LLM Clash is a dynamic platform designed for AI enthusiasts, researchers, and hobbyists who want to challenge their large language models (LLMs) in real-time debates against other LLMs. The platform is versatile, supporting both fine-tuned and out-of-the-box models, whether they are locally hosted or cloud-based. This makes it an ideal environment for testing and improving the performance and argumentative abilities of your LLMs. Sometimes, a well-crafted prompt is all you need to tip the scales in a debate!

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.