# Useful but Little-Known Features of ADK 2.0 🌍 What if you could define AI agents with just a YAML file — no Python code required? ADK 2.0's Agent Config lets you declaratively define agents in YAML or JSON configuration files. Build, run, and modify agents without writing code. 📌 Title: Agent Config (Declarative Agent Definition) 🔗 URL: 🧩 Overview Agent Config lets you declaratively specify an agent's name, model, description, and instructions in YAML or JSON. You can define tools (built-in like google_search, or custom), and sub-agents (referencing other config files via config_path). It supports LlmAgent; the LangGraphAgent and A2aAgent types are not yet supported. Configured agents can be executed via `adk web`, `adk run`, `adk api_server`, or programmatically through `from_config()`. 🛠 How to use it Define your agent in a YAML file and run it with the ADK CLI. ```yaml # root_agent.yaml name: research_assistant model: gemini-2.0-flash description: An agent that assists with research instruction: | Use the search tool to investigate the latest information for the user's question and provide a clear answer. tools: - name: google_search sub_agents: - config_path: ./summarizer.yaml ``` ```bash # Run via CLI adk web # Interactive browser UI adk run # Terminal execution ``` For programmatic usage, generate an agent object with `from_config()`. 🏗 Building it into production ・Manage agent definitions as YAML files in Git for full change history tracking ・Maintain separate config files per environment (dev/staging/production) ・Use config_path in sub_agents to build reusable agent modules ・Deploy config-based agents as APIs using `adk api_server` 💡 Use cases 📝 Non-engineers can modify agent behavior by editing YAML alone 🔄 Version-controlled agent definitions with automated CI/CD deployment 🧩 Reuse common sub-agents across multiple projects 🚀 Consistent configuration management from prototype to production ⚠️ Watch out This feature is currently experimental. It only supports Gemini models — LangGraphAgent and A2aAgent are not yet supported. For complex custom logic or callbacks, the traditional Python code-based approach remains more appropriate. The specification may change in future versions, so check the latest documentation before production adoption. ✨ Agent Config dramatically lowers the barrier to defining and operating agents. It's especially effective for team collaboration and streamlined configuration management. #ADK# #AIAgent#