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
110 lines
3.4 KiB
Python
110 lines
3.4 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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"""Video Transcription Example
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This example demonstrates how to use the video recording tools to capture
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and analyze video content from a mobile device screen.
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The agent can:
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1. Start a screen recording
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2. Perform actions while recording
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3. Stop the recording and analyze its content using Gemini models
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Use case: Recording a video playing on the screen and transcribing its content.
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"""
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import asyncio
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from artemis.config import LLM, LLMConfig, LLMConfigUtils, LLMWithFallback
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from artemis.sdk.agent import Agent
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from artemis.sdk.builders.agent_config_builder import AgentConfigBuilder
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from artemis.sdk.types.agent import AgentConfig
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from artemis.sdk.types.task import AgentProfile, TaskRequest
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def get_video_capable_llm_config() -> LLMConfig:
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"""Returns an LLM config with video_analyzer configured.
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The video_analyzer must use a video-capable Gemini model:
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- gemini-3.6-flash (recommended - fast and capable)
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- gemini-3-pro-preview
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- gemini-2.5-flash
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- gemini-2.5-pro
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- gemini-2.0-flash
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"""
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return LLMConfig(
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planner=LLMWithFallback(
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provider="openai",
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model="gpt-5-nano",
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fallback=LLM(provider="openai", model="gpt-5-mini"),
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),
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utils=LLMConfigUtils(
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outputter=LLMWithFallback(
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provider="openai",
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model="gpt-5-nano",
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fallback=LLM(provider="openai", model="gpt-5-mini"),
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),
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hopper=LLMWithFallback(
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provider="openai",
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model="gpt-5-nano",
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fallback=LLM(provider="openai", model="gpt-5-mini"),
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),
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video_analyzer=LLMWithFallback(
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provider="google",
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model="gemini-3.6-flash",
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fallback=LLM(provider="google", model="gemini-2.5-flash"),
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),
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),
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)
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async def main():
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config: AgentConfig = (
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AgentConfigBuilder()
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.add_profile(
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AgentProfile(
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name="VideoCapable",
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llm_config=get_video_capable_llm_config(),
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)
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)
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.with_video_recording_tools()
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.build()
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)
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agent = Agent(config=config)
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try:
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await agent.init()
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result = await agent.run_task(
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request=TaskRequest(
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goal="""
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1. Open YouTube app
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2. Search for "Python tutorial"
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3. Start recording the screen
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4. Play the first video
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5. Wait for the first 30 seconds of the video to play
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6. Stop recording and tell me what was said in the video
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""",
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profile="VideoCapable",
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)
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)
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print(f"Task result: {result}")
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finally:
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await agent.clean()
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if __name__ == "__main__":
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asyncio.run(main())
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