LiteMultiAgent offers a modular SDK for building and running multiple AI agents in parallel or sequence, each assigned unique roles and responsibilities. It provides out-of-the-box memory stores, messaging pipelines, plugin adapters, and execution loops to manage complex inter-agent communication. Users can customize agent behaviors, plug in external tools or APIs, and monitor conversations through logs. The framework’s lightweight design and dependency management make it ideal for rapid prototyping and production deployment of collaborative AI workflows.

CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.

Dial4JaCa is a Java library plugin for the JaCaMo multi-agent platform that intercepts inter-agent messages, encodes agent intentions, and routes them through LLM backends (OpenAI, local models). It manages dialogue context, updates belief bases, and integrates response generation directly into AgentSpeak(L) reasoning cycles. Developers can customize prompts, define dialogue artifacts, and handle asynchronous calls, enabling agents to interpret user utterances, coordinate tasks, and retrieve external information in natural language. Its modular design supports error handling, logging, and multi-LLM selection, ideal for research, education, and rapid prototyping of conversational MAS.

The project uses the JADE (Java Agent DEvelopment) framework to build a multi-agent environment. It defines agents that register with the platform’s AMS and DF, exchange ACL messages, and execute behaviors like cyclic, one-shot, and FSM. Example scenarios include buyer-seller negotiations, contract net protocols, and task allocation. A GUI agent container helps monitor runtime agent states and message flows.

MultiAgentes provides a modular architecture for defining environments and agents, supporting synchronous and asynchronous multi-agent interactions. It includes base classes for environments and agents, predefined scenarios for cooperative and competitive tasks, tools for customizing reward functions, and APIs for agent communication and observation sharing. Visualization utilities allow real-time monitoring of agent behaviors, while logging modules record performance metrics for analysis. The framework integrates seamlessly with Gym-compatible reinforcement learning libraries, enabling users to train agents using existing algorithms. MultiAgentes is designed for extensibility, allowing developers to add new environment templates, agent types, and communication protocols to suit diverse research and educational use cases.