Merge branch 'main' of https://github.com/a-holm/OpenManus into fix-for-search-rate-limits

.github/ISSUE_TEMPLATE/request_new_features.md

@@ -1,14 +0,0 @@
----
-name: "🤔 Request new features"
-about: Suggest ideas or features you’d like to see implemented in OpenManus.
-title: ''
-labels: kind/features
-assignees: ''
----
-
-**Feature description**
-<!-- Provide a clear and concise description of the proposed feature -->
-
-**Your Feature**
-<!-- Explain your idea or implementation process. Optionally, include a Pull Request URL. -->
-<!-- Ensure accompanying docs/tests/examples are provided for review. -->

.github/ISSUE_TEMPLATE/request_new_features.yaml

@@ -0,0 +1,21 @@
+name: "🤔 Request new features"
+description: Suggest ideas or features you’d like to see implemented in OpenManus.
+labels: enhancement
+body:
+  - type: textarea
+    id: feature-description
+    attributes:
+      label: Feature description
+      description: |
+        Provide a clear and concise description of the proposed feature
+    validations:
+      required: true
+  - type: textarea
+    id: your-feature
+    attributes:
+      label: Your Feature
+      description: |
+        Explain your idea or implementation process, if any. Optionally, include a Pull Request URL.
+        Ensure accompanying docs/tests/examples are provided for review.
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/show_me_the_bug.md

@@ -1,25 +0,0 @@
----
-name: "🪲 Show me the Bug"
-about: Report a bug encountered while using OpenManus and seek assistance.
-title: ''
-labels: kind/bug
-assignees: ''
----
-
-**Bug description**
-<!-- Clearly describe the bug you encountered -->
-
-**Bug solved method**
-<!-- If resolved, explain the solution. Optionally, include a Pull Request URL. -->
-<!-- If unresolved, provide additional details to aid investigation -->
-
-**Environment information**
-<!-- System: e.g., Ubuntu 22.04, Python: e.g., 3.12, OpenManus version: e.g., 0.1.0 -->
-
-- System version:
-- Python version:
-- OpenManus version or branch:
-- Installation method (e.g., `pip install -r requirements.txt` or `pip install -e .`):
-
-**Screenshots or logs**
-<!-- Attach screenshots or logs to help diagnose the issue -->

.github/ISSUE_TEMPLATE/show_me_the_bug.yaml

@@ -0,0 +1,44 @@
+name: "🪲 Show me the Bug"
+description: Report a bug encountered while using OpenManus and seek assistance.
+labels: bug
+body:
+  - type: textarea
+    id: bug-description
+    attributes:
+      label: Bug Description
+      description: |
+        Clearly describe the bug you encountered
+    validations:
+      required: true
+  - type: textarea
+    id: solve-method
+    attributes:
+      label: Bug solved method
+      description: |
+        If resolved, explain the solution. Optionally, include a Pull Request URL.
+        If unresolved, provide additional details to aid investigation
+    validations:
+      required: true
+  - type: textarea
+    id: environment-information
+    attributes:
+      label: Environment information
+      description: |
+        System: e.g., Ubuntu 22.04
+        Python: e.g., 3.12
+        OpenManus version: e.g., 0.1.0
+      value: |
+        - System version:
+        - Python version:
+        - OpenManus version or branch:
+        - Installation method (e.g., `pip install -r requirements.txt` or `pip install -e .`):
+    validations:
+      required: true
+  - type: textarea
+    id: extra-information
+    attributes:
+      label: Extra information
+      description: |
+        For example, attach screenshots or logs to help diagnose the issue
+    validations:
+      required: false

