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# -*- coding: utf-8 -*-
# @Time : 2025/1/2
# @Author : wenshao
# @ProjectName: browser-use-webui
# @FileName: custom_agent.py
import asyncio
import base64
import io
import json
import logging
import os
import pdb
import textwrap
import time
import uuid
from io import BytesIO
from pathlib import Path
from typing import Any, Optional, Type, TypeVar
from dotenv import load_dotenv
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
BaseMessage,
SystemMessage,
)
from openai import RateLimitError
from PIL import Image, ImageDraw, ImageFont
from pydantic import BaseModel, ValidationError
from browser_use.agent.message_manager.service import MessageManager
from browser_use.agent.prompts import AgentMessagePrompt, SystemPrompt
from browser_use.agent.service import Agent
from browser_use.agent.views import (
ActionResult,
AgentError,
AgentHistory,
AgentHistoryList,
AgentOutput,
AgentStepInfo,
)
from browser_use.browser.browser import Browser
from browser_use.browser.context import BrowserContext
from browser_use.browser.views import BrowserState, BrowserStateHistory
from browser_use.controller.registry.views import ActionModel
from browser_use.controller.service import Controller
from browser_use.dom.history_tree_processor.service import (
DOMHistoryElement,
HistoryTreeProcessor,
)
from browser_use.telemetry.service import ProductTelemetry
from browser_use.telemetry.views import (
AgentEndTelemetryEvent,
AgentRunTelemetryEvent,
AgentStepErrorTelemetryEvent,
)
from browser_use.utils import time_execution_async
from .custom_views import CustomAgentOutput, CustomAgentStepInfo
from .custom_massage_manager import CustomMassageManager
logger = logging.getLogger(__name__)
class CustomAgent(Agent):
def __init__(
self,
task: str,
llm: BaseChatModel,
add_infos: str = '',
browser: Browser | None = None,
browser_context: BrowserContext | None = None,
controller: Controller = Controller(),
use_vision: bool = True,
save_conversation_path: Optional[str] = None,
max_failures: int = 5,
retry_delay: int = 10,
system_prompt_class: Type[SystemPrompt] = SystemPrompt,
max_input_tokens: int = 128000,
validate_output: bool = False,
include_attributes: list[str] = [
'title',
'type',
'name',
'role',
'tabindex',
'aria-label',
'placeholder',
'value',
'alt',
'aria-expanded',
],
max_error_length: int = 400,
max_actions_per_step: int = 10,
):
super().__init__(task, llm, browser, browser_context, controller, use_vision, save_conversation_path,
max_failures, retry_delay, system_prompt_class, max_input_tokens, validate_output,
include_attributes, max_error_length, max_actions_per_step)
self.add_infos = add_infos
self.message_manager = CustomMassageManager(
llm=self.llm,
task=self.task,
action_descriptions=self.controller.registry.get_prompt_description(),
system_prompt_class=self.system_prompt_class,
max_input_tokens=self.max_input_tokens,
include_attributes=self.include_attributes,
max_error_length=self.max_error_length,
max_actions_per_step=self.max_actions_per_step,
)
def _setup_action_models(self) -> None:
"""Setup dynamic action models from controller's registry"""
# Get the dynamic action model from controller's registry
self.ActionModel = self.controller.registry.create_action_model()
# Create output model with the dynamic actions
self.AgentOutput = CustomAgentOutput.type_with_custom_actions(self.ActionModel)
def _log_response(self, response: CustomAgentOutput) -> None:
"""Log the model's response"""
if 'Success' in response.current_state.prev_action_evaluation:
emoji = '✅'
elif 'Failed' in response.current_state.prev_action_evaluation:
emoji = '❌'
else:
emoji = '🤷'
logger.info(f'{emoji} Eval: {response.current_state.prev_action_evaluation}')
logger.info(f'🧠 New Memory: {response.current_state.important_contents}')
logger.info(f'⏳ Task Progress: {response.current_state.completed_contents}')
