The classics

Before the boom of chatgpt, there were frameworks used popular in research and academia …

Overview of multi-agent AI frameworks used in software development involves discussing various tools and technologies that enable the creation and management of AI agents that can interact with each other.

These frameworks are essential in developing complex systems where multiple AI agents work together, simulating real-world scenarios or solving complex problems.

1. MASDK (Multi-Agent Software Development Kit)

• Purpose: Provides tools for developing, testing, and deploying multi-agent systems.
• Features: Includes libraries for agent communication, coordination, and decision-making algorithms.
• Use Cases: Used in simulations, distributed problem solving, and complex system modeling.

2. JADE (Java Agent DEvelopment Framework)

• Purpose: Facilitates the development of multi-agent systems using the Java language.
• Features: Offers a runtime environment, agent lifecycle management, and messaging system.
• Use Cases: Common in enterprise applications, IoT systems, and distributed AI applications.

3. ADE (Agent Development Environment)

• Purpose: A framework for developing and managing robust multi-agent systems.
• Features: Supports various programming languages, real-time monitoring, and agent mobility.
• Use Cases: Robotics, distributed computing, and educational tools.

4. NetLogo

• Purpose: A multi-agent programmable modeling environment.
• Features: User-friendly interface, built-in programming language, and extensive modeling capabilities.
• Use Cases: Used in research for complex systems, social sciences, and biological modeling.

5. GAMA Platform

• Purpose: An open-source development environment for building spatially explicit multi-agent simulations.
• Features: GIS integration, 3D visualization, and a rich modeling language.
• Use Cases: Urban planning, environmental modeling, and epidemiology studies.

6. Jason

• Purpose: An interpreter for an extended version of AgentSpeak, a language for developing multi-agent systems.
• Features: BDI (Beliefs-Desires-Intentions) architecture, integration with Java, and environment modeling.
• Use Cases: Research in AI, educational purposes, and complex system simulations.

7. SPADE (Smart Python multi-Agent Development Environment)

• Purpose: A platform for developing multi-agent systems in Python.
• Features: XMPP-based communication, agent lifecycle management, and web-based tools.
• Use Cases: IoT applications, distributed AI systems, and academic research.

8. FIPA-OS (Foundation for Intelligent Physical Agents - Operating System)

• Purpose: Provides a basis for developing systems compliant with FIPA standards for multi-agent systems.
• Features: Agent communication language and interaction protocols.
• Use Cases: Standards-compliant AI systems, interoperable agent-based applications.

Conclusion

These frameworks offer diverse functionalities, from simple agent interactions to complex simulations and real-world problem-solving. The choice of framework depends on the specific requirements of the project, such as the programming language, scalability, and the level of complexity in agent interactions.