.github/workflows/pr-autodiff.yaml

@@ -15,21 +15,20 @@ jobs:
       (github.event_name == 'pull_request') ||
       (github.event_name == 'issue_comment' &&
        contains(github.event.comment.body, '!pr-diff') &&
-       (github.event.comment.author_association == 'COLLABORATOR' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') &&
+       (github.event.comment.author_association == 'CONTRIBUTOR' || github.event.comment.author_association == 'COLLABORATOR' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') &&
        github.event.issue.pull_request)
     steps:
       - name: Get PR head SHA
         id: get-pr-sha
         run: |
-          if [ "${{ github.event_name }}" == "pull_request" ]; then
+          PR_URL="${{ github.event.issue.pull_request.url || github.event.pull_request.url }}"
-            echo "pr_sha=${{ github.event.pull_request.head.sha }}" >> $GITHUB_OUTPUT
+          # https://api.github.com/repos/OpenManus/pulls/1
-            echo "Retrieved PR head SHA: ${{ github.event.pull_request.head.sha }}"
+          RESPONSE=$(curl -s -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" $PR_URL)
-          else
+          SHA=$(echo $RESPONSE | jq -r '.head.sha')
-            PR_URL="${{ github.event.issue.pull_request.url }}"
+          TARGET_BRANCH=$(echo $RESPONSE | jq -r '.base.ref')
-            SHA=$(curl -s -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" $PR_URL | jq -r '.head.sha')
           echo "pr_sha=$SHA" >> $GITHUB_OUTPUT
-            echo "Retrieved PR head SHA from API: $SHA"
+          echo "target_branch=$TARGET_BRANCH" >> $GITHUB_OUTPUT
-          fi
+          echo "Retrieved PR head SHA from API: $SHA, target branch: $TARGET_BRANCH"
       - name: Check out code
         uses: actions/checkout@v4
         with:
@@ -49,6 +48,7 @@ jobs:
           OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}
           GH_TOKEN: ${{ github.token }}
           PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
+          TARGET_BRANCH: ${{ steps.get-pr-sha.outputs.target_branch }}
         run: |-
           cat << 'EOF' > /tmp/_workflow_core.py
           import os
@@ -59,7 +59,7 @@ jobs:
 
           def get_diff():
               result = subprocess.run(
-                  ['git', 'diff', 'origin/main...HEAD'],
+                  ['git', 'diff', 'origin/' + os.getenv('TARGET_BRANCH') + '...HEAD'],
                   capture_output=True, text=True, check=True)
               return '\n'.join(
                   line for line in result.stdout.split('\n')
@@ -86,6 +86,17 @@ jobs:
 
               ### Spelling/Offensive Content Check
               - No spelling mistakes or offensive content found in the code or comments.
+
+              ## 中文（简体）
+              - 新增了 `ABC` 类
+              - `foo` 模块中的 `f()` 行为已修复
+
+              ### 评论高亮
+              - `config.toml` 需要正确配置才能确保新功能正常运行。
+
+              ### 内容检查
+              - 没有发现代码或注释中的拼写错误或不当措辞。
+
           3. Highlight non-English comments
           4. Check for spelling/offensive content'''
 

app/agent/toolcall.py

@@ -71,40 +71,42 @@ class ToolCallAgent(ReActAgent):
                 return False
             raise
 
-        self.tool_calls = response.tool_calls
+        self.tool_calls = tool_calls = (
+            response.tool_calls if response and response.tool_calls else []
+        )
+        content = response.content if response and response.content else ""
 
         # Log response info
-        logger.info(f"✨ {self.name}'s thoughts: {response.content}")
+        logger.info(f"✨ {self.name}'s thoughts: {content}")
         logger.info(
-            f"🛠️ {self.name} selected {len(response.tool_calls) if response.tool_calls else 0} tools to use"
+            f"🛠️ {self.name} selected {len(tool_calls) if tool_calls else 0} tools to use"
         )
-        if response.tool_calls:
+        if tool_calls:
             logger.info(
-                f"🧰 Tools being prepared: {[call.function.name for call in response.tool_calls]}"
+                f"🧰 Tools being prepared: {[call.function.name for call in tool_calls]}"
-            )
-            logger.info(
-                f"🔧 Tool arguments: {response.tool_calls[0].function.arguments}"
             )
+            logger.info(f"🔧 Tool arguments: {tool_calls[0].function.arguments}")
 
         try:
+            if response is None:
+                raise RuntimeError("No response received from the LLM")
+
             # Handle different tool_choices modes
             if self.tool_choices == ToolChoice.NONE:
-                if response.tool_calls:
+                if tool_calls:
                     logger.warning(
                         f"🤔 Hmm, {self.name} tried to use tools when they weren't available!"
                     )
-                if response.content:
+                if content:
-                    self.memory.add_message(Message.assistant_message(response.content))
+                    self.memory.add_message(Message.assistant_message(content))
                     return True
                 return False
 
