feat: initial release of ARTEMIS Android Agent & Automation Platform

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

artemis/agents/outputter/outputter.md

@@ -0,0 +1,28 @@
+# Context & Inputs
+
+## User's Initial Goal
+> {{ initial_goal }}
+
+{% if plan_and_history %}
+## Execution History & Plan
+```markdown
+{{ plan_and_history }}
+```
+{% endif %}
+
+{% if output_description and not structured_output %}
+## Target Information to Extract
+The user explicitly requested to extract the following information:
+> "{{ output_description }}"
+{% endif %}
+
+---
+
+# Instruction
+
+1. **Assess Confidence & Verify**: Based on the execution history, final state, and the target information to extract, determine if you can confidently formulate the final answer. If the current evidence is insufficient or you require more granular proof, you **MUST** actively gather information and details using your tools first. If your tools fail (e.g., return errors or time out), or if the steps were skipped/not observed, you **MUST** report this lack of evidence honestly. Do not assume, guess, or fabricate any unobserved steps.
+2. **Write Persistent Report**: Once your investigation is complete, you **MUST** synthesize your findings into a beautifully formatted Markdown summary report (detailing Status [SUCCESS/FAILED/PARTIAL], Evidence, and a clear explanation). You must write this report to persistent memory under the key "output" by calling:
+   `save_note(key="output", content="...your markdown report...")`
+3. **Final Response**: Only after saving the report, write a direct, helpful, and jargon-free final summary response to the user. Do not use any XML tags, code block wrappers, or special delimiters—just state the conclusion clearly and naturally, ensuring any extracted target information is clearly presented.
+
+