somew1nd
206a9ce921
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
106 lines
3.7 KiB
Python
106 lines
3.7 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 ast
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import logging
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from pathlib import Path
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import queue
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import time
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import jupyter_client
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logger = logging.getLogger(__name__)
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def validate_coder_script(code_str: str):
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"""Validates that the generated python code does not violate constraints.
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Specifically checks for usage of cv2.resize and numpy slicing which could
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mess up the transformation tracking.
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"""
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try:
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tree = ast.parse(code_str)
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except SyntaxError as e:
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raise ValueError(f"Syntax error in code: {e}")
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for node in ast.walk(tree):
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# Prevent cv2.resize
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if isinstance(node, ast.Attribute):
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if node.attr == "resize":
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# Quick check if it might be cv2.resize
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raise ValueError(
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"SECURITY/VALIDATION ERROR: Direct usage of resize() is"
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" forbidden. You MUST use canvas.resize_by_factor(factor)."
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)
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# Prevent slicing (very aggressive, but forces them to use canvas.crop)
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# Note: We cannot easily block all slicing because they might slice other arrays.
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# But we can look for specific variable names or just warn.
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# Let's be lenient on slicing but strict in the prompt.
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return True
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class PythonExecutor:
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def __init__(self, session_dir: Path | None = None):
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self.session_dir = session_dir
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try:
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self.km = jupyter_client.KernelManager(kernel_name="python3")
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self.km.start_kernel()
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self.kc = self.km.client()
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self.kc.start_channels()
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self.kc.wait_for_ready(timeout=10)
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except Exception as e:
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logger.error(f"Failed to start Jupyter kernel: {e}")
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raise RuntimeError(f"Kernel startup failed: {e}")
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def execute(self, code: str) -> str:
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# First validate code
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try:
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validate_coder_script(code)
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except ValueError as ve:
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return str(ve)
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start_time = time.time()
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self.kc.execute(code)
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output_text = []
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while True:
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try:
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msg = self.kc.get_iopub_msg(timeout=15)
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msg_type = msg["header"]["msg_type"]
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if msg_type == "stream":
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output_text.append(msg["content"]["text"])
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elif msg_type == "error":
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traceback = "\n".join(msg["content"]["traceback"])
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output_text.append(f"Traceback:\n{traceback}")
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elif msg_type in ("display_data", "execute_result"):
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data = msg["content"]["data"]
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if "text/plain" in data:
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output_text.append(data["text/plain"])
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elif msg_type == "status" and msg["content"]["execution_state"] == "idle":
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break
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except queue.Empty:
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output_text.append("[Execution Timeout Error]")
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break
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return "".join(output_text)
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def close(self):
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try:
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self.kc.stop_channels()
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self.km.shutdown_kernel()
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except Exception as e:
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logger.error(f"Failed to cleanly shutdown kernel: {e}")
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