ai-agents-using-ADK

ai-agents-using-ADK

A collection of Jupyter notebooks that demonstrate concepts, patterns, and practical examples for building, running, and operating AI agents using an Agent Development Kit (ADK). The repository is organized as lightweight tutorials and reference examples to help you learn about agent architectures, tools, sessions, deployment, and observability.

I inspected the repository and used the notebooks present to draft this README. Below you'll find a short description of each notebook, basic setup and usage instructions, and guidance for contributing or running the examples locally.

Notebooks (contents of this repo)

• Agent_Architectures.ipynb

  • Explores different agent design patterns and architectures: single-agent vs multi-agent setups, internal components (planner, memory, tool use), and example diagrams and code snippets.
  • Link: https://github.com/rajkumar-sankarachinthamani/ai-agents-using-ADK/blob/main/Agent_Architectures.ipynb

• Agent_Tools.ipynb

  • Demonstrates integration of external tools and plugins that agents can call (APIs, local utilities, chain-of-thought tools), plus examples of safe tool invocation and tool response handling.
  • Link: https://github.com/rajkumar-sankarachinthamani/ai-agents-using-ADK/blob/main/Agent_Tools.ipynb

• Agent_Sessions.ipynb

  • Shows session management patterns for agents: conversational state, memory management, session lifecycles, and best practices for storing and resuming context.
  • Link: https://github.com/rajkumar-sankarachinthamani/ai-agents-using-ADK/blob/main/Agent_Sessions.ipynb

• Agent_Observability.ipynb

  • Demonstrates how to instrument and observe agent behavior: logging, tracing, metrics, dashboards, and examples of recording conversations, actions, and tool calls for debugging and monitoring.
  • Link: https://github.com/rajkumar-sankarachinthamani/ai-agents-using-ADK/blob/main/Agent_Observability.ipynb

• Agent_Deployment.ipynb

  • Covers deployment patterns for agents: containerization, exposing agent endpoints, scaling considerations, and sample deployment workflows (local and cloud).
  • Link: https://github.com/rajkumar-sankarachinthamani/ai-agents-using-ADK/blob/main/Agent_Deployment.ipynb

Getting started

These notebooks are intended to be run interactively. Recommended steps to get started locally:

1. Clone the repository

   • git clone https://github.com/rajkumar-sankarachinthamani/ai-agents-using-ADK.git
   • cd ai-agents-using-ADK

2. Create and activate a Python virtual environment

   • python3 -m venv .venv
   • source .venv/bin/activate (Linux/macOS)
   • .venv\Scripts\activate (Windows PowerShell)

3. Install dependencies

   • If a requirements.txt is present, install it:
     • pip install -r requirements.txt
   • If not present, install typical notebook and developer dependencies:
     • pip install jupyterlab notebook ipykernel pandas numpy matplotlib
   • Add any ADK-specific SDKs or packages required by the notebooks (check the first cells of each notebook for exact imports and packages to install).

4. Start Jupyter

   • jupyter lab
   • or jupyter notebook

5. Open and run the notebooks in your browser. Run the top cells first to ensure package imports and any API keys or credentials are configured.

Notes:

• Some notebooks may require API keys or credentials (for example, cloud providers, LLM APIs, or monitoring backends). Check each notebook's top cell for required environment variables or configuration steps.
• For reproducibility, consider creating an IPython kernel for this environment:
  • python -m ipykernel install --user --name=ai-agents-adk

Practical tips & environment variables

• Keep credentials out of notebooks. Use environment variables (e.g., export OPENAI_API_KEY=...) or a local .env file loaded at runtime.
• If a notebook integrates with a logging/observability backend (Prometheus, Datadog, OpenTelemetry), verify and configure endpoints and API keys before running observability examples.
• For deployment examples, Docker and Kubernetes CLIs may be required. Install Docker Desktop and kubectl if you want to try container/deployment notebooks.

Suggested workflow

• Start with Agent_Architectures.ipynb to understand the design patterns.
• Move to Agent_Tools.ipynb and Agent_Sessions.ipynb to see how agents interact with tools and persist context.
• Use Agent_Observability.ipynb to add instrumentation and monitoring to your agent.
• Finally, review Agent_Deployment.ipynb to learn recommended approaches for running agents in production or at scale.

Contributing

Contributions are welcome! Suggested ways to contribute:

• Add missing requirements or pin package versions (create a requirements.txt).
• Add small runnable examples for each notebook (clear setup cells and sample inputs).
• Improve documentation with step-by-step guides or a demo script that runs an example end-to-end.
• Open issues for bugs, enhancements, or questions.

When contributing:

• Fork the repository and create a branch with a descriptive name.
• Include notebooks with clean, rerunnable cells and no sensitive keys or tokens.
• Open a pull request describing the changes, linked to any relevant issue.

Troubleshooting

• If a notebook fails on import, check the first cells for required packages and install them into your active venv.
• If an API call fails, verify that required environment variables or credentials are set and that network access is available.
• If longer-running cells time out in Jupyter, increase the kernel timeout or run the heavy work in a separate script.

License

Specify the project license here (e.g., MIT, Apache-2.0). If the repository already has a LICENSE file, follow that license. If not present, add one before distributing widely.

Acknowledgements

• This repository collects demos and examples; credit goes to the maintainers and contributors who assembled the notebooks.
• If these notebooks are based on or inspired by a particular ADK or vendor SDK, include that vendor's name and links here.

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