DevLooper is designed to simplify the end-to-end lifecycle of AI agent projects. With a single command you can generate boilerplate code for task-specific agents and step-by-step workflows. It leverages Modal’s cloud-native execution environment to run agents as scalable, stateless functions, while offering local run and debugging modes for fast iteration. DevLooper handles stateful data flows, periodic scheduling, and integrated observability out of the box. By abstracting infrastructure details, it lets teams focus on agent logic, testing, and optimization. Seamless integration with existing Python libraries and Modal’s SDK ensures secure, reproducible deployments across development, staging, and production environments.

Java Action Generic is a lightweight, modular library that allows developers to implement autonomous agent behaviors in Java by defining generic actions. Actions are parameterized units of work that agents can execute, schedule, and compose at runtime. The framework offers a consistent action interface, allowing developers to create custom actions, handle action parameters, and integrate with LightJason’s agent lifecycle management. With support for event-driven execution and concurrency, agents can perform tasks such as dynamic decision-making, interaction with external services, and complex behavior orchestration. The library promotes reusability and modular design, making it suitable for research, simulations, IoT, and game AI applications on any JVM-supported platform.