AI Agents & Automation

Terminus Group specializes in AIoT (Artificial Intelligence of Things) solutions, focusing on the development and implementation of smart technologies for urban environments. Their offerings include AI robots designed for various applications such as patrolling, monitoring e-scooter violations, and even announcing Ramadan fasting times. The company is a prominent partner in initiatives like Expo 2020 Dubai, showcasing their commitment to green and smart technologies. Terminus Group also develops urban smart operating systems like TacOS 3.0, large model platforms like BitInfra, and AGI agents. They are actively involved in research and development, collaborating with institutions and promoting sustainable digitalization across various sectors, from industrial parks to cultural scenarios.

superglue is an AI-powered tool builder designed to create integrations and tools using natural language. It simplifies the development of production-grade solutions for both long-tail and enterprise systems by abstracting away complexities like authentication, documentation handling, and data mapping between different systems. A key differentiator is its self-healing capability, where tools can auto-repair failures caused by upstream API changes, ensuring continuous operation. Users can interact with superglue via a web application, an SDK for programmatic execution, or an MCP server for discoverability and execution of pre-built tools. It supports use cases from data syncing to enterprise GPT tools.

ElasticDL is a Kubernetes-native deep learning framework designed to enhance TensorFlow and PyTorch with fault-tolerance and elastic scheduling. It allows deep learning tasks to continue running even if some processes fail, eliminating the need for frequent checkpointing and recovery. By integrating directly with Kubernetes, ElasticDL leverages its priority-based preemption to achieve elastic scheduling, significantly improving cluster utilization. This framework supports TensorFlow Estimator, TensorFlow Keras, and PyTorch, offering a minimalist interface for distributed model training via command line. It's ideal for machine learning engineers working with Kubernetes clusters who need robust and scalable solutions for large-scale deep learning.

EvoAgentX is an open-source framework designed for building, evaluating, and evolving LLM-based agents and agentic workflows. It moves beyond static prompt chaining by introducing a self-evolving ecosystem where AI agents are constructed, assessed, and optimized through iterative feedback loops. Key features include automatic workflow construction from a single prompt, built-in evaluation mechanisms, and a self-evolution engine that improves workflows over time. It offers plug-and-play compatibility with popular LLMs like OpenAI, Qwen, Claude, and Deepseek, and supports local LLM deployment via LiteLLM. EvoAgentX also provides a rich set of built-in tools for interacting with real-world environments, memory modules for short-term and long-term recall, and human-in-the-loop interactions for supervision and guidance.

Sigmoid is an AI consulting company specializing in AI-first data & analytics, data engineering, Agentic AI, and Generative AI solutions. They assist organizations in modernizing their data infrastructure and operationalizing AI to achieve tangible business value. Sigmoid offers services ranging from AI strategy and Generative AI implementation to AI-led data engineering and advanced analytics. They also provide industry-specific solutions for CPG & Retail, Life Sciences, and Financial Services, alongside accelerators like Reconica for data harmonization, DataGuard for data quality, and RAPID for accelerated ML model deployment. Their approach focuses on delivering measurable ROI from AI investments.

Match Profile to Positions is an open-source project demonstrating the CrewAI framework's capability to automate the complex task of matching candidate CVs to job proposals. By orchestrating autonomous AI agents, the tool efficiently extracts relevant information from CVs and aligns it with job opportunities, ensuring an optimal fit. This project is particularly useful for enhancing recruitment workflows and streamlining the candidate selection process. It requires access to GPT-4o by default, though users can configure it to use other models. The project provides clear instructions for environment setup, dependency installation, and customization of agents and tasks through YAML configuration files, making it adaptable for various recruitment needs.

Traxen's iQ-Cruise is an intelligent speed control system designed for heavy-duty trucking, leveraging AI to optimize fuel consumption and enhance safety. The system automates longitudinal speed, adapting to various conditions like road grades, curves, weather, and traffic. It aims to reduce fuel costs by up to 10%, saving an average of $9,000 per truck annually. Beyond efficiency, iQ-Cruise improves drivability by providing a human-like driving style and offering relevant warnings without overwhelming the driver. The technology includes features like connected predictive/adaptive cruise control, AI driver system software for scenario recognition and optimization, and intelligent data capture for driver performance insights. It also incorporates driver monitoring and training agents, along with environmental data from on-board and external sensors, all connected to a cloud for continuous learning and over-the-air updates.

