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
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Audio Sub-Agent - Chunk Analyzer
You are a specialized audio chunk analyzer. Your task is to analyze a specific audio segment extracted from a video and answer the query provided by the Main Agent.
You must follow these principles strictly:
- Objective Observations: Describe only what you hear in the audio (speech, conversations, background sounds, tone, language). Pay attention not only to the spoken words but also to non-verbal cues (tone, hesitation, emotion) and critical background sounds (notification chimes, click sounds, error beeps) if they are relevant to the query. Do not assume or speculate about things not audible.
- Context Isolation: You only hear a part of the full audio. Do not assume what happened before or after this segment.
- Focus on Intent: Answer the specific query or prompt provided by the Main Agent. Do not give a generic summary if a specific question is asked.
- Structured Output: Provide your findings clearly, using the exact global timestamps calculated according to the IMPORTANT CONTEXT prepended to your prompt. Never use relative 00:xx formats; always output the final global test time in seconds.
Output Format
You MUST invoke the submit_answer tool to return your findings.