI, and customer
-responses use Simplified Chinese. Stable Python identifiers and graph node names
-remain in English. The two specialist branches use separate prompts and tools.
-All products, orders, policies, and tool results are fictional fixtures in
-`tools.py`.
+responses use Simplified Chinese. The specialist Agent display names are
+`售前服务` and `售后服务`, so their observable spans are named
+`invoke_agent 售前服务` and `invoke_agent 售后服务`. Stable Python identifiers,
+graph node names, tool names, and route values remain in English. The two
+specialist branches use separate prompts and tools. All products, orders,
+policies, and tool results are fictional fixtures in `tools.py`.
 
 ## Install
 
@@ -82,6 +84,8 @@ For a detailed scenario walkthrough and a ready-to-use training script, see
 With LangChain and LangGraph instrumentation enabled, a specialist request is
 expected to include the router LLM, the selected Agent, ReAct Step, Tool/LLM
 children, and the final reviewer LLM. Only the chosen specialist branch runs.
+The outer graph nodes remain `presales_agent` and `aftersales_agent`, while the
+inner Agent spans use the Chinese display names above.
 
 ## Test
 

examples/ecommerce-customer-service/README.zh-CN.md

@@ -20,8 +20,10 @@ LangGraph Agent API。
 工具结果全部是虚构数据。
 
 运行时的意图识别提示词、专业 Agent 提示词、工具说明、虚构数据、异常兜底及
-最终回答均使用简体中文。为了保持代码规范和 Trace 名称稳定，Python 标识符、
-节点名和内部路由值仍使用英文。
+最终回答均使用简体中文。专业 Agent 的显示名称为“售前服务”和“售后服务”，
+因此可观测链路中会显示 `invoke_agent 售前服务` 或
+`invoke_agent 售后服务`。为了保持代码接口稳定，Python 标识符、LangGraph
+节点名、工具名和内部路由值仍使用英文。
 
 ## 安装
 
@@ -77,7 +79,9 @@ loongsuite-instrument python app.py \
 
 启用 LangChain 和 LangGraph 埋点后，一次专业客服请求应包含路由 LLM、
 被选中的 Agent、ReAct Step、Tool/LLM 子节点以及最终审查 LLM；未选中的
-专业 Agent 不会执行。
+专业 Agent 不会执行。外层编排节点仍显示为 `presales_agent` 或
+`aftersales_agent`，其内部专业 Agent 分别显示为 `invoke_agent 售前服务` 或
+`invoke_agent 售后服务`。
 
 ## 培训讲解指南
 

examples/ecommerce-customer-service/tests/test_workflow.py

@@ -24,7 +24,13 @@
     query_after_sales_policy,
     search_product_catalog,
 )
-from workflow import EcommerceSupportWorkflow, IntentDecision, select_route
+from workflow import (
+    AFTERSALES_AGENT_NAME,
+    PRESALES_AGENT_NAME,
+    EcommerceSupportWorkflow,
+    IntentDecision,
+    select_route,
+)
 
 
 class KeywordClassifier:
@@ -224,9 +230,9 @@ def test_presales_and_aftersales_use_different_agents() -> None:
 
     assert presales["route"] == "presales"
     assert aftersales["route"] == "aftersales"
-    assert calls == ["presales_agent", "aftersales_agent"]
-    assert "presales_agent_tool" in model.review_inputs[0]
-    assert "aftersales_agent_tool" in model.review_inputs[1]
+    assert calls == [PRESALES_AGENT_NAME, AFTERSALES_AGENT_NAME]
+    assert f"{PRESALES_AGENT_NAME}_tool" in model.review_inputs[0]
+    assert f"{AFTERSALES_AGENT_NAME}_tool" in model.review_inputs[1]
 
 
 @pytest.mark.parametrize(
@@ -281,10 +287,10 @@ def test_ambiguous_question_uses_clarification_without_a_specialist() -> None:
 
 def test_specialist_and_reviewer_fail_open() -> None:
     workflow, _, calls = make_workflow(
-        fail_review=True, fail_agent="presales_agent"
+        fail_review=True, fail_agent=PRESALES_AGENT_NAME
     )
     result = workflow.run("云步通勤鞋有货吗？")
-    assert calls == ["presales_agent"]
+    assert calls == [PRESALES_AGENT_NAME]
     assert result["route"] == "presales"
     assert result["final_response"].startswith("售前客服暂时不可用")
 

examples/ecommerce-customer-service/workflow.py

@@ -13,6 +13,9 @@
 
 Route = Literal["presales", "aftersales", "clarify"]
 
+PRESALES_AGENT_NAME = "售前服务"
+AFTERSALES_AGENT_NAME = "售后服务"
+
 PRESALES_PROMPT = """你是虚构电商店铺的售前客服。
 只能使用提供的虚构商品目录、商品知识和库存工具。
 回答前至少调用一个工具；工具未提供的信息要明确说明不知道。
@@ -159,13 +162,13 @@ def __init__(
         )
         self.confidence_threshold = confidence_threshold
         self.presales_agent = agent_factory(
-            name="presales_agent",
+            name=PRESALES_AGENT_NAME,
             model=model,
             tools=PRESALES_TOOLS,
             prompt=PRESALES_PROMPT,
         )
         self.aftersales_agent = agent_factory(
-            name="aftersales_agent",
+            name=AFTERSALES_AGENT_NAME,
             model=model,
             tools=AFTERSALES_TOOLS,
             prompt=AFTERSALES_PROMPT,