AAPMOR Inc. is dedicated to transforming visions into reality by offering advanced automation and AI solutions. The company focuses on empowering businesses with the necessary tools and expertise to navigate and thrive in the complex digital landscape. By leveraging artificial intelligence and automation, AAPMOR aims to streamline operations, enhance efficiency, and drive digital transformation for its clients. While specific features are not detailed on the provided website content, the overarching goal is to provide comprehensive solutions that enable businesses to achieve their strategic objectives through technological innovation.

awesome-claude-skills is a comprehensive, curated list of Claude Skills, resources, and tools designed to customize and enhance Claude AI workflows, with a particular focus on Claude Code. Claude Skills are specialized folders containing instructions, scripts, and resources that Claude dynamically discovers and loads when relevant to tasks. This open-source GitHub repository details how Skills work, their progressive disclosure architecture for efficiency, and provides guides for getting started via the Claude.ai web interface, Claude Code CLI, or Claude API. It features official skills for document processing (docx, pdf, pptx, xlsx), design (algorithmic-art, canvas-design), development (frontend-design, web-artifacts-builder), communication, and skill creation. The repository also highlights community-contributed skills, tools for skill creation, best practices, and security guidelines, emphasizing the importance of vetting skills due to arbitrary code execution capabilities.

The Marketing Strategy Generator is an open-source project built on the CrewAI framework, designed to automate the creation of detailed marketing strategies. It orchestrates autonomous AI agents to collaborate on complex tasks, from analyzing market trends to developing compelling marketing content. Users can configure environment variables, install dependencies, and customize agent inputs and tasks through YAML files. The tool uses GPT-4o by default, allowing for advanced AI capabilities in generating strategic insights. It provides a structured approach to marketing strategy development, making it a valuable resource for those looking to leverage AI for efficient planning.

Melting Pot is an open-source suite of test scenarios specifically designed for multi-agent reinforcement learning (MARL). Developed by Google DeepMind, it offers researchers a robust platform to train and evaluate AI agents in complex social situations. The tool includes over 50 multi-agent games (substrates) and more than 256 unique test scenarios, allowing for the assessment of generalization to novel social interactions like cooperation, competition, and trust. It is built on DeepMind Lab2D and provides tools for interactive play, evaluation of trained models, and example training scripts using frameworks like RLlib. Melting Pot aims to become a standard benchmark for MARL research, with ongoing development to expand its coverage of social interactions and generalization scenarios.

SS&C Blue Prism provides agentic automation solutions for enterprises, specializing in robotic process automation (RPA), business process management (BPM), and artificial intelligence (AI). The platform is designed to handle high-stakes, high-compliance environments across various industries like banking, healthcare, and insurance. It emphasizes built-in governance, proven execution, and a clear path to value, helping businesses operate faster, safer, and smarter. Blue Prism's agentic AI allows agents to make decisions and take actions autonomously, reducing the need for constant human oversight. The platform integrates with various AI tools and offers a Digital Exchange with over 2,000 automation software components, including generative AI and agentic AI.

Atmos AI is an agentic AI marketing engine designed to autonomously plan, execute, and optimize marketing campaigns for mid-market companies. It features over 180 specialized AI agents that work together across 36 marketing modules, covering areas like content, ads, email, SEO, lead generation, and analytics. Users can access this engine through three distinct brands: Marketing Titan for the full platform, Lead Titan AI for lead intelligence and outreach, and Darwin AI for a natural-language AI Chief of Staff. The platform offers flexible control levels, from full human approval to guided autonomy and full automation, allowing users to set the desired level per campaign or module. Built over 2.5 years, Atmos AI aims to provide a comprehensive solution that runs marketing rather than just recommending actions.

DetGPT is an innovative AI tool designed for object detection through advanced reasoning capabilities. Unlike traditional object detection systems, DetGPT not only identifies objects but also understands complex instructions, allowing it to locate targets based on abstract concepts. For instance, it can identify "blood pressure-reducing foods" in an image by recognizing potassium-rich items like bananas. This ability to provide answers beyond human common sense, such as identifying unfamiliar fruits rich in potassium, makes it a powerful tool for various applications. The project is built upon the open-vocabulary detector GroundingDino and the multimodal conversation model MiniGPT-4, leveraging large language models (LLMs) for its reasoning prowess. It is available as an open-source project on GitHub, providing installation instructions and an online demo for users to explore its features.

