Fetch.ai is an open-source platform and software development kit designed for building autonomous agents that represent digital twins on a decentralized network. It provides a Python and Rust SDK, an Open Economic Framework (OEF) for peer discovery, and seamless integration with its ledger for secure transactions. Developers can define custom agent skills—such as market making, data provision, or task bidding—and deploy them to testnets or mainnets. Fetch.ai agents autonomously communicate, negotiate, and execute smart contracts, enabling powerful multi-agent coordination for supply chains, IoT ecosystems, mobility services, energy grids, and beyond.

SARL isms for decision-making and supports the dynamic with the Eclipse IDE, offering editor support, code generation, debugging, and testing tools. The runtime engine can target various platforms, including simulation frameworks (e.g., MadKit, Janus) and real-world systems in robotics and IoT. Developers can structure complex MAS applications by assembling modular skills and protocols, simplifying the development of adaptive, distributed AI systems.

Coaty is an open-source toolkit written in TypeScript for developing collaborative, decentralized IoT applications using software agents. It delivers a container runtime that hosts agent instances, a discovery and registry service for dynamic resource lookup, and pub/sub communication layers for event distribution. Built-in storage adapters synchronize state across devices, while a flexible data model allows you to extend and share domain objects. Coaty supports multiple transport protocols like MQTT and WebSocket, enabling robust, real-time interoperability between edge, fog, and cloud environments without central points of failure.

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.