somew1nd
9a5aaf99b7
- Showcase UI Reorganization: * Move web UI dashboard from root directory into apps/showcase_ui/ for cleaner project structure * Add 'artemis ui' CLI command (artemis/interfaces/cli/commands/ui.py) and launcher scripts for the interactive showcase console - Standalone MCP Server & Notifiers (mcp_server/): * Implement dedicated MCP server package with mobile automation tools (device_state, inspect_trace, task_manager, task_runner) * Add multi-channel notifiers (AgentAPI, Desktop notifications, Webhooks, File output, and Composite routing) * Introduce background task runner and trace store for autonomous mobile execution monitoring - Cross-Platform Runtime & Toolchain Support: * Add artemis/platform/ with OS-specific implementations for Linux, macOS (Darwin), and Windows * Add artemis/runtime/supervisor.py for resilient process and agent runtime supervision * Add artemis/toolchain/ with dependency resolution and SDK descriptors * Add cross-platform launcher scripts (start.sh, start.bat) and installer scripts (scripts/install_deps.*) - Core Enhancements & Diagnostics: * Enhance coordinate normalization, OCR API fallback, and perception pipeline * Add credential validation (credentials_validator.py) and system diagnostics * Update bilingual documentation (README.md, README_CN.md) and architecture diagrams - Test Suite Expansion: * Add integration tests for cloud gateway, remote ADB, and Artemis login * Add unit tests covering core diagnostics, platform detection, toolchain, credentials validator, optional OCR, and MCP tools
111 lines
3.4 KiB
Python
111 lines
3.4 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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"""Video Transcription Example
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This example demonstrates how to use the video recording tools to capture
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and analyze video content from a mobile device screen.
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The agent can:
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1. Start a screen recording
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2. Perform actions while recording
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3. Stop the recording and analyze its content using Gemini models
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Use case: Recording a video playing on the screen and transcribing its content.
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"""
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import asyncio
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from artemis.config import LLM, LLMConfig, LLMConfigUtils, LLMWithFallback
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from artemis.sdk.agent import Agent
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from artemis.sdk.builders.agent_config_builder import AgentConfigBuilder
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from artemis.sdk.types.agent import AgentConfig
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from artemis.sdk.types.task import AgentProfile, TaskRequest
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def get_video_capable_llm_config() -> LLMConfig:
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"""Returns an LLM config with video_analyzer configured.
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The video_analyzer must use a video-capable Gemini model:
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- gemini-3.7-flash (recommended - fast and capable)
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- gemini-3.6-flash
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- gemini-3-pro-preview
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- gemini-2.5-flash
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- gemini-2.5-pro
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- gemini-2.0-flash
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"""
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return LLMConfig(
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planner=LLMWithFallback(
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provider="openai",
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model="gpt-5-nano",
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fallback=LLM(provider="openai", model="gpt-5-mini"),
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),
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utils=LLMConfigUtils(
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outputter=LLMWithFallback(
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provider="openai",
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model="gpt-5-nano",
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fallback=LLM(provider="openai", model="gpt-5-mini"),
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),
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hopper=LLMWithFallback(
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provider="openai",
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model="gpt-5-nano",
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fallback=LLM(provider="openai", model="gpt-5-mini"),
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),
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video_analyzer=LLMWithFallback(
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provider="google",
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model="gemini-3.7-flash",
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fallback=LLM(provider="google", model="gemini-3.6-flash"),
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),
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),
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)
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async def main():
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config: AgentConfig = (
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AgentConfigBuilder()
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.add_profile(
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AgentProfile(
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name="VideoCapable",
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llm_config=get_video_capable_llm_config(),
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)
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)
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.with_video_recording_tools()
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.build()
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)
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agent = Agent(config=config)
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try:
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await agent.init()
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result = await agent.run_task(
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request=TaskRequest(
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goal="""
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1. Open YouTube app
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2. Search for "Python tutorial"
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3. Start recording the screen
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4. Play the first video
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5. Wait for the first 30 seconds of the video to play
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6. Stop recording and tell me what was said in the video
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""",
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profile="VideoCapable",
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)
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)
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print(f"Task result: {result}")
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finally:
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await agent.clean()
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if __name__ == "__main__":
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asyncio.run(main())
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