Aeiva is a developer-first platform that enables you to create, deploy, and evaluate autonomous AI agents within flexible simulation environments. It features a plugin-based engine for environment definition, intuitive APIs to customize agent decision loops, and built-in metrics collection for performance analysis. The framework supports integration with OpenAI Gym, PyTorch, and TensorFlow, plus real-time web UI for monitoring live simulations. Aeiva’s benchmarking tools let you organize agent tournaments, record results, and visualize agent behaviors to fine-tune strategies and accelerate multi-agent AI research.

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.

Examify is an innovative AI-powered platform created to assist educators in designing, generating, and assessing exams with ease. It harnesses advanced AI technology to offer customizable test templates, automated grading, and insightful data analysis for enhanced test efficiency and effectiveness. Whether you’re a teacher, academic institution, or training provider, Examify ensures accurate and fair assessments while saving time and effort in exam management.

GridWorldEnvs offers a comprehensive suite of grid-world environments to support the design, testing, and benchmarking of reinforcement learning and multi-agent systems. Users can easily configure grid dimensions, agent start positions, goal locations, obstacles, reward structures, and action spaces. The library includes ready-to-use templates such as classic grid navigation, obstacle avoidance, and cooperative tasks, while also allowing custom scenario definitions via JSON or Python classes. Seamless integration with the OpenAI Gym API means that standard RL algorithms can be applied directly. Additionally, GridWorldEnvs supports single-agent and multi-agent experiments, logging, and visualization utilities for tracking agent performance.

HoneyHive is a comprehensive platform providing AI evaluation, testing, and observability tools, primarily aimed at teams building and maintaining GenAI applications. It enables developers to automatically test, evaluate, and benchmark models, agents, and RAG pipelines against safety and performance criteria. By aggregating production data such as traces, evaluations, and user feedback, HoneyHive facilitates anomaly detection, thorough testing, and iterative improvements in AI systems, ensuring they are production-ready and reliable.

LifelongAgentBench is designed to simulate real-world continuous learning environments, enabling developers to test AI agents across a sequence of evolving tasks. The framework offers a plug-and-play API to define new scenarios, load datasets, and configure memory management policies. Built-in evaluation modules compute metrics like forward transfer, backward transfer, forgetting rate, and cumulative performance. Users can deploy baseline implementations or integrate proprietary agents, facilitating direct comparison under identical settings. Results are exported as standardized reports, featuring interactive plots and tables. The modular architecture supports extensions with custom dataloaders, metrics, and visualization plugins, ensuring researchers and engineers can adapt the platform to varied application domains.

MARL-DPP is an open-source framework enabling multi-agent reinforcement learning (MARL) with enforced diversity through Determinantal Point Processes (DPP). Traditional MARL approaches often suffer from policy convergence to similar behaviors; MARL-DPP addresses this by incorporating DPP-based measures to encourage agents to maintain diverse action distributions. The toolkit provides modular code for embedding DPP in training objectives, sampling policies, and managing exploration. It includes ready-to-use integration with standard OpenAI Gym environments and the Multi-Agent Particle Environment (MPE), along with utilities for hyperparameter management, logging, and visualization of diversity metrics. Researchers can evaluate the impact of diversity constraints on cooperative tasks, resource allocation, and competitive games. The extensible design supports custom environments and advanced algorithms, facilitating exploration of novel MARL-DPP variants.

Mock Exam AI is a cutting-edge platform that leverages the power of Artificial Intelligence to help users create customized mock exams with ease. Users can manually add questions, generate new ones, and even include references in the form of links and PDFs. Premium users have no limit on question generation and can make their exams private. It’s an ideal tool for anyone preparing for upcoming exams who wants a streamlined and flexible testing experience.

MultiAgentSystems is designed to simplify the process of building and evaluating multi-agent reinforcement learning (MARL) applications. The platform includes implementations of state-of-the-art algorithms like MADDPG, QMIX, VDN, and centralized training with decentralized execution. It features modular environment wrappers compatible with OpenAI Gym, communication protocols for agent interaction, and logging utilities to track metrics such as reward shaping and convergence rates. Researchers can customize agent architectures, tune hyperparameters, and simulate settings including cooperative navigation, resource allocation, and adversarial games. With built-in support for PyTorch, GPU acceleration, and TensorBoard integration, MultiAgentSystems accelerates experimentation and benchmarking in collaborative and competitive multi-agent domains.

