AI Otaku LABO is a leading media platform specializing in AI tool reviews and guides. Managed by professionals, it rigorously tests over 100 paid and free AI generators to verify their practical usability. The website ensures readers receive accurate and reliable data from proven experiments, making it a go-to source for those seeking in-depth knowledge and the latest updates in AI technology.

PommerLearn enables researchers and developers to train multi-agent RL bots in the Pommerman game environment. It includes ready-to-use implementations of popular algorithms (PPO, DQN), flexible configuration files for hyperparameters, automatic logging and visualization of training metrics, model checkpointing, and evaluation scripts. Its modular architecture makes it easy to extend with new algorithms, customize environments, and integrate with standard ML libraries such as PyTorch.

ThreeAgents is built in Python, leveraging OpenAI's chat completions API to instantiate multiple AI agents with distinct roles (system, assistant, user). It provides abstractions for agent prompting, role-based message handling, and context memory management. Developers can define custom prompt templates, configure agent personalities, and chain interactions to simulate realistic dialogues or task-oriented workflows. The framework handles message passing, context window management, and logging, enabling experiments in collaborative decision-making or hierarchical task decomposition. With support for environment variables and modular agents, ThreeAgents allows seamless swapping between OpenAI and local LLM backends, facilitating rapid prototyping of multi-agent AI systems. It ships with example scripts and Docker support for quick setup.

Agents-Deep-Research is designed to streamline the development and testing of autonomous AI agents by offering a modular, extensible codebase. It features a task planning engine that decomposes user-defined goals into sub-tasks, a long-term memory module that stores and retrieves context, and a tool integration layer that allows agents to interact with external APIs and simulated environments. The framework also provides evaluation scripts and benchmarking tools to measure agent performance across diverse scenarios. Built on Python and adaptable to various LLM backends, it enables researchers and developers to rapidly prototype novel agent architectures, conduct reproducible experiments, and compare different planning strategies under controlled conditions.