feat: add python result and error handling

* fix: fix package error

Change-Id: I0f7a962656df7e45da03b591296fbf3afc398b64

* fix: rollback uv.lock

Change-Id: I465849c0d3d7a4d0757ecd79ff156feab1217f70

.env.example

@@ -17,5 +17,11 @@ VOLCENGINE_TTS_ACCESS_TOKEN=xxx
 # VOLCENGINE_TTS_CLUSTER=volcano_tts # Optional, default is volcano_tts
 # VOLCENGINE_TTS_VOICE_TYPE=BV700_V2_streaming # Optional, default is BV700_V2_streaming
 
+# Option, for langsmith tracing and monitoring
+# LANGSMITH_TRACING=true
+# LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+# LANGSMITH_API_KEY="xxx"
+# LANGSMITH_PROJECT="xxx"
+
 # [!NOTE]
 # For model settings and other configurations, please refer to `docs/configuration_guide.md`

README.md

@@ -347,6 +347,25 @@ When you submit a research topic in the Studio UI, you'll be able to see the ent
 - The research and writing phases for each section
 - The final report generation
 
+### Enabling LangSmith Tracing
+
+DeerFlow supports LangSmith tracing to help you debug and monitor your workflows. To enable LangSmith tracing:
+
+1. Make sure your `.env` file has the following configurations (see `.env.example`):
+   ```bash
+   LANGSMITH_TRACING=true
+   LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+   LANGSMITH_API_KEY="xxx"
+   LANGSMITH_PROJECT="xxx"
+   ```
+
+2. Start tracing and visualize the graph locally with LangSmith by running:
+   ```bash
+   langgraph dev
+   ```
+
+This will enable trace visualization in LangGraph Studio and send your traces to LangSmith for monitoring and analysis.
+
 ## Docker
 
 You can also run this project with Docker.

README_de.md

@@ -333,6 +333,25 @@ Wenn Sie ein Forschungsthema in der Studio UI einreichen, können Sie die gesamt
 - Die Forschungs- und Schreibphasen für jeden Abschnitt
 - Die Erstellung des endgültigen Berichts
 
+### Aktivieren von LangSmith-Tracing
+
+DeerFlow unterstützt LangSmith-Tracing, um Ihnen beim Debuggen und Überwachen Ihrer Workflows zu helfen. Um LangSmith-Tracing zu aktivieren:
+
+1. Stellen Sie sicher, dass Ihre `.env`-Datei die folgenden Konfigurationen enthält (siehe `.env.example`):
+   ```bash
+   LANGSMITH_TRACING=true
+   LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+   LANGSMITH_API_KEY="xxx"
+   LANGSMITH_PROJECT="xxx"
+   ```
+
+2. Starten Sie das Tracing mit LangSmith lokal, indem Sie folgenden Befehl ausführen:
+   ```bash
+   langgraph dev
+   ```
+
+Dies aktiviert die Trace-Visualisierung in LangGraph Studio und sendet Ihre Traces zur Überwachung und Analyse an LangSmith.
+
 ## Beispiele
 
 Die folgenden Beispiele demonstrieren die Fähigkeiten von DeerFlow:

README_ja.md

@@ -322,6 +322,25 @@ Studio UI で研究トピックを送信すると、次を含む全ワークフ
 - 各セクションの研究と執筆段階
 - 最終レポート生成
 
+### LangSmith トレースの有効化
+
+DeerFlow は LangSmith トレース機能をサポートしており、ワークフローのデバッグとモニタリングに役立ちます。LangSmith トレースを有効にするには：
+
+1. `.env` ファイルに次の設定があることを確認してください（`.env.example` を参照）：
+   ```bash
+   LANGSMITH_TRACING=true
+   LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+   LANGSMITH_API_KEY="xxx"
+   LANGSMITH_PROJECT="xxx"
+   ```
+
+2. 次のコマンドを実行して LangSmith トレースを開始します：
+   ```bash
+   langgraph dev
+   ```
+
+これにより、LangGraph Studio でトレース可視化が有効になり、トレースがモニタリングと分析のために LangSmith に送信されます。
+
 ## Docker
 
 このプロジェクトは Docker でも実行できます。

README_zh.md

@@ -322,6 +322,25 @@ langgraph dev
 - 每个部分的研究和写作阶段
 - 最终报告生成
 
+### 启用 LangSmith 追踪
+
+DeerFlow 支持 LangSmith 追踪功能，帮助您调试和监控工作流。要启用 LangSmith 追踪：
+
+1. 确保您的 `.env` 文件中有以下配置（参见 `.env.example`）：
+   ```bash
+   LANGSMITH_TRACING=true
+   LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+   LANGSMITH_API_KEY="xxx"
+   LANGSMITH_PROJECT="xxx"
+   ```
+
+2. 通过运行以下命令本地启动 LangSmith 追踪：
+   ```bash
+   langgraph dev
+   ```
+
+这将在 LangGraph Studio 中启用追踪可视化，并将您的追踪发送到 LangSmith 进行监控和分析。
+
 ## Docker
 
 您也可以使用 Docker 运行此项目。

src/tools/search.py

@@ -9,7 +9,7 @@ from langchain_community.tools import BraveSearch, DuckDuckGoSearchResults
 from langchain_community.tools.arxiv import ArxivQueryRun
 from langchain_community.utilities import ArxivAPIWrapper, BraveSearchWrapper
 
-from src.config import SEARCH_MAX_RESULTS
+from src.config import SEARCH_MAX_RESULTS, SearchEngine
 from src.tools.tavily_search.tavily_search_results_with_images import (
     TavilySearchResultsWithImages,
 )
@@ -19,13 +19,16 @@ from src.tools.decorators import create_logged_tool
 logger = logging.getLogger(__name__)
 
 LoggedTavilySearch = create_logged_tool(TavilySearchResultsWithImages)
-tavily_search_tool = LoggedTavilySearch(
+if os.getenv("SEARCH_API", "") == SearchEngine.TAVILY.value:
+    tavily_search_tool = LoggedTavilySearch(
         name="web_search",
         max_results=SEARCH_MAX_RESULTS,
         include_raw_content=True,
         include_images=True,
         include_image_descriptions=True,
-)
+    )
+else:
+    tavily_search_tool = None
 
 LoggedDuckDuckGoSearch = create_logged_tool(DuckDuckGoSearchResults)
 duckduckgo_search_tool = LoggedDuckDuckGoSearch(

web/src/app/chat/components/research-activities-block.tsx

@@ -304,10 +304,58 @@ function PythonToolCall({ toolCall }: { toolCall: ToolCallRuntime }) {
           </SyntaxHighlighter>
         </div>
       </div>
+      {toolCall.result && <PythonToolCallResult result={toolCall.result} />}
     </section>
   );
 }
 
+function PythonToolCallResult({ result }: { result: string }) {
+  const { resolvedTheme } = useTheme();
+  const hasError = useMemo(
+    () => result.includes("Error executing code:\n"),
+    [result],
+  );
+  const error = useMemo(() => {
+    if (hasError) {
+      const parts = result.split("```\nError: ");
+      if (parts.length > 1) {
+        return parts[1]!.trim();
+      }
+    }
+    return null;
+  }, [result, hasError]);
+  const stdout = useMemo(() => {
+    if (!hasError) {
+      const parts = result.split("```\nStdout: ");
+      if (parts.length > 1) {
+        return parts[1]!.trim();
+      }
+    }
+    return null;
+  }, [result, hasError]);
+  return (
+    <>
+      <div className="mt-4 font-medium italic">
+        {hasError ? "Error when executing the above code" : "Execution output"}
+      </div>
+      <div className="bg-accent mt-2 max-h-[400px] max-w-[calc(100%-120px)] overflow-y-auto rounded-md p-2 text-sm">
+        <SyntaxHighlighter
+          language="plaintext"
+          style={resolvedTheme === "dark" ? dark : docco}
+          customStyle={{
+            color: hasError ? "red" : "inherit",
+            background: "transparent",
+            border: "none",
+            boxShadow: "none",
+          }}
+        >
+          {error ?? stdout ?? "(empty)"}
+        </SyntaxHighlighter>
+      </div>
+    </>
+  );
+}
+
 function MCPToolCall({ toolCall }: { toolCall: ToolCallRuntime }) {
   const tool = useMemo(() => findMCPTool(toolCall.name), [toolCall.name]);
   const { resolvedTheme } = useTheme();

web/src/components/deer-flow/markdown.tsx

@@ -19,6 +19,19 @@ import { cn } from "~/lib/utils";
 import Image from "./image";
 import { Tooltip } from "./tooltip";
 
+const components: ReactMarkdownOptions["components"] = {
+  a: ({ href, children }) => (
+    <a href={href} target="_blank" rel="noopener noreferrer">
+      {children}
+    </a>
+  ),
+  img: ({ src, alt }) => (
+    <a href={src as string} target="_blank" rel="noopener noreferrer">
+      <Image className="rounded" src={src as string} alt={alt ?? ""} />
+    </a>
+  ),
+};
+
 export function Markdown({
   className,
   children,
@@ -49,18 +62,7 @@ export function Markdown({
       <ReactMarkdown
         remarkPlugins={[remarkGfm, remarkMath]}
         rehypePlugins={rehypePlugins}
-        components={{
-          a: ({ href, children }) => (
-            <a href={href} target="_blank" rel="noopener noreferrer">
-              {children}
-            </a>
-          ),
-          img: ({ src, alt }) => (
-            <a href={src as string} target="_blank" rel="noopener noreferrer">
-              <Image className="rounded" src={src as string} alt={alt ?? ""} />
-            </a>
-          ),
-        }}
+        components={components}
         {...props}
       >
         {autoFixMarkdown(