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
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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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from collections.abc import Callable
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import inspect
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import time
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from typing import Annotated, Any, get_args, get_origin
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from unittest.mock import MagicMock, Mock
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import uuid
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from langchain_core.tools import BaseTool
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from langchain_core.tools.base import InjectedToolCallId
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from langgraph.prebuilt import InjectedState
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from langgraph.types import Command
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from pydantic import BaseModel
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from artemis.constants import VALIDATOR_MESSAGES_KEY
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from artemis.context import ArtemisContext
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from artemis.data_engine import engine as engine_mod
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from artemis.data_engine.trace import CURRENT_TRACE_ID, smart_serialize
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from artemis.graph.state import State
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from artemis.tools.types import CyFunctionDetector
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class ToolWrapper(BaseModel):
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"""Wrapper holding a tool factory and lifecycle callbacks."""
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model_config = {"ignored_types": (CyFunctionDetector,)}
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tool_fn_getter: Callable[[ArtemisContext], BaseTool]
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on_success_fn: Callable[..., str]
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on_failure_fn: Callable[..., str]
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class CompositeToolWrapper(ToolWrapper):
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"""Wrapper holding a composite tool factory and lifecycle callbacks."""
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composite_tools_fn_getter: Callable[[ArtemisContext], list[BaseTool]]
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# pylint: disable=too-many-branches,too-many-locals,too-many-statements
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async def invoke_tool_with_injection(
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tool: BaseTool,
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args: dict,
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tool_call_id: str,
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state: State | None = None,
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record_trace: bool = True,
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) -> Any:
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"""Invokes a LangChain tool, manually injecting arguments marked with
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InjectedState or InjectedToolCallId if calling the underlying function
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directly.
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Automatically records type='tool' trace in DataEngine.
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"""
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final_args = dict(args)
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if isinstance(tool, (Mock, MagicMock)):
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has_real_coro = hasattr(tool, "coroutine") and not isinstance(
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tool.coroutine, (Mock, MagicMock)
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)
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has_real_func = hasattr(tool, "func") and not isinstance(tool.func, (Mock, MagicMock))
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if not has_real_coro and not has_real_func:
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if (
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state is not None
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and hasattr(tool, "args")
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and isinstance(tool.args, dict)
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and "state" in tool.args
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):
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final_args["state"] = state
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return await tool.ainvoke(final_args)
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# For StructuredTool, the original function might be in tool.func or tool.coroutine
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func = None
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if hasattr(tool, "coroutine") and tool.coroutine:
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func = tool.coroutine
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elif hasattr(tool, "func") and tool.func:
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func = tool.func
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if func:
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try:
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sig = inspect.signature(func)
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for param_name, param in sig.parameters.items():
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annotation = param.annotation
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# Handle Annotated types
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if get_origin(annotation) is Annotated:
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args_list = get_args(annotation)
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if InjectedState in args_list:
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final_args[param_name] = state
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elif InjectedToolCallId in args_list:
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final_args[param_name] = tool_call_id
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except Exception: # pylint: disable=broad-exception-caught
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# If inspection fails, fall back to just using provided args
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pass
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trace_id = uuid.uuid4()
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parent_id = CURRENT_TRACE_ID.get()
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token = CURRENT_TRACE_ID.set(trace_id)
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start_time = time.time()
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live_engine = getattr(engine_mod, "_CURRENT_DATA_ENGINE", None)
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if record_trace and live_engine:
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step_id = getattr(live_engine, "current_step_id", None)
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live_engine.record_trace(
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type="tool",
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name=tool.name,
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payload={"args": {k: smart_serialize(v) for k, v in args.items()}},
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status="running",
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parent_trace_id=parent_id,
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step_id=step_id,
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trace_id=trace_id,
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)
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execution_status = "running"
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execution_result = None
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execution_error = None
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try:
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# Call the function
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if inspect.iscoroutinefunction(func):
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result = await func(**final_args)
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else:
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result = func(**final_args)
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execution_status = "success"
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execution_result = result
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return result
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except Exception as e:
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execution_status = "failed"
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execution_error = e
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raise e
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finally:
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duration = time.time() - start_time
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if record_trace and live_engine:
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step_id = getattr(live_engine, "current_step_id", None)
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payload = {"args": {k: smart_serialize(v) for k, v in args.items()}}
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if execution_status == "success":
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payload["result"] = smart_serialize(get_tool_result_content(execution_result))
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elif execution_status == "failed":
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payload["error"] = str(execution_error)
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else:
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execution_status = "terminated"
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payload["error"] = "Execution terminated unexpectedly."
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live_engine.record_trace(
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type="tool",
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name=tool.name,
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payload=payload,
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status=execution_status,
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duration=duration,
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parent_trace_id=parent_id,
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step_id=step_id,
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trace_id=trace_id,
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)
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CURRENT_TRACE_ID.reset(token)
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else:
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# Fallback to ainvoke if no direct func available
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return await tool.ainvoke(final_args)
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def get_tool_result_content(result: Any) -> Any:
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"""Extracts the content from a tool result, handling Command objects."""
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if isinstance(result, Command):
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updates = result.update
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if VALIDATOR_MESSAGES_KEY in updates:
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msgs = updates[VALIDATOR_MESSAGES_KEY]
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if msgs and hasattr(msgs[0], "content"):
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return msgs[0].content
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return result
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return result
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