feat: introduce MCP server, cross-platform runtime, and Showcase UI reorganization
- Showcase UI Reorganization: * Move web UI dashboard from root directory into apps/showcase_ui/ for cleaner project structure * Add 'artemis ui' CLI command (artemis/interfaces/cli/commands/ui.py) and launcher scripts for the interactive showcase console - Standalone MCP Server & Notifiers (mcp_server/): * Implement dedicated MCP server package with mobile automation tools (device_state, inspect_trace, task_manager, task_runner) * Add multi-channel notifiers (AgentAPI, Desktop notifications, Webhooks, File output, and Composite routing) * Introduce background task runner and trace store for autonomous mobile execution monitoring - Cross-Platform Runtime & Toolchain Support: * Add artemis/platform/ with OS-specific implementations for Linux, macOS (Darwin), and Windows * Add artemis/runtime/supervisor.py for resilient process and agent runtime supervision * Add artemis/toolchain/ with dependency resolution and SDK descriptors * Add cross-platform launcher scripts (start.sh, start.bat) and installer scripts (scripts/install_deps.*) - Core Enhancements & Diagnostics: * Enhance coordinate normalization, OCR API fallback, and perception pipeline * Add credential validation (credentials_validator.py) and system diagnostics * Update bilingual documentation (README.md, README_CN.md) and architecture diagrams - Test Suite Expansion: * Add integration tests for cloud gateway, remote ADB, and Artemis login * Add unit tests covering core diagnostics, platform detection, toolchain, credentials validator, optional OCR, and MCP tools
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# 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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"""LLM & Multimodal Vision Credentials Readiness Probe."""
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from typing import Any
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from artemis.config import settings
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from artemis.core.diagnostics.probes.base import BaseProbe
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from artemis.core.diagnostics.schema import (
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ProbeAction,
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ProbeCategory,
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ProbeResult,
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ProbeStatus,
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)
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class LLMCredentialsProbe(BaseProbe):
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"""Probe verifying Gemini and multi-provider multimodal LLM credentials."""
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@property
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def probe_id(self) -> str:
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return "gemini_api_key"
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@property
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def category(self) -> ProbeCategory:
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return ProbeCategory.CREDENTIALS
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@property
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def is_blocker(self) -> bool:
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return True
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def _mask_key(self, key_str: str) -> str:
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"""Helper to safely mask an API credential for display."""
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if len(key_str) > 10:
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return f"{key_str[:6]}...{key_str[-4:]}"
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return "***"
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async def probe(self) -> ProbeResult:
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import os
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from artemis.config.settings import is_placeholder_key
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gemini_key = settings.get_api_key("google")
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openai_key = settings.get_api_key("openai")
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claude_key = settings.get_api_key("anthropic")
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openrouter_key = settings.get_api_key("openrouter")
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xai_key = settings.get_api_key("xai")
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ocr_key = settings.get_api_key("ocr")
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configured_providers: list[dict[str, Any]] = []
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api_keys_map: dict[str, str] = {}
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if gemini_key and not is_placeholder_key(gemini_key):
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g_val = gemini_key.get_secret_value()
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configured_providers.append(
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{
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"provider": "google",
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"label": "Gemini",
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"masked": self._mask_key(g_val),
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"raw_key": g_val,
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"key": g_val,
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}
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)
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api_keys_map["google"] = g_val
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api_keys_map["gemini"] = g_val
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if openai_key and not is_placeholder_key(openai_key):
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o_val = openai_key.get_secret_value()
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configured_providers.append(
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{
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"provider": "openai",
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"label": "ChatGPT",
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"masked": self._mask_key(o_val),
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"raw_key": o_val,
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"key": o_val,
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}
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)
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api_keys_map["openai"] = o_val
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if claude_key and not is_placeholder_key(claude_key):
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c_val = claude_key.get_secret_value()
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configured_providers.append(
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{
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"provider": "anthropic",
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"label": "Claude",
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"masked": self._mask_key(c_val),
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"raw_key": c_val,
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"key": c_val,
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}
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)
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api_keys_map["anthropic"] = c_val
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if openrouter_key and not is_placeholder_key(openrouter_key):
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or_val = openrouter_key.get_secret_value()
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configured_providers.append(
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{
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"provider": "openrouter",
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"label": "OpenRouter",
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"masked": self._mask_key(or_val),
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"raw_key": or_val,
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"key": or_val,
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}
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)
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api_keys_map["openrouter"] = or_val
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if xai_key and not is_placeholder_key(xai_key):
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x_val = xai_key.get_secret_value()
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configured_providers.append(
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{
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"provider": "xai",
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"label": "xAI (Grok)",
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"masked": self._mask_key(x_val),
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"raw_key": x_val,
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"key": x_val,
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}
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)
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api_keys_map["xai"] = x_val
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# Detect any custom model endpoints or environment variables defined in files
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for env_var, label, prov_id in [
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("DEEPSEEK_API_KEY", "DeepSeek", "deepseek"),
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("GROQ_API_KEY", "Groq", "groq"),
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("OPENAI_BASE_URL", "Custom OpenAI Endpoint", "custom"),
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("OLLAMA_BASE_URL", "Local Ollama", "ollama"),
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("VLLM_BASE_URL", "vLLM Endpoint", "vllm"),
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("VERTEX_AI_PROJECT", "Google Cloud Vertex AI", "vertexai"),
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]:
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val = os.environ.get(env_var)
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if val and val.strip() and not is_placeholder_key(val.strip()):
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configured_providers.append(
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{
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"provider": prov_id,
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"label": label,
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"masked": self._mask_key(val.strip()),
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"raw_key": val.strip(),
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"key": val.strip(),
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}
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)
