At its core, LionAGI provides a modular architecture for defining and executing dependent task stages, breaking complex problems into logical components that can be processed sequentially or in parallel. Each stage can leverage a custom prompt, memory storage, and decision logic to adapt behavior based on previous results. Developers can integrate any supported LLM API or self-hosted model, configure observation spaces, and define action mappings to create agents that plan, reason, and learn over multiple cycles. Built-in logging, error recovery, and analytics tools enable real-time monitoring and iterative refinement. Whether automating research workflows, generating reports, or orchestrating autonomous processes, LionAGI accelerates the delivery of intelligent, adaptable AI agents with minimal boilerplate.

LiteSwarm is a comprehensive AI agent orchestration framework designed to facilitate collaboration among multiple specialized agents. Users define individual agents with distinct roles—such as data fetching, analysis, summarization, or external API calls—and link them within a visual workflow. LiteSwarm handles inter-agent communication, persistent memory storage, error recovery, and logging. It supports API integration, custom code extensions, and real-time monitoring, so teams can prototype, test, and deploy complex multi-agent solutions without extensive engineering overhead.

Llama Guard is an AI-driven agent focused on cybersecurity. It continuously monitors network activity, identifies potential threats, and automatically responds to mitigate risks. By utilizing machine learning algorithms, Llama Guard adapts to new vulnerabilities, providing real-time protection for organizations. Its functionalities include threat analysis, incident response, and compliance management, making it an essential tool for safeguarding critical information and minimizing security breaches.

LotusEye provides an advanced AI anomaly detection system that automatically learns patterns from normal sensor data. Users can easily create their AI models by uploading data, with no prior AI knowledge required. The platform is free to try, and offers rich features like email notifications, data uploads via API, and multi-member management. Anomaly detection is simple and effective in three steps: upload training data, upload test data, and review the anomaly scores. Free model creation allows you to verify its effectiveness before committing to a paid plan.

MapZot.AI is an advanced AI-powered GIS platform that specializes in retail site selection and market analysis. It integrates vast amounts of internal data with cutting-edge algorithms to provide real-time insights and actionable recommendations. Businesses can leverage MapZot.AI to track their portfolio in real-time, forecast revenue, and optimize site selection based on hyperlocal data and emerging trends. The platform is built to be user-friendly, requiring no prior training to use effectively, making it an invaluable tool for data-driven business decisions.

MarketPlan.io is an all-in-one digital marketing platform that enables users to map out marketing funnels, plan strategies, execute campaigns, and analyze performance. It offers a virtual simulation tool to compare different scenarios and predict profitability, ensuring congruence with your marketing message. Features include market analysis tools, strategic planning modules, budgeting features, and collaboration functionalities. Ideal for marketers and agencies, MarketPlan.io helps enhance campaign outcomes through better planning and real-time monitoring.

MaxLearn is a microlearning platform that simplifies the creation and delivery of engaging training content. It incorporates gamified learning methodologies and AI-driven tools to provide an interactive learning experience. Users can easily design courses using various media types and track progress in real-time. By leveraging spaced repetition techniques, MaxLearn enhances knowledge retention and skill application, perfect for educating employees or students in an increasingly digital world.

Mera Monitor is a sophisticated workforce analytics solution designed to help organizations track and improve employee productivity. This software offers a comprehensive suite of features such as real-time monitoring, screenshot capture, application usage tracking, and keystroke logging. By deploying these tools, Mera Monitor helps managers gain insights into day-to-day activities, identify areas for improvement, and promote a culture of transparency and accountability. Its user-friendly interface makes it accessible for organizations of all sizes, ensuring that businesses can adapt to the dynamic nature of modern work environments, including remote and hybrid scenarios.

Milk Infrastructure is an AI-powered system that automates the deployment, management, and scaling of production-grade Kubernetes clusters across any cloud environment. With intuitive solutions, it eliminates the need for human involvement in DevOps processes, streamlining infrastructure management. This platform not only simplifies operations but also enhances scalability, allowing developers to easily adapt and grow their applications in dynamic cloud settings. By using Milk Infrastructure, companies can achieve efficient resource usage and minimize operational overhead, ensuring high performance and reliability.

Moonhub provides an intelligent platform for managing projects efficiently. It integrates advanced AI features for task automation, team collaboration, and progress tracking. Users can assign tasks, set deadlines, and monitor outcomes in real-time. This AI agent is particularly useful for enhancing productivity and maintaining organized workflows across diverse teams, making it ideal for both small groups and large enterprises.

