In today’s AI-driven world, building intelligent, modular, and scalable systems is easier than ever—thanks to ADK (Agent Development Kit) by Google. Whether you’re working on a travel assistant, customer support bot, or any multi-agent system, ADK gives you the tools to make it real.
Let’s walk you through how to use ADK step-by-step in your project.
What is ADK?
ADK (Agent Development Kit) is an open-source Python framework designed for creating intelligent agents and multi-agent workflows. It supports LLM-powered agents, tool usage, agent-to-agent communication, and more—all ready for real-world deployment.
Step 1: Install ADK & Setup Project Structure
Start by installing the ADK package using pip.
Once installed, structure your project like this:
- agents/ – Includes different agents like host_agent, flight_agent, etc.
- common/ – Shared logic or tools
- shared/ – Configs or utilities
- streamlit_app.py – Optional frontend
- .well-known/agent.json – Metadata file for each agent
A clean structure helps in scalability and debugging later.
Step 2: Define Tools for Each Agent
Tools are just Python functions that give your agent the ability to perform real tasks (like checking the weather, searching flights, etc.).
Each tool should include a clear docstring so the agent’s LLM knows exactly when and how to use it.
Step 3: Create Agents Using ADK
Use ADK’s built-in Agent class to define your agents.
Each agent requires:
- A unique name
- An LLM model (like Gemini, GPT-4o, Claude)
- A description
- Instructions on how the agent should behave
- A list of tools it can use
You can also add sub-agents to form a complete workflow system.
Step 4: Orchestrate Multi-Agent Workflows
ADK allows you to create:
- LLM Agents (single use-case)
- Workflow Agents (sequence or parallel operations)
- Custom Agents (combine logic as needed)
Use a host agent to control the flow and delegate tasks to sub-agents like flight_agent or stay_agent.
Step 5: Enable Agent-to-Agent Communication (A2A)
To allow agents to communicate:
- Expose a /run endpoint via HTTP
- Include metadata in .well-known/agent.json
This enables agents to work together or be called from other apps or services.
Step 6: Run and Test Your Agents
You can test agents in 3 ways:
- CLI Command: Run agents with adk run
- Web UI: Visual interface via adk web
- Programmatically: Use ADK’s Runner class to run agents in Python
Perfect for debugging and quick testing.
Step 7: Add State, Memory & Safety Controls
ADK supports session state, meaning agents can remember past inputs across sessions.
Add callbacks to control how agents use tools or interact with users. This ensures reliability and safety in your agent’s responses.
Step 8: Create a Frontend (Optional)
Build a frontend using tools like Streamlit or any web framework.
This allows real users to chat with your host agent, ask questions, and receive structured results—all live.
Example Use Case: Travel Assistant
Imagine this setup:
- host_agent: Manages the conversation
- flight_agent: Finds flights
- stay_agent: Recommends hotels
- activities_agent: Suggests local activities
When a user asks for a trip plan, the host agent coordinates the response using all other agents.
Best Practices
Use descriptive names for your agents
Write helpful docstrings for tools
Keep logic modular for easier scaling
Use ADK’s Web UI & CLI for rapid testing
Conclusion
ADK is powerful, modular, and developer-friendly. By using it, you can create LLM agents that are not only smart—but also secure, collaborative, and ready for production.
Start small, build one agent, then scale it into a system. That’s the ADK way.
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