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api_keys_map[prov_id] = val.strip()
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if ocr_key and not is_placeholder_key(ocr_key):
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api_keys_map["ocr"] = ocr_key.get_secret_value()
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current_active_key = (
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gemini_key.get_secret_value()
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if gemini_key
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else (configured_providers[0]["key"] if configured_providers else "")
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)
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metadata = {
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"configured_count": len(configured_providers),
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"providers": configured_providers,
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"has_ocr_key": ocr_key is not None,
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"current_key": current_active_key,
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"current_gemini_key": gemini_key.get_secret_value() if gemini_key else "",
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"api_keys": api_keys_map,
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}
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# Case 1: Gemini API Key configured (Standard / Recommended)
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if gemini_key:
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masked = self._mask_key(gemini_key.get_secret_value())
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return ProbeResult(
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id=self.probe_id,
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category=self.category,
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title="Multimodal LLM API Key",
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status=ProbeStatus.PASS,
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is_blocker=self.is_blocker,
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summary="Active (Gemini)",
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description=f"Gemini multimodal API credential is active ({masked}) and ready for vision and reasoning.",
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metadata=metadata,
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actions=[
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ProbeAction(
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action_type="hint",
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label="Provider Active",
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payload="Gemini 2.5 Flash / Pro Multimodal Engine is enabled.",
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)
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],
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)
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# Case 2: Other LLM provider configured
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if configured_providers:
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first_p = configured_providers[0]
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return ProbeResult(
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id=self.probe_id,
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category=self.category,
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title="Multimodal LLM API Key",
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status=ProbeStatus.PASS,
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is_blocker=self.is_blocker,
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summary=f"Active ({first_p['label']})",
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description=f"{first_p['label']} multimodal API credential is active ({first_p['masked']}).",
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metadata=metadata,
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actions=[
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ProbeAction(
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action_type="hint",
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label="Provider Active",
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payload=f"{first_p['label']} multimodal vision endpoint is active.",
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)
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],
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)
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# Case 3: No LLM key configured
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return ProbeResult(
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id=self.probe_id,
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category=self.category,
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title="Multimodal LLM API Key",
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status=ProbeStatus.FAIL,
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is_blocker=self.is_blocker,
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summary="Key Missing",
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description="No Multimodal LLM credential (e.g. GEMINI_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY) found in environment or .env file.",
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metadata=metadata,
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actions=[
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ProbeAction(
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action_type="command",
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label="Run Artemis Init",
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payload="artemis init",
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),
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ProbeAction(
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action_type="link",
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label="Get Google AI Studio Key",
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payload="https://aistudio.google.com/app/apikey",
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),
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],
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)
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class VisionOCRProbe(BaseProbe):
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"""Probe verifying optional Google Cloud Vision OCR credentials."""
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@property
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def probe_id(self) -> str:
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return "vision_ocr_key"
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@property
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def category(self) -> ProbeCategory:
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return ProbeCategory.CREDENTIALS
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@property
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def is_blocker(self) -> bool:
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return False
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def _mask_key(self, key_str: str) -> str:
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"""Helper to safely mask an API credential for display."""
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if len(key_str) > 10:
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return f"{key_str[:6]}...{key_str[-4:]}"
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return "***"
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async def probe(self) -> ProbeResult:
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from artemis.utils.ocr_api import is_ocr_configured
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ocr_key = settings.get_api_key("ocr")
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is_configured = is_ocr_configured() and ocr_key is not None
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if is_configured and ocr_key:
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val = ocr_key.get_secret_value()
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masked = self._mask_key(val)
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return ProbeResult(
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id=self.probe_id,
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category=self.category,
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title="Vision OCR API Key (Optional)",
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status=ProbeStatus.PASS,
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is_blocker=False,
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summary="Active & Configured",
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description=f"Google Cloud Vision OCR ({masked}) is active for image text recognition.",
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metadata={"configured": True, "masked_key": masked, "key": val, "raw_key": val},
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actions=[
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ProbeAction(
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action_type="hint",
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label="OCR Enabled",
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payload="OCR is active and will fuse text with UI XML hierarchy.",
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)
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],
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)
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return ProbeResult(
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id=self.probe_id,
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category=self.category,
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title="Vision OCR API Key (Optional)",
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status=ProbeStatus.PASS,
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is_blocker=False,
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summary="Not Configured (Optional)",
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description="OCR_API_KEY is not set. Perception operates seamlessly in pure UI XML hierarchy mode.",
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metadata={"configured": False},
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actions=[
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ProbeAction(
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action_type="hint",
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label="Standard XML Perception",
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payload="Artemis uses pure UI layout parsing and Set-of-Marks visual grounding.",
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)
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],
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)
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