Multi-Agent Surveillance offers a flexible simulation framework where multiple AI agents act as predators or evaders in a discrete grid world. Users can configure environment parameters such as grid dimensions, number of agents, detection radii, and reward structures. The repository includes Python classes for agent behavior, scenario generation scripts, built-in visualization via matplotlib, and seamless integration with popular reinforcement learning libraries. This makes it easy to benchmark multi-agent coordination, develop custom surveillance strategies, and conduct reproducible experiments.

Multi-Agents System from Scratch provides a comprehensive set of Python modules to build, customize, and evaluate multi-agent environments from the ground up. Users can define world models, create agent classes with unique sensory inputs and action capabilities, and establish flexible communication protocols for cooperation or competition. The framework supports dynamic task allocation, strategic planning modules, and real-time performance tracking. Its modular architecture allows easy integration of custom algorithms, reward functions, and learning mechanisms. With built-in visualization tools and logging utilities, developers can monitor agent interactions and diagnose behavior patterns. Designed for extensibility and clarity, the system caters to both researchers exploring distributed AI and educators teaching agent-based modeling.

Crewai provides a Python-based library to design and execute multi-AI agent systems. Users can define individual agents with specialized roles, configure messaging channels for inter-agent communication, and implement dynamic planners to allocate tasks based on real-time context. Its modular architecture enables plugging in different LLMs or custom models for each agent. Built-in logging and monitoring tools track conversations and decisions, allowing seamless debugging and iterative refinement of agent behaviors.

Multi-Agent-AI-Models-and-Path-Planning provides a comprehensive toolkit for developing and testing multi-agent systems combined with classical and modern path planning methods. It includes implementations of algorithms such as A*, Dijkstra, RRT, and potential fields, alongside customizable agent behavior models. The framework features simulation and visualization modules, allowing seamless scenario creation, real-time monitoring, and performance analysis. Designed for extensibility, users can plug in new planning algorithms or agent decision models to evaluate cooperative navigation and task allocation in complex environments.

Multi-Agent AI Orchestration allows teams of autonomous AI agents to work together on predefined or dynamic goals. Each agent can be configured with unique roles, capabilities, and memory stores, interacting through a central orchestrator. The framework integrates with LLM providers (e.g., OpenAI, Cohere), vector databases (e.g., Pinecone, Weaviate), and custom user-defined tools. It supports extending agent behaviors, real-time monitoring, and logging for audit trails and debugging. Ideal for complex workflows, such as multi-step question answering, automated content generation pipelines, or distributed decision-making systems, it accelerates development by abstracting inter-agent communication and providing a pluggable architecture for rapid experimentation and production deployment.

This open-source project delivers a complete multi-agent reinforcement learning framework built on PyTorch and Unity ML-Agents. It offers decentralized DDPG algorithms, environment wrappers, and training scripts. Users can configure agent policies, critic networks, replay buffers, and parallel training workers. Logging hooks allow TensorBoard monitoring, while modular code supports custom reward functions and environment parameters. The repository includes sample Unity scenes demonstrating collaborative navigation tasks, making it ideal for extending and benchmarking multi-agent scenarios in simulation.

MultiAgentes provides a modular architecture for defining environments and agents, supporting synchronous and asynchronous multi-agent interactions. It includes base classes for environments and agents, predefined scenarios for cooperative and competitive tasks, tools for customizing reward functions, and APIs for agent communication and observation sharing. Visualization utilities allow real-time monitoring of agent behaviors, while logging modules record performance metrics for analysis. The framework integrates seamlessly with Gym-compatible reinforcement learning libraries, enabling users to train agents using existing algorithms. MultiAgentes is designed for extensibility, allowing developers to add new environment templates, agent types, and communication protocols to suit diverse research and educational use cases.

Netify is a robust network intelligence platform that uses Deep Packet Inspection (DPI) combined with cloud analytics to monitor and analyze network traffic in real-time. It identifies applications, user behavior, and network performance issues, offering detailed visibility into network operations. This advanced tooling is essential for enhancing network security, optimizing performance, and ensuring reliable connectivity. Netify is particularly useful for ISPs, enterprises, and IT professionals who need a reliable solution to manage and secure their network infrastructures.

NeXent is a flexible AI agent framework that lets you define custom digital workers via YAML or Python SDK. You can integrate multiple LLMs, external APIs, and toolchains into modular pipelines. Built-in memory modules enable stateful interactions, while a monitoring dashboard provides real-time insights. NeXent supports local and cloud deployment, Docker containers, and scales horizontally for enterprise workloads. The open-source design encourages extensibility and community-driven plugins.

No Code Scraper is an intuitive web scraping tool that allows users to collect and monitor data from multiple websites without the need for programming knowledge. It streamlines the data extraction process with a point-and-click interface, enabling users to set up scraping tasks quickly and efficiently. This tool is ideal for technical and non-technical users looking to automate data collection for various applications, including market research, competitor analysis, and content aggregation.