             # Create and add assistant message
             assistant_msg = (
-                Message.from_tool_calls(
+                Message.from_tool_calls(content=content, tool_calls=self.tool_calls)
-                    content=response.content, tool_calls=self.tool_calls
-                )
                 if self.tool_calls
-                else Message.assistant_message(response.content)
+                else Message.assistant_message(content)
             )
             self.memory.add_message(assistant_msg)
 
@@ -113,7 +115,7 @@ class ToolCallAgent(ReActAgent):
 
             # For 'auto' mode, continue with content if no commands but content exists
             if self.tool_choices == ToolChoice.AUTO and not self.tool_calls:
-                return bool(response.content)
+                return bool(content)
 
             return bool(self.tool_calls)
         except Exception as e:
@@ -209,7 +211,7 @@ class ToolCallAgent(ReActAgent):
             return f"Error: {error_msg}"
         except Exception as e:
             error_msg = f"⚠️ Tool '{name}' encountered a problem: {str(e)}"
-            logger.error(error_msg)
+            logger.exception(error_msg)
             return f"Error: {error_msg}"
 
     async def _handle_special_tool(self, name: str, result: Any, **kwargs):

app/config.py

@@ -25,7 +25,7 @@ class LLMSettings(BaseModel):
         description="Maximum input tokens to use across all requests (None for unlimited)",
     )
     temperature: float = Field(1.0, description="Sampling temperature")
-    api_type: str = Field(..., description="AzureOpenai or Openai")
+    api_type: str = Field(..., description="Azure, Openai, or Ollama")
     api_version: str = Field(..., description="Azure Openai version if AzureOpenai")
 
 

app/llm.py

@@ -10,6 +10,7 @@ from openai import (
     OpenAIError,
     RateLimitError,
 )
+from openai.types.chat.chat_completion_message import ChatCompletionMessage
 from tenacity import (
     retry,
     retry_if_exception_type,
@@ -30,6 +31,14 @@ from app.schema import (
 
 
 REASONING_MODELS = ["o1", "o3-mini"]
+MULTIMODAL_MODELS = [
+    "gpt-4-vision-preview",
+    "gpt-4o",
+    "gpt-4o-mini",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-20240307",
+]
 
 
 class TokenCounter:
@@ -259,12 +268,15 @@ class LLM:
         return "Token limit exceeded"
 
     @staticmethod
-    def format_messages(messages: List[Union[dict, Message]]) -> List[dict]:
+    def format_messages(
+        messages: List[Union[dict, Message]], supports_images: bool = False
+    ) -> List[dict]:
         """
         Format messages for LLM by converting them to OpenAI message format.
 
         Args:
             messages: List of messages that can be either dict or Message objects
+            supports_images: Flag indicating if the target model supports image inputs
 
         Returns:
             List[dict]: List of formatted messages in OpenAI format
@@ -288,20 +300,20 @@ class LLM:
             if isinstance(message, Message):
                 message = message.to_dict()
 
-            if not isinstance(message, dict):
+            if isinstance(message, dict):
-                raise TypeError(f"Unsupported message type: {type(message)}")
+                # If message is a dict, ensure it has required fields
-
-            # Validate required fields
                 if "role" not in message:
                     raise ValueError("Message dict must contain 'role' field")
 
-            # Process base64 images if present
+                # Process base64 images if present and model supports images
-            if message.get("base64_image"):
+                if supports_images and message.get("base64_image"):
                     # Initialize or convert content to appropriate format
                     if not message.get("content"):
                         message["content"] = []
                     elif isinstance(message["content"], str):
-                    message["content"] = [{"type": "text", "text": message["content"]}]
+                        message["content"] = [
+                            {"type": "text", "text": message["content"]}
+                        ]
                     elif isinstance(message["content"], list):
                         # Convert string items to proper text objects
                         message["content"] = [
@@ -325,17 +337,21 @@ class LLM:
 
                     # Remove the base64_image field
                     del message["base64_image"]
+                # If model doesn't support images but message has base64_image, handle gracefully
+                elif not supports_images and message.get("base64_image"):
+                    # Just remove the base64_image field and keep the text content
+                    del message["base64_image"]
 