logger.info(f'🤔 Thought: {response.current_state.thought}')
logger.info(f'🎯 Summary: {response.current_state.summary}')
for i, action in enumerate(response.action):
logger.info(
f'🛠️ Action {i + 1}/{len(response.action)}: {action.model_dump_json(exclude_unset=True)}'
)
def update_step_info(self, model_output: CustomAgentOutput, step_info: CustomAgentStepInfo = None):
"""
update step info
"""
if step_info is None:
return
step_info.step_number += 1
important_contents = model_output.current_state.important_contents
if important_contents and 'None' not in important_contents and important_contents not in step_info.memory:
step_info.memory += important_contents + '\n'
completed_contents = model_output.current_state.completed_contents
if completed_contents and 'None' not in completed_contents:
step_info.task_progress = completed_contents
@time_execution_async('--get_next_action')
async def get_next_action(self, input_messages: list[BaseMessage]) -> AgentOutput:
"""Get next action from LLM based on current state"""
ret = self.llm.invoke(input_messages)
parsed_json = json.loads(ret.content.replace('```json', '').replace("```", ""))
parsed: AgentOutput = self.AgentOutput(**parsed_json)
# cut the number of actions to max_actions_per_step
parsed.action = parsed.action[: self.max_actions_per_step]
self._log_response(parsed)
self.n_steps += 1
return parsed
@time_execution_async('--step')
async def step(self, step_info: Optional[CustomAgentStepInfo] = None) -> None:
"""Execute one step of the task"""
logger.info(f'\n📍 Step {self.n_steps}')
state = None
model_output = None
result: list[ActionResult] = []
try:
state = await self.browser_context.get_state(use_vision=self.use_vision)
self.message_manager.add_state_message(state, self._last_result, step_info)
input_messages = self.message_manager.get_messages()
model_output = await self.get_next_action(input_messages)
self.update_step_info(model_output, step_info)
logger.info(f'🧠 All Memory: {step_info.memory}')
self._save_conversation(input_messages, model_output)
self.message_manager._remove_last_state_message() # we dont want the whole state in the chat history
self.message_manager.add_model_output(model_output)
result: list[ActionResult] = await self.controller.multi_act(
model_output.action, self.browser_context
)
self._last_result = result
if len(result) > 0 and result[-1].is_done:
logger.info(f'📄 Result: {result[-1].extracted_content}')
self.consecutive_failures = 0
except Exception as e:
result = self._handle_step_error(e)
self._last_result = result
finally:
if not result:
return
for r in result:
if r.error:
self.telemetry.capture(
AgentStepErrorTelemetryEvent(
agent_id=self.agent_id,
error=r.error,
)
)
if state:
self._make_history_item(model_output, state, result)
async def run(self, max_steps: int = 100) -> AgentHistoryList:
"""Execute the task with maximum number of steps"""
try:
logger.info(f'🚀 Starting task: {self.task}')
self.telemetry.capture(
AgentRunTelemetryEvent(
agent_id=self.agent_id,
task=self.task,
)
)
step_info = CustomAgentStepInfo(task=self.task,
add_infos=self.add_infos,
step_number=1,
max_steps=max_steps,
memory='',
task_progress=''
)
for step in range(max_steps):
if self._too_many_failures():
break
await self.step(step_info)
if self.history.is_done():
if (
self.validate_output and step < max_steps - 1
): # if last step, we dont need to validate
if not await self._validate_output():
continue
logger.info('✅ Task completed successfully')
break
else:
logger.info('❌ Failed to complete task in maximum steps')
return self.history
finally:
self.telemetry.capture(
AgentEndTelemetryEvent(
agent_id=self.agent_id,
task=self.task,
success=self.history.is_done(),
steps=len(self.history.history),
)
)
if not self.injected_browser_context:
await self.browser_context.close()
if not self.injected_browser and self.browser:
await self.browser.close()
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