Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use Google ADK Python to build agents, Workflow graphs, tools, runtime services, CLI apps, evaluations, deployments, and ADK repository changes.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use this repo skill when a task involves Google Agent Development Kit for Python (google-adk / google.adk), ADK 2.0 Agent and Workflow APIs, ADK CLI commands, tool integrations, runtime services, evaluation/debugging, or modifying the ADK Python repository itself.
pip install google-adk on Python 3.10+.all by default, such as google-adk[db], google-adk[mcp], google-adk[eval], google-adk[gcp], or google-adk[extensions].python -c "import google.adk; print(google.adk.__version__)" and adk --help.Agent/LlmAgent, instructions, model settings, callbacks, schemas, task/single-turn modes, sub-agents, and multi-agent delegation.Workflow, BaseNode, function nodes, graph edges, JoinNode, dynamic nodes, parallel worker, HITL, retry, checkpoint/resume, and event flow.FunctionTool, ToolContext, toolsets, confirmation/long-running tools, auth, MCP, OpenAPI, Google API/cloud integrations, A2A, and optional extras.Runner, App, sessions, memory, artifacts, plugins, telemetry, code executors, environments, event persistence, and service lifecycles.adk run, adk web, adk api_server, adk test, adk eval, adk deploy, app discovery, YAML config, service URIs, and safe CLI/schema inspection.adk test, adk eval, event summaries, traces, session inspection, flaky evals, and assertion design.repo-skills-router during import.--help, and dry-run diagnostics before starting servers, deployments, cloud calls, model-backed evals, or database migrations.google-adk; diagnose missing modules and install only what the selected workflow requires.Create a simple agent:
from google.adk import Agent
root_agent = Agent(
name="greeter",
model="gemini-2.5-flash",
instruction="Greet the user and answer briefly.",
)
Create a simple workflow:
from google.adk import Workflow
root_agent = Workflow(
name="fruit_flow",
edges=[("START", pick_fruit, explain_benefit)],
)
Run local checks:
python scripts/check_adk_install.py --json
adk --help
adk run path/to/my_agent --help
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
中文简介正在准备中。当前显示原文说明。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
中文简介正在准备中。当前显示原文说明。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
中文简介正在准备中。当前显示原文说明。