Introduction to ADK

Building Reliable & Scalable AI Agents

Agent Development Kit (ADK)

What is ADK?

ADK

Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is:

1. model-agnostic, deployment-agnostic, and
2. is built for compatibility with other frameworks

Where does ADK fit in the GenAI Project?

ADK fits in the “Application Integration” phase of the GenAI Project Lifecycle.

ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.

• Before diving into tools, we must understand the lifecycle.
• ADK is primarily for the “Application Integration” phase, but helps with “Evaluate” and “Select” too.

Features

1. Flexible Orchestration
2. Multi-Agent Architecture
3. Rich Tool Ecosystem
4. Deployment Ready
5. Built-in Evaluation
6. Building Safe and Secure Agents

• Flexible Orchestration:
  Define workflows using workflow agents (Sequential, Parallel, Loop) for predictable execution pipelines, or leverage LLM-driven dynamic routing (LlmAgent transfer) for more adaptive behavior.

• Multi-Agent Architecture:
  Compose multiple specialized agents in a hierarchy to build modular, scalable applications. This enables complex coordination, delegation, and specialization of responsibilities.

• Rich Tool Ecosystem:
  Agents can access a broad set of capabilities: use pre-built tools (Search, Code Exec), create custom Python functions, integrate with 3rd-party libraries, or even designate other agents as tools.

• Deployment Ready:
  Containerize and deploy agents anywhere: run locally, scale to production with Vertex AI Agent Engine, or integrate into existing infrastructure with Cloud Run or Docker.

• Built-in Evaluation:
  ADK enables systematic evaluation of agents—not just the final answer, but the full execution trace—against predefined test cases, ensuring both quality and reliability.

• Building Safe and Secure Agents:
  Adopt battle-tested patterns and security/safety best practices to build trustworthy agents, with controls for privacy, cost, and safe tool usage.

Framework Comparison

Feature	ADK	LangChain
Philosophy	Engineering-first	Prototyping-first
Modularity	High	Mixed
Ecosystem	Integrated (Google/Vertex)	Broad (Community-driven)
Stability	Enterprise-focused APIs	Fast-moving, breaking changes

• LangChain is great for quick experiments and vast integrations.
• ADK is better for building production-grade, maintainable systems, especially in the Google ecosystem.

No-Code Agent Design

Visual Builder

• Drag-and-Drop: Design workflows visually.
• AI Assistant: Start building with natural language.
• Exportable: Generates Python/Java code for your project.

• New experimental feature.
• Lowers barrier to entry. Great for prototyping.

Get started

See Python Quick Start for ADK

• Install dependencies.
• Set up API keys.
• Run your first agent.

• Let’s get our hands on the keyboard.
• Follow the guide.
• Ensure everyone has a working environment.

Coding with AI

• LLM-Optimized Docs: Supports /llms.txt.
• Integration:
  • Cursor: Add docs directly to context.
  • Gemini CLI: Query docs from terminal.
• Benefit: AI assistants understand ADK perfectly.

• ADK is built for AI, with AI.
• Using llms.txt makes your coding partner smarter.

Core Concepts

ADK Components

1. Core Reasoning & Identity
2. Context & History
3. Capabilities & Actions
4. Coordination & Control

See: https://google.github.io/adk-docs/get-started/about/

1. Core Reasoning & Identity

• Agents (The Specialist): The fundamental unit. An agent is a persona designed for a specific job (e.g., a “Travel Agent” or “Coding Assistant”).
• Models (The Intelligence): The underlying LLM (like Gemini). This is the “IQ” of the agent that allows it to understand language and reason through problems.

Analogy: If the Agent is a doctor, the Model is the medical degree and years of study in their head.

2. Context & History

• Events (The Transcript): A chronological record of everything that happened—what the user said, what the agent thought, and what the tools returned.
• Sessions (The Appointment): A specific instance of interaction. It groups events together so the agent knows who it is talking to right now.

Relating Topics: Sessions are the “folders” that contain the Events.

• Memory (The Patient File): Unlike a session (which might end), Memory persists. It allows the agent to remember your name or preferences across different sessions weeks apart.

3. Capabilities & Actions

• Tools (The Toolbox): External functions the agent can call, like a calculator, a Google Search API, or a database query.
• Code Execution (The Sandbox): A specific tool that allows the agent to write and run code (Python) to solve math or data problems that are too hard for “talk” alone.
• Artifacts (The Briefcase): These handle files. If the agent generates a PDF or needs to read a CSV, that file is an Artifact.

Connecting the Dots: If an agent needs to analyze a budget, it uses Tools to read the Artifact (file), and uses Code Execution to calculate the totals.

4. Coordination & Control

• Planning (The Blueprint): For complex asks, the agent doesn’t just “guess.” It creates a step-by-step plan before acting.
• Runner (The Orchestrator): This is the engine that actually executes the plan, feeding information between the Model, Tools, and Memory.
• Callbacks (The Status Reports): Hooks that trigger at specific moments (e.g., “Agent started thinking” or “Tool failed”).

Example: If a tool fails, a Callback catches the error so the Runner can ask the Model for a new Plan.

Key Takeaways

1. Agents are the building blocks of ADK.
2. Models power reasoning.
3. Events form the conversation history.
4. Tools extend their capabilities.
5. Sessions manage context.
6. Memory keeps long-term information.
7. Planning breaks down complex goals.
8. Runner coordinates the execution flow.
9. Callbacks help with logging and error handling.
10. Artifacts handle files.
11. Code Execution allows agents to generate and execute code.

See: Core Concepts & Key Capabilities