Research Navigator is an AI-driven tool that automates literature review tasks for researchers, students, and professionals. Leveraging advanced NLP and knowledge graph technologies, it retrieves and filters relevant scientific articles based on user-defined queries. It extracts salient points, methodologies, and results to generate concise summaries, highlights differences across studies, and provides side-by-side comparisons. The platform supports citation export in multiple formats and integrates with existing documentation workflows via API or CLI. With customizable search parameters, users can focus on specific domains, publication years, or keywords. The agent also maintains session-based memory, enabling follow-up queries and incremental refinement of research topics.

Videmak Research AI is an advanced academic tool that leverages artificial intelligence to optimize research workflow. It offers tailored tools such as literature search, article writing, proposal generation, autocitations, and data analysis. Designed for efficiency, Videmak allows researchers to quickly produce high-quality content, manage projects, and collaborate with teams. With features like AI chat bots, grammar checkers, and content rewriters, Videmak ensures that your research tasks are completed with speed and accuracy. This platform supports multilingual content generation and offers comprehensive templates across various research needs.

Multiagent_system offers a comprehensive toolkit for constructing and managing multi-agent environments. Users can define custom simulation scenarios, specify agent behaviors, and leverage pre-implemented algorithms such as DQN, PPO, and MADDPG. The framework supports synchronous and asynchronous training, enabling agents to interact concurrently or in turn-based setups. Built-in communication modules facilitate message passing between agents for cooperative strategies. Experiment configuration is streamlined via YAML files, and results are logged automatically to CSV or TensorBoard. Visualization scripts help interpret agent trajectories, reward evolution, and communication patterns. Designed for research and production workflows, Multiagent_system seamlessly scales from single-machine prototypes to distributed training on GPU clusters.