import asyncio import base64 import json import os import random import re import string from asyncio.exceptions import CancelledError from dataclasses import dataclass from datetime import UTC, datetime from pathlib import Path from typing import Any, Tuple, cast import httpx import structlog from openai.types.responses.response import Response as OpenAIResponse from playwright._impl._errors import TargetClosedError from playwright.async_api import Page from skyvern import analytics from skyvern.config import settings from skyvern.constants import ( BROWSER_DOWNLOAD_TIMEOUT, BROWSER_DOWNLOADING_SUFFIX, DEFAULT_MAX_SCREENSHOT_SCROLLS, GET_DOWNLOADED_FILES_TIMEOUT, SAVE_DOWNLOADED_FILES_TIMEOUT, SCRAPE_TYPE_ORDER, SPECIAL_FIELD_VERIFICATION_CODE, ScrapeType, ) from skyvern.errors.errors import ( GetTOTPVerificationCodeError, ReachMaxRetriesError, ReachMaxStepsError, TimeoutGetTOTPVerificationCodeError, UserDefinedError, ) from skyvern.exceptions import ( BrowserSessionNotFound, DownloadFileMaxWaitingTime, EmptyScrapePage, FailedToGetTOTPVerificationCode, FailedToNavigateToUrl, FailedToParseActionInstruction, FailedToSendWebhook, FailedToTakeScreenshot, InvalidTaskStatusTransition, InvalidWorkflowTaskURLState, MissingBrowserStatePage, MissingExtractActionsResponse, NoTOTPVerificationCodeFound, ScrapingFailed, SkyvernException, StepTerminationError, StepUnableToExecuteError, TaskAlreadyCanceled, TaskAlreadyTimeout, TaskNotFound, UnsupportedActionType, UnsupportedTaskType, ) from skyvern.forge import app from skyvern.forge.async_operations import AgentPhase, AsyncOperationPool from skyvern.forge.prompts import prompt_engine from skyvern.forge.sdk.api.aws import aws_client from skyvern.forge.sdk.api.files import ( get_path_for_workflow_download_directory, list_downloading_files_in_directory, list_files_in_directory, rename_file, wait_for_download_finished, ) from skyvern.forge.sdk.api.llm.api_handler_factory import LLMAPIHandlerFactory, LLMCaller, LLMCallerManager from skyvern.forge.sdk.api.llm.config_registry import LLMConfigRegistry from skyvern.forge.sdk.api.llm.exceptions import LLM_PROVIDER_ERROR_RETRYABLE_TASK_TYPE, LLM_PROVIDER_ERROR_TYPE from skyvern.forge.sdk.api.llm.ui_tars_llm_caller import UITarsLLMCaller from skyvern.forge.sdk.api.llm.vertex_cache_manager import get_cache_manager from skyvern.forge.sdk.artifact.models import ArtifactType from skyvern.forge.sdk.core import skyvern_context from skyvern.forge.sdk.core.security import generate_skyvern_webhook_signature from skyvern.forge.sdk.core.skyvern_context import SkyvernContext from skyvern.forge.sdk.db.enums import TaskType from skyvern.forge.sdk.log_artifacts import save_step_logs, save_task_logs from skyvern.forge.sdk.models import SpeculativeLLMMetadata, Step, StepStatus from skyvern.forge.sdk.schemas.files import FileInfo from skyvern.forge.sdk.schemas.organizations import Organization from skyvern.forge.sdk.schemas.tasks import Task, TaskRequest, TaskResponse, TaskStatus from skyvern.forge.sdk.schemas.totp_codes import OTPType from skyvern.forge.sdk.trace import TraceManager from skyvern.forge.sdk.workflow.context_manager import WorkflowRunContext from skyvern.forge.sdk.workflow.models.block import ( ActionBlock, BaseTaskBlock, FileDownloadBlock, ValidationBlock, ) from skyvern.forge.sdk.workflow.models.workflow import Workflow, WorkflowRun, WorkflowRunStatus from skyvern.schemas.runs import CUA_ENGINES, RunEngine from skyvern.schemas.steps import AgentStepOutput from skyvern.services import run_service, service_utils from skyvern.services.action_service import get_action_history from skyvern.services.otp_service import poll_otp_value from skyvern.utils.image_resizer import Resolution from skyvern.utils.prompt_engine import MaxStepsReasonResponse, load_prompt_with_elements from skyvern.webeye.actions.action_types import ActionType from skyvern.webeye.actions.actions import ( Action, ActionStatus, CompleteAction, CompleteVerifyResult, DecisiveAction, ExtractAction, GotoUrlAction, ReloadPageAction, TerminateAction, WebAction, ) from skyvern.webeye.actions.handler import ActionHandler from skyvern.webeye.actions.models import DetailedAgentStepOutput from skyvern.webeye.actions.parse_actions import ( parse_actions, parse_anthropic_actions, parse_cua_actions, parse_ui_tars_actions, ) from skyvern.webeye.actions.responses import ActionResult, ActionSuccess from skyvern.webeye.browser_factory import BrowserState from skyvern.webeye.scraper.scraper import ElementTreeFormat, ScrapedPage, scrape_website from skyvern.webeye.utils.page import SkyvernFrame LOG = structlog.get_logger() EXTRACT_ACTION_TEMPLATE = "extract-action" EXTRACT_ACTION_PROMPT_NAME = "extract-actions" EXTRACT_ACTION_CACHE_KEY_PREFIX = f"{EXTRACT_ACTION_TEMPLATE}-static" @dataclass class SpeculativePlan: scraped_page: ScrapedPage extract_action_prompt: str use_caching: bool llm_json_response: dict[str, Any] | None llm_metadata: SpeculativeLLMMetadata | None = None class ActionLinkedNode: def __init__(self, action: Action) -> None: self.action = action self.next: ActionLinkedNode | None = None class ForgeAgent: def __init__(self) -> None: self.async_operation_pool = AsyncOperationPool() async def create_task_and_step_from_block( self, task_block: BaseTaskBlock, workflow: Workflow, workflow_run: WorkflowRun, workflow_run_context: WorkflowRunContext, task_order: int, task_retry: int, ) -> tuple[Task, Step]: task_block_parameters = task_block.parameters navigation_payload = {} for parameter in task_block_parameters: navigation_payload[parameter.key] = workflow_run_context.get_value(parameter.key) task_url = task_block.url if task_url is None: browser_state = app.BROWSER_MANAGER.get_for_workflow_run( workflow_run_id=workflow_run.workflow_run_id, parent_workflow_run_id=workflow_run.parent_workflow_run_id ) if browser_state is not None: working_page = await browser_state.get_working_page() if not working_page: LOG.error( "BrowserState has no page", workflow_run_id=workflow_run.workflow_run_id, ) raise MissingBrowserStatePage(workflow_run_id=workflow_run.workflow_run_id) if working_page.url == "about:blank": raise InvalidWorkflowTaskURLState(workflow_run.workflow_run_id) task_url = working_page.url else: LOG.info("No browser state found for workflow run, setting task url to empty string") task_url = "" task = await app.DATABASE.create_task( url=task_url, task_type=task_block.task_type, complete_criterion=task_block.complete_criterion, terminate_criterion=task_block.terminate_criterion, title=task_block.title or task_block.label, webhook_callback_url=None, totp_verification_url=task_block.totp_verification_url, totp_identifier=task_block.totp_identifier, navigation_goal=task_block.navigation_goal, data_extraction_goal=task_block.data_extraction_goal, navigation_payload=navigation_payload, organization_id=workflow_run.organization_id, proxy_location=workflow_run.proxy_location, extracted_information_schema=task_block.data_schema, workflow_run_id=workflow_run.workflow_run_id, order=task_order, retry=task_retry, max_steps_per_run=task_block.max_steps_per_run, error_code_mapping=task_block.error_code_mapping, include_action_history_in_verification=task_block.include_action_history_in_verification, model=task_block.model, max_screenshot_scrolling_times=workflow_run.max_screenshot_scrolls, extra_http_headers=workflow_run.extra_http_headers, browser_address=workflow_run.browser_address, browser_session_id=workflow_run.browser_session_id, download_timeout=task_block.download_timeout, ) LOG.info( "Created a new task for workflow run", workflow_id=workflow.workflow_id, workflow_run_id=workflow_run.workflow_run_id, task_id=task.task_id, url=task.url, title=task.title, proxy_location=task.proxy_location, task_order=task_order, task_retry=task_retry, ) # Update task status to running task = await app.DATABASE.update_task( task_id=task.task_id, organization_id=task.organization_id, status=TaskStatus.running, ) step = await app.DATABASE.create_step( task.task_id, order=0, retry_index=0, organization_id=task.organization_id, ) LOG.info( "Created new step for workflow run", workflow_id=workflow.workflow_id, workflow_run_id=workflow_run.workflow_run_id, order=step.order, retry_index=step.retry_index, ) return task, step async def create_task(self, task_request: TaskRequest, organization_id: str) -> Task: webhook_callback_url = str(task_request.webhook_callback_url) if task_request.webhook_callback_url else None totp_verification_url = str(task_request.totp_verification_url) if task_request.totp_verification_url else None # validate browser session id if task_request.browser_session_id: browser_session = await app.DATABASE.get_persistent_browser_session( session_id=task_request.browser_session_id, organization_id=organization_id, ) if not browser_session: raise BrowserSessionNotFound(browser_session_id=task_request.browser_session_id) task = await app.DATABASE.create_task( url=str(task_request.url), title=task_request.title, webhook_callback_url=webhook_callback_url, totp_verification_url=totp_verification_url, totp_identifier=task_request.totp_identifier, navigation_goal=task_request.navigation_goal, complete_criterion=task_request.complete_criterion, terminate_criterion=task_request.terminate_criterion, data_extraction_goal=task_request.data_extraction_goal, navigation_payload=task_request.navigation_payload, organization_id=organization_id, proxy_location=task_request.proxy_location, extracted_information_schema=task_request.extracted_information_schema, error_code_mapping=task_request.error_code_mapping, application=task_request.application, include_action_history_in_verification=task_request.include_action_history_in_verification, model=task_request.model, max_screenshot_scrolling_times=task_request.max_screenshot_scrolls, extra_http_headers=task_request.extra_http_headers, browser_session_id=task_request.browser_session_id, browser_address=task_request.browser_address, ) LOG.info( "Created new task", task_id=task.task_id, url=task.url, proxy_location=task.proxy_location, organization_id=organization_id, ) return task async def register_async_operations(self, organization: Organization, task: Task, page: Page) -> None: operations = await app.AGENT_FUNCTION.generate_async_operations(organization, task, page) self.async_operation_pool.add_operations(task.task_id, operations) @TraceManager.traced_async( ignore_inputs=["api_key", "close_browser_on_completion", "task_block", "cua_response", "llm_caller"] ) async def execute_step( self, organization: Organization, task: Task, step: Step, api_key: str | None = None, close_browser_on_completion: bool = True, task_block: BaseTaskBlock | None = None, browser_session_id: str | None = None, complete_verification: bool = True, engine: RunEngine = RunEngine.skyvern_v1, cua_response: OpenAIResponse | None = None, llm_caller: LLMCaller | None = None, ) -> Tuple[Step, DetailedAgentStepOutput | None, Step | None]: # set the step_id and task_id in the context context = skyvern_context.ensure_context() context.step_id = step.step_id context.task_id = task.task_id # do not need to do complete verification when it's a CUA task # 1. CUA executes only one action step by step -- it's pretty less likely to have a hallucination for completion or forget to return a complete # 2. It will significantly slow down CUA tasks if engine in CUA_ENGINES: complete_verification = False close_browser_on_completion = ( close_browser_on_completion and browser_session_id is None and not task.browser_address ) workflow_run: WorkflowRun | None = None if task.workflow_run_id: workflow_run = await app.DATABASE.get_workflow_run( workflow_run_id=task.workflow_run_id, organization_id=organization.organization_id, ) if workflow_run and workflow_run.status == WorkflowRunStatus.canceled: LOG.info( "Workflow run is canceled, stopping execution inside task", workflow_run_id=workflow_run.workflow_run_id, ) step = await self.update_step( step, status=StepStatus.canceled, is_last=True, ) task = await self.update_task( task, status=TaskStatus.canceled, ) return step, None, None if workflow_run and workflow_run.status == WorkflowRunStatus.timed_out: LOG.info( "Workflow run is timed out, stopping execution inside task", workflow_run_id=workflow_run.workflow_run_id, ) step = await self.update_step( step, status=StepStatus.canceled, is_last=True, ) task = await self.update_task( task, status=TaskStatus.timed_out, ) return step, None, None refreshed_task = await app.DATABASE.get_task(task_id=task.task_id, organization_id=organization.organization_id) if refreshed_task: task = refreshed_task if task.status == TaskStatus.canceled: LOG.info( "Task is canceled, stopping execution", task_id=task.task_id, ) step = await self.update_step( step, status=StepStatus.canceled, is_last=True, ) await self.clean_up_task( task=task, last_step=step, api_key=api_key, need_call_webhook=True, browser_session_id=browser_session_id, close_browser_on_completion=close_browser_on_completion, ) return step, None, None override_max_steps_per_run = context.max_steps_override or None max_steps_per_run = ( override_max_steps_per_run or task.max_steps_per_run or organization.max_steps_per_run or settings.MAX_STEPS_PER_RUN ) if max_steps_per_run and task.max_steps_per_run != max_steps_per_run: await app.DATABASE.update_task( task_id=task.task_id, organization_id=organization.organization_id, max_steps_per_run=max_steps_per_run, ) next_step: Step | None = None detailed_output: DetailedAgentStepOutput | None = None list_files_before: list[str] = [] try: if task.workflow_run_id: list_files_before = list_files_in_directory( get_path_for_workflow_download_directory( context.run_id if context and context.run_id else task.workflow_run_id ) ) if task.browser_session_id: browser_session_downloaded_files = await app.STORAGE.list_downloaded_files_in_browser_session( organization_id=organization.organization_id, browser_session_id=task.browser_session_id, ) list_files_before = list_files_before + browser_session_downloaded_files # Check some conditions before executing the step, throw an exception if the step can't be executed await app.AGENT_FUNCTION.validate_step_execution(task, step) ( step, browser_state, detailed_output, ) = await self.initialize_execution_state(task, step, workflow_run, browser_session_id) # mark step as completed and mark task as completed if ( not task.navigation_goal and not task.data_extraction_goal and not task.complete_criterion and not task.terminate_criterion ): # most likely a GOTO_URL task block page = await browser_state.must_get_working_page() current_url = page.url if current_url.rstrip("/") != task.url.rstrip("/"): await page.goto(task.url) step = await self.update_step( step, status=StepStatus.completed, is_last=True, output=AgentStepOutput(action_results=[]) ) task = await self.update_task(task, status=TaskStatus.completed) await self.clean_up_task( task=task, last_step=step, api_key=api_key, need_call_webhook=True, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return step, detailed_output, None if page := await browser_state.get_working_page(): await self.register_async_operations(organization, task, page) if engine == RunEngine.anthropic_cua and not llm_caller: # see if the llm_caller is already set in memory llm_caller = LLMCallerManager.get_llm_caller(task.task_id) if not llm_caller: # if not, create a new llm_caller llm_key = task.llm_key llm_caller = LLMCaller( llm_key=llm_key or settings.ANTHROPIC_CUA_LLM_KEY, screenshot_scaling_enabled=True ) if engine == RunEngine.ui_tars and not llm_caller: # see if the llm_caller is already set in memory llm_caller = LLMCallerManager.get_llm_caller(task.task_id) if not llm_caller: # create a new UI-TARS llm_caller llm_key = task.llm_key or settings.VOLCENGINE_CUA_LLM_KEY ui_tars_llm_caller = UITarsLLMCaller(llm_key=llm_key, screenshot_scaling_enabled=True) ui_tars_llm_caller.initialize_conversation(task) llm_caller = ui_tars_llm_caller # TODO: remove the code after migrating everything to llm callers # currently, only anthropic cua and ui_tars tasks use llm_caller if engine in [RunEngine.anthropic_cua, RunEngine.ui_tars] and llm_caller: LLMCallerManager.set_llm_caller(task.task_id, llm_caller) step, detailed_output = await self.agent_step( task, step, browser_state, organization=organization, task_block=task_block, complete_verification=complete_verification, engine=engine, cua_response=cua_response, llm_caller=llm_caller, ) await app.AGENT_FUNCTION.post_step_execution(task, step) task = await self.update_task_errors_from_detailed_output(task, detailed_output) # type: ignore retry = False download_detected = False if task_block and task_block.complete_on_download and task.workflow_run_id: workflow_download_directory = get_path_for_workflow_download_directory( context.run_id if context and context.run_id else task.workflow_run_id ) downloading_files = list_downloading_files_in_directory(workflow_download_directory) if task.browser_session_id: browser_session_downloading_files = await app.STORAGE.list_downloading_files_in_browser_session( organization_id=organization.organization_id, browser_session_id=task.browser_session_id, ) downloading_files = downloading_files + browser_session_downloading_files if len(downloading_files) > 0: LOG.info( "Detecting files are still downloading, waiting for files to be completely downloaded.", downloading_files=downloading_files, ) try: await wait_for_download_finished( downloading_files=downloading_files, timeout=task_block.download_timeout or BROWSER_DOWNLOAD_TIMEOUT, ) except DownloadFileMaxWaitingTime as e: LOG.warning( "There're several long-time downloading files, these files might be broken", downloading_files=e.downloading_files, workflow_run_id=task.workflow_run_id, ) list_files_after = list_files_in_directory(workflow_download_directory) if task.browser_session_id: browser_session_downloaded_files_after = await app.STORAGE.list_downloaded_files_in_browser_session( organization_id=organization.organization_id, browser_session_id=task.browser_session_id, ) list_files_after = list_files_after + browser_session_downloaded_files_after if len(list_files_after) > len(list_files_before): download_detected = True files_to_rename = list(set(list_files_after) - set(list_files_before)) for file in files_to_rename: if file.startswith("s3://"): file_data = await aws_client.download_file(file, log_exception=False) if not file_data: continue file = file.split("/")[-1] # Extract filename from the end of S3 URI with open(os.path.join(workflow_download_directory, file), "wb") as f: f.write(file_data) file_extension = Path(file).suffix if file_extension == BROWSER_DOWNLOADING_SUFFIX: LOG.warning( "Detecting incompleted download file, skip the rename", file=file, task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) continue if task_block.download_suffix: # Use download_suffix as the complete filename (without extension) final_file_name = task_block.download_suffix else: # Fallback to random filename if no download_suffix provided random_file_id = "".join(random.choices(string.ascii_uppercase + string.digits, k=4)) final_file_name = f"download-{datetime.now().strftime('%Y%m%d%H%M%S%f')}-{random_file_id}" # Check if file with this name already exists final_file_name = final_file_name target_path = os.path.join(workflow_download_directory, final_file_name + file_extension) counter = 1 while os.path.exists(target_path): # If file exists, append counter to filename final_file_name = f"{final_file_name}_{counter}" target_path = os.path.join(workflow_download_directory, final_file_name + file_extension) counter += 1 rename_file(os.path.join(workflow_download_directory, file), final_file_name + file_extension) LOG.info( "Task marked as completed due to download", task_id=task.task_id, num_files_before=len(list_files_before), num_files_after=len(list_files_after), new_files=files_to_rename, ) last_step = await self.update_step(step, is_last=True) completed_task = await self.update_task( task, status=TaskStatus.completed, ) await self.clean_up_task( task=completed_task, last_step=last_step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return last_step, detailed_output, None if ( task_block and isinstance(task_block, FileDownloadBlock) and task_block.complete_on_download and task.workflow_run_id and not download_detected ): handled, fallback_last_step = await self._handle_file_download_verification_fallback( organization=organization, task=task, step=step, browser_state=browser_state, task_block=task_block, detailed_output=detailed_output, engine=engine, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) if handled and fallback_last_step: return fallback_last_step, detailed_output, None # If the step failed, mark the step as failed and retry if step.status == StepStatus.failed: maybe_next_step = await self.handle_failed_step(organization, task, step) # If there is no next step, it means that the task has failed if maybe_next_step: next_step = maybe_next_step retry = True else: await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return step, detailed_output, None elif step.status == StepStatus.completed: # TODO (kerem): keep the task object uptodate at all times so that clean_up_task can just use it ( is_task_completed, maybe_last_step, maybe_next_step, ) = await self.handle_completed_step( organization=organization, task=task, step=step, page=await browser_state.get_working_page(), task_block=task_block, browser_state=browser_state, scraped_page=detailed_output.scraped_page if detailed_output else None, engine=engine, complete_verification=complete_verification, ) if is_task_completed is not None and maybe_last_step: last_step = maybe_last_step await self.clean_up_task( task=task, last_step=last_step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return last_step, detailed_output, None elif maybe_next_step: next_step = maybe_next_step retry = False else: LOG.error( "Step completed but task is not completed and next step is not created.", is_task_completed=is_task_completed, maybe_last_step=maybe_last_step, maybe_next_step=maybe_next_step, ) else: LOG.error( "Unexpected step status after agent_step", step_status=step.status, ) cua_response_param = detailed_output.cua_response if detailed_output else None if not cua_response_param and cua_response: cua_response_param = cua_response if retry and next_step: return await self.execute_step( organization, task, next_step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, task_block=task_block, complete_verification=complete_verification, engine=engine, cua_response=cua_response_param, llm_caller=llm_caller, ) elif settings.execute_all_steps() and next_step: return await self.execute_step( organization, task, next_step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, task_block=task_block, complete_verification=complete_verification, engine=engine, cua_response=cua_response_param, llm_caller=llm_caller, ) else: LOG.info( "Step executed but continuous execution is disabled.", is_cloud_env=settings.is_cloud_environment(), execute_all_steps=settings.execute_all_steps(), next_step_id=next_step.step_id if next_step else None, ) return step, detailed_output, next_step # TODO (kerem): Let's add other exceptions that we know about here as custom exceptions as well except StepUnableToExecuteError: LOG.exception("Step cannot be executed. Task execution stopped") raise except TaskAlreadyTimeout: LOG.warning("Task is timed out, stopping execution") await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return step, detailed_output, None except StepTerminationError as e: LOG.warning( "Step cannot be executed, marking task as failed", exc_info=True, ) is_task_marked_as_failed = await self.fail_task(task, step, e.message) if is_task_marked_as_failed: await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) else: LOG.warning("Task isn't marked as failed, after step termination. NOT clean up the task") return step, detailed_output, None except FailedToSendWebhook: LOG.exception( "Failed to send webhook", task=task, step=step, ) return step, detailed_output, next_step except FailedToNavigateToUrl as e: # Fail the task if we can't navigate to the URL and send the response LOG.exception( "Failed to navigate to URL, marking task as failed, and sending webhook response", url=e.url, ) failure_reason = f"Failed to navigate to URL. URL:{e.url}, Error:{e.error_message}" is_task_marked_as_failed = await self.fail_task(task, step, failure_reason) if is_task_marked_as_failed: await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, need_final_screenshot=False, browser_session_id=browser_session_id, ) else: LOG.warning("Task isn't marked as failed, after navigation failure. NOT clean up the task") return step, detailed_output, next_step except TaskAlreadyCanceled: LOG.info( "Task is already canceled, stopping execution", task_id=task.task_id, ) await self.clean_up_task( task=task, last_step=step, api_key=api_key, need_call_webhook=False, browser_session_id=browser_session_id, close_browser_on_completion=close_browser_on_completion, ) return step, detailed_output, None except InvalidTaskStatusTransition: LOG.warning("Invalid task status transition") # TODO: shall we send task response here? await self.clean_up_task( task=task, last_step=step, api_key=api_key, need_call_webhook=False, browser_session_id=browser_session_id, close_browser_on_completion=close_browser_on_completion, ) return step, detailed_output, None except (UnsupportedActionType, UnsupportedTaskType, FailedToParseActionInstruction) as e: LOG.warning( "unsupported task type or action type, marking the task as failed", step_order=step.order, step_retry=step.retry_index, ) await self.fail_task(task, step, e.message) await self.clean_up_task( task=task, last_step=step, api_key=api_key, need_call_webhook=False, browser_session_id=browser_session_id, close_browser_on_completion=close_browser_on_completion, ) return step, detailed_output, None except ScrapingFailed as sfe: LOG.warning( "Scraping failed, marking the task as failed", exc_info=True, ) await self.fail_task( task, step, sfe.reason or "Skyvern failed to load the website. This usually happens when the website is not properly designed, and crashes the browser as a result.", ) await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return step, detailed_output, None except MissingBrowserStatePage: LOG.warning("Missing browser state page, marking the task as failed") await self.fail_task( task, step, "The browser does not have a valid page for skyvern to operate. This may be due to the website being empty or the browser crashing.", ) await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return step, detailed_output, None except Exception as e: LOG.exception("Got an unexpected exception in step, marking task as failed") failure_reason = f"Unexpected error: {str(e)}" if isinstance(e, SkyvernException): failure_reason = f"unexpected SkyvernException({e.__class__.__name__}): {str(e)}" is_task_marked_as_failed = await self.fail_task(task, step, failure_reason) if is_task_marked_as_failed: await self.clean_up_task( task=task, last_step=step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) else: LOG.warning("Task isn't marked as failed, after unexpected exception. NOT clean up the task") return step, detailed_output, None finally: # remove the step_id from the context context = skyvern_context.ensure_context() context.step_id = None context.task_id = None async def fail_task(self, task: Task, step: Step | None, reason: str | None) -> bool: try: if step is not None: await self.update_step( step=step, status=StepStatus.failed, ) await self.update_task( task, status=TaskStatus.failed, failure_reason=reason, ) return True except TaskAlreadyCanceled: LOG.info( "Task is already canceled. Can't fail the task.", ) return False except InvalidTaskStatusTransition: LOG.warning( "Invalid task status transition while failing a task", ) return False except Exception: LOG.exception( "Failed to update status and failure reason in database. Task might going to be time_out", reason=reason, ) return True @TraceManager.traced_async( ignore_inputs=["browser_state", "organization", "task_block", "cua_response", "llm_caller"] ) async def _handle_file_download_verification_fallback( self, *, organization: Organization, task: Task, step: Step, browser_state: BrowserState, task_block: FileDownloadBlock, detailed_output: DetailedAgentStepOutput | None, engine: RunEngine, api_key: str | None, close_browser_on_completion: bool, browser_session_id: str | None, ) -> tuple[bool, Step | None]: if detailed_output is None or detailed_output.scraped_page is None: return False, None try: distinct_id = task.workflow_run_id if task.workflow_run_id else task.task_id use_termination_prompt = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "USE_TERMINATION_AWARE_COMPLETE_VERIFICATION", distinct_id, properties={"organization_id": task.organization_id}, ) except Exception as error: # pragma: no cover - defensive logging LOG.warning( "Failed to check USE_TERMINATION_AWARE_COMPLETE_VERIFICATION experiment; skipping download fallback verification", task_id=task.task_id, workflow_run_id=task.workflow_run_id, error=str(error), ) return False, None if not use_termination_prompt: return False, None try: page = await browser_state.get_working_page() if page is None: page = await browser_state.must_get_working_page() except Exception: LOG.warning( "File download fallback verification could not fetch working page, skipping verification", task_id=task.task_id, workflow_run_id=task.workflow_run_id, exc_info=True, ) return False, None try: fallback_action = await self.check_user_goal_complete( page=page, scraped_page=detailed_output.scraped_page, task=task, step=step, task_block=task_block, ) except Exception: LOG.warning( "File download fallback verification failed, continuing with standard flow", task_id=task.task_id, workflow_run_id=task.workflow_run_id, exc_info=True, ) return False, None if fallback_action is None: LOG.info( "File download fallback verification completed with continue status", task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) return False, None LOG.info( "File download fallback verification returned decisive action", task_id=task.task_id, workflow_run_id=task.workflow_run_id, action_type=fallback_action.action_type if isinstance(fallback_action, Action) else "unknown", ) if step.output is None: step.output = AgentStepOutput(action_results=[], actions_and_results=[], errors=[]) if step.output.action_results is None: step.output.action_results = [] if step.output.actions_and_results is None: step.output.actions_and_results = [] if step.output.errors is None: step.output.errors = [] if detailed_output.actions_and_results is None: detailed_output.actions_and_results = [] persisted_action = cast(Action, fallback_action) if isinstance(persisted_action, (CompleteAction, TerminateAction)): persisted_action.organization_id = task.organization_id persisted_action.workflow_run_id = task.workflow_run_id persisted_action.task_id = task.task_id persisted_action.step_id = step.step_id persisted_action.step_order = step.order persisted_action.action_order = len(step.output.actions_and_results) action_results = await ActionHandler.handle_action( detailed_output.scraped_page, task, step, page, persisted_action, ) await self.record_artifacts_after_action(task, step, browser_state, engine) step.output.action_results.extend(action_results) step.output.actions_and_results.append((persisted_action, action_results)) detailed_output.actions_and_results.append((persisted_action, action_results)) if isinstance(persisted_action, DecisiveAction) and persisted_action.errors: step.output.errors.extend(persisted_action.errors) if isinstance(persisted_action, TerminateAction): LOG.warning( "File download fallback verification determined workflow should terminate", task_id=task.task_id, workflow_run_id=task.workflow_run_id, reasoning=persisted_action.reasoning, ) last_step = await self.update_step(step, output=step.output, is_last=True) task_errors = None if persisted_action.errors: task_errors = [error.model_dump() for error in persisted_action.errors] failure_reason = persisted_action.reasoning if persisted_action.errors: failure_reason = "; ".join(error.reasoning for error in persisted_action.errors) updated_task = await self.update_task( task, status=TaskStatus.terminated, failure_reason=failure_reason, errors=task_errors, ) await self.clean_up_task( task=updated_task, last_step=last_step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return True, last_step LOG.info( "File download fallback verification marked task as complete", task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) last_step = await self.update_step(step, output=step.output, is_last=True) extracted_information = await self.get_extracted_information_for_task(task) updated_task = await self.update_task( task, status=TaskStatus.completed, extracted_information=extracted_information, ) await self.clean_up_task( task=updated_task, last_step=last_step, api_key=api_key, close_browser_on_completion=close_browser_on_completion, browser_session_id=browser_session_id, ) return True, last_step async def agent_step( self, task: Task, step: Step, browser_state: BrowserState, engine: RunEngine = RunEngine.skyvern_v1, organization: Organization | None = None, task_block: BaseTaskBlock | None = None, complete_verification: bool = True, cua_response: OpenAIResponse | None = None, llm_caller: LLMCaller | None = None, ) -> tuple[Step, DetailedAgentStepOutput]: detailed_agent_step_output = DetailedAgentStepOutput( scraped_page=None, extract_action_prompt=None, llm_response=None, actions=None, action_results=None, actions_and_results=None, cua_response=None, ) try: LOG.info( "Starting agent step", step_order=step.order, step_retry=step.retry_index, ) # Update context with step_id for auto action/screenshot creation context = skyvern_context.current() if context: context.step_id = step.step_id step = await self.update_step(step=step, status=StepStatus.running) await app.AGENT_FUNCTION.prepare_step_execution( organization=organization, task=task, step=step, browser_state=browser_state ) speculative_plan: SpeculativePlan | None = None reuse_speculative_llm_response = False speculative_llm_metadata: SpeculativeLLMMetadata | None = None if context: speculative_plan = context.speculative_plans.pop(step.step_id, None) if speculative_plan: step.is_speculative = False scraped_page = speculative_plan.scraped_page extract_action_prompt = speculative_plan.extract_action_prompt use_caching = speculative_plan.use_caching json_response = speculative_plan.llm_json_response reuse_speculative_llm_response = json_response is not None speculative_llm_metadata = speculative_plan.llm_metadata else: ( scraped_page, extract_action_prompt, use_caching, ) = await self.build_and_record_step_prompt( task, step, browser_state, engine, ) json_response = None detailed_agent_step_output.scraped_page = scraped_page detailed_agent_step_output.extract_action_prompt = extract_action_prompt actions: list[Action] if engine == RunEngine.openai_cua: actions, new_cua_response = await self._generate_cua_actions( task=task, step=step, scraped_page=scraped_page, previous_response=cua_response, engine=engine, ) detailed_agent_step_output.cua_response = new_cua_response elif engine == RunEngine.anthropic_cua: assert llm_caller is not None actions = await self._generate_anthropic_actions( task=task, step=step, scraped_page=scraped_page, llm_caller=llm_caller, ) elif