refactor: clean up legacy code, rename hopper to entity_extractor, and add third-party notices

- Add NOTICE file and README license references for mobile-use and finalrun-agent
- Add derivative attribution headers to 17 files derived from mobile-use
- Rename hopper agent to entity_extractor across prompts, tools, and configs
- Remove obsolete adb_tunnel, ApiBaseUrl, and legacy CLI utility scripts
- Replace legacy examples with native Artemis diagnostic and workflow examples
- Update unit and tool tests to validate entity_extractor and SDK builders

artemis/agents/entity_extractor/entity_extractor.py

@@ -0,0 +1,90 @@
+# 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.
+
+"""Lightweight entity extraction and package resolver for ARTEMIS.
+
+Dispatches bounded LLM extraction turns to identify application package identifiers
+and configuration attributes from raw device listings without manual parsing heuristics.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import cast
+
+from jinja2 import Template
+from langchain_core.messages import HumanMessage, SystemMessage
+from pydantic import BaseModel, Field
+
+from artemis.context import ArtemisContext
+from artemis.data_engine.trace import trace
+from artemis.services.llm import get_llm, invoke_llm_with_timeout_message, with_fallback
+from artemis.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class ExtractionResult(BaseModel):
+    """Structured extraction outcome for entity resolution."""
+
+    found: bool = Field(description="True if the requested entity was definitively identified.")
+    output: str | None = Field(default=None, description="Exact extracted identifier or string value.")
+    reason: str = Field(description="Brief justification of the match criteria applied.")
+
+
+@trace(type="agent", name="entity_extractor")
+async def extract_entity(
+    ctx: ArtemisContext,
+    request: str,
+    data: str,
+    use_fallback: bool = True,
+) -> ExtractionResult:
+    """Extract a specific identifier or entity from raw batch device data."""
+    logger.info(f"Invoking Artemis Entity Extractor (fallback={use_fallback})")
+    template_path = Path(__file__).parent / "entity_extractor.md"
+    system_prompt = Template(template_path.read_text(encoding="utf-8")).render()
+
+    messages = [
+        SystemMessage(content=system_prompt),
+        HumanMessage(content=f"Target Query:\n{request}\n\nCandidate Raw Dataset:\n{data}"),
+    ]
+
+    primary_llm = get_llm(ctx=ctx, name="entity_extractor", is_utils=True).with_structured_output(
+        ExtractionResult
+    )
+
+    try:
+        if use_fallback:
+            fallback_llm = get_llm(
+                ctx=ctx, name="entity_extractor", is_utils=True, use_fallback=True
+            ).with_structured_output(ExtractionResult)
+            raw = await with_fallback(
+                main_call=lambda: invoke_llm_with_timeout_message(primary_llm.ainvoke(messages)),
+                fallback_call=lambda: invoke_llm_with_timeout_message(fallback_llm.ainvoke(messages)),
+            )
+        else:
+            raw = await invoke_llm_with_timeout_message(primary_llm.ainvoke(messages))
+
+        if isinstance(raw, ExtractionResult):
+            return raw
+        if isinstance(raw, dict):
+            return ExtractionResult.model_validate(raw)
+        return cast(ExtractionResult, raw)
+    except Exception as exc:
+        logger.error(f"Entity extraction turn failed: {exc}")
+        return ExtractionResult(
+            found=False,
+            output=None,
+            reason=f"LLM extraction error: {str(exc)[:400]}",
+        )