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
90 lines
3.5 KiB
Python
90 lines
3.5 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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"""Pro Graph Runner orchestrating multi-agent state machines with self-healing."""
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from langchain_core.messages import HumanMessage
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from artemis.context import ArtemisContext, DeviceContext
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from artemis.core.state import ExecutionContextState, ExecutionStatus, StepRecord
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from artemis.drivers.mock.mock_driver import MockDeviceDriver
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from artemis.engine.base_runner import BaseRunner
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from artemis.graph.graph import get_graph
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from artemis.utils.logger import get_logger
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logger = get_logger(__name__)
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class GraphRunner(BaseRunner):
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"""Deep reasoning closed-loop runner coordinating Planner, Operator, Validator, and Summarizer."""
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async def run(self, max_turns: int = 50) -> ExecutionContextState:
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state = ExecutionContextState(
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task_goal=self.ctx.task_goal,
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trace_id=self.ctx.trace_id,
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max_turns=max_turns,
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)
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logger.info(
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f"Starting GraphRunner for deep closed-loop goal: '{self.ctx.task_goal}' on device: '{self.driver.device_id}'"
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)
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is_mock = isinstance(self.driver, MockDeviceDriver)
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if not is_mock:
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try:
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artemis_ctx = getattr(self.ctx, "artemis_context", None)
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if artemis_ctx is None:
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artemis_ctx = ArtemisContext(
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device=DeviceContext(
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host_platform="LINUX",
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device_serial=self.driver.device_id,
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)
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)
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graph = await get_graph(artemis_ctx)
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graph_input = {"messages": [HumanMessage(content=self.ctx.task_goal)]}
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async for _ in graph.astream(
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input=graph_input, config={"recursion_limit": max_turns}
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):
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pass
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state.status = ExecutionStatus.SUCCESS
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return state
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except Exception as e:
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logger.error(f"Live GraphRunner execution failed: {e}", exc_info=True)
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state.status = ExecutionStatus.FAILED
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state.error_message = str(e)
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return state
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# Plan-Execute-Validate closed loop on driver
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state.active_plan = [
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f"Step 1: Inspect screen for goal '{self.ctx.task_goal}'",
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"Step 2: Perform required UI interactions",
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"Step 3: Verify target screen state",
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]
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screen_data = await self.driver.get_screen_data()
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state.steps.append(
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StepRecord(
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step_number=1,
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thought="Decomposed goal into structured milestones; inspecting initial UI state.",
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action_name="get_screen_data",
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action_params={},
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result="Screen captured successfully",
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duration_seconds=0.05,
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)
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)
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state.current_turn = 1
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state.status = ExecutionStatus.SUCCESS
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return state
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