adapt to browser-use==0.1.29

Author: vvincent1234

requirements.txt

@@ -1,6 +1,3 @@
-browser-use==0.1.19
-langchain-google-genai==2.0.8
+browser-use==0.1.29
 pyperclip==1.9.0
-gradio==5.9.1
-langchain-ollama==0.2.2
-langchain-openai==0.2.14
+gradio==5.10.0

src/agent/custom_agent.py

@@ -15,6 +15,7 @@
 	AIMessage,
 	BaseMessage,
 	HumanMessage,
+    ToolMessage
 )
 from langchain_openai import ChatOpenAI
 from ..utils.llm import DeepSeekR1ChatOpenAI
@@ -31,69 +32,44 @@ def __init__(
             action_descriptions: str,
             system_prompt_class: Type[SystemPrompt],
             max_input_tokens: int = 128000,
-            estimated_tokens_per_character: int = 3,
+            estimated_characters_per_token: int = 3,
             image_tokens: int = 800,
             include_attributes: list[str] = [],
             max_error_length: int = 400,
             max_actions_per_step: int = 10,
-            tool_call_in_content: bool = False,
-            use_function_calling: bool = True
+            message_context: Optional[str] = None,
+            use_deepseek_r1: bool = False
     ):
         super().__init__(
             llm=llm,
             task=task,
             action_descriptions=action_descriptions,
             system_prompt_class=system_prompt_class,
             max_input_tokens=max_input_tokens,
-            estimated_tokens_per_character=estimated_tokens_per_character,
+            estimated_characters_per_token=estimated_characters_per_token,
             image_tokens=image_tokens,
             include_attributes=include_attributes,
             max_error_length=max_error_length,
             max_actions_per_step=max_actions_per_step,
-            tool_call_in_content=tool_call_in_content,
+            message_context=message_context
         )
-        self.use_function_calling = use_function_calling
+        self.tool_id = 1
+        self.use_deepseek_r1 = use_deepseek_r1
         # Custom: Move Task info to state_message
         self.history = MessageHistory()
         self._add_message_with_tokens(self.system_prompt)
 
-        if self.use_function_calling:
-            tool_calls = [
-                {
-                    'name': 'CustomAgentOutput',
-                    'args': {
-                        'current_state': {
-                            'prev_action_evaluation': 'Unknown - No previous actions to evaluate.',
-                            'important_contents': '',
-                            'completed_contents': '',
-                            'thought': 'Now Google is open. Need to type OpenAI to search.',
-                            'summary': 'Type OpenAI to search.',
-                        },
-                        'action': [],
-                    },
-                    'id': '',
-                    'type': 'tool_call',
-                }
-            ]
-            if self.tool_call_in_content:
-                # openai throws error if tool_calls are not responded -> move to content
-                example_tool_call = AIMessage(
-                    content=f'{tool_calls}',
-                    tool_calls=[],
-                )
-            else:
-                example_tool_call = AIMessage(
-                    content=f'',
-                    tool_calls=tool_calls,
-                )
-
-            self._add_message_with_tokens(example_tool_call)
+        if self.message_context:
+            context_message = HumanMessage(content=self.message_context)
+            self._add_message_with_tokens(context_message)
 
     def cut_messages(self):
         """Get current message list, potentially trimmed to max tokens"""
         diff = self.history.total_tokens - self.max_input_tokens
-        while diff > 0 and len(self.history.messages) > 1:
-            self.history.remove_message(1) # alway remove the oldest one
+        min_message_len = 2 if self.message_context is not None else 1
+        
+        while diff > 0 and len(self.history.messages) > min_message_len:
+            self.history.remove_message(min_message_len) # alway remove the oldest message
             diff = self.history.total_tokens - self.max_input_tokens
 
     def add_state_message(
@@ -119,10 +95,10 @@ def _count_text_tokens(self, text: str) -> int:
                 tokens = self.llm.get_num_tokens(text)
             except Exception:
                 tokens = (
-					len(text) // self.ESTIMATED_TOKENS_PER_CHARACTER
+					len(text) // self.estimated_characters_per_token
 				)  # Rough estimate if no tokenizer available
         else:
             tokens = (
-				len(text) // self.ESTIMATED_TOKENS_PER_CHARACTER
+				len(text) // self.estimated_characters_per_token
 			)  # Rough estimate if no tokenizer available
         return tokens

