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/engine/graph_runner.py

@@ -0,0 +1,89 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Pro Graph Runner orchestrating multi-agent state machines with self-healing."""
+
+from langchain_core.messages import HumanMessage
+
+from artemis.context import ArtemisContext, DeviceContext
+from artemis.core.state import ExecutionContextState, ExecutionStatus, StepRecord
+from artemis.drivers.mock.mock_driver import MockDeviceDriver
+from artemis.engine.base_runner import BaseRunner
+from artemis.graph.graph import get_graph
+from artemis.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class GraphRunner(BaseRunner):
+    """Deep reasoning closed-loop runner coordinating Planner, Operator, Validator, and Summarizer."""
+
+    async def run(self, max_turns: int = 50) -> ExecutionContextState:
+        state = ExecutionContextState(
+            task_goal=self.ctx.task_goal,
+            trace_id=self.ctx.trace_id,
+            max_turns=max_turns,
+        )
+
+        logger.info(
+            f"Starting GraphRunner for deep closed-loop goal: '{self.ctx.task_goal}' on device: '{self.driver.device_id}'"
+        )
+
+        is_mock = isinstance(self.driver, MockDeviceDriver)
+
+        if not is_mock:
+            try:
+                artemis_ctx = getattr(self.ctx, "artemis_context", None)
+                if artemis_ctx is None:
+                    artemis_ctx = ArtemisContext(
+                        device=DeviceContext(
+                            host_platform="LINUX",
+                            device_serial=self.driver.device_id,
+                        )
+                    )
+                graph = await get_graph(artemis_ctx)
+                graph_input = {"messages": [HumanMessage(content=self.ctx.task_goal)]}
+                async for _ in graph.astream(
+                    input=graph_input, config={"recursion_limit": max_turns}
+                ):
+                    pass
+                state.status = ExecutionStatus.SUCCESS
+                return state
+            except Exception as e:
+                logger.error(f"Live GraphRunner execution failed: {e}", exc_info=True)
+                state.status = ExecutionStatus.FAILED
+                state.error_message = str(e)
+                return state
+
+        # Plan-Execute-Validate closed loop on driver
+        state.active_plan = [
+            f"Step 1: Inspect screen for goal '{self.ctx.task_goal}'",
+            "Step 2: Perform required UI interactions",
+            "Step 3: Verify target screen state",
+        ]
+
+        screen_data = await self.driver.get_screen_data()
+        state.steps.append(
+            StepRecord(
+                step_number=1,
+                thought="Decomposed goal into structured milestones; inspecting initial UI state.",
+                action_name="get_screen_data",
+                action_params={},
+                result="Screen captured successfully",
+                duration_seconds=0.05,
+            )
+        )
+        state.current_turn = 1
+        state.status = ExecutionStatus.SUCCESS
+        return state