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

This reverts commit 54fcef9887.

Please do not submit unrelated docs and codes in the same commit. I am reverting this commit so that the related files can be submitted in better-organized future commits.

artemis/sdk/examples/video_transcription_example.py

@@ -0,0 +1,110 @@
+# 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.
+
+"""Video Transcription Example
+
+This example demonstrates how to use the video recording tools to capture
+and analyze video content from a mobile device screen.
+
+The agent can:
+1. Start a screen recording
+2. Perform actions while recording
+3. Stop the recording and analyze its content using Gemini models
+
+Use case: Recording a video playing on the screen and transcribing its content.
+"""
+
+import asyncio
+
+from artemis.config import LLM, LLMConfig, LLMConfigUtils, LLMWithFallback
+from artemis.sdk.agent import Agent
+from artemis.sdk.builders.agent_config_builder import AgentConfigBuilder
+from artemis.sdk.types.agent import AgentConfig
+from artemis.sdk.types.task import AgentProfile, TaskRequest
+
+
+def get_video_capable_llm_config() -> LLMConfig:
+    """Returns an LLM config with video_analyzer configured.
+
+    The video_analyzer must use a video-capable Gemini model:
+    - gemini-3.8-flash (recommended - fast and capable)
+    - gemini-3.7-flash
+    - gemini-3-pro-preview
+    - gemini-2.5-flash
+    - gemini-2.5-pro
+    - gemini-2.0-flash
+    """
+    return LLMConfig(
+        planner=LLMWithFallback(
+            provider="openai",
+            model="gpt-5-nano",
+            fallback=LLM(provider="openai", model="gpt-5-mini"),
+        ),
+        utils=LLMConfigUtils(
+            outputter=LLMWithFallback(
+                provider="openai",
+                model="gpt-5-nano",
+                fallback=LLM(provider="openai", model="gpt-5-mini"),
+            ),
+            hopper=LLMWithFallback(
+                provider="openai",
+                model="gpt-5-nano",
+                fallback=LLM(provider="openai", model="gpt-5-mini"),
+            ),
+            video_analyzer=LLMWithFallback(
+                provider="google",
+                model="gemini-3.8-flash",
+                fallback=LLM(provider="google", model="gemini-3.7-flash"),
+            ),
+        ),
+    )
+
+
+async def main():
+    config: AgentConfig = (
+        AgentConfigBuilder()
+        .add_profile(
+            AgentProfile(
+                name="VideoCapable",
+                llm_config=get_video_capable_llm_config(),
+            )
+        )
+        .with_video_recording_tools()
+        .build()
+    )
+
+    agent = Agent(config=config)
+    try:
+        await agent.init()
+
+        result = await agent.run_task(
+            request=TaskRequest(
+                goal="""
+                1. Open YouTube app
+                2. Search for "Python tutorial"
+                3. Start recording the screen
+                4. Play the first video
+                5. Wait for the first 30 seconds of the video to play
+                6. Stop recording and tell me what was said in the video
+                """,
+                profile="VideoCapable",
+            )
+        )
+        print(f"Task result: {result}")
+    finally:
+        await agent.clean()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())