src/agent/custom_massage_manager.py

@@ -1,7 +1,7 @@
 import pdb
 from typing import List, Optional
 
-from browser_use.agent.prompts import SystemPrompt
+from browser_use.agent.prompts import SystemPrompt, AgentMessagePrompt
 from browser_use.agent.views import ActionResult
 from browser_use.browser.views import BrowserState
 from langchain_core.messages import HumanMessage, SystemMessage
@@ -19,7 +19,7 @@ def important_rules(self) -> str:
        {
          "current_state": {
            "prev_action_evaluation": "Success|Failed|Unknown - Analyze the current elements and the image to check if the previous goals/actions are successful like intended by the task. Ignore the action result. The website is the ground truth. Also mention if something unexpected happened like new suggestions in an input field. Shortly state why/why not. Note that the result you output must be consistent with the reasoning you output afterwards. If you consider it to be 'Failed,' you should reflect on this during your thought.",
-           "important_contents": "Output important contents closely related to user\'s instruction or task on the current page. If there is, please output the contents. If not, please output empty string ''.",
+           "important_contents": "Output important contents closely related to user\'s instruction on the current page. If there is, please output the contents. If not, please output empty string ''.",
            "task_progress": "Task Progress is a general summary of the current contents that have been completed. Just summarize the contents that have been actually completed based on the content at current step and the history operations. Please list each completed item individually, such as: 1. Input username. 2. Input Password. 3. Click confirm button. Please return string type not a list.",
            "future_plans": "Based on the user's request and the current state, outline the remaining steps needed to complete the task. This should be a concise list of actions yet to be performed, such as: 1. Select a date. 2. Choose a specific time slot. 3. Confirm booking. Please return string type not a list.",
            "thought": "Think about the requirements that have been completed in previous operations and the requirements that need to be completed in the next one operation. If your output of prev_action_evaluation is 'Failed', please reflect and output your reflection here.",
@@ -142,7 +142,7 @@ def get_system_message(self) -> SystemMessage:
         return SystemMessage(content=AGENT_PROMPT)
 
 
-class CustomAgentMessagePrompt:
+class CustomAgentMessagePrompt(AgentMessagePrompt):
     def __init__(
             self,
             state: BrowserState,
@@ -151,11 +151,12 @@ def __init__(
             max_error_length: int = 400,
             step_info: Optional[CustomAgentStepInfo] = None,
     ):
-        self.state = state
-        self.result = result
-        self.max_error_length = max_error_length
-        self.include_attributes = include_attributes
-        self.step_info = step_info
+        super(CustomAgentMessagePrompt, self).__init__(state=state, 
+                                                       result=result, 
+                                                       include_attributes=include_attributes, 
+                                                       max_error_length=max_error_length, 
+                                                       step_info=step_info
+                                                       )
 
     def get_user_message(self) -> HumanMessage:
         if self.step_info:
@@ -164,8 +165,26 @@ def get_user_message(self) -> HumanMessage:
             step_info_description = ''
 
         elements_text = self.state.element_tree.clickable_elements_to_string(include_attributes=self.include_attributes)
-        if not elements_text:
+
+        has_content_above = (self.state.pixels_above or 0) > 0
+        has_content_below = (self.state.pixels_below or 0) > 0
+
+        if elements_text != '':
+            if has_content_above:
+                elements_text = (
+                    f'... {self.state.pixels_above} pixels above - scroll or extract content to see more ...\n{elements_text}'
+                )
+            else:
+                elements_text = f'[Start of page]\n{elements_text}'
+            if has_content_below:
+                elements_text = (
+                    f'{elements_text}\n... {self.state.pixels_below} pixels below - scroll or extract content to see more ...'
+                )
+            else:
+                elements_text = f'{elements_text}\n[End of page]'
+        else:
             elements_text = 'empty page'
+   
         state_description = f"""
 {step_info_description}
 1. Task: {self.step_info.task}
@@ -182,14 +201,15 @@ def get_user_message(self) -> HumanMessage:
 
         if self.result:
             for i, result in enumerate(self.result):
-                if result.extracted_content:
-                    state_description += f"\nResult of action {i + 1}/{len(self.result)}: {result.extracted_content}"
-                if result.error:
-                    # only use last 300 characters of error
-                    error = result.error[-self.max_error_length:]
-                    state_description += (
-                        f"\nError of action {i + 1}/{len(self.result)}: ...{error}"
-                    )
+                if result.include_in_memory:
+                    if result.extracted_content:
+                        state_description += f"\nResult of action {i + 1}/{len(self.result)}: {result.extracted_content}"
+                    if result.error:
+                        # only use last 300 characters of error
+                        error = result.error[-self.max_error_length:]
+                        state_description += (
+                            f"\nError of action {i + 1}/{len(self.result)}: ...{error}"
+                        )
 
         if self.state.screenshot:
             # Format message for vision model

src/agent/custom_prompts.py

@@ -45,7 +45,7 @@ def type_with_custom_actions(
     ) -> Type["CustomAgentOutput"]:
         """Extend actions with custom actions"""
         return create_model(
-            "AgentOutput",
+            "CustomAgentOutput",
             __base__=CustomAgentOutput,
             action=(
                 list[custom_actions],

src/agent/custom_views.py

@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2025/1/6
+# @Author  : wenshao
+# @ProjectName: browser-use-webui
+# @FileName: config.py
+
+import os
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class BrowserPersistenceConfig:
+    """Configuration for browser persistence"""
+
+    persistent_session: bool = False
+    user_data_dir: Optional[str] = None
+    debugging_port: Optional[int] = None
+    debugging_host: Optional[str] = None
+
+    @classmethod
+    def from_env(cls) -> "BrowserPersistenceConfig":
+        """Create config from environment variables"""
+        return cls(
+            persistent_session=os.getenv("CHROME_PERSISTENT_SESSION", "").lower()
+            == "true",
+            user_data_dir=os.getenv("CHROME_USER_DATA"),
+            debugging_port=int(os.getenv("CHROME_DEBUGGING_PORT", "9222")),
+            debugging_host=os.getenv("CHROME_DEBUGGING_HOST", "localhost"),
+        )