-            # Only include messages with content or tool_calls
                 if "content" in message or "tool_calls" in message:
                     formatted_messages.append(message)
+                # else: do not include the message
+            else:
+                raise TypeError(f"Unsupported message type: {type(message)}")
 
-        # Validate all roles
+        # Validate all messages have required fields
-        invalid_roles = [
+        for msg in formatted_messages:
-            msg for msg in formatted_messages if msg["role"] not in ROLE_VALUES
+            if msg["role"] not in ROLE_VALUES:
-        ]
+                raise ValueError(f"Invalid role: {msg['role']}")
-        if invalid_roles:
-            raise ValueError(f"Invalid role: {invalid_roles[0]['role']}")
 
         return formatted_messages
 
@@ -372,12 +388,15 @@ class LLM:
             Exception: For unexpected errors
         """
         try:
-            # Format system and user messages
+            # Check if the model supports images
+            supports_images = self.model in MULTIMODAL_MODELS
+
+            # Format system and user messages with image support check
             if system_msgs:
-                system_msgs = self.format_messages(system_msgs)
+                system_msgs = self.format_messages(system_msgs, supports_images)
-                messages = system_msgs + self.format_messages(messages)
+                messages = system_msgs + self.format_messages(messages, supports_images)
             else:
-                messages = self.format_messages(messages)
+                messages = self.format_messages(messages, supports_images)
 
             # Calculate input token count
             input_tokens = self.count_message_tokens(messages)
@@ -403,9 +422,9 @@ class LLM:
 
             if not stream:
                 # Non-streaming request
-                params["stream"] = False
+                response = await self.client.chat.completions.create(
-
+                    **params, stream=False
-                response = await self.client.chat.completions.create(**params)
+                )
 
                 if not response.choices or not response.choices[0].message.content:
                     raise ValueError("Empty or invalid response from LLM")
@@ -420,8 +439,7 @@ class LLM:
             # Streaming request, For streaming, update estimated token count before making the request
             self.update_token_count(input_tokens)
 
-            params["stream"] = True
+            response = await self.client.chat.completions.create(**params, stream=True)
-            response = await self.client.chat.completions.create(**params)
 
             collected_messages = []
             completion_text = ""
@@ -448,11 +466,11 @@ class LLM:
         except TokenLimitExceeded:
             # Re-raise token limit errors without logging
             raise
-        except ValueError as ve:
+        except ValueError:
-            logger.error(f"Validation error: {ve}")
+            logger.exception(f"Validation error")
             raise
         except OpenAIError as oe:
-            logger.error(f"OpenAI API error: {oe}")
+            logger.exception(f"OpenAI API error")
             if isinstance(oe, AuthenticationError):
                 logger.error("Authentication failed. Check API key.")
             elif isinstance(oe, RateLimitError):
@@ -460,8 +478,8 @@ class LLM:
             elif isinstance(oe, APIError):
                 logger.error(f"API error: {oe}")
             raise
-        except Exception as e:
+        except Exception:
-            logger.error(f"Unexpected error in ask: {e}")
+            logger.exception(f"Unexpected error in ask")
             raise
 
     @retry(
@@ -499,8 +517,15 @@ class LLM:
             Exception: For unexpected errors
         """
         try:
-            # Format messages
+            # For ask_with_images, we always set supports_images to True because
-            formatted_messages = self.format_messages(messages)
+            # this method should only be called with models that support images
+            if self.model not in MULTIMODAL_MODELS:
+                raise ValueError(
+                    f"Model {self.model} does not support images. Use a model from {MULTIMODAL_MODELS}"
+                )
+
+            # Format messages with image support
+            formatted_messages = self.format_messages(messages, supports_images=True)
 
             # Ensure the last message is from the user to attach images
             if not formatted_messages or formatted_messages[-1]["role"] != "user":
@@ -539,7 +564,10 @@ class LLM:
 
             # Add system messages if provided
             if system_msgs:
-                all_messages = self.format_messages(system_msgs) + formatted_messages
+                all_messages = (
+                    self.format_messages(system_msgs, supports_images=True)
+                    + formatted_messages
+                )
             else:
                 all_messages = formatted_messages
 
@@ -626,7 +654,7 @@ class LLM:
         tool_choice: TOOL_CHOICE_TYPE = ToolChoice.AUTO,  # type: ignore
         temperature: Optional[float] = None,
         **kwargs,
-    ):
+    ) -> ChatCompletionMessage | None:
         """
         Ask LLM using functions/tools and return the response.
 
