update README and format code

README.md

@@ -143,6 +143,9 @@ Join our networking group on Feishu and share your experience with other develop
 Thanks to [anthropic-computer-use](https://github.com/anthropics/anthropic-quickstarts/tree/main/computer-use-demo)
 and [browser-use](https://github.com/browser-use/browser-use) for providing basic support for this project!
 
+Additionally, we are grateful to [AAAJ](https://github.com/metauto-ai/agent-as-a-judge)
+and [MetaGPT](https://github.com/mannaandpoem/MetaGPT).
+
 OpenManus is built by contributors from MetaGPT. Huge thanks to this agent community!
 
 ## Cite

README_zh.md

@@ -145,4 +145,7 @@ python run_flow.py
 特别感谢 [anthropic-computer-use](https://github.com/anthropics/anthropic-quickstarts/tree/main/computer-use-demo)
 和 [browser-use](https://github.com/browser-use/browser-use) 为本项目提供的基础支持！
 
+此外，我们感谢 [AAAJ](https://github.com/metauto-ai/agent-as-a-judge)
+和 [MetaGPT](https://github.com/mannaandpoem/MetaGPT).
+
 OpenManus 由 MetaGPT 社区的贡献者共同构建，感谢这个充满活力的智能体开发者社区！

app/llm/inference.py

@@ -1,6 +1,6 @@
-from typing import Dict, List, Literal, Optional, Union, Tuple, Any
-import os
 import base64
+import os
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 import litellm
 from litellm import completion, completion_cost
@@ -9,12 +9,17 @@ from litellm.exceptions import (
     RateLimitError,
     ServiceUnavailableError,
 )
-from tenacity import retry, stop_after_attempt, wait_random_exponential, retry_if_exception_type
+from tenacity import (
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_random_exponential,
+)
 
 from app.config import LLMSettings, config
+from app.llm.cost import Cost
 from app.logger import logger
 from app.schema import Message
-from app.llm.cost import Cost
 
 
 class LLM:
@@ -32,7 +37,9 @@ class LLM:
     def __init__(
         self, config_name: str = "default", llm_config: Optional[LLMSettings] = None
     ):
-        if not hasattr(self, "initialized"):  # Only initialize if not already initialized
+        if not hasattr(
+                self, "initialized"
+        ):  # Only initialize if not already initialized
             llm_config = llm_config or config.llm
             llm_config = llm_config.get(config_name, llm_config["default"])
 
@@ -41,7 +48,9 @@ class LLM:
             self.temperature = getattr(llm_config, "temperature", 0.7)
             self.top_p = getattr(llm_config, "top_p", 0.9)
             self.api_type = getattr(llm_config, "api_type", "openai")
-            self.api_key = getattr(llm_config, "api_key", os.environ.get("OPENAI_API_KEY", ""))
+            self.api_key = getattr(
+                llm_config, "api_key", os.environ.get("OPENAI_API_KEY", "")
+            )
             self.api_version = getattr(llm_config, "api_version", "")
             self.base_url = getattr(llm_config, "base_url", "https://api.openai.com/v1")
             self.timeout = getattr(llm_config, "timeout", 60)
@@ -183,7 +192,9 @@ class LLM:
             # Add the cost to our tracker
             if cost > 0:
                 self.cost_tracker.add_cost(cost)
-                logger.info(f"Added cost: ${cost:.6f}, Total: ${self.cost_tracker.accumulated_cost:.6f}")
+                logger.info(
+                    f"Added cost: ${cost:.6f}, Total: ${self.cost_tracker.accumulated_cost:.6f}"
+                )
 
             return cost
         except Exception as e:
@@ -202,7 +213,9 @@ class LLM:
                 substring in self.base_url
                 for substring in ["localhost", "127.0.0.1", "0.0.0.0"]
             )
-        if self.model and (self.model.startswith("ollama") or "local" in self.model.lower()):
+        if self.model and (
+                self.model.startswith("ollama") or "local" in self.model.lower()
+        ):
             return True
         return False
 
@@ -234,7 +247,9 @@ class LLM:
         with open(image_path, "rb") as image_file:
             return base64.b64encode(image_file.read()).decode("utf-8")
 
-    def prepare_messages(self, text: str, image_path: Optional[str] = None) -> List[dict]:
+    def prepare_messages(
+            self, text: str, image_path: Optional[str] = None
+    ) -> List[dict]:
         """
         Prepare messages for completion, including multimodal content if needed.
 
@@ -257,7 +272,9 @@ class LLM:
             ]
         return messages
 
-    def do_multimodal_completion(self, text: str, image_path: str) -> Tuple[Any, float, float]:
+    def do_multimodal_completion(
+            self, text: str, image_path: str
+    ) -> Tuple[Any, float, float]:
         """
         Perform a multimodal completion with text and image.
 
@@ -342,7 +359,7 @@ class LLM:
                 print(chunk_message, end="", flush=True)
 
             # For streaming responses, cost is calculated on the last chunk
-            if hasattr(chunk, 'usage') and chunk.usage:
+            if hasattr(chunk, "usage") and chunk.usage:
                 self._calculate_and_track_cost(chunk)
 
             print()  # Newline after streaming
@@ -484,6 +501,7 @@ class LLM:
 if __name__ == "__main__":
     # Load environment variables if needed
     from dotenv import load_dotenv
+
     load_dotenv()
 
     # Create LLM instance
@@ -501,5 +519,7 @@ if __name__ == "__main__":
         multimodal_response, mm_cost, mm_total_cost = llm.do_multimodal_completion(
             "What's in this image?", image_path
         )
-        print(f"Multimodal response: {multimodal_response['choices'][0]['message']['content']}")
+        print(
+            f"Multimodal response: {multimodal_response['choices'][0]['message']['content']}"
+        )
         print(f"Cost: ${mm_cost:.6f}, Total cost: ${mm_total_cost:.6f}")

app/tool/browser_use_tool.py

@@ -11,6 +11,7 @@ from pydantic_core.core_schema import ValidationInfo
 
 from app.tool.base import BaseTool, ToolResult
 
+
 MAX_LENGTH = 2000
 
 _BROWSER_DESCRIPTION = """
@@ -181,7 +182,9 @@ class BrowserUseTool(BaseTool):
 
                 elif action == "get_html":
                     html = await context.get_page_html()
-                    truncated = html[:MAX_LENGTH] + "..." if len(html) > MAX_LENGTH else html
+                    truncated = (
+                        html[:MAX_LENGTH] + "..." if len(html) > MAX_LENGTH else html
+                    )
                     return ToolResult(output=truncated)
 
                 elif action == "get_text":