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
208 lines
6.1 KiB
Python
208 lines
6.1 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 json
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import os
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from pathlib import Path
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import subprocess
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import tempfile
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from artemis.utils.logger import get_logger
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from PIL import Image
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logger = get_logger(__name__)
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USE_FFMPEG_GIF = os.environ.get("USE_FFMPEG_GIF", "").lower() in (
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"1",
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"true",
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"yes",
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)
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def quantize_and_save_gif_from_paths(
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image_paths: list[Path],
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output_path: Path,
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colors: int = 128,
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duration: int = 100,
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) -> None:
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"""Create an optimized GIF from image file paths.
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By default uses PIL (loads all frames into memory).
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Set USE_FFMPEG_GIF=1 env var to use ffmpeg for memory-efficient streaming.
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Args:
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image_paths: List of paths to image files (must be sorted in desired
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order)
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output_path: Path where the GIF will be saved
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colors: Number of colors to use in quantization (lower = smaller file)
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duration: Duration of each frame in milliseconds
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Raises:
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ValueError: If image_paths list is empty
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RuntimeError: If ffmpeg fails (when USE_FFMPEG_GIF is enabled)
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"""
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if not image_paths:
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raise ValueError("image_paths must not be empty")
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if USE_FFMPEG_GIF:
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_save_gif_ffmpeg(image_paths, output_path, colors, duration)
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else:
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_save_gif_pillow(image_paths, output_path, colors, duration)
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def _save_gif_pillow(
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image_paths: list[Path],
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output_path: Path,
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colors: int,
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duration: int,
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) -> None:
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"""Create GIF using PIL (loads all frames into memory)."""
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def frame_generator():
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for path in image_paths:
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with Image.open(path) as img:
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if img.mode != "RGB":
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img = img.convert("RGB")
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yield img.quantize(colors=colors, method=2)
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frames = frame_generator()
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first_frame = next(frames)
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first_frame.save(
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output_path,
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save_all=True,
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append_images=frames,
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loop=0,
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optimize=True,
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duration=duration,
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)
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def _save_gif_ffmpeg(
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image_paths: list[Path],
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output_path: Path,
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colors: int,
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duration: int,
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) -> None:
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"""Create GIF using ffmpeg (memory-efficient streaming)."""
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fps = 1000 / duration
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with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
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for i, path in enumerate(image_paths):
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f.write(f"file '{path.absolute()}'\n")
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# Last file needs no duration (ffmpeg concat demuxer uses it as the final frame)
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if i < len(image_paths) - 1:
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f.write(f"duration {duration / 1000}\n")
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# Repeat last file to ensure it's included (ffmpeg concat demuxer quirk)
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f.write(f"file '{image_paths[-1].absolute()}'\n")
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concat_file = Path(f.name)
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palette_path = output_path.with_suffix(".palette.png")
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try:
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result = subprocess.run(
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[
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"ffmpeg",
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"-y",
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"-f",
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"concat",
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"-safe",
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"0",
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"-i",
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str(concat_file),
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"-vf",
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f"palettegen=max_colors={min(colors, 256)}:stats_mode=diff",
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str(palette_path),
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],
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capture_output=True,
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text=True,
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)
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if result.returncode != 0:
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raise RuntimeError(f"ffmpeg palette generation failed: {result.stderr}")
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result = subprocess.run(
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[
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"ffmpeg",
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"-y",
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"-f",
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"concat",
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"-safe",
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"0",
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"-i",
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str(concat_file),
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"-i",
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str(palette_path),
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"-lavfi",
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f"fps={fps},paletteuse=dither=bayer:bayer_scale=5",
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"-loop",
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"0",
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str(output_path),
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],
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capture_output=True,
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text=True,
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)
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if result.returncode != 0:
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raise RuntimeError(f"ffmpeg GIF creation failed: {result.stderr}")
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finally:
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concat_file.unlink(missing_ok=True)
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if palette_path.exists():
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palette_path.unlink()
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def create_gif_from_trace_folder(trace_folder_path: Path):
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image_files: list[Path] = []
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for file in trace_folder_path.iterdir():
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if file.suffix == ".jpeg":
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image_files.append(file)
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image_files.sort(key=lambda f: int(f.stem))
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logger.info(f"Found {len(image_files)} images to compile")
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if not image_files:
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return
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gif_path = trace_folder_path / "trace.gif"
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quantize_and_save_gif_from_paths(image_files, gif_path)
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logger.info(f"GIF created at {gif_path}")
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def remove_images_from_trace_folder(trace_folder_path: Path):
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for file in trace_folder_path.iterdir():
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if file.suffix == ".jpeg":
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file.unlink()
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def create_steps_json_from_trace_folder(trace_folder_path: Path):
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steps = []
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for file in trace_folder_path.iterdir():
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if file.suffix == ".json":
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with open(file, encoding="utf-8", errors="ignore") as f:
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json_content = f.read()
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steps.append({"timestamp": int(file.stem), "data": json_content})
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steps.sort(key=lambda f: f["timestamp"])
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logger.info(f"Found {len(steps)} steps to compile")
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with open(trace_folder_path / "steps.json", "w", encoding="utf-8", errors="ignore") as f:
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f.write(json.dumps(steps))
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def remove_steps_json_from_trace_folder(trace_folder_path: Path):
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for file in trace_folder_path.iterdir():
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if file.suffix == ".json" and file.name != "steps.json":
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file.unlink()
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