@@ -653,12 +681,15 @@ class LLM:
             if tool_choice not in TOOL_CHOICE_VALUES:
                 raise ValueError(f"Invalid tool_choice: {tool_choice}")
 
+            # Check if the model supports images
+            supports_images = self.model in MULTIMODAL_MODELS
+
             # Format messages
             if system_msgs:
-                system_msgs = self.format_messages(system_msgs)
+                system_msgs = self.format_messages(system_msgs, supports_images)
-                messages = system_msgs + self.format_messages(messages)
+                messages = system_msgs + self.format_messages(messages, supports_images)
             else:
-                messages = self.format_messages(messages)
+                messages = self.format_messages(messages, supports_images)
 
             # Calculate input token count
             input_tokens = self.count_message_tokens(messages)
@@ -701,12 +732,15 @@ class LLM:
                     temperature if temperature is not None else self.temperature
                 )
 
-            response = await self.client.chat.completions.create(**params)
+            response: ChatCompletion = await self.client.chat.completions.create(
+                **params, stream=False
+            )
 
             # Check if response is valid
             if not response.choices or not response.choices[0].message:
                 print(response)
-                raise ValueError("Invalid or empty response from LLM")
+                # raise ValueError("Invalid or empty response from LLM")
+                return None
 
             # Update token counts
             self.update_token_count(

app/tool/browser_use_tool.py

@@ -1,4 +1,5 @@
 import asyncio
+import base64
 import json
 from typing import Generic, Optional, TypeVar
 
@@ -418,17 +419,7 @@ class BrowserUseTool(BaseTool, Generic[Context]):
 
                         # Create prompt for LLM
                         prompt_text = """
-Your task is to extract the content of the page. You will be given a page and a goal, and you should extract all relevant information around this goal from the page.
+Your task is to extract the content of the page. You will be given a page and a goal, and you should extract all relevant information around this goal from the page. If the goal is vague, summarize the page. Respond in json format.
-
-Examples of extraction goals:
-- Extract all company names
-- Extract specific descriptions
-- Extract all information about a topic
-- Extract links with companies in structured format
-- Extract all links
-
-If the goal is vague, summarize the page. Respond in JSON format.
-
 Extraction goal: {goal}
 
 Page content:
@@ -445,10 +436,54 @@ Page content:
 
                         messages = [Message.user_message(formatted_prompt)]
 
-                        # Use LLM to extract content based on the goal
+                        # Define extraction function for the tool
-                        response = await self.llm.ask(messages)
+                        extraction_function = {
+                            "type": "function",
+                            "function": {
+                                "name": "extract_content",
+                                "description": "Extract specific information from a webpage based on a goal",
+                                "parameters": {
+                                    "type": "object",
+                                    "properties": {
+                                        "extracted_content": {
+                                            "type": "object",
+                                            "description": "The content extracted from the page according to the goal",
+                                        }
+                                    },
+                                    "required": ["extracted_content"],
+                                },
+                            },
+                        }
+
+                        # Use LLM to extract content with required function calling
+                        response = await self.llm.ask_tool(
+                            messages,
+                            tools=[extraction_function],
+                            tool_choice="required",
+                        )
+
+                        # Extract content from function call response
+                        if (
+                            response
+                            and response.tool_calls
+                            and len(response.tool_calls) > 0
+                        ):
+                            # Get the first tool call arguments
+                            tool_call = response.tool_calls[0]
+                            # Parse the JSON arguments
+                            try:
+                                args = json.loads(tool_call.function.arguments)
+                                extracted_content = args.get("extracted_content", {})
+                                # Format extracted content as JSON string
+                                content_json = json.dumps(
+                                    extracted_content, indent=2, ensure_ascii=False
+                                )
+                                msg = f"Extracted from page:\n{content_json}\n"
+                            except Exception as e:
+                                msg = f"Error parsing extraction result: {str(e)}\nRaw response: {tool_call.function.arguments}"
+                        else:
+                            msg = "No content was extracted from the page."
 
