Build with ADK streamlines the creation of AI agents by providing a CLI scaffolding tool, workflow definitions, LLM integration modules, testing utilities, logging, and deployment support. Developers can initialize agent projects, select AI models, configure prompts, connect external tools or APIs, run local tests, and push their agents to production or container platforms—all with simple commands. The modular architecture allows easy extension with plugins and supports multiple programming languages for maximum flexibility.

Defang is an AI-enabled cloud deployment tool that allows developers to easily and securely deploy applications to their cloud of choice using a single command. It transforms any Docker Compose-compatible project into a live deployment instantly, provides AI-guided debugging, and supports any programming language or framework. Whether you use AWS, GCP, or DigitalOcean, Defang ensures your deployments are secure, scalable, and cost-efficient. The platform supports various environments like development, staging, and production, making it ideal for projects of any scale.

e2e-chainlit-chatbot is a sample project demonstrating the complete development lifecycle of a conversational AI agent using Chainlit. The repository includes end-to-end code for launching a local web server that hosts an interactive chat interface, integrating with large language models for responses, and managing conversation context across messages. It features customizable prompt templates, multi-agent workflows, and real-time streaming of responses. Developers can configure API keys, adjust model parameters, and extend the system with custom logic or integrations. With minimal dependencies and clear documentation, this project accelerates experimentation with AI-driven chatbots and provides a solid foundation for production-grade conversational assistants. It also includes examples for customizing front-end components, logging, and error handling. Designed for seamless integration with cloud platforms, it supports both prototype and production use cases.

AgentSmithy is designed to streamline the development lifecycle of AI agents by offering modular components for memory management, task planning, and execution orchestration. The framework leverages Google Cloud Storage or Firestore for persistent memory, Cloud Functions for event-driven triggers, and Pub/Sub for scalable messaging. Handlers define agent behaviors, while planners manage multi-step task execution. Observability modules track performance metrics and logs. Developers can integrate bespoke plugins to enhance capabilities such as custom data sources, specialized LLMs, or domain-specific tools. AgentSmithy’s cloud-native architecture ensures high availability and elasticity, allowing deployment across development, testing, and production environments seamlessly. With built-in security and role-based access controls, teams can maintain governance while rapidly iterating on intelligent agent solutions.