FMAS (Flexible Multi-Agent System) is an open-source Python library for building, running, and visualizing multi-agent simulations. You can define agents with custom decision logic, configure an environment model, set up messaging channels for communication, and execute scalable simulation runs. FMAS provides hooks for monitoring agent state, debugging interactions, and exporting results. Its modular architecture supports plugins for visualization, metrics collection, and integration with external data sources, making it ideal for research, education, and real-world prototypes of autonomous systems.

NavGround Learning provides a comprehensive toolkit for developing and benchmarking reinforcement learning agents in navigation tasks. It supports multi-agent simulation, collision modeling, and customizable sensors and actuators. Users can select from predefined policy templates or implement custom architectures, train with state-of-the-art RL algorithms, and visualize performance metrics. Its integration with OpenAI Gym and Stable Baselines3 simplifies experiment management, while built-in logging and visualization tools allow in-depth analysis of agent behavior and training dynamics.

Askman is an innovative browser extension that allows users to integrate AI functionality directly into their webpages. By leveraging the power of OpenAI-compatible protocols, users can set up customizable prompts and queries using page titles, content, and selected text. This tool also supports free trial APIs offered by SiliconFlow and is open source, allowing the community to contribute to its development. It's a versatile tool ideal for enhancing browsing with advanced chat and information retrieval functions.

LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.

Agentin is an open-source Python library designed to help developers build intelligent agents that can plan, act, and learn. It provides abstractions for managing conversational memory, integrating external tools or APIs, and orchestrating multiple agents in parallel or hierarchical workflows. With configurable planner modules and support for custom tool wrappers, Agentin enables rapid prototyping of autonomous data-processing agents, customer service bots, or research assistants. The framework also offers extensible logging and monitoring hooks, making it easy to track agent decisions and troubleshoot complex multi-step interactions.

JaCaMo provides a unified environment for designing and running multi-agent systems (MAS) by integrating three core components: the Jason agent programming language for BDI-based agents, CArtAgO for artifact-based environmental modeling, and Moise for specifying organizational structures and roles. Developers can write agent plans, define artifacts with operations, and organize groups of agents under normative frameworks. The platform includes tooling for simulation, debugging, and visualization of MAS interactions. With support for distributed execution, artifact repositories, and flexible messaging, JaCaMo enables rapid prototyping and research in areas like swarm intelligence, collaborative robotics, and distributed decision-making. Its modular design ensures scalability and extensibility across academic and industrial projects.