-                        msg = f"Extracted from page:\n{response}\n"
                         return ToolResult(output=msg)
                     except Exception as e:
                         # Provide a more helpful error message
@@ -518,7 +553,16 @@ Page content:
                 viewport_height = ctx.config.browser_window_size.get("height", 0)
 
             # Take a screenshot for the state
-            screenshot = await ctx.take_screenshot(full_page=True)
+            page = await ctx.get_current_page()
+
+            await page.bring_to_front()
+            await page.wait_for_load_state()
+
+            screenshot = await page.screenshot(
+                full_page=True, animations="disabled", type="jpeg", quality=100
+            )
+
+            screenshot = base64.b64encode(screenshot).decode("utf-8")
 
             # Build the state info with all required fields
             state_info = {

app/tool/file_operators.py

@@ -42,17 +42,19 @@ class FileOperator(Protocol):
 class LocalFileOperator(FileOperator):
     """File operations implementation for local filesystem."""
 
+    encoding: str = "utf-8"
+
     async def read_file(self, path: PathLike) -> str:
         """Read content from a local file."""
         try:
-            return Path(path).read_text()
+            return Path(path).read_text(encoding=self.encoding)
         except Exception as e:
             raise ToolError(f"Failed to read {path}: {str(e)}") from None
 
     async def write_file(self, path: PathLike, content: str) -> None:
         """Write content to a local file."""
         try:
-            Path(path).write_text(content)
+            Path(path).write_text(content, encoding=self.encoding)
         except Exception as e:
             raise ToolError(f"Failed to write to {path}: {str(e)}") from None
 

app/tool/search/__init__.py

@@ -1,5 +1,6 @@
 from app.tool.search.baidu_search import BaiduSearchEngine
 from app.tool.search.base import WebSearchEngine
+from app.tool.search.bing_search import BingSearchEngine
 from app.tool.search.duckduckgo_search import DuckDuckGoSearchEngine
 from app.tool.search.google_search import GoogleSearchEngine
 
@@ -9,4 +10,5 @@ __all__ = [
     "BaiduSearchEngine",
     "DuckDuckGoSearchEngine",
     "GoogleSearchEngine",
+    "BingSearchEngine",
 ]