engine == RunEngine.ui_tars and not await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "DISABLE_UI_TARS_CUA", task.workflow_run_id or task.task_id, properties={"organization_id": task.organization_id}, ): assert llm_caller is not None actions = await self._generate_ui_tars_actions( task=task, step=step, scraped_page=scraped_page, llm_caller=llm_caller, ) else: if not task.navigation_goal and not isinstance(task_block, ValidationBlock): actions = [await self.create_extract_action(task, step, scraped_page)] else: llm_key_override = task.llm_key # FIXME: Redundant engine check? if engine in CUA_ENGINES: self.async_operation_pool.run_operation(task.task_id, AgentPhase.llm) llm_key_override = None llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler( llm_key_override, default=app.LLM_API_HANDLER ) # Add caching flag to context for monitoring if use_caching: context = skyvern_context.current() if context: context.use_prompt_caching = True if not reuse_speculative_llm_response: json_response = await llm_api_handler( prompt=extract_action_prompt, prompt_name="extract-actions", step=step, screenshots=scraped_page.screenshots, ) else: LOG.debug( "Using speculative extract-actions response", step_id=step.step_id, ) if json_response is None: raise MissingExtractActionsResponse() try: otp_json_response, otp_actions = await self.handle_potential_OTP_actions( task, step, scraped_page, browser_state, json_response ) if otp_actions: detailed_agent_step_output.llm_response = otp_json_response actions = otp_actions else: actions = parse_actions( task, step.step_id, step.order, scraped_page, json_response["actions"] ) if context: context.pop_totp_code(task.task_id) except NoTOTPVerificationCodeFound: actions = [ TerminateAction( organization_id=task.organization_id, workflow_run_id=task.workflow_run_id, task_id=task.task_id, step_id=step.step_id, step_order=step.order, action_order=0, reasoning="No TOTP verification code found. Going to terminate.", intention="No TOTP verification code found. Going to terminate.", errors=[TimeoutGetTOTPVerificationCodeError().to_user_defined_error()], ) ] except FailedToGetTOTPVerificationCode as e: actions = [ TerminateAction( reasoning=f"Failed to get TOTP verification code. Going to terminate. Reason: {e.reason}", intention=f"Failed to get TOTP verification code. Going to terminate. Reason: {e.reason}", organization_id=task.organization_id, workflow_run_id=task.workflow_run_id, task_id=task.task_id, step_id=step.step_id, step_order=step.order, action_order=0, errors=[GetTOTPVerificationCodeError(reason=e.reason).to_user_defined_error()], ) ] if reuse_speculative_llm_response and speculative_llm_metadata: await self._persist_speculative_llm_metadata( step, speculative_llm_metadata, screenshots=scraped_page.screenshots, ) speculative_llm_metadata = None detailed_agent_step_output.actions = actions if len(actions) == 0: LOG.info( "No actions to execute, marking step as failed", step_order=step.order, step_retry=step.retry_index, ) step = await self.update_step( step=step, status=StepStatus.failed, output=detailed_agent_step_output.to_agent_step_output(), ) return step, detailed_agent_step_output # Execute the actions LOG.info( "Executing actions", step_order=step.order, step_retry=step.retry_index, actions=actions, ) action_results: list[ActionResult] = [] detailed_agent_step_output.action_results = action_results # filter out wait action if there are other actions in the list # we do this because WAIT action is considered as a failure # which will block following actions if we don't remove it from the list # if the list only contains WAIT action, we will execute WAIT action(s) if len(actions) > 1: wait_actions_to_skip = [action for action in actions if action.action_type == ActionType.WAIT] wait_actions_len = len(wait_actions_to_skip) # if there are wait actions and there are other actions in the list, skip wait actions # if we are using cached action plan, we don't skip wait actions if wait_actions_len > 0 and wait_actions_len < len(actions): actions = [action for action in actions if action.action_type != ActionType.WAIT] LOG.info( "Skipping wait actions", wait_actions_to_skip=wait_actions_to_skip, actions=actions, ) # initialize list of tuples and set actions as the first element of each tuple so that in the case # of an exception, we can still see all the actions detailed_agent_step_output.actions_and_results = [(action, []) for action in actions] # build a linked action chain by the action_idx action_linked_list: list[ActionLinkedNode] = [] element_id_to_action_index: dict[str, int] = dict() for action_idx, action in enumerate(actions): node = ActionLinkedNode(action=action) action_linked_list.append(node) if not isinstance(action, WebAction): continue previous_action_idx = element_id_to_action_index.get(action.element_id) if previous_action_idx is not None: previous_node = action_linked_list[previous_action_idx] previous_node.next = node element_id_to_action_index[action.element_id] = action_idx element_id_to_last_action: dict[str, int] = dict() for action_idx, action_node in enumerate(action_linked_list): context = skyvern_context.ensure_context() if context.refresh_working_page: LOG.warning( "Detected the signal to reload the page, going to reload and skip the rest of the actions", step_order=step.order, ) await browser_state.reload_page() context.refresh_working_page = False action_result = ActionSuccess() action_result.step_order = step.order action_result.step_retry_number = step.retry_index action = ReloadPageAction( reasoning="Something wrong with the current page, reload to continue", status=ActionStatus.completed, organization_id=task.organization_id, workflow_run_id=task.workflow_run_id, task_id=task.task_id, step_id=step.step_id, step_order=step.order, action_order=action_idx, ) detailed_agent_step_output.actions_and_results[action_idx] = (action, [action_result]) await app.DATABASE.create_action(action=action) await self.record_artifacts_after_action(task, step, browser_state, engine) break action = action_node.action if isinstance(action, WebAction): previous_action_idx = element_id_to_last_action.get(action.element_id) if previous_action_idx is not None: LOG.warning( "Duplicate action element id.", step_order=step.order, action=action, ) previous_action, previous_result = detailed_agent_step_output.actions_and_results[ previous_action_idx ] if len(previous_result) > 0 and previous_result[-1].success: LOG.info( "Previous action succeeded, but we'll still continue.", step_order=step.order, previous_action=previous_action, previous_result=previous_result, ) else: LOG.warning( "Previous action failed, so handle the next action.", step_order=step.order, previous_action=previous_action, previous_result=previous_result, ) element_id_to_last_action[action.element_id] = action_idx if engine != RunEngine.openai_cua: self.async_operation_pool.run_operation(task.task_id, AgentPhase.action) current_page = await browser_state.must_get_working_page() if isinstance(action, CompleteAction) and not complete_verification: # Do not verify the complete action when complete_verification is False # set verified to True will skip the completion verification action.verified = True # Pass TOTP secret to handler for multi-field TOTP sequences # Handler will generate TOTP at execution time if ( action.action_type == ActionType.INPUT_TEXT and self._is_multi_field_totp_sequence(actions) and (totp_secret := skyvern_context.ensure_context().totp_codes.get(f"{task.task_id}_secret")) ): # Pass TOTP secret to handler for execution-time generation action.totp_timing_info = { "is_totp_sequence": True, "action_index": action_idx, "totp_secret": totp_secret, "is_retry": step.retry_index > 0, } results = await ActionHandler.handle_action( scraped_page=scraped_page, task=task, step=step, page=current_page, action=action, ) await app.AGENT_FUNCTION.post_action_execution(action) detailed_agent_step_output.actions_and_results[action_idx] = ( action, results, ) # Determine wait time between actions wait_time = random.uniform(0.5, 1.0) # For multi-field TOTP sequences, use zero delay between all digits for fast execution if action.action_type == ActionType.INPUT_TEXT and self._is_multi_field_totp_sequence(actions): current_text = action.text if hasattr(action, "text") else None if current_text and len(current_text) == 1 and current_text.isdigit(): # Zero delay between all TOTP digits for fast execution wait_time = 0.0 LOG.debug( "TOTP: zero delay for digit", task_id=task.task_id, action_idx=action_idx, digit=current_text, ) await asyncio.sleep(wait_time) await self.record_artifacts_after_action(task, step, browser_state, engine) for result in results: result.step_retry_number = step.retry_index result.step_order = step.order step.output = detailed_agent_step_output.to_agent_step_output() action_results.extend(results) # Check the last result for this action. If that succeeded, assume the entire action is successful if results and results[-1].success: LOG.info( "Action succeeded", step_order=step.order, step_retry=step.retry_index, action_idx=action_idx, action=action, action_result=results, ) if results[-1].skip_remaining_actions: LOG.warning( "Going to stop executing the remaining actions", step_order=step.order, step_retry=step.retry_index, action_idx=action_idx, action=action, action_result=results, ) break elif results and isinstance(action, DecisiveAction): LOG.warning( "DecisiveAction failed, but not stopping execution and not retrying the step", step_order=step.order, step_retry=step.retry_index, action_idx=action_idx, action=action, action_result=results, ) elif results and not results[-1].success and not results[-1].stop_execution_on_failure: LOG.warning( "Action failed, but not stopping execution", step_order=step.order, step_retry=step.retry_index, action_idx=action_idx, action=action, action_result=results, ) else: if action_node.next is not None: LOG.warning( "Action failed, but have duplicated element id in the action list. Continue excuting.", step_order=step.order, step_retry=step.retry_index, action_idx=action_idx, action=action, next_action=action_node.next.action, action_result=results, ) continue LOG.warning( "Action failed, marking step as failed", step_order=step.order, step_retry=step.retry_index, action_idx=action_idx, action=action, action_result=results, actions_and_results=detailed_agent_step_output.actions_and_results, ) # if the action failed, don't execute the rest of the actions, mark the step as failed, and retry failed_step = await self.update_step( step=step, status=StepStatus.failed, output=detailed_agent_step_output.to_agent_step_output(), ) return failed_step, detailed_agent_step_output.get_clean_detailed_output() LOG.info( "Actions executed successfully, marking step as completed", step_order=step.order, step_retry=step.retry_index, action_results=action_results, ) # Clean up TOTP cache after multi-field TOTP sequence completion if self._is_multi_field_totp_sequence(actions): context = skyvern_context.ensure_context() cache_key = f"{task.task_id}_totp_cache" if cache_key in context.totp_codes: context.totp_codes.pop(cache_key) LOG.debug( "Cleaned up TOTP cache after multi-field sequence completion", task_id=task.task_id, ) secret_key = f"{task.task_id}_secret" if secret_key in context.totp_codes: context.totp_codes.pop(secret_key) # Check if Skyvern already returned a complete action, if so, don't run user goal check has_decisive_action = False if detailed_agent_step_output and detailed_agent_step_output.actions_and_results: for action, results in detailed_agent_step_output.actions_and_results: if isinstance(action, DecisiveAction): has_decisive_action = True break task_completes_on_download = task_block and task_block.complete_on_download and task.workflow_run_id enable_parallel_verification = False if ( not has_decisive_action and not task_completes_on_download and not isinstance(task_block, ActionBlock) and complete_verification and (task.navigation_goal or task.complete_criterion) ): disable_user_goal_check = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "DISABLE_USER_GOAL_CHECK", task.task_id, properties={"task_url": task.url, "organization_id": task.organization_id}, ) # Check if parallel verification is enabled distinct_id = task.workflow_run_id if task.workflow_run_id else task.task_id enable_parallel_verification = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "ENABLE_PARALLEL_USER_GOAL_CHECK", distinct_id, properties={"organization_id": task.organization_id, "task_url": task.url}, ) if not disable_user_goal_check and not enable_parallel_verification: # Standard synchronous verification working_page = await browser_state.must_get_working_page() complete_action = await self.check_user_goal_complete( page=working_page, scraped_page=scraped_page, task=task, step=step, task_block=task_block, ) if complete_action is not None: LOG.info("User goal achieved, executing complete action") complete_action.organization_id = task.organization_id complete_action.workflow_run_id = task.workflow_run_id complete_action.task_id = task.task_id complete_action.step_id = step.step_id complete_action.step_order = step.order complete_action.action_order = len(detailed_agent_step_output.actions_and_results) complete_results = await ActionHandler.handle_action( scraped_page, task, step, working_page, complete_action ) detailed_agent_step_output.actions_and_results.append((complete_action, complete_results)) await self.record_artifacts_after_action(task, step, browser_state, engine) elif enable_parallel_verification: # Parallel verification enabled - defer check to handle_completed_step LOG.info( "Parallel verification enabled, deferring user goal check to handle_completed_step", step_id=step.step_id, task_id=task.task_id, ) # if the last action is complete and is successful, check if there's a data extraction goal # if task has navigation goal and extraction goal at the same time, handle ExtractAction before marking step as completed if ( task.navigation_goal and task.data_extraction_goal and self.step_has_completed_goal(detailed_agent_step_output) ): working_page = await browser_state.must_get_working_page() # refresh task in case the extracted information is updated previously refreshed_task = await app.DATABASE.get_task(task.task_id, task.organization_id) assert refreshed_task is not None task = refreshed_task extract_action = await self.create_extract_action(task, step, scraped_page) extract_results = await ActionHandler.handle_action( scraped_page, task, step, working_page, extract_action ) await app.AGENT_FUNCTION.post_action_execution(extract_action) detailed_agent_step_output.actions_and_results.append((extract_action, extract_results)) # If no action errors return the agent state and output completed_step = await self.update_step( step=step, status=StepStatus.completed, output=detailed_agent_step_output.to_agent_step_output(), ) if enable_parallel_verification: completed_step.speculative_original_status = StepStatus.completed return completed_step, detailed_agent_step_output.get_clean_detailed_output() except CancelledError: LOG.exception( "CancelledError in agent_step, marking step as failed", step_order=step.order, step_retry=step.retry_index, ) detailed_agent_step_output.step_exception = "CancelledError" failed_step = await self.update_step( step=step, status=StepStatus.failed, output=detailed_agent_step_output.to_agent_step_output(), ) return failed_step, detailed_agent_step_output.get_clean_detailed_output() except ( UnsupportedActionType, UnsupportedTaskType, FailedToParseActionInstruction, ScrapingFailed, MissingBrowserStatePage, ): raise except Exception as e: LOG.exception( "Unexpected exception in agent_step, marking step as failed", step_order=step.order, step_retry=step.retry_index, ) detailed_agent_step_output.step_exception = e.__class__.__name__ failed_step = await self.update_step( step=step, status=StepStatus.failed, output=detailed_agent_step_output.to_agent_step_output(), ) return failed_step, detailed_agent_step_output.get_clean_detailed_output() async def _generate_cua_actions( self, task: Task, step: Step, scraped_page: ScrapedPage, previous_response: OpenAIResponse | None = None, engine: RunEngine = RunEngine.openai_cua, ) -> tuple[list[Action], OpenAIResponse | None]: if not previous_response: # this is the first step first_response: OpenAIResponse = await app.OPENAI_CLIENT.responses.create( model="computer-use-preview", tools=[ { "type": "computer_use_preview", "display_width": settings.BROWSER_WIDTH, "display_height": settings.BROWSER_HEIGHT, "environment": "browser", } ], input=[ { "role": "user", "content": task.navigation_goal, } ], reasoning={ "generate_summary": "concise", }, truncation="auto", temperature=0, ) previous_response = first_response input_tokens = first_response.usage.input_tokens or 0 output_tokens = first_response.usage.output_tokens or 0 first_response.usage.total_tokens or 0 cached_tokens = first_response.usage.input_tokens_details.cached_tokens or 0 reasoning_tokens = first_response.usage.output_tokens_details.reasoning_tokens or 0 llm_cost = (3.0 / 1000000) * input_tokens + (12.0 / 1000000) * output_tokens await app.DATABASE.update_step( task_id=task.task_id, step_id=step.step_id, organization_id=task.organization_id, incremental_cost=llm_cost, incremental_input_tokens=input_tokens if input_tokens > 0 else None, incremental_output_tokens=output_tokens if output_tokens > 0 else None, incremental_reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None, incremental_cached_tokens=cached_tokens if cached_tokens > 0 else None, ) if not scraped_page.screenshots: return [], previous_response computer_calls = [item for item in previous_response.output if item.type == "computer_call"] reasonings = [item for item in previous_response.output if item.type == "reasoning"] assistant_messages = [ item for item in previous_response.output if item.type == "message" and item.role == "assistant" ] last_call_id = None if computer_calls: last_call_id = computer_calls[-1].call_id screenshot_base64 = base64.b64encode(scraped_page.screenshots[0]).decode("utf-8") if last_call_id is None: current_context = skyvern_context.ensure_context() resp_content = None if task.task_id in current_context.totp_codes: verification_code = current_context.totp_codes[task.task_id] current_context.totp_codes.pop(task.task_id) LOG.info( "Using verification code from context", task_id=task.task_id, verification_code=verification_code, ) resp_content = f"Here is the verification code: {verification_code}" else: # try address the conversation with the context we have reasoning = reasonings[0].summary[0].text if reasonings and reasonings[0].summary else None assistant_message = assistant_messages[0].content[0].text if assistant_messages else None skyvern_repsonse_prompt = load_prompt_with_elements( element_tree_builder=scraped_page, prompt_engine=prompt_engine, template_name="cua-answer-question", navigation_goal=task.navigation_goal, assistant_reasoning=reasoning, assistant_message=assistant_message, ) skyvern_response = await app.LLM_API_HANDLER( prompt=skyvern_repsonse_prompt, prompt_name="cua-answer-question", step=step, screenshots=scraped_page.screenshots, ) LOG.info("Skyvern response to CUA question", skyvern_response=skyvern_response) resp_content = skyvern_response.get("answer") if not resp_content: resp_content = "I don't know. Can you help me make the best decision to achieve the goal?" current_response = await app.OPENAI_CLIENT.responses.create( model="computer-use-preview", previous_response_id=previous_response.id, tools=[ { "type": "computer_use_preview", "display_width": settings.BROWSER_WIDTH, "display_height": settings.BROWSER_HEIGHT, "environment": "browser", } ], input=[ {"role": "user", "content": resp_content}, ], reasoning={"generate_summary": "concise"}, truncation="auto", temperature=0, ) else: last_computer_call = computer_calls[-1] computer_call_input = { "call_id": last_call_id, "type": "computer_call_output", "output": { "type": "input_image", "image_url": f"data:image/png;base64,{screenshot_base64}", }, } if last_computer_call.pending_safety_checks: pending_checks = [check.model_dump() for check in last_computer_call.pending_safety_checks] computer_call_input["acknowledged_safety_checks"] = pending_checks current_response = await app.OPENAI_CLIENT.responses.create( model="computer-use-preview", previous_response_id=previous_response.id, tools=[ { "type": "computer_use_preview", "display_width": settings.BROWSER_WIDTH, "display_height": settings.BROWSER_HEIGHT, "environment": "browser", } ], input=[computer_call_input], reasoning={ "generate_summary": "concise", }, truncation="auto", temperature=0, ) input_tokens = current_response.usage.input_tokens or 0 output_tokens = current_response.usage.output_tokens or 0 current_response.usage.total_tokens or 0 cached_tokens = current_response.usage.input_tokens_details.cached_tokens or 0 reasoning_tokens = current_response.usage.output_tokens_details.reasoning_tokens or 0 llm_cost = (3.0 / 1000000) * input_tokens + (12.0 / 1000000) * output_tokens await app.DATABASE.update_step( task_id=task.task_id, step_id=step.step_id, organization_id=task.organization_id, incremental_cost=llm_cost, incremental_input_tokens=input_tokens if input_tokens > 0 else None, incremental_output_tokens=output_tokens if output_tokens > 0 else None, incremental_reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None, incremental_cached_tokens=cached_tokens if cached_tokens > 0 else None, ) return await parse_cua_actions(task, step, current_response), current_response async def _generate_anthropic_actions( self, task: Task, step: Step, scraped_page: ScrapedPage, llm_caller: LLMCaller, ) -> list[Action]: LOG.info( "Anthropic CU call starts", tool_results=llm_caller.current_tool_results, message_length=len(llm_caller.message_history), ) if llm_caller.current_tool_results: llm_caller.message_history.append({"role": "user", "content": llm_caller.current_tool_results}) llm_caller.clear_tool_results() LOG.info( "Anthropic CU call - appended tool result message to message history and cleared cached tool results", message=llm_caller.current_tool_results, message_length=len(llm_caller.message_history), ) tools = [ { "type": "computer_20250124", "name": "computer", "display_height_px": settings.BROWSER_HEIGHT, "display_width_px": settings.BROWSER_WIDTH, } ] thinking = {"type": "enabled", "budget_tokens": 1024} betas = ["computer-use-2025-01-24"] window_dimension = cast(Resolution, scraped_page.window_dimension) if scraped_page.window_dimension else None if not llm_caller.message_history: llm_response = await llm_caller.call( prompt=task.navigation_goal, step=step, screenshots=scraped_page.screenshots, use_message_history=True, tools=tools, raw_response=True, betas=betas, thinking=thinking, window_dimension=window_dimension, ) else: current_context = skyvern_context.ensure_context() resp_content = None if task.task_id in current_context.totp_codes: verification_code = current_context.totp_codes[task.task_id] current_context.totp_codes.pop(task.task_id) LOG.info( "Using verification code from context for anthropic CU call", task_id=task.task_id, verification_code=verification_code, ) resp_content = f"Here is the verification code: {verification_code}" llm_response = await llm_caller.call( prompt=resp_content, step=step, screenshots=scraped_page.screenshots, use_message_history=True, tools=tools, raw_response=True, betas=betas, thinking=thinking, window_dimension=window_dimension, ) assistant_content = llm_response["content"] llm_caller.message_history.append({"role": "assistant", "content": assistant_content}) actions = await parse_anthropic_actions( task, step, assistant_content, window_dimension or llm_caller.browser_window_dimension, llm_caller.get_screenshot_resize_target_dimension(window_dimension), ) return actions async def _generate_ui_tars_actions( self, task: Task, step: Step, scraped_page: ScrapedPage, llm_caller: LLMCaller, ) -> list[Action]: """Generate actions using UI-TARS (Seed1.5-VL) model through the LLMCaller pattern.""" LOG.info( "UI-TARS action generation starts", step_order=step.order, ) # Ensure we have a UITarsLLMCaller instance if not isinstance(llm_caller, UITarsLLMCaller): raise ValueError(f"Expected UITarsLLMCaller, got {type(llm_caller)}") # Add the current screenshot to conversation if scraped_page.screenshots: llm_caller.add_screenshot(scraped_page.screenshots[0]) else: LOG.error("No screenshots found, skipping UI-TARS action generation") raise ValueError("No screenshots found, skipping UI-TARS action generation") # Generate response using the LLMCaller response_content = await llm_caller.generate_ui_tars_response(step) LOG.info(f"UI-TARS raw response: {response_content}") window_dimension = ( cast(Resolution, scraped_page.window_dimension) if scraped_page.window_dimension else Resolution(width=1920, height=1080) ) LOG.info(f"UI-TARS browser window dimension: {window_dimension}") actions = await parse_ui_tars_actions(task, step, response_content, window_dimension) LOG.info( "UI-TARS action generation completed", actions_count=len(actions), ) return actions async def _should_skip_screenshot_annotations(self, task: Task, draw_boxes: bool) -> bool: """ Check PostHog feature flag to determine if screenshot annotations should be skipped. Args: task: The task being executed draw_boxes: Current value indicating if boxes should be drawn Returns: bool: True if annotations should be drawn, False if they should be skipped """ if not draw_boxes: # Only check if we were going to draw boxes return draw_boxes try: distinct_id = task.workflow_run_id if task.workflow_run_id else task.task_id skip_annotations = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "SKIP_SCREENSHOT_ANNOTATIONS", distinct_id, properties={"organization_id": task.organization_id}, ) if skip_annotations: LOG.info( "Skipping screenshot annotations per SKIP_SCREENSHOT_ANNOTATIONS feature flag", task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) return False except Exception: LOG.warning( "Failed to check SKIP_SCREENSHOT_ANNOTATIONS feature flag, using default behavior", task_id=task.task_id, exc_info=True, ) return draw_boxes async def _speculate_next_step_plan( self, task: Task, current_step: Step, next_step: Step, browser_state: BrowserState, engine: RunEngine, ) -> SpeculativePlan | None: if engine in CUA_ENGINES: LOG.info( "Skipping speculative extract-actions for CUA engine", step_id=current_step.step_id, task_id=task.task_id, ) return None try: next_step.is_speculative = True scraped_page, extract_action_prompt, use_caching = await self.build_and_record_step_prompt( task, next_step, browser_state, engine, persist_artifacts=False, ) llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler( task.llm_key, default=app.LLM_API_HANDLER, ) llm_json_response = await llm_api_handler( prompt=extract_action_prompt, prompt_name="extract-actions", step=next_step, screenshots=scraped_page.screenshots, ) LOG.info( "Speculative extract-actions completed", current_step_id=current_step.step_id, synthetic_step_id=next_step.step_id, ) metadata_copy = None if next_step.speculative_llm_metadata is not None: metadata_copy = next_step.speculative_llm_metadata.model_copy() next_step.speculative_llm_metadata = None next_step.is_speculative = False return SpeculativePlan( scraped_page=scraped_page, extract_action_prompt=extract_action_prompt, use_caching=use_caching, llm_json_response=llm_json_response, llm_metadata=metadata_copy, ) except Exception: LOG.warning( "Failed to run speculative extract-actions", step_id=current_step.step_id, exc_info=True, ) next_step.is_speculative = False return None async def _persist_speculative_llm_metadata( self, step: Step, metadata: SpeculativeLLMMetadata, *, screenshots: list[bytes] | None = None, ) -> None: if not metadata: return LOG.debug("Persisting speculative LLM metadata") if metadata.prompt: await app.ARTIFACT_MANAGER.create_llm_artifact( data=metadata.prompt.encode("utf-8"), artifact_type=ArtifactType.LLM_PROMPT, screenshots=screenshots, step=step, ) if metadata.llm_request_json: await app.ARTIFACT_MANAGER.create_llm_artifact( data=metadata.llm_request_json.encode("utf-8"), artifact_type=ArtifactType.LLM_REQUEST, step=step, ) if metadata.llm_response_json: await app.ARTIFACT_MANAGER.create_llm_artifact( data=metadata.llm_response_json.encode("utf-8"), artifact_type=ArtifactType.LLM_RESPONSE, step=step, ) if metadata.parsed_response_json: await app.ARTIFACT_MANAGER.create_llm_artifact( data=metadata.parsed_response_json.encode("utf-8"), artifact_type=ArtifactType.LLM_RESPONSE_PARSED, step=step, ) if metadata.rendered_response_json: await app.ARTIFACT_MANAGER.create_llm_artifact( data=metadata.rendered_response_json.encode("utf-8"), artifact_type=ArtifactType.LLM_RESPONSE_RENDERED, step=step, ) incremental_cost = metadata.llm_cost if metadata.llm_cost and metadata.llm_cost > 0 else None incremental_input_tokens = ( metadata.input_tokens if metadata.input_tokens and metadata.input_tokens > 0 else None ) incremental_output_tokens = ( metadata.output_tokens if metadata.output_tokens and metadata.output_tokens > 0 else None ) incremental_reasoning_tokens = ( metadata.reasoning_tokens if metadata.reasoning_tokens and metadata.reasoning_tokens > 0 else None ) incremental_cached_tokens = ( metadata.cached_tokens if metadata.cached_tokens and metadata.cached_tokens > 0 else None ) if ( incremental_cost is not None or incremental_input_tokens is not None or incremental_output_tokens is not None or incremental_reasoning_tokens is not None or incremental_cached_tokens is not None ): await app.DATABASE.update_step( task_id=step.task_id, step_id=step.step_id, organization_id=step.organization_id, incremental_cost=incremental_cost, incremental_input_tokens=incremental_input_tokens, incremental_output_tokens=incremental_output_tokens, incremental_reasoning_tokens=incremental_reasoning_tokens, incremental_cached_tokens=incremental_cached_tokens, ) if incremental_input_tokens: step.input_token_count += incremental_input_tokens if incremental_output_tokens: step.output_token_count += incremental_output_tokens if incremental_reasoning_tokens: step.reasoning_token_count = (step.reasoning_token_count or 0) + incremental_reasoning_tokens if incremental_cached_tokens: step.cached_token_count = (step.cached_token_count or 0) + incremental_cached_tokens if incremental_cost: step.step_cost += incremental_cost step.speculative_llm_metadata = None async def _persist_speculative_metadata_for_discarded_plan( self, step: Step, speculative_task: asyncio.Future[SpeculativePlan | None], *, cancel_step: bool = False, ) -> None: try: plan = await asyncio.shield(speculative_task) except CancelledError: LOG.debug( "Speculative extract-actions cancelled before metadata persistence", step_id=step.step_id, ) step.is_speculative = False if cancel_step: await self._cancel_speculative_step(step) return except Exception: LOG.debug( "Speculative extract-actions failed before metadata persistence", step_id=step.step_id, exc_info=True, ) step.is_speculative = False if cancel_step: await self._cancel_speculative_step(step) return if not plan or not plan.llm_metadata: step.is_speculative = False if cancel_step: await self._cancel_speculative_step(step) return try: await self._persist_speculative_llm_metadata( step, plan.llm_metadata, ) step.is_speculative = False if cancel_step: await self._cancel_speculative_step(step) except Exception: LOG.warning( "Failed to persist speculative llm metadata for discarded plan", step_id=step.step_id, exc_info=True, ) async def _cancel_speculative_step(self, step: Step) -> None: if step.status == StepStatus.canceled: return try: updated_step = await self.update_step(step, status=StepStatus.canceled) step.status = updated_step.status step.is_speculative = False except Exception: LOG.warning( "Failed to cancel speculative step", step_id=step.step_id, exc_info=True, ) async def complete_verify( self, page: Page, scraped_page: ScrapedPage, task: Task, step: Step, task_block: BaseTaskBlock | None = None, *, use_termination_prompt: bool = False, ) -> CompleteVerifyResult: LOG.info( "Checking if user goal is achieved after re-scraping the page", workflow_run_id=task.workflow_run_id, ) scroll = True llm_key_override = task.llm_key if await service_utils.is_cua_task(task=task): scroll = False llm_key_override = None scraped_page_refreshed = await scraped_page.refresh(draw_boxes=False, scroll=scroll) actions_and_results_str = "" if task.include_action_history_in_verification: actions_and_results_str = await self._get_action_results(task, current_step=step) # Select the appropriate template based on experiment template_name = "check-user-goal-with-termination" if use_termination_prompt else "check-user-goal" prompt_name = "check-user-goal-with-termination" if use_termination_prompt else "check-user-goal" verification_prompt = load_prompt_with_elements( element_tree_builder=scraped_page_refreshed, prompt_engine=prompt_engine, template_name=template_name, navigation_goal=task.navigation_goal, navigation_payload=task.navigation_payload, complete_criterion=task.complete_criterion, terminate_criterion=task.terminate_criterion, action_history=actions_and_results_str, local_datetime=datetime.now(skyvern_context.ensure_context().tz_info).isoformat(), ) # This prompt is critical for our agent, we probably should use the primary LLM handler # but we're experimenting with using the dedicated check-user-goal handler use_check_user_goal_handler = False try: # Use task_id or workflow_run_id as distinct_id distinct_id = task.workflow_run_id if task.workflow_run_id else task.task_id use_check_user_goal_handler = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "USE_CHECK_USER_GOAL_HANDLER_FOR_VERIFICATION", distinct_id, properties={"organization_id": task.organization_id}, ) if use_check_user_goal_handler: LOG.info( "Experiment enabled: using CHECK_USER_GOAL_LLM_API_HANDLER for complete verification", task_id=task.task_id, workflow_run_id=task.workflow_run_id, organization_id=task.organization_id, ) except Exception as e: LOG.warning( "Failed to check USE_CHECK_USER_GOAL_HANDLER_FOR_VERIFICATION experiment; using legacy behavior", task_id=task.task_id, workflow_run_id=task.workflow_run_id, error=str(e), ) if use_check_user_goal_handler: # Use the dedicated check-user-goal handler (new behavior) llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler( llm_key_override, default=app.CHECK_USER_GOAL_LLM_API_HANDLER ) else: # Use the primary LLM handler (legacy behavior) llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler( llm_key_override, default=app.LLM_API_HANDLER ) verification_result = await llm_api_handler( prompt=verification_prompt, step=step, screenshots=scraped_page_refreshed.screenshots, prompt_name=prompt_name, ) return CompleteVerifyResult.model_validate(verification_result) async def check_user_goal_complete( self, page: Page, scraped_page: ScrapedPage, task: Task, step: Step, task_block: BaseTaskBlock | None = None, *, use_termination_prompt: bool = False, ) -> CompleteAction | TerminateAction | None: try: verification_result = await self.complete_verify( page=page, scraped_page=scraped_page, task=task, step=step, task_block=task_block, use_termination_prompt=use_termination_prompt, ) # Check if we should terminate instead of complete if verification_result.is_terminate: if use_termination_prompt: LOG.warning( "Periodic verification determined task should terminate (termination-aware experiment)", workflow_run_id=task.workflow_run_id, thoughts=verification_result.thoughts, status=verification_result.status if verification_result.status else "legacy", ) else: LOG.warning( "Periodic verification determined task should terminate", workflow_run_id=task.workflow_run_id, thoughts=verification_result.thoughts, ) return TerminateAction( reasoning=verification_result.thoughts, ) # We don't want to return a complete action if the user goal is not achieved since we're checking at every step if not verification_result.is_complete: return None return CompleteAction( reasoning=verification_result.thoughts, data_extraction_goal=task.data_extraction_goal, verified=True, ) except Exception: LOG.exception( "Failed to check user goal complete, skipping", workflow_run_id=task.workflow_run_id, ) return None async def record_artifacts_after_action( self, task: Task, step: Step, browser_state: BrowserState, engine: RunEngine, ) -> None: working_page = await browser_state.get_working_page() if not working_page: raise MissingBrowserStatePage() context = skyvern_context.ensure_context() scrolling_number = context.max_screenshot_scrolls if scrolling_number is None: scrolling_number = DEFAULT_MAX_SCREENSHOT_SCROLLS if engine in CUA_ENGINES: scrolling_number = 0 try: screenshot = await browser_state.take_post_action_screenshot( scrolling_number=scrolling_number, use_playwright_fullpage=await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "ENABLE_PLAYWRIGHT_FULLPAGE", task.workflow_run_id or task.task_id, properties={"organization_id": task.organization_id}, ), ) await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.SCREENSHOT_ACTION, data=screenshot, ) except Exception: LOG.error( "Failed to record screenshot after action", exc_info=True, ) try: skyvern_frame = await SkyvernFrame.create_instance(frame=working_page) html = await skyvern_frame.get_content() await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.HTML_ACTION, data=html.encode(), ) except Exception: LOG.exception("Failed to record html after action") try: video_artifacts = await app.BROWSER_MANAGER.get_video_artifacts( task_id=task.task_id, browser_state=browser_state ) for video_artifact in video_artifacts: await app.ARTIFACT_MANAGER.update_artifact_data( artifact_id=video_artifact.video_artifact_id, organization_id=task.organization_id, data=video_artifact.video_data, ) except Exception: LOG.exception("Failed to record video after action") async def initialize_execution_state( self, task: Task, step: Step, workflow_run: WorkflowRun | None = None, browser_session_id: str | None = None, ) -> tuple[Step, BrowserState, DetailedAgentStepOutput]: if workflow_run: browser_state = await app.BROWSER_MANAGER.get_or_create_for_workflow_run( workflow_run=workflow_run, url=task.url, browser_session_id=browser_session_id, browser_profile_id=workflow_run.browser_profile_id, ) else: browser_state = await app.BROWSER_MANAGER.get_or_create_for_task( task=task, browser_session_id=browser_session_id, ) # Initialize video artifact for the task here, afterwards it'll only get updated if browser_state and browser_state.browser_artifacts: video_artifacts = await app.BROWSER_MANAGER.get_video_artifacts( task_id=task.task_id, browser_state=browser_state ) for idx, video_artifact in enumerate(video_artifacts): if video_artifact.video_artifact_id: continue video_artifact_id = await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.RECORDING, data=video_artifact.video_data, ) video_artifacts[idx].video_artifact_id = video_artifact_id app.BROWSER_MANAGER.set_video_artifact_for_task(task, video_artifacts) detailed_output = DetailedAgentStepOutput( scraped_page=None, extract_action_prompt=None, llm_response=None, actions=None, action_results=None, actions_and_results=None, step_exception=None, ) return step, browser_state, detailed_output async def _scrape_with_type( self, task: Task, step: Step, browser_state: BrowserState, scrape_type: ScrapeType, engine: RunEngine, ) -> ScrapedPage: if scrape_type == ScrapeType.NORMAL: pass elif scrape_type == ScrapeType.STOPLOADING: LOG.info("Try to stop loading the page before scraping") await browser_state.stop_page_loading() elif scrape_type == ScrapeType.RELOAD: LOG.info("Try to reload the page before scraping") await browser_state.reload_page() max_screenshot_number = settings.MAX_NUM_SCREENSHOTS draw_boxes = True scroll = True if engine in CUA_ENGINES: max_screenshot_number = 1 draw_boxes = False scroll = False # Check PostHog feature flag to skip screenshot annotations draw_boxes = await self._should_skip_screenshot_annotations(task, draw_boxes) return await scrape_website( browser_state, task.url, app.AGENT_FUNCTION.cleanup_element_tree_factory(task=task, step=step), scrape_exclude=app.scrape_exclude, max_screenshot_number=max_screenshot_number, draw_boxes=draw_boxes, scroll=scroll, ) async def build_and_record_step_prompt( self, task: Task, step: Step, browser_state: BrowserState, engine: RunEngine, *, persist_artifacts: bool = True, ) -> tuple[ScrapedPage, str, bool]: # Check if we have pre-scraped data from parallel verification optimization context = skyvern_context.current() scraped_page: ScrapedPage | None = None if ( context and context.next_step_pre_scraped_data and context.next_step_pre_scraped_data.get("step_id") == step.step_id ): scraped_page = context.next_step_pre_scraped_data.get("scraped_page") if scraped_page: timestamp = context.next_step_pre_scraped_data.get("timestamp") age_seconds = (datetime.now(UTC) - timestamp).total_seconds() if timestamp else 0 LOG.info( "Using pre-scraped data from parallel verification optimization", step_id=step.step_id, num_elements=len(scraped_page.elements), age_seconds=age_seconds, ) # Clear the cached data context.next_step_pre_scraped_data = None # If we don't have pre-scraped data, scrape normally if scraped_page is None: # Check PostHog for speed optimizations BEFORE scraping # This decision will be used in both: # 1. SVG conversion skip (in agent_functions.py cleanup) # 2. Tree selection (economy vs regular tree) # By checking once and storing in context, we ensure perfect coordination if context: try: distinct_id = task.workflow_run_id if task.workflow_run_id else task.task_id context.enable_speed_optimizations = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "ENABLE_SPEED_OPTIMIZATIONS", distinct_id, properties={"organization_id": task.organization_id}, ) except Exception: LOG.warning( "Failed to check ENABLE_SPEED_OPTIMIZATIONS feature flag", exc_info=True, task_id=task.task_id, ) context.enable_speed_optimizations = False # start the async tasks while running scrape_website if engine not in CUA_ENGINES: self.async_operation_pool.run_operation(task.task_id, AgentPhase.scrape) # Scrape the web page and get the screenshot and the elements # HACK: try scrape_website three time to handle screenshot timeout # first time: normal scrape to take screenshot # second time: try again the normal scrape, (stopping window loading before scraping barely helps, but causing problem) # third time: reload the page before scraping extract_action_prompt = "" use_caching = False for idx, scrape_type in enumerate(SCRAPE_TYPE_ORDER): try: scraped_page = await self._scrape_with_type( task=task, step=step, browser_state=browser_state, scrape_type=scrape_type, engine=engine, ) break except (FailedToTakeScreenshot, ScrapingFailed) as e: if idx < len(SCRAPE_TYPE_ORDER) - 1: continue LOG.exception(f"{e.__class__.__name__} happened in two normal attempts and reload-page retry") raise e if scraped_page is None: raise EmptyScrapePage() extract_action_prompt = "" use_caching = False if persist_artifacts: await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.HTML_SCRAPE, data=scraped_page.html.encode(), ) LOG.info( "Scraped website", step_order=step.order, step_retry=step.retry_index, num_elements=len(scraped_page.elements), url=task.url, ) # TODO: we only use HTML element for now, introduce a way to switch in the future enable_speed_optimizations = getattr(context, "enable_speed_optimizations", False) element_tree_format = ElementTreeFormat.HTML # OPTIMIZATION: Use economy tree (skip SVGs) when ENABLE_SPEED_OPTIMIZATIONS is enabled # Economy tree removes all SVG elements from the DOM tree sent to LLM # - SVGs are decorative (icons, logos, graphics) - not needed for action planning # - Even for charts/graphs: LLM sees them in screenshots, not SVG code # - Saves ~8s per SVG x ~15 SVGs = ~120s per workflow (30% speedup!) # # RETRY STRATEGY: Use economy tree on first attempt only # - retry_index 0: Use economy tree (fast, no SVGs) # - retry_index 1+: Use regular tree (SVGs loaded from existing 4-week cache) # Note: SVG conversions are already cached globally with 4-week TTL, so retries are fast # # COORDINATION: The enable_speed_optimizations decision is made ONCE before scraping # and stored in context. Both SVG conversion skip (agent_functions.py) and tree # selection (here) use the SAME value, ensuring perfect coordination. element_tree_in_prompt: str = "" # Use the speed optimization decision from context (set before scraping) enable_speed_optimizations = context.enable_speed_optimizations if context else False if not enable_speed_optimizations: # Optimization disabled - use regular tree always element_tree_in_prompt = scraped_page.build_element_tree(element_tree_format) elif step.retry_index == 0: # First attempt - use economy tree (fast, no SVG conversion) # Note: SVG conversion was already skipped in cleanup_element_tree_func # based on the same context.enable_speed_optimizations value element_tree_in_prompt = scraped_page.build_economy_elements_tree(element_tree_format) LOG.info( "Speed optimization: Using economy element tree (skipping SVGs)", step_order=step.order, step_retry=step.retry_index, task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) else: # Retry 1+ - use regular tree (SVGs will be loaded from existing 4-week cache) element_tree_in_prompt = scraped_page.build_element_tree(element_tree_format) LOG.info( "Speed optimization: Using regular tree on retry (SVGs from global cache)", step_order=step.order, step_retry=step.retry_index, task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) extract_action_prompt = "" if engine not in CUA_ENGINES: extract_action_prompt, use_caching = await self._build_extract_action_prompt( task, step, browser_state, scraped_page, verification_code_check=bool(task.totp_verification_url or task.totp_identifier), expire_verification_code=True, ) if persist_artifacts: await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.VISIBLE_ELEMENTS_ID_CSS_MAP, data=json.dumps(scraped_page.id_to_css_dict, indent=2).encode(), ) await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.VISIBLE_ELEMENTS_ID_FRAME_MAP, data=json.dumps(scraped_page.id_to_frame_dict, indent=2).encode(), ) await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.VISIBLE_ELEMENTS_TREE, data=json.dumps(scraped_page.element_tree, indent=2).encode(), ) await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.VISIBLE_ELEMENTS_TREE_TRIMMED, data=json.dumps(scraped_page.element_tree_trimmed, indent=2).encode(), ) await app.ARTIFACT_MANAGER.create_artifact( step=step, artifact_type=ArtifactType.VISIBLE_ELEMENTS_TREE_IN_PROMPT, data=element_tree_in_prompt.encode(), ) return scraped_page, extract_action_prompt, use_caching async def _create_vertex_cache_for_task( self, task: Task, static_prompt: str, context: SkyvernContext, llm_key_override: str | None ) -> None: """ Create a Vertex AI cache for the task's static prompt. Uses llm_key as cache key to enable cache sharing across tasks with the same model. Args: task: The task to create cache for static_prompt: The static prompt content to cache context: The Skyvern context to store the cache name in """ # Early return if task doesn't have an llm_key # This should not happen given the guard at the call site, but being defensive resolved_llm_key = llm_key_override or task.llm_key if not resolved_llm_key: LOG.warning( "Cannot create Vertex AI cache without llm_key, skipping cache creation", task_id=task.task_id, ) return try: LOG.info( "Attempting Vertex AI cache creation", task_id=task.task_id, llm_key=resolved_llm_key, ) cache_manager = get_cache_manager() # Use llm_key as cache_key so all tasks with the same model share the same cache # This maximizes cache reuse and reduces cache storage costs cache_key = f"{EXTRACT_ACTION_CACHE_KEY_PREFIX}-{resolved_llm_key}" # Get the actual model name from LLM config to ensure correct format # (e.g., "gemini-2.5-flash" with decimal, not "gemini-2-5-flash") model_name = "gemini-2.5-flash" # Default try: llm_config = LLMConfigRegistry.get_config(resolved_llm_key) extracted_name = None # Try to extract from model_name if it contains "vertex_ai/" or starts with "gemini-" if hasattr(llm_config, "model_name") and isinstance(llm_config.model_name, str): if "vertex_ai/" in llm_config.model_name: # Direct Vertex config: "vertex_ai/gemini-2.5-flash" -> "gemini-2.5-flash" extracted_name = llm_config.model_name.split("/")[-1] elif llm_config.model_name.startswith("gemini-"): # Already in correct format extracted_name = llm_config.model_name # For router/fallback configs, extract from api_base or infer from key name if not extracted_name and hasattr(llm_config, "litellm_params") and llm_config.litellm_params: params = llm_config.litellm_params api_base = getattr(params, "api_base", None) if api_base and isinstance(api_base, str) and "/models/" in api_base: # Extract from URL: .../models/gemini-2.5-flash -> "gemini-2.5-flash" extracted_name = api_base.split("/models/")[-1] # For router configs without api_base, infer from the llm_key itself if not extracted_name: # Extract version from llm_key (e.g., VERTEX_GEMINI_1_5_FLASH -> "1_5" or VERTEX_GEMINI_2.5_FLASH -> "2.5") # Pattern: GEMINI_{version}_{flavor} where version can use dots, underscores, or dashes version_match = re.search(r"GEMINI[_-](\d+[._-]\d+)", resolved_llm_key, re.IGNORECASE) version = version_match.group(1).replace("_", ".").replace("-", ".") if version_match else "2.5" # Determine flavor if "_PRO_" in resolved_llm_key or resolved_llm_key.endswith("_PRO"): extracted_name = f"gemini-{version}-pro" elif "_FLASH_LITE_" in resolved_llm_key or resolved_llm_key.endswith("_FLASH_LITE"): extracted_name = f"gemini-{version}-flash-lite" else: # Default to flash flavor extracted_name = f"gemini-{version}-flash" if extracted_name: model_name = extracted_name except Exception as e: LOG.debug("Failed to extract model name from config, using default", error=str(e)) # Normalize model name to the canonical Vertex identifier (e.g., gemini-2.5-pro) match = re.search(r"(gemini-\d+(?:\.\d+)?-(?:flash-lite|flash|pro))", model_name, re.IGNORECASE) if match: model_name = match.group(1).lower() # Create cache for this task # Use asyncio.to_thread to offload blocking HTTP request (requests.post) # This prevents freezing the event loop during cache creation cache_data = await asyncio.to_thread( cache_manager.create_cache, model_name=model_name, static_content=static_prompt, cache_key=cache_key, ttl_seconds=3600, # 1 hour ) # Store cache resource name in context context.vertex_cache_name = cache_data["name"] LOG.info( "Created Vertex AI cache for task", task_id=task.task_id, cache_key=cache_key, cache_name=cache_data["name"], model_name=model_name, ) except Exception as e: LOG.warning( "Failed to create Vertex AI cache, proceeding without caching", task_id=task.task_id, error=str(e), exc_info=True, ) async def _build_extract_action_prompt( self, task: Task, step: Step, browser_state: BrowserState, scraped_page: ScrapedPage, verification_code_check: bool = False, expire_verification_code: bool = False, ) -> tuple[str, bool]: actions_and_results_str = await self._get_action_results(task) # Generate the extract action prompt navigation_goal = task.navigation_goal starting_url = task.url page = await browser_state.get_working_page() current_url = ( await SkyvernFrame.evaluate(frame=page, expression="() => document.location.href") if page else starting_url ) final_navigation_payload = self._build_navigation_payload( task, expire_verification_code=expire_verification_code, step=step, scraped_page=scraped_page ) navigation_payload_str = json.dumps(final_navigation_payload) task_type = task.task_type if task.task_type else TaskType.general template = "" if task_type == TaskType.general: template = EXTRACT_ACTION_TEMPLATE elif task_type == TaskType.validation: template = "decisive-criterion-validate" elif task_type == TaskType.action: prompt = prompt_engine.load_prompt( "infer-action-type", navigation_goal=navigation_goal, prompt_name="infer-action-type" ) llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler( task.llm_key, default=app.LLM_API_HANDLER ) json_response = await llm_api_handler(prompt=prompt, step=step, prompt_name="infer-action-type") if json_response.get("error"): raise FailedToParseActionInstruction( reason=json_response.get("thought"), error_type=json_response.get("error") ) inferred_actions: list[dict[str, Any]] = json_response.get("inferred_actions", []) if not inferred_actions: raise FailedToParseActionInstruction(reason=json_response.get("thought"), error_type="EMPTY_ACTION") action_type: str = inferred_actions[0].get("action_type") or "" action_type = ActionType[action_type.upper()] if action_type == ActionType.CLICK: template = "single-click-action" elif action_type == ActionType.INPUT_TEXT: template = "single-input-action" elif action_type == ActionType.UPLOAD_FILE: template = "single-upload-action" elif action_type == ActionType.SELECT_OPTION: template = "single-select-action" else: raise UnsupportedActionType(action_type=action_type) if not template: raise UnsupportedTaskType(task_type=task_type) context = skyvern_context.ensure_context() # Reset cached prompt by default; we will set it below if caching is enabled. context.cached_static_prompt = None # Check if prompt caching is enabled for extract-action use_caching = False prompt_caching_settings = LLMAPIHandlerFactory._prompt_caching_settings or {} effective_llm_key = task.llm_key if not effective_llm_key: handler_for_key = LLMAPIHandlerFactory.get_override_llm_api_handler( task.llm_key, default=app.LLM_API_HANDLER ) effective_llm_key = getattr(handler_for_key, "llm_key", None) cache_enabled = prompt_caching_settings.get(EXTRACT_ACTION_PROMPT_NAME) or prompt_caching_settings.get( EXTRACT_ACTION_TEMPLATE ) LOG.info( "Extract-action prompt caching evaluation", template=template, cache_enabled=cache_enabled, prompt_caching_settings=prompt_caching_settings, task_llm_key=task.llm_key, effective_llm_key=effective_llm_key, ) enable_speed_optimizations = context.enable_speed_optimizations element_tree_format = ElementTreeFormat.HTML if enable_speed_optimizations: if step.retry_index == 0: elements_for_prompt = scraped_page.build_economy_elements_tree(element_tree_format) else: elements_for_prompt = scraped_page.build_element_tree(element_tree_format) else: elements_for_prompt = scraped_page.build_element_tree(element_tree_format) if template == EXTRACT_ACTION_TEMPLATE and cache_enabled: try: # Try to load split templates for caching prompt_kwargs = { "navigation_goal": navigation_goal, "navigation_payload_str": navigation_payload_str, "starting_url": starting_url, "current_url": current_url, "data_extraction_goal": task.data_extraction_goal, "action_history": actions_and_results_str, "error_code_mapping_str": ( json.dumps(task.error_code_mapping) if task.error_code_mapping else None ), "local_datetime": datetime.now(context.tz_info).isoformat(), "verification_code_check": verification_code_check, "complete_criterion": task.complete_criterion.strip() if task.complete_criterion else None, "terminate_criterion": task.terminate_criterion.strip() if task.terminate_criterion else None, "parse_select_feature_enabled": context.enable_parse_select_in_extract, "has_magic_link_page": context.has_magic_link_page(task.task_id), } static_prompt = prompt_engine.load_prompt(f"{template}-static", **prompt_kwargs) dynamic_prompt = prompt_engine.load_prompt( f"{template}-dynamic", elements=elements_for_prompt, **prompt_kwargs, ) # Store static prompt for caching and continue sending it alongside the dynamic section. # Vertex explicit caching expects the static content to still be present in the request so the # first call succeeds even if the cache is cold. The cached reference simply lets the service # reuse the static portion internally. context.cached_static_prompt = static_prompt context.use_prompt_caching = True use_caching = True # Create Vertex AI cache for Gemini models if effective_llm_key and "GEMINI" in effective_llm_key: await self._create_vertex_cache_for_task(task, static_prompt, context, effective_llm_key) combined_prompt = f"{static_prompt.rstrip()}\n\n{dynamic_prompt.lstrip()}" LOG.info( "Using cached prompt", task_id=task.task_id, prompt_name=EXTRACT_ACTION_PROMPT_NAME, ) return combined_prompt, use_caching except Exception as e: LOG.warning("Failed to load cached prompt templates, falling back to original", error=str(e)) # Fall through to original behavior # Original behavior - load full prompt full_prompt = load_prompt_with_elements( element_tree_builder=scraped_page, prompt_engine=prompt_engine, template_name=template, navigation_goal=navigation_goal, navigation_payload_str=navigation_payload_str, starting_url=starting_url, current_url=current_url, data_extraction_goal=task.data_extraction_goal, action_history=actions_and_results_str, error_code_mapping_str=(json.dumps(task.error_code_mapping) if task.error_code_mapping else None), local_datetime=datetime.now(context.tz_info).isoformat(), verification_code_check=verification_code_check, complete_criterion=task.complete_criterion.strip() if task.complete_criterion else None, terminate_criterion=task.terminate_criterion.strip() if task.terminate_criterion else None, parse_select_feature_enabled=context.enable_parse_select_in_extract, has_magic_link_page=context.has_magic_link_page(task.task_id), ) return full_prompt, use_caching def _should_process_totp(self, scraped_page: ScrapedPage | None) -> bool: """Detect TOTP pages by checking for multiple input fields or verification keywords.""" if not scraped_page: return False try: # Count input fields that could be for TOTP (more flexible than maxlength="1") input_fields = [ element for element in scraped_page.elements if element.get("tagName", "").lower() == "input" and element.get("attributes", {}).get("type", "text").lower() in ["text", "number", "tel"] ] # Check for multiple input fields (potential multi-field TOTP) if len(input_fields) >= 4: # Additional check: look for patterns that suggest multi-field TOTP # Check if inputs are close together or have similar attributes has_maxlength_1 = any(elem.get("attributes", {}).get("maxlength") == "1" for elem in input_fields) # Check for input fields with numeric patterns (type="number", pattern for digits) has_numeric_patterns = any( elem.get("attributes", {}).get("type") == "number" or elem.get("attributes", {}).get("pattern", "").isdigit() or "digit" in elem.get("attributes", {}).get("pattern", "").lower() for elem in input_fields ) if has_maxlength_1 or has_numeric_patterns: return True # Check for TOTP-related keywords in page content page_text = scraped_page.html.lower() if scraped_page.html else "" totp_keywords = [ "verification code", "authentication code", "security code", "2fa", "two-factor", "totp", "authenticator", "verification", "enter code", "verification number", "security number", ] keyword_matches = sum(1 for keyword in totp_keywords if keyword in page_text) # If we have multiple TOTP keywords and multiple input fields, likely TOTP if keyword_matches >= 2 and len(input_fields) >= 6: return True # Strong single keyword match with multiple inputs strong_keywords = ["verification code", "authentication code", "2fa", "two-factor"] if any(keyword in page_text for keyword in strong_keywords) and len(input_fields) >= 3: return True return False except Exception: return False def _is_multi_field_totp_sequence(self, actions: list) -> bool: """ Check if the action sequence represents a multi-field TOTP input (6 single-digit fields). Args: actions: List of actions to analyze Returns: bool: True if this is a multi-field TOTP sequence """ # Must have at least 4 actions (minimum for TOTP) if len(actions) < 4: return False # Check if we have multiple consecutive single-digit INPUT_TEXT actions consecutive_single_digits = 0 max_consecutive = 0 for action in actions: if ( action.action_type == ActionType.INPUT_TEXT and hasattr(action, "text") and action.text and len(action.text) == 1 and action.text.isdigit() ): consecutive_single_digits += 1 max_consecutive = max(max_consecutive, consecutive_single_digits) else: # If we hit a non-single-digit action, reset consecutive counter consecutive_single_digits = 0 # Consider it a multi-field TOTP if we have 4+ consecutive single-digit inputs # This is more reliable than just counting total single digits # We use 4+ as the threshold to avoid false positives with single TOTP fields is_multi_field_totp = max_consecutive >= 4 if is_multi_field_totp: LOG.debug( "Detected multi-field TOTP sequence", max_consecutive=max_consecutive, total_actions=len(actions), ) return is_multi_field_totp def _build_navigation_payload( self, task: Task, expire_verification_code: bool = False, step: Step | None = None, scraped_page: ScrapedPage | None = None, ) -> dict[str, Any] | list | str | None: final_navigation_payload = task.navigation_payload current_context = skyvern_context.ensure_context() verification_code = current_context.totp_codes.get(task.task_id) if (task.totp_verification_url or task.totp_identifier) and verification_code: if ( isinstance(final_navigation_payload, dict) and SPECIAL_FIELD_VERIFICATION_CODE not in final_navigation_payload ): final_navigation_payload[SPECIAL_FIELD_VERIFICATION_CODE] = verification_code elif ( isinstance(final_navigation_payload, str) and SPECIAL_FIELD_VERIFICATION_CODE not in final_navigation_payload ): final_navigation_payload = ( final_navigation_payload + "\n" + str({SPECIAL_FIELD_VERIFICATION_CODE: verification_code}) ) if expire_verification_code: current_context.totp_codes.pop(task.task_id) # Store TOTP secrets and provide placeholder TOTP for LLM to see format # Only when on a TOTP page to avoid premature processing if ( task.workflow_run_id and step and isinstance(final_navigation_payload, dict) and self._should_process_totp(scraped_page) ): workflow_run_context = app.WORKFLOW_CONTEXT_MANAGER.get_workflow_run_context(task.workflow_run_id) for key, value in list(final_navigation_payload.items()): if isinstance(value, dict) and "totp" in value: totp_placeholder = value.get("totp") if totp_placeholder and isinstance(totp_placeholder, str): totp_secret_key = workflow_run_context.totp_secret_value_key(totp_placeholder) totp_secret = workflow_run_context.get_original_secret_value_or_none(totp_secret_key) if totp_secret: # Store TOTP secret for handler to use during execution current_context = skyvern_context.ensure_context() current_context.totp_codes[f"{task.task_id}_secret"] = totp_secret # Send a placeholder TOTP for the LLM to see the format final_navigation_payload[key]["totp"] = "123456" return final_navigation_payload async def _get_action_results(self, task: Task, current_step: Step | None = None) -> str: return json.dumps(await get_action_history(task=task, current_step=current_step)) async def get_extracted_information_for_task(self, task: Task) -> dict[str, Any] | list | str | None: """ Find the last successful ScrapeAction for the task and return the extracted information. """ # TODO: make sure we can get extracted information with the ExtractAction change steps = await app.DATABASE.get_task_steps( task_id=task.task_id, organization_id=task.organization_id, ) for step in reversed(steps): if step.status != StepStatus.completed: continue if not step.output or not step.output.actions_and_results: continue for action, action_results in step.output.actions_and_results: if action.action_type != ActionType.EXTRACT: continue for action_result in action_results: if action_result.success: LOG.info( "Extracted information for task", extracted_information=action_result.data, ) return action_result.data if task.data_extraction_goal: LOG.warning( "Failed to find extracted information for task", task_id=task.task_id, ) return None async def get_failure_reason_for_task(self, task: Task) -> str | None: """ Find the TerminateAction for the task and return the reasoning. # TODO (kerem): Also return meaningful exceptions when we add them [WYV-311] """ steps = await app.DATABASE.get_task_steps( task_id=task.task_id, organization_id=task.organization_id, ) for step in reversed(steps): if step.status != StepStatus.completed: continue if not step.output: continue if step.output.actions_and_results: for action, action_results in step.output.actions_and_results: if action.action_type == ActionType.TERMINATE: return action.reasoning LOG.error( "Failed to find failure reasoning for task", task_id=task.task_id, ) return None async def clean_up_task( self, task: Task, last_step: Step, api_key: str | None = None, need_call_webhook: bool = True, close_browser_on_completion: bool = True, need_final_screenshot: bool = True, browser_session_id: str | None = None, ) -> None: """ send the task response to the webhook callback url """ # refresh the task from the db to get the latest status try: refreshed_task = await app.DATABASE.get_task(task_id=task.task_id, organization_id=task.organization_id) if not refreshed_task: LOG.error("Failed to get task from db when clean up task", task_id=task.task_id) raise TaskNotFound(task_id=task.task_id) except Exception as e: LOG.exception( "Failed to get task from db when clean up task", task_id=task.task_id, ) raise TaskNotFound(task_id=task.task_id) from e task = refreshed_task # Caches expire based on TTL (1 hour) or can be cleaned up via scheduled job # This allows multiple tasks with the same llm_key to share the same cache # log the task status as an event analytics.capture("skyvern-oss-agent-task-status", {"status": task.status}) # Add task completion tag to trace TraceManager.add_task_completion_tag(task.status.value) if need_final_screenshot: # Take one last screenshot and create an artifact before closing the browser to see the final state # We don't need the artifacts and send the webhook response directly only when there is an issue with the browser # initialization. In this case, we don't have any artifacts to send and we can't take final screenshots etc. # since the browser is not initialized properly or the proxy is not working. browser_state = app.BROWSER_MANAGER.get_for_task(task.task_id) if browser_state is not None and await browser_state.get_working_page() is not None: try: screenshot = await browser_state.take_fullpage_screenshot( use_playwright_fullpage=await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "ENABLE_PLAYWRIGHT_FULLPAGE", task.workflow_run_id or task.task_id, properties={"organization_id": task.organization_id}, ) ) await app.ARTIFACT_MANAGER.create_artifact( step=last_step, artifact_type=ArtifactType.SCREENSHOT_FINAL, data=screenshot, ) except TargetClosedError: LOG.warning( "Failed to take screenshot before sending task response, page is closed", ) except Exception: LOG.exception("Failed to take screenshot before sending task response") if task.organization_id: try: async with asyncio.timeout(SAVE_DOWNLOADED_FILES_TIMEOUT): context = skyvern_context.current() await app.STORAGE.save_downloaded_files( organization_id=task.organization_id, run_id=context.run_id if context and context.run_id else task.workflow_run_id or task.task_id, ) except asyncio.TimeoutError: LOG.warning( "Timeout to save downloaded files", task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) except Exception: LOG.warning( "Failed to save downloaded files", exc_info=True, task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) # if it's a task block from workflow run, # we don't need to close the browser, save browser artifacts, or call webhook if task.workflow_run_id: LOG.info( "Task is part of a workflow run, not sending a webhook response", task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) return await self.async_operation_pool.remove_task(task.task_id) await self.cleanup_browser_and_create_artifacts( close_browser_on_completion, last_step, task, browser_session_id=browser_session_id ) # Wait for all tasks to complete before generating the links for the artifacts await app.ARTIFACT_MANAGER.wait_for_upload_aiotasks([task.task_id]) if need_call_webhook: await self.execute_task_webhook(task=task, api_key=api_key) async def execute_task_webhook( self, task: Task, api_key: str | None, ) -> None: if not api_key: LOG.warning( "Request has no api key. Not sending task response", task_id=task.task_id, ) return if not task.webhook_callback_url: LOG.warning( "Task has no webhook callback url. Not sending task response", task_id=task.task_id, ) return last_step = await app.DATABASE.get_latest_step(task.task_id, organization_id=task.organization_id) task_response = await self.build_task_response(task=task, last_step=last_step) # try to build the new TaskRunResponse for backward compatibility task_run_response_json: str | None = None try: run_response = await run_service.get_run_response( run_id=task.task_id, organization_id=task.organization_id, ) if run_response is not None: task_run_response_json = run_response.model_dump_json(exclude={"run_request"}) # send task_response to the webhook callback url payload_json = task_response.model_dump_json(exclude={"request"}) payload_dict = json.loads(payload_json) if task_run_response_json: payload_dict.update(json.loads(task_run_response_json)) signed_data = generate_skyvern_webhook_signature(payload=payload_dict, api_key=api_key) LOG.info( "Sending task response to webhook callback url", task_id=task.task_id, webhook_callback_url=task.webhook_callback_url, payload=signed_data.signed_payload, headers=signed_data.headers, ) async with httpx.AsyncClient() as client: resp = await client.post( task.webhook_callback_url, data=signed_data.signed_payload, headers=signed_data.headers, timeout=httpx.Timeout(30.0), ) if resp.status_code >= 200 and resp.status_code < 300: LOG.info( "Webhook sent successfully", task_id=task.task_id, resp_code=resp.status_code, resp_text=resp.text, ) await app.DATABASE.update_task( task_id=task.task_id, organization_id=task.organization_id, webhook_failure_reason="", ) else: LOG.info( "Webhook failed", task_id=task.task_id, resp=resp, resp_code=resp.status_code, resp_text=resp.text, ) await app.DATABASE.update_task( task_id=task.task_id, organization_id=task.organization_id, webhook_failure_reason=f"Webhook failed with status code {resp.status_code}, error message: {resp.text}", ) except Exception as e: raise FailedToSendWebhook(task_id=task.task_id) from e async def build_task_response( self, task: Task, last_step: Step | None = None, failure_reason: str | None = None, need_browser_log: bool = False, ) -> TaskResponse: # no last step means the task didn't start, so we don't have any other artifacts if last_step is None: return task.to_task_response( failure_reason=failure_reason, ) screenshot_url = None recording_url = None browser_console_log_url: str | None = None latest_action_screenshot_urls: list[str] | None = None downloaded_files: list[FileInfo] | None = None # get the artifact of the screenshot and get the screenshot_url screenshot_artifact = await app.DATABASE.get_artifact( task_id=task.task_id, step_id=last_step.step_id, artifact_type=ArtifactType.SCREENSHOT_FINAL, organization_id=task.organization_id, ) if screenshot_artifact: screenshot_url = await app.ARTIFACT_MANAGER.get_share_link(screenshot_artifact) first_step = await app.DATABASE.get_first_step(task_id=task.task_id, organization_id=task.organization_id) if first_step: recording_artifact = await app.DATABASE.get_artifact( task_id=task.task_id, step_id=first_step.step_id, artifact_type=ArtifactType.RECORDING, organization_id=task.organization_id, ) if recording_artifact: recording_url = await app.ARTIFACT_MANAGER.get_share_link(recording_artifact) # get the artifact of the last TASK_RESPONSE_ACTION_SCREENSHOT_COUNT screenshots and get the screenshot_url latest_action_screenshot_artifacts = await app.DATABASE.get_latest_n_artifacts( task_id=task.task_id, organization_id=task.organization_id, artifact_types=[ArtifactType.SCREENSHOT_ACTION], n=settings.TASK_RESPONSE_ACTION_SCREENSHOT_COUNT, ) if latest_action_screenshot_artifacts: latest_action_screenshot_urls = await app.ARTIFACT_MANAGER.get_share_links( latest_action_screenshot_artifacts ) if task.organization_id: try: async with asyncio.timeout(GET_DOWNLOADED_FILES_TIMEOUT): context = skyvern_context.current() downloaded_files = await app.STORAGE.get_downloaded_files( organization_id=task.organization_id, run_id=context.run_id if context and context.run_id else task.workflow_run_id or task.task_id, ) except asyncio.TimeoutError: LOG.warning( "Timeout to get downloaded files", task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) except Exception: LOG.warning( "Failed to get downloaded files", exc_info=True, task_id=task.task_id, workflow_run_id=task.workflow_run_id, ) if need_browser_log: browser_console_log = await app.DATABASE.get_latest_artifact( task_id=task.task_id, artifact_types=[ArtifactType.BROWSER_CONSOLE_LOG], organization_id=task.organization_id, ) if browser_console_log: browser_console_log_url = await app.ARTIFACT_MANAGER.get_share_link(browser_console_log) # get the latest task from the db to get the latest status, extracted_information, and failure_reason task_from_db = await app.DATABASE.get_task(task_id=task.task_id, organization_id=task.organization_id) if not task_from_db: LOG.error("Failed to get task from db when sending task response") raise TaskNotFound(task_id=task.task_id) task = task_from_db return task.to_task_response( action_screenshot_urls=latest_action_screenshot_urls, screenshot_url=screenshot_url, recording_url=recording_url, browser_console_log_url=browser_console_log_url, downloaded_files=downloaded_files, failure_reason=failure_reason, ) async def cleanup_browser_and_create_artifacts( self, close_browser_on_completion: bool, last_step: Step, task: Task, browser_session_id: str | None = None, ) -> None: """ Developer notes: we should not expect any exception to be raised here. This function should handle exceptions gracefully. If errors are raised and not caught inside this function, please catch and handle them. """ # We need to close the browser even if there is no webhook callback url or api key browser_state = await app.BROWSER_MANAGER.cleanup_for_task( task.task_id, close_browser_on_completion, browser_session_id, task.organization_id, ) if browser_state: # Update recording artifact after closing the browser, so we can get an accurate recording video_artifacts = await app.BROWSER_MANAGER.get_video_artifacts( task_id=task.task_id, browser_state=browser_state ) for video_artifact in video_artifacts: await app.ARTIFACT_MANAGER.update_artifact_data( artifact_id=video_artifact.video_artifact_id, organization_id=task.organization_id, data=video_artifact.video_data, ) har_data = await app.BROWSER_MANAGER.get_har_data(task_id=task.task_id, browser_state=browser_state) if har_data: await app.ARTIFACT_MANAGER.create_artifact( step=last_step, artifact_type=ArtifactType.HAR, data=har_data, ) browser_log = await app.BROWSER_MANAGER.get_browser_console_log( task_id=task.task_id, browser_state=browser_state ) if browser_log: await app.ARTIFACT_MANAGER.create_artifact( step=last_step, artifact_type=ArtifactType.BROWSER_CONSOLE_LOG, data=browser_log, ) if browser_state.browser_context and browser_state.browser_artifacts.traces_dir: trace_path = f"{browser_state.browser_artifacts.traces_dir}/{task.task_id}.zip" await app.ARTIFACT_MANAGER.create_artifact( step=last_step, artifact_type=ArtifactType.TRACE, path=trace_path, ) else: LOG.warning( "BrowserState is missing before sending response to webhook_callback_url", web_hook_url=task.webhook_callback_url, ) async def update_step( self, step: Step, status: StepStatus | None = None, output: AgentStepOutput | None = None, is_last: bool | None = None, retry_index: int | None = None, ) -> Step: step.validate_update(status, output, is_last) updates: dict[str, Any] = {} if status is not None: updates["status"] = status if output is not None: updates["output"] = output if is_last is not None: updates["is_last"] = is_last if retry_index is not None: updates["retry_index"] = retry_index update_comparison = { key: {"old": getattr(step, key), "new": value} for key, value in updates.items() if getattr(step, key) != value and key != "output" } LOG.debug( "Updating step in db", diff=update_comparison, ) # Track step duration when step is completed or failed if status in [StepStatus.completed, StepStatus.failed]: duration_seconds = (datetime.now(UTC) - step.created_at.replace(tzinfo=UTC)).total_seconds() LOG.info( "Step duration metrics", duration_seconds=duration_seconds, step_status=status, organization_id=step.organization_id, ) await save_step_logs(step.step_id) return await app.DATABASE.update_step( task_id=step.task_id, step_id=step.step_id, organization_id=step.organization_id, **updates, ) async def update_task( self, task: Task, status: TaskStatus, extracted_information: dict[str, Any] | list | str | None = None, failure_reason: str | None = None, webhook_failure_reason: str | None = None, errors: list[dict[str, Any]] | None = None, ) -> Task: # refresh task from db to get the latest status task_from_db = await app.DATABASE.get_task(task_id=task.task_id, organization_id=task.organization_id) if task_from_db: task = task_from_db task.validate_update(status, extracted_information, failure_reason) updates: dict[str, Any] = {} if status is not None: updates["status"] = status if extracted_information is not None: updates["extracted_information"] = extracted_information if failure_reason is not None: updates["failure_reason"] = failure_reason if errors is not None: updates["errors"] = errors update_comparison = { key: {"old": getattr(task, key), "new": value} for key, value in updates.items() if getattr(task, key) != value } # Track task duration when task is completed, failed, or terminated if status in [TaskStatus.completed, TaskStatus.failed, TaskStatus.terminated]: start_time = task.started_at.replace(tzinfo=UTC) if task.started_at else task.created_at.replace(tzinfo=UTC) queued_seconds = (start_time - task.created_at.replace(tzinfo=UTC)).total_seconds() duration_seconds = (datetime.now(UTC) - start_time).total_seconds() LOG.info( "Task duration metrics", task_id=task.task_id, workflow_run_id=task.workflow_run_id, duration_seconds=duration_seconds, queued_seconds=queued_seconds, task_status=status, organization_id=task.organization_id, ) await save_task_logs(task.task_id) LOG.info("Updating task in db", task_id=task.task_id, diff=update_comparison) return await app.DATABASE.update_task( task.task_id, organization_id=task.organization_id, **updates, ) async def _handle_completed_step_with_parallel_verification( self, organization: Organization, task: Task, step: Step, page: Page | None, browser_state: BrowserState, scraped_page: ScrapedPage, engine: RunEngine, task_block: BaseTaskBlock | None = None, ) -> tuple[bool | None, Step | None, Step | None]: """ Handle completed step with parallel verification optimization. Runs two tasks in parallel: 1. Verify if user goal is complete (check-user-goal) 2. Pre-scrape page for next step If goal is complete, cancel pre-scraping and mark task done. If goal not complete, use pre-scraped data for next step execution. Note: This should only be called when verification is needed (i.e., when the standard flow would have called check_user_goal_complete in agent_step). """ LOG.info( "Starting parallel user goal verification with speculative extract-actions", step_id=step.step_id, task_id=task.task_id, ) verification_task = asyncio.create_task( self.check_user_goal_complete( page=page, scraped_page=scraped_page, task=task, step=step, task_block=task_block, ), name=f"verify_goal_{step.step_id}", ) next_step = await app.DATABASE.create_step( task_id=task.task_id, order=step.order + 1, retry_index=0, organization_id=task.organization_id, ) LOG.debug( "Waiting before launching speculative plan", step_id=step.step_id, task_id=task.task_id, ) await asyncio.sleep(1.0) speculative_task = asyncio.create_task( self._speculate_next_step_plan( task=task, current_step=step, next_step=next_step, browser_state=browser_state, engine=engine, ), name=f"speculate_next_step_{step.step_id}", ) try: complete_action = await verification_task except Exception: LOG.warning( "User goal verification failed in parallel mode, will continue with next step", step_id=step.step_id, exc_info=True, ) complete_action = None if complete_action is not None: asyncio.create_task( self._persist_speculative_metadata_for_discarded_plan( next_step, speculative_task, cancel_step=True, ) ) working_page = page or await browser_state.must_get_working_page() if step.output is None: step.output = AgentStepOutput(action_results=[], actions_and_results=[], errors=[]) if step.output.action_results is None: step.output.action_results = [] if step.output.actions_and_results is None: step.output.actions_and_results = [] persisted_action = cast(Action, complete_action) if isinstance(persisted_action, (CompleteAction, TerminateAction)): persisted_action.organization_id = task.organization_id persisted_action.workflow_run_id = task.workflow_run_id persisted_action.task_id = task.task_id persisted_action.step_id = step.step_id persisted_action.step_order = step.order persisted_action.action_order = len(step.output.actions_and_results) action_results = await ActionHandler.handle_action(scraped_page, task, step, working_page, persisted_action) await self.record_artifacts_after_action(task, step, browser_state, engine) step.output.action_results.extend(action_results) step.output.actions_and_results.append((persisted_action, action_results)) if isinstance(persisted_action, DecisiveAction) and persisted_action.errors: step.output.errors.extend(persisted_action.errors) if isinstance(persisted_action, TerminateAction): LOG.warning( "Parallel verification: termination required, marking task as terminated", step_id=step.step_id, task_id=task.task_id, reasoning=complete_action.reasoning, ) final_status = step.speculative_original_status or StepStatus.completed step.speculative_original_status = None step.status = final_status last_step = await self.update_step( step, status=final_status, output=step.output, is_last=True, ) task_errors = None if persisted_action.errors: task_errors = [error.model_dump() for error in persisted_action.errors] failure_reason = persisted_action.reasoning if persisted_action.errors: failure_reason = "; ".join(error.reasoning for error in persisted_action.errors) await self.update_task( task, status=TaskStatus.terminated, failure_reason=failure_reason, errors=task_errors, ) return True, last_step, None if isinstance(persisted_action, CompleteAction) and task.navigation_goal and task.data_extraction_goal: task = await self._run_data_extraction_after_complete_action( task=task, step=step, scraped_page=scraped_page, working_page=working_page, ) LOG.info( "Parallel verification: goal achieved, marking task as completed", step_id=step.step_id, task_id=task.task_id, ) final_status = step.speculative_original_status or StepStatus.completed step.speculative_original_status = None step.status = final_status last_step = await self.update_step( step, status=final_status, output=step.output, is_last=True, ) extracted_information = await self.get_extracted_information_for_task(task) await self.update_task( task, status=TaskStatus.completed, extracted_information=extracted_information, ) return True, last_step, None LOG.info( "Parallel verification: goal not achieved, awaiting speculative extract-actions", step_id=step.step_id, task_id=task.task_id, ) try: speculative_plan = await speculative_task except CancelledError: LOG.debug("Speculative extract-actions cancelled after verification finished", step_id=step.step_id) speculative_plan = None except Exception: LOG.warning( "Speculative extract-actions failed, next step will run sequentially", step_id=step.step_id, exc_info=True, ) speculative_plan = None context = skyvern_context.current() override_max_steps_per_run = context.max_steps_override if context else None max_steps_per_run = ( override_max_steps_per_run or task.max_steps_per_run or organization.max_steps_per_run or settings.MAX_STEPS_PER_RUN ) if step.order + 1 >= max_steps_per_run: LOG.info( "Step completed but max steps reached, marking task as failed", step_order=step.order, step_retry=step.retry_index, max_steps=max_steps_per_run, ) final_status = step.speculative_original_status or StepStatus.completed step.speculative_original_status = None step.status = final_status last_step = await self.update_step( step, status=final_status, output=step.output, is_last=True, ) generated_failure_reason = await self.summary_failure_reason_for_max_steps( organization=organization, task=task, step=step, page=page, ) failure_reason = f"Reached the maximum steps ({max_steps_per_run}). Possible failure reasons: {generated_failure_reason.reasoning}" errors = [ReachMaxStepsError().model_dump()] + [ error.model_dump() for error in generated_failure_reason.errors ] await self._cancel_speculative_step(next_step) await self.update_task( task, status=TaskStatus.failed, failure_reason=failure_reason, errors=errors, ) return False, last_step, None if speculative_plan: context = skyvern_context.ensure_context() context.speculative_plans[next_step.step_id] = speculative_plan LOG.info( "Stored speculative extract-actions plan for next step", current_step_id=step.step_id, next_step_id=next_step.step_id, ) step.status = step.speculative_original_status or StepStatus.completed step.speculative_original_status = None return None, None, next_step async def handle_failed_step(self, organization: Organization, task: Task, step: Step) -> Step | None: max_retries_per_step = ( organization.max_retries_per_step # we need to check by None because 0 is a valid value for max_retries_per_step if organization.max_retries_per_step is not None else settings.MAX_RETRIES_PER_STEP ) if step.retry_index >= max_retries_per_step: LOG.warning( "Step failed after max retries, marking task as failed", step_order=step.order, step_retry=step.retry_index, max_retries=settings.MAX_RETRIES_PER_STEP, ) browser_state = app.BROWSER_MANAGER.get_for_task(task_id=task.task_id, workflow_run_id=task.workflow_run_id) page = None if browser_state is not None: page = await browser_state.get_working_page() failure_reason = await self.summary_failure_reason_for_max_retries( organization=organization, task=task, step=step, page=page, max_retries=max_retries_per_step, ) await self.update_task( task, TaskStatus.failed, failure_reason=( f"Max retries per step ({max_retries_per_step}) exceeded. Possible failure reasons: {failure_reason}" ), errors=[ReachMaxRetriesError().model_dump()], ) return None else: LOG.warning( "Step failed, retrying", step_order=step.order, step_retry=step.retry_index, ) next_step = await app.DATABASE.create_step( task_id=task.task_id, organization_id=task.organization_id, order=step.order, retry_index=step.retry_index + 1, ) return next_step async def summary_failure_reason_for_max_steps( self, organization: Organization, task: Task, step: Step, page: Page | None, ) -> MaxStepsReasonResponse: steps_results = [] llm_errors: list[str] = [] try: steps = await app.DATABASE.get_task_steps( task_id=task.task_id, organization_id=organization.organization_id ) for step_cnt, step in enumerate(steps): if step.output is None: continue if len(step.output.errors) > 0: failure_reason = ";".join([repr(err) for err in step.output.errors]) return MaxStepsReasonResponse( page_info="", reasoning=failure_reason, errors=step.output.errors, ) if step.output.actions_and_results is None: continue action_result_summary: list[str] = [] step_result: dict[str, Any] = { "order": step_cnt, } for action, action_results in step.output.actions_and_results: if len(action_results) == 0: continue last_result = action_results[-1] # Check if this is an LLM provider error if not last_result.success: exception_type = last_result.exception_type or "" exception_message = last_result.exception_message or "" if ( exception_type in (LLM_PROVIDER_ERROR_TYPE, LLM_PROVIDER_ERROR_RETRYABLE_TASK_TYPE) or "LLMProvider" in exception_message ): llm_errors.append(f"Step {step_cnt}: {exception_message}") action_result_summary.append( f"{action.reasoning}(action_type={action.action_type}, result={'success' if last_result.success else 'failed'})" ) step_result["actions_result"] = action_result_summary steps_results.append(step_result) # If we detected LLM errors, return a clear message without calling the LLM if llm_errors: llm_error_details = "; ".join(llm_errors) return MaxStepsReasonResponse( page_info="", reasoning=( f"The task failed due to LLM service errors. The LLM provider encountered errors and was unable to process the requests. " f"This is typically caused by rate limiting, service outages, or resource exhaustion from the LLM provider. " f"Error details: {llm_error_details}" ), errors=[], ) scroll = True if await service_utils.is_cua_task(task=task): scroll = False screenshots: list[bytes] = [] if page is not None: screenshots = await SkyvernFrame.take_split_screenshots(page=page, url=page.url, scroll=scroll) prompt = prompt_engine.load_prompt( "summarize-max-steps-reason", step_count=len(steps), navigation_goal=task.navigation_goal, navigation_payload=task.navigation_payload, steps=steps_results, error_code_mapping_str=(json.dumps(task.error_code_mapping) if task.error_code_mapping else None), local_datetime=datetime.now(skyvern_context.ensure_context().tz_info).isoformat(), ) json_response = await app.LLM_API_HANDLER( prompt=prompt, screenshots=screenshots, step=step, prompt_name="summarize-max-steps-reason" ) return MaxStepsReasonResponse.model_validate(json_response) except Exception: LOG.warning("Failed to summary the failure reason") # Check if we have LLM errors even if the summarization failed if llm_errors: llm_error_details = "; ".join(llm_errors) return MaxStepsReasonResponse( page_info="", reasoning=( f"The task failed due to LLM service errors. The LLM provider encountered errors and was unable to process the requests. " f"Error details: {llm_error_details}" ), errors=[], ) if steps_results: last_step_result = steps_results[-1] return MaxStepsReasonResponse( page_info="", reasoning=f"Step {last_step_result['order']}: {last_step_result['actions_result']}", errors=[], ) return MaxStepsReasonResponse( page_info="", reasoning="", errors=[], ) async def summary_failure_reason_for_max_retries( self, organization: Organization, task: Task, step: Step, page: Page | None, max_retries: int, ) -> str: html = "" screenshots: list[bytes] = [] steps_results = [] llm_errors: list[str] = [] steps_without_actions = 0 try: steps = await app.DATABASE.get_task_steps( task_id=task.task_id, organization_id=organization.organization_id ) # Check for LLM provider errors in the failed steps for step_cnt, cur_step in enumerate(steps[-max_retries:]): if cur_step.status == StepStatus.failed: # If step failed with no actions, it might be an LLM error during action extraction if not cur_step.output or not cur_step.output.actions_and_results: steps_without_actions += 1 if cur_step.output and cur_step.output.actions_and_results: action_result_summary: list[str] = [] step_result: dict[str, Any] = { "order": step_cnt, } for action, action_results in cur_step.output.actions_and_results: if len(action_results) == 0: continue last_result = action_results[-1] if last_result.success: continue reason = last_result.exception_message or "" # Check if this is an LLM provider error exception_type = last_result.exception_type or "" if ( exception_type in (LLM_PROVIDER_ERROR_TYPE, LLM_PROVIDER_ERROR_RETRYABLE_TASK_TYPE) or "LLMProvider" in reason ): llm_errors.append(f"Step {step_cnt}: {reason}") action_result_summary.append( f"{action.reasoning}(action_type={action.action_type}, result=failed, reason={reason})" ) step_result["actions_result"] = action_result_summary steps_results.append(step_result) # If we detected LLM errors, return a clear message without calling the LLM if llm_errors: llm_error_details = "; ".join(llm_errors) return ( f"The task failed due to LLM service errors. The LLM provider encountered errors and was unable to process the requests. " f"This is typically caused by rate limiting, service outages, or resource exhaustion from the LLM provider. " f"Error details: {llm_error_details}" ) # If multiple steps failed without producing any actions, it's likely an LLM error during action extraction if steps_without_actions >= max_retries: return ( f"The task failed because all {max_retries} retry attempts failed to generate actions. " f"This is typically caused by LLM service errors during action extraction, such as rate limiting, " f"service outages, or resource exhaustion from the LLM provider. Please check the LLM service status and try again." ) if page is not None: skyvern_frame = await SkyvernFrame.create_instance(frame=page) html = await skyvern_frame.get_content() screenshots = await SkyvernFrame.take_split_screenshots(page=page, url=page.url) prompt = prompt_engine.load_prompt( "summarize-max-retries-reason", navigation_goal=task.navigation_goal, navigation_payload=task.navigation_payload, steps=steps_results, page_html=html, max_retries=max_retries, local_datetime=datetime.now(skyvern_context.ensure_context().tz_info).isoformat(), ) json_response = await app.SECONDARY_LLM_API_HANDLER( prompt=prompt, screenshots=screenshots, step=step, prompt_name="summarize-max-retries-reason", ) return json_response.get("reasoning", "") except Exception: LOG.warning("Failed to summarize the failure reason for max retries") # Check if we have LLM errors even if the summarization failed if llm_errors: llm_error_details = "; ".join(llm_errors) return ( f"The task failed due to LLM service errors. The LLM provider encountered errors and was unable to process the requests. " f"Error details: {llm_error_details}" ) # If multiple steps failed without actions during summarization failure, still report it if steps_without_actions >= max_retries: return ( f"The task failed because all {max_retries} retry attempts failed to generate actions. " f"This is typically caused by LLM service errors during action extraction." ) if steps_results: last_step_result = steps_results[-1] return f"Retry Step {last_step_result['order']}: {last_step_result['actions_result']}" return "" async def handle_completed_step( self, organization: Organization, task: Task, step: Step, page: Page | None, task_block: BaseTaskBlock | None = None, browser_state: BrowserState | None = None, scraped_page: ScrapedPage | None = None, engine: RunEngine = RunEngine.skyvern_v1, complete_verification: bool = True, ) -> tuple[bool | None, Step | None, Step | None]: # Check if parallel verification should be used # Only use it when we have the required data AND when verification would normally happen should_verify = ( complete_verification and not step.is_goal_achieved() and not step.is_terminated() and not isinstance(task_block, ActionBlock) and (task.navigation_goal or task.complete_criterion) ) if should_verify and browser_state and scraped_page: try: distinct_id = task.workflow_run_id if task.workflow_run_id else task.task_id enable_parallel_verification = await app.EXPERIMENTATION_PROVIDER.is_feature_enabled_cached( "ENABLE_PARALLEL_USER_GOAL_CHECK", distinct_id, properties={"organization_id": task.organization_id, "task_url": task.url}, ) if enable_parallel_verification: LOG.info( "Parallel verification enabled, using optimized flow", step_id=step.step_id, task_id=task.task_id, ) return await self._handle_completed_step_with_parallel_verification( organization=organization, task=task, step=step, page=page, browser_state=browser_state, scraped_page=scraped_page, engine=engine, task_block=task_block, ) except Exception: LOG.warning( "Failed to check parallel verification feature flag, using standard flow", step_id=step.step_id, exc_info=True, ) if step.is_goal_achieved(): LOG.info( "Step completed and goal achieved, marking task as completed", step_order=step.order, step_retry=step.retry_index, output=step.output, ) last_step = await self.update_step(step, is_last=True) extracted_information = await self.get_extracted_information_for_task(task) await self.update_task( task, status=TaskStatus.completed, extracted_information=extracted_information, ) return True, last_step, None if step.is_terminated(): LOG.info( "Step completed and terminated by the agent, marking task as terminated", step_order=step.order, step_retry=step.retry_index, output=step.output, ) last_step = await self.update_step(step, is_last=True) failure_reason = await self.get_failure_reason_for_task(task) await self.update_task(task, status=TaskStatus.terminated, failure_reason=failure_reason) return False, last_step, None # If the max steps are exceeded, mark the current step as the last step and conclude the task context = skyvern_context.current() override_max_steps_per_run = context.max_steps_override if context else None max_steps_per_run = ( override_max_steps_per_run or task.max_steps_per_run or organization.max_steps_per_run or settings.MAX_STEPS_PER_RUN ) # HACK: action block only have one step to execute without complete action, so we consider the task is completed as long as the step is completed if isinstance(task_block, ActionBlock) and step.is_success(): LOG.info( "Step completed for the action block, marking task as completed", step_order=step.order, step_retry=step.retry_index, output=step.output, ) last_step = await self.update_step(step, is_last=True) await self.update_task( task, status=TaskStatus.completed, ) return True, last_step, None if step.order + 1 >= max_steps_per_run: LOG.info( "Step completed but max steps reached, marking task as failed", step_order=step.order, step_retry=step.retry_index, max_steps=max_steps_per_run, ) last_step = await self.update_step(step, is_last=True) generated_failure_reason = await self.summary_failure_reason_for_max_steps( organization=organization, task=task, step=step, page=page, ) failure_reason = f"Reached the maximum steps ({max_steps_per_run}). Possible failure reasons: {generated_failure_reason.reasoning}" errors = [ReachMaxStepsError().model_dump()] + [ error.model_dump() for error in generated_failure_reason.errors ] await self.update_task( task, status=TaskStatus.failed, failure_reason=failure_reason, errors=errors, ) return False, last_step, None else: LOG.info( "Step completed, creating next step", step_order=step.order, step_retry=step.retry_index, ) next_step = await app.DATABASE.create_step( task_id=task.task_id, order=step.order + 1, retry_index=0, organization_id=task.organization_id, ) if step.order == int(max_steps_per_run * settings.LONG_RUNNING_TASK_WARNING_RATIO - 1): LOG.info( "Long running task warning", order=step.order, max_steps=max_steps_per_run, warning_ratio=settings.LONG_RUNNING_TASK_WARNING_RATIO, ) return None, None, next_step async def handle_potential_OTP_actions( self, task: Task, step: Step, scraped_page: ScrapedPage, browser_state: BrowserState, json_response: dict[str, Any], ) -> tuple[dict[str, Any], list[Action]]: if not task.organization_id: return json_response, [] if not task.totp_verification_url and not task.totp_identifier: return json_response, [] should_verify_by_magic_link = json_response.get("should_verify_by_magic_link") place_to_enter_verification_code = json_response.get("place_to_enter_verification_code") should_enter_verification_code = json_response.get("should_enter_verification_code") if ( not should_verify_by_magic_link and not place_to_enter_verification_code and not should_enter_verification_code ): return json_response, [] if place_to_enter_verification_code and should_enter_verification_code: json_response = await self.handle_potential_verification_code( task, step, scraped_page, browser_state, json_response ) actions = parse_actions(task, step.step_id, step.order, scraped_page, json_response["actions"]) return json_response, actions if should_verify_by_magic_link: actions = await self.handle_potential_magic_link(task, step, scraped_page, browser_state, json_response) return json_response, actions return json_response, [] async def handle_potential_magic_link( self, task: Task, step: Step, scraped_page: ScrapedPage, browser_state: BrowserState, json_response: dict[str, Any], ) -> list[Action]: should_verify_by_magic_link = json_response.get("should_verify_by_magic_link") if not should_verify_by_magic_link: return [] LOG.info("Handling magic link verification", task_id=task.task_id) otp_value = await poll_otp_value( organization_id=task.organization_id, task_id=task.task_id, workflow_run_id=task.workflow_run_id, totp_verification_url=task.totp_verification_url, totp_identifier=task.totp_identifier, ) if not otp_value or otp_value.get_otp_type() != OTPType.MAGIC_LINK: return [] # always open a new tab to navigate to the magic link page = await browser_state.new_page() context = skyvern_context.ensure_context() context.add_magic_link_page(task.task_id, page) return [ GotoUrlAction( reasoning="Navigating to the magic link URL to verify the login", intention="Navigating to the magic link URL to verify the login", url=otp_value.value, organization_id=task.organization_id, workflow_run_id=task.workflow_run_id, task_id=task.task_id, step_id=step.step_id, step_order=step.order, action_order=0, is_magic_link=True, ), ] async def handle_potential_verification_code( self, task: Task, step: Step, scraped_page: ScrapedPage, browser_state: BrowserState, json_response: dict[str, Any], ) -> dict[str, Any]: place_to_enter_verification_code = json_response.get("place_to_enter_verification_code") should_enter_verification_code = json_response.get("should_enter_verification_code") if ( place_to_enter_verification_code and should_enter_verification_code and (task.totp_verification_url or task.totp_identifier) and task.organization_id ): LOG.info("Need verification code") workflow_id = workflow_permanent_id = None if task.workflow_run_id: workflow_run = await app.DATABASE.get_workflow_run(task.workflow_run_id) if workflow_run: workflow_id = workflow_run.workflow_id workflow_permanent_id = workflow_run.workflow_permanent_id otp_value = await poll_otp_value( organization_id=task.organization_id, task_id=task.task_id, workflow_id=workflow_id, workflow_run_id=task.workflow_run_id, workflow_permanent_id=workflow_permanent_id, totp_verification_url=task.totp_verification_url, totp_identifier=task.totp_identifier, ) if not otp_value or otp_value.get_otp_type() != OTPType.TOTP: return json_response current_context = skyvern_context.ensure_context() current_context.totp_codes[task.task_id] = otp_value.value extract_action_prompt, use_caching = await self._build_extract_action_prompt( task, step, browser_state, scraped_page, verification_code_check=False, ) llm_key_override = task.llm_key if await service_utils.is_cua_task(task=task): llm_key_override = None llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler( llm_key_override, default=app.LLM_API_HANDLER ) # Add caching flag to context for monitoring if use_caching: context = skyvern_context.current() if context: context.use_prompt_caching = True return await llm_api_handler( prompt=extract_action_prompt, step=step, screenshots=scraped_page.screenshots, prompt_name="extract-actions", ) return json_response @staticmethod async def get_task_errors(task: Task) -> list[UserDefinedError]: steps = await app.DATABASE.get_task_steps(task_id=task.task_id, organization_id=task.organization_id) errors = [] for step in steps: if step.output and step.output.errors: errors.extend(step.output.errors) return errors @staticmethod async def update_task_errors_from_detailed_output( task: Task, detailed_step_output: DetailedAgentStepOutput ) -> Task: task_errors = task.errors step_errors = detailed_step_output.extract_errors() or [] task_errors.extend([error.model_dump() for error in step_errors]) return await app.DATABASE.update_task( task_id=task.task_id, organization_id=task.organization_id, errors=task_errors, ) @staticmethod async def create_extract_action(task: Task, step: Step, scraped_page: ScrapedPage) -> ExtractAction: context = skyvern_context.ensure_context() # generate reasoning by prompt llm to think briefly what data to extract prompt = prompt_engine.load_prompt( "data-extraction-summary", data_extraction_goal=task.data_extraction_goal, data_extraction_schema=task.extracted_information_schema, current_url=scraped_page.url, local_datetime=datetime.now(context.tz_info).isoformat(), ) data_extraction_summary_resp = await app.EXTRACTION_LLM_API_HANDLER( prompt=prompt, step=step, prompt_name="data-extraction-summary" ) return ExtractAction( reasoning=data_extraction_summary_resp.get("summary", "Extracting information from the page"), data_extraction_goal=task.data_extraction_goal, data_extraction_schema=task.extracted_information_schema, organization_id=task.organization_id, task_id=task.task_id, workflow_run_id=task.workflow_run_id, step_id=step.step_id, step_order=step.order, action_order=0, confidence_float=1.0, ) @staticmethod def step_has_completed_goal(detailed_agent_step_output: DetailedAgentStepOutput) -> bool: if not detailed_agent_step_output.actions_and_results: return False last_action, last_action_results = detailed_agent_step_output.actions_and_results[-1] if last_action.action_type not in [ActionType.COMPLETE, ActionType.EXTRACT]: return False return any(action_result.success for action_result in last_action_results) async def _run_data_extraction_after_complete_action( self, task: Task, step: Step, scraped_page: ScrapedPage, working_page: Page, ) -> Task: """ Run the extraction flow when a task with a data extraction goal completes during parallel verification. """ refreshed_task = await app.DATABASE.get_task(task.task_id, task.organization_id) if refreshed_task: task = refreshed_task extract_action = await self.create_extract_action(task, step, scraped_page) extract_results = await ActionHandler.handle_action(scraped_page, task, step, working_page, extract_action) await app.AGENT_FUNCTION.post_action_execution(extract_action) if step.output is None: step.output = AgentStepOutput(action_results=[], actions_and_results=[], errors=[]) if step.output.action_results is None: step.output.action_results = [] if step.output.actions_and_results is None: step.output.actions_and_results = [] step.output.action_results.extend(extract_results) step.output.actions_and_results.append((extract_action, extract_results)) return task