Nonfinito.xyz is a platform designed to facilitate the comparison and evaluation of multimodal models. It provides users with comprehensive tools to run and share evaluations, going beyond traditional language models (LLMs) to include various multimodal models. This helps in gaining deeper insights and improving performance by leveraging a wide range of parameters and metrics. Nonfinito aims to streamline the evaluative process and make it accessible to researchers, developers, and data scientists looking to optimize their models.

OpenSpiel is a research framework that provides a wide range of environments (from simple matrix games to complex board games such as Chess, Go, and Poker) and implements various reinforcement learning and search algorithms (e.g., value iteration, policy gradient methods, MCTS). Its modular C++ core and Python bindings allow users to plug in custom algorithms, define new games, and compare performance across standard benchmarks. Designed for extensibility, it supports single and multi-agent settings, enabling study of cooperative and competitive scenarios. Researchers leverage OpenSpiel to prototype algorithms quickly, run large-scale experiments, and share reproducible code.

OpenAgent offers a comprehensive framework for developing autonomous AI agents that can understand tasks, plan multi-step actions, and interact with external services. By integrating with LLMs such as OpenAI and Anthropic, it enables natural language reasoning and decision-making. The platform features a pluggable tool system for executing HTTP requests, file operations, and custom Python functions. Memory management modules allow agents to store and retrieve contextual information across sessions. Developers can extend functionality via plugins, configure real-time streaming of responses, and utilize built-in logging and evaluation tools to monitor agent performance. OpenAgent simplifies orchestration of complex workflows, accelerates prototyping of intelligent assistants, and ensures modular architecture for scalable AI applications.

Questgen.ai is a sophisticated AI-driven platform that generates quizzes from any text swiftly and effortlessly. Tailored for educators and trainers, it supports various question types including Multiple Choice Questions (MCQs), True/False, Fill-in-the-blanks, and Higher-Order questions. Utilizing advanced NLP algorithms, Questgen ensures high-quality, contextually relevant questions, boosting learner engagement and assessment accuracy.

Qwizzard is a comprehensive tool designed to make quiz and assessment creation, sharing, and analysis simple and effective. It allows users to engage their audience through interactive and customizable quizzes, making it ideal for educators, marketers, and businesses. With Qwizzard, creating quizzes is straightforward, and the platform supports robust analytics to provide deep insights into participant performance. Share your quizzes seamlessly with customizable options, and gather meaningful data to enhance your strategies and improve engagement.

Quizify leverages advanced AI technology to streamline quiz creation for educators. By automating the generation of quiz questions and formats, Quizify saves teachers valuable time and ensures consistently high-quality assessments. Users can effortlessly create, customize, and share quizzes, which can be personalized to suit different learning environments and objectives. The platform supports various question types, such as multiple-choice, true/false, and short answer, providing a comprehensive tool for a range of educational needs. Furthermore, Quizify offers analytical tools to track performance and identify areas for improvement.

Wise Agents offers a comprehensive, searchable catalog of AI agent frameworks and platforms. It features filtering by category, programming language, license type, and more to help users zero in on the right tool. Each agent entry includes a detailed profile, key capabilities, GitHub and documentation links, and community ratings. The site is regularly updated through community contributions, ensuring the latest agent releases and developments are always available in one centralized resource.

Yunkao AI is a state-of-the-art online examination platform designed to facilitate secure and efficient evaluations using advanced AI technologies. The system is equipped with features like facial recognition authentication, dual-device invigilation, exam mode, and AI-driven evaluations. It caters to a wide range of organizations including educational institutions, government bodies, and enterprises, ensuring reliable and streamlined exam processes. With support for multiple devices and operating systems, Yunkao AI aims to provide flexible and scalable assessment solutions.

Jinshuju is a comprehensive online form tool designed to streamline data collection, management, and analysis. Whether you need to conduct surveys, academic research, or customer feedback collection, Jinshuju offers a wide range of features to make the process quick and easy. With customizable templates and powerful analytics, it helps users uncover valuable insights from their data.

Asker-I is an innovative AI-based tool designed to create questions rapidly and efficiently. By simply uploading your materials or specifying topics, the AI takes over the tedious process of question formation. Asker-I can handle large documents, supports various question types, and promises high customization to meet diverse needs. This makes it an invaluable resource for educators, researchers, and anyone in need of quick and reliable question generation.

CommNet is a research-oriented library that implements the CommNet architecture, allowing multiple agents to share hidden states at each timestep and learn to coordinate actions in cooperative environments. It includes PyTorch model definitions, training and evaluation scripts, environment wrappers for OpenAI Gym, and utilities for customizing communication channels, agent counts, and network depths. Researchers and developers can use CommNet to prototype and benchmark inter-agent communication strategies on navigation, pursuit–evasion, and resource-collection tasks.