Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.

LLM Playground serves as a comprehensive tool for researchers and developers interested in Large Language Models (LLMs). Users can experiment with different prompts, evaluate model responses, and deploy applications. The platform supports a range of LLMs and includes features for performance comparison, allowing users to see which model suits their needs best. With its accessible interface, LLM Playground aims to simplify the process of engaging with sophisticated machine learning technologies, making it a valuable resource for both education and experimentation.

MARFT is a Python-based LLMs, enabling reproducible experiments and rapid prototyping of collaborative AI systems.

Reinforcement Learning Agents for PettingZoo Games is a Python-based code library delivering off-the-shelf DQN, PPO, and A2C algorithms for multi-agent reinforcement learning on PettingZoo environments. It features standardized training and evaluation scripts, configurable hyperparameters, integrated TensorBoard logging, and support for both competitive and cooperative games. Researchers and developers can clone the repo, adjust environment and algorithm parameters, run training sessions, and visualize metrics to benchmark and iterate quickly on their multi-agent RL experiments.

Vanilla Agents is a lightweight PyTorch-based framework that delivers modular and extensible implementations of core reinforcement learning agents. It supports algorithms like DQN, Double DQN, PPO, and A2C, with pluggable environment wrappers compatible with OpenAI Gym. Users can configure hyperparameters, log training metrics, save checkpoints, and visualize learning curves. The codebase is organized for clarity, making it ideal for research prototyping, educational use, and benchmarking new ideas in RL.

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.