app/tool/search/bing_search.py

@@ -0,0 +1,146 @@
+from typing import List
+
+import requests
+from bs4 import BeautifulSoup
+
+from app.logger import logger
+from app.tool.search.base import WebSearchEngine
+
+
+ABSTRACT_MAX_LENGTH = 300
+
+USER_AGENTS = [
+    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36",
+    "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)",
+    "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/49.0.2623.108 Chrome/49.0.2623.108 Safari/537.36",
+    "Mozilla/5.0 (Windows; U; Windows NT 5.1; pt-BR) AppleWebKit/533.3 (KHTML, like Gecko) QtWeb Internet Browser/3.7 http://www.QtWeb.net",
+    "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36",
+    "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/532.2 (KHTML, like Gecko) ChromePlus/4.0.222.3 Chrome/4.0.222.3 Safari/532.2",
+    "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.4pre) Gecko/20070404 K-Ninja/2.1.3",
+    "Mozilla/5.0 (Future Star Technologies Corp.; Star-Blade OS; x86_64; U; en-US) iNet Browser 4.7",
+    "Mozilla/5.0 (Windows; U; Windows NT 6.1; rv:2.2) Gecko/20110201",
+    "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.13) Gecko/20080414 Firefox/2.0.0.13 Pogo/2.0.0.13.6866",
+]
+
+HEADERS = {
+    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
+    "Content-Type": "application/x-www-form-urlencoded",
+    "User-Agent": USER_AGENTS[0],
+    "Referer": "https://www.bing.com/",
+    "Accept-Encoding": "gzip, deflate",
+    "Accept-Language": "zh-CN,zh;q=0.9",
+}
+
+BING_HOST_URL = "https://www.bing.com"
+BING_SEARCH_URL = "https://www.bing.com/search?q="
+
+
+class BingSearchEngine(WebSearchEngine):
+    session: requests.Session = None
+
+    def __init__(self, **data):
+        """Initialize the BingSearch tool with a requests session."""
+        super().__init__(**data)
+        self.session = requests.Session()
+        self.session.headers.update(HEADERS)
+
+    def _search_sync(self, query: str, num_results: int = 10) -> List[str]:
+        """
+        Synchronous Bing search implementation to retrieve a list of URLs matching a query.
+
+        Args:
+            query (str): The search query to submit to Bing. Must not be empty.
+            num_results (int, optional): The maximum number of URLs to return. Defaults to 10.
+
+        Returns:
+            List[str]: A list of URLs from the search results, capped at `num_results`.
+                       Returns an empty list if the query is empty or no results are found.
+
+        Notes:
+            - Pagination is handled by incrementing the `first` parameter and following `next_url` links.
+            - If fewer results than `num_results` are available, all found URLs are returned.
+        """
+        if not query:
+            return []
+
+        list_result = []
+        first = 1
+        next_url = BING_SEARCH_URL + query
+
+        while len(list_result) < num_results:
+            data, next_url = self._parse_html(
+                next_url, rank_start=len(list_result), first=first
+            )
+            if data:
+                list_result.extend([item["url"] for item in data])
+            if not next_url:
+                break
+            first += 10
+
+        return list_result[:num_results]
+
+    def _parse_html(self, url: str, rank_start: int = 0, first: int = 1) -> tuple:
+        """
+        Parse Bing search result HTML synchronously to extract search results and the next page URL.
+
+        Args:
+            url (str): The URL of the Bing search results page to parse.
+            rank_start (int, optional): The starting rank for numbering the search results. Defaults to 0.
+            first (int, optional): Unused parameter (possibly legacy). Defaults to 1.
+        Returns:
+            tuple: A tuple containing:
+                - list: A list of dictionaries with keys 'title', 'abstract', 'url', and 'rank' for each result.
+                - str or None: The URL of the next results page, or None if there is no next page.
+        """
+        try:
+            res = self.session.get(url=url)
+            res.encoding = "utf-8"
+            root = BeautifulSoup(res.text, "lxml")
+
+            list_data = []
+            ol_results = root.find("ol", id="b_results")
+            if not ol_results:
+                return [], None
+
+            for li in ol_results.find_all("li", class_="b_algo"):
+                title = ""
+                url = ""
+                abstract = ""
+                try:
+                    h2 = li.find("h2")
+                    if h2:
+                        title = h2.text.strip()
+                        url = h2.a["href"].strip()
+
+                    p = li.find("p")
+                    if p:
+                        abstract = p.text.strip()
+
+                    if ABSTRACT_MAX_LENGTH and len(abstract) > ABSTRACT_MAX_LENGTH:
+                        abstract = abstract[:ABSTRACT_MAX_LENGTH]
+
+                    rank_start += 1
+                    list_data.append(
+                        {
+                            "title": title,
+                            "abstract": abstract,
+                            "url": url,
+                            "rank": rank_start,
+                        }
+                    )
+                except Exception:
+                    continue
+
+            next_btn = root.find("a", title="Next page")
+            if not next_btn:
+                return list_data, None
+
+            next_url = BING_HOST_URL + next_btn["href"]
+            return list_data, next_url
+        except Exception as e:
+            logger.warning(f"Error parsing HTML: {e}")
+            return [], None
+
+    def perform_search(self, query, num_results=10, *args, **kwargs):
+        """Bing search engine."""
+        return self._search_sync(query, num_results=num_results)

app/tool/web_search.py

@@ -8,6 +8,7 @@ from app.logger import logger
 from app.tool.base import BaseTool
 from app.tool.search import (
     BaiduSearchEngine,
+    BingSearchEngine,
     DuckDuckGoSearchEngine,
     GoogleSearchEngine,
     WebSearchEngine,
@@ -38,6 +39,7 @@ class WebSearch(BaseTool):
         "google": GoogleSearchEngine(),
         "baidu": BaiduSearchEngine(),
         "duckduckgo": DuckDuckGoSearchEngine(),
+        "bing": BingSearchEngine(),
     }
 
     async def execute(self, query: str, num_results: int = 10) -> List[str]: