ARTEMIS is an autonomous multimodal Android agent and mobile UI automation platform engineered for physical devices and emulators. It combines state-of-the-art multimodal Vision-Language Models (VLMs) with hierarchical closed-loop planning, resilient self-healing, and deep Android subsystem integration. Key Features & Architectural Highlights: - Dual-Engine Orchestration: * Pro Engine: Multi-agent closed-loop (Planner -> Operator -> Validator/Failure Analyzer -> Summarizer) powered by LangGraph. * Flash Engine: Ultra-fast reactive loop for deterministic, low-latency UI execution (<35 steps). - Multimodal Perception & Grounding: * Hybrid visual screenshot grounding, real-time OCR extraction, and XML accessibility hierarchy parsing. * Dynamic-First, Coordinate-Fallback locating strategy for maximum resilience against UI drift. - Autonomous Self-Healing & Diagnostics: * Failure analyzer and safety net for popup dismissal, crash recovery, and auto-retry. * Diagnostic sub-agents for log analysis, video recording inspection, and object detection. - Unified CLI & Python SDK: * Rich CLI with commands: init, doctor, run, trace, batch, bench, server, and mcp. * Fluent Python SDK with Pydantic structured output models and package locking. - Multi-Provider LLM Architecture: * Support for Google Gemini, OpenAI, Anthropic Claude, Vertex AI, OpenRouter, xAI Grok, and local endpoints (Ollama/vLLM). * Centralized configuration in config/artemis.jsonc. - Native MCP & Observability: * Model Context Protocol (MCP) server endpoints for AI IDEs. * Full trace recording with before/after screenshots, action overlays, and execution replays. Change-Id: I430b93ee2b76b54cc5a3c6eeccd15d2cf29830a1
188 lines
6.8 KiB
Python
188 lines
6.8 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import asyncio
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import base64
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import hashlib
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import json
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from pathlib import Path
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from artemis.context import ArtemisContext
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from artemis.controllers.unified_controller import UnifiedMobileController
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from artemis.data_engine.trace import trace
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from artemis.graph.state import State
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from artemis.utils.logger import get_logger
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from artemis.utils.ocr_api import perform_ocr
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from artemis.utils.ocr_xml_fusion import (
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_crop_image_remove_status_bar,
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_detect_status_bar_height,
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_map_coordinates_back,
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fuse_ocr_with_xml,
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)
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logger = get_logger(__name__)
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def _should_skip_settling(state: State) -> bool:
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"""Determine if we should skip screen settling based on the last executed action.
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Purely logical, non-UI-drawing, or wait operations don't require dynamic
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settling checks.
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"""
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if not state or not state.structured_decisions:
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# First turn or no prior action, skip settling
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return True
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try:
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actions = json.loads(state.structured_decisions)
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if not actions or not isinstance(actions, list):
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return True
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last_action = actions[-1]
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action_name = last_action.get("action")
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# Actions that are known to have already completed full wait loops or don't render UI
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skip_actions = {
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"wait_for_delay",
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}
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if action_name in skip_actions:
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logger.info(f"Last action was '{action_name}'. Skipping dynamic screen settling.")
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return True
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return False
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except Exception as e:
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logger.warning(
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f"Failed to parse last action for settling heuristic: {e}."
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" Defaulting to settling check."
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)
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return False
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@trace(type="agent", name="perception")
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def _check_injected_instruction_file(base_dir: str) -> str | None:
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instruction_path = Path(base_dir) / "injected_instruction.json"
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if instruction_path.exists():
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with open(instruction_path, encoding="utf-8") as f:
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data = json.load(f)
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injected_instruction = data.get("instruction")
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instruction_path.unlink()
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logger.info(f"Read and deleted injected instruction: '{injected_instruction}'")
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return injected_instruction
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return None
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async def perception_node(state: State, ctx: ArtemisContext) -> dict:
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"""Perception node (fast path): captures screenshot, extracts XML hierarchy, and performs OCR.
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Always executes at the start of an operator cycle.
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"""
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logger.info("Entering Perception Node...")
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if not ctx.data_engine.current_step_id:
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ctx.data_engine.allocate_step_id()
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logger.info(f"Pre-allocated step ID in Perception Node: {ctx.data_engine.current_step_id}")
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# Check for injected instruction without blocking event loop
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injected_instruction = None
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if ctx.data_engine and ctx.data_engine.base_dir:
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injected_instruction = await asyncio.to_thread(
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_check_injected_instruction_file, str(ctx.data_engine.base_dir)
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)
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controller = UnifiedMobileController(ctx)
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# 1. Capture screenshot with hybrid settling strategy
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if _should_skip_settling(state):
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logger.info("Skipping settling. Retrieving screen data directly...")
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device_data = await controller.get_screen_data()
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latest_screenshot_b64 = device_data.base64
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xml_hierarchy = device_data.elements
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else:
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logger.info("Screen settling required. Delaying 0.4s and capturing screen data...")
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await asyncio.sleep(0.4)
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device_data = await controller.get_screen_data()
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latest_screenshot_b64 = device_data.base64
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xml_hierarchy = device_data.elements
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latest_screenshot_bytes = base64.b64decode(latest_screenshot_b64)
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# Update context device dimensions dynamically
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if ctx.device:
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ctx.device.device_width = device_data.width
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ctx.device.device_height = device_data.height
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logger.info(f"Updated context device dimensions: {device_data.width}x{device_data.height}")
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# 3. Perform OCR
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screen_height = device_data.height
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status_bar_height = _detect_status_bar_height(xml_hierarchy, screen_height)
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if status_bar_height > 0:
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logger.info(
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f"Detected status bar height: {status_bar_height}. Cropping screenshot for OCR."
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)
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cropped_b64, _, _ = await asyncio.to_thread(
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_crop_image_remove_status_bar,
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latest_screenshot_b64,
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status_bar_height,
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)
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raw_ocr_results = await perform_ocr(cropped_b64)
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ocr_results = _map_coordinates_back(raw_ocr_results, status_bar_height)
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else:
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logger.info("No status bar detected. Performing OCR on full screenshot.")
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ocr_results = await perform_ocr(latest_screenshot_b64)
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# 4. Fuse OCR with XML
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fused_xml = fuse_ocr_with_xml(xml_hierarchy, ocr_results)
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# 5. Save to Data Engine in background thread
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image_name = None
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screenshot_path = None
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if ctx.data_engine:
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image_name = hashlib.sha256(latest_screenshot_bytes).hexdigest()
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screenshot_path = str(ctx.data_engine.get_image_path(image_name))
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asyncio.create_task(
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asyncio.to_thread(
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ctx.data_engine.get_or_create_image,
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latest_screenshot_bytes,
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ui_tree=xml_hierarchy,
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ocr_result=ocr_results,
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)
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)
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logger.info(f"Offloaded perception data saving to background with image_name: {image_name}")
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# Cache latest perception data on ArtemisContext for downstream zero-latency reuse (e.g. Checker)
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ctx.latest_perception_data = {
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"screenshot_b64": latest_screenshot_b64,
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"latest_ui_hierarchy": fused_xml,
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"xml_hierarchy": xml_hierarchy if xml_hierarchy is not None else [],
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"ocr_results": ocr_results,
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"width": device_data.width,
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"height": device_data.height,
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}
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# Return state update
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update = {
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"latest_screenshot": screenshot_path,
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"latest_ui_hierarchy": fused_xml,
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"operator_raw_data": {
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"screenshot_b64": latest_screenshot_b64,
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"xml_hierarchy": xml_hierarchy if xml_hierarchy is not None else [],
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"ocr_results": ocr_results,
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"width": device_data.width,
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"height": device_data.height,
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},
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"injected_instruction": injected_instruction,
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}
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return update
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