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adk-expert

Orchestrate AI agents with Google ADK 2.0 for log analysis.

Updated Jan 10, 2026
Search Tags:#workflow#python#agent#orchestration#adk#duckdb#loglensai
One-click install
npx skills add https://github.com/serguei9090/Agentic-Governance-Framework --skill adk-expert
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: adk-expert
Source: https://github.com/serguei9090/Agentic-Governance-Framework/tree/main/MasterPrompts/agent/.agents/skills/adk-expert
Command: npx skills add https://github.com/serguei9090/Agentic-Governance-Framework --skill adk-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

ADK expert enables building and orchestrating AI agents with ADK 2.0 to automate complex log analysis and tool coordination within the LogLensAi sidecar.

Core Features & Use Cases

  • Graph-based agent orchestration using LlmAgent, Runner, and SessionService for end-to-end workflows.
  • Tool integration by binding Python functions and databases (e.g., DuckDB) as agent tools for diagnostics.
  • Debugging and migration guidance for ADK 2.0, including pattern references and implementation tips.

Quick Start

Create a minimal LlmAgent, attach it to a Runner, and run a diagnostic session.

Frequently Asked Questions about adk-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate AI agents using Google ADK 2.0 in Python?▼

Agent orchestration with Google ADK 2.0 involves configuring an LlmAgent, attaching it to a Runner, and managing sessions via SessionService to execute end-to-end workflows across graph-based diagnostics.

How do I bind a Python function or DuckDB database as an agent tool?▼

Binding Python functions and DuckDB databases as agent tools requires well-typed Python tool definitions integrated within the LlmAgent configuration to enable automated diagnostics and data queries.

What is the best way to debug multi-tool agent workflows in a sidecar environment?▼

Debugging multi-tool agent workflows in a sidecar environment is supported through pattern references and implementation tips that ensure predictable orchestration, session management, and error handling.

Do I need specific session management configurations for predictable agent orchestration?▼

Predictable agent orchestration requires explicit session management using SessionService alongside a minimal agent configuration to coordinate multi-tool workflows and handle errors effectively.

How does graph-based agent orchestration work with LlmAgent and Runner?▼

Graph-based agent orchestration works by defining an LlmAgent, attaching it to a Runner, and utilizing SessionService to manage state across complex diagnostic workflows and tool integrations.

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