DROO is an open-source project providing a Deep Reinforcement Learning algorithm for online computation offloading in Wireless Powered Mobile-Edge Computing (WPMEC) networks. The tool takes time-varying wireless channel gains as input and generates binary offloading decisions. It includes DNN structures for WPMEC, along with training and testing functionalities. Implementations are available for Tensorflow 1.x, Tensorflow 2, and PyTorch, making it accessible to developers with different deep learning framework preferences. The repository also provides data sets and demo files to evaluate performance under various conditions, such as alternating weights and random device turn-on/off scenarios.

GigaWorld-0 is an open-source, unified world model framework designed as a data engine to empower embodied AI, specifically for Vision-Language-Action (VLA) learning. It integrates two synergistic components: GigaWorld-0-Video, which generates diverse, texture-rich, and temporally coherent embodied sequences with fine-grained control over appearance, camera viewpoint, and action semantics; and GigaWorld-0-3D, which combines 3D generative modeling, 3D Gaussian Splatting reconstruction, physically differentiable system identification, and executable motion planning to ensure geometric consistency and physical realism. The framework is built upon GigaTrain, GigaDatasets, and GigaModels, offering a comprehensive solution for researchers and developers in embodied AI.

Minion AI is a web agent designed to revolutionize human-computer interaction by providing a personal AI assistant capable of automating a wide range of tasks. This tool focuses on streamlining workflows and offering intelligent assistance, positioning itself at the forefront of AI advancements. While specific features are not detailed, its core purpose is to act as an autonomous agent, performing actions and managing information on behalf of the user. Minion AI is built to enhance productivity and simplify complex operations, making advanced AI capabilities accessible for everyday use.

GITM (Ghost in the Minecraft) is an innovative AI agent framework designed to tackle complex, long-horizon tasks within open-world environments, specifically demonstrated in Minecraft. It integrates Large Language Models (LLMs) with text-based knowledge and memory to enable generally capable agents. Unlike previous RL-based agents that struggle with mapping complex goals to low-level operations, GITM employs a hierarchical approach, breaking down goals into sub-goals, structured actions, and finally keyboard/mouse operations. This framework features an LLM Decomposer, LLM Planner, and LLM Interface, which collectively manage goal decomposition, action planning, and environmental interaction. GITM boasts broad task coverage, achieving 100% completion of the Minecraft Overworld technology tree, significantly outperforming previous methods. It also demonstrates a high success rate on challenging tasks like "ObtainDiamond" and remarkable training efficiency, requiring only a single CPU node for two days, a stark contrast to the extensive GPU training days needed by other leading agents.

InternNav is an all-in-one open-source toolbox built on PyTorch, Habitat, and Isaac Sim, designed for embodied navigation. It provides modular support for the entire navigation system, including vision-language navigation with discrete action space (VLN-CE), visual navigation (VN) with various goal types, and full VLN systems with continuous trajectory outputs. The platform is compatible with mainstream simulation platforms, catering to diverse training and evaluation needs. It offers comprehensive datasets, models, and benchmarks, including the advanced InternData-N1 dataset and the dual-system navigation foundation model, InternVLA-N1, which demonstrates leading performance and zero-shot generalization capabilities in real-world scenarios. InternNav also supports distributed evaluation and provides resources for real-world deployment.

This repository offers open-source code for developing Intrusion Detection Systems (IDS) using a range of machine learning algorithms. It's designed for general IDS and anomaly detection applications, particularly in the context of the Internet of Vehicles (IoV). The project includes implementations of tree-based algorithms like Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost, as well as unsupervised learning with k-means, and ensemble methods such as stacking and the proposed LCCDE. It also incorporates hyperparameter optimization techniques like Bayesian optimization. The code is accompanied by published research papers detailing three specific IDS models: a tree-based IDS, MTH-IDS (a multi-tiered hybrid IDS), and LCCDE (a decision-based ensemble framework). Datasets like CICIDS2017 and CAN-intrusion are used for experimentation, making it a valuable resource for cybersecurity researchers and developers.

HALOs (Human-Centered Loss Functions) is a Python library designed to facilitate the alignment of Large Language Models (LLMs) with human preferences. It provides extensible implementations of popular alignment methods such as DPO, KTO, PPO, and ORPO. The library emphasizes modularity, separating dataloading, training, and sampling, and extensibility, allowing users to quickly implement custom dataloaders or new alignment losses. HALOs is built for simplicity, making it easy to hack on, and has been tested with LLMs ranging from 1B to 30B parameters. It supports LoRA training, reference logit caching to reduce memory, and integrates with tools like Hydra for configuration and Accelerate for job launching with FSDP. The repository also includes scripts for evaluation with AlpacaEval and LMEval.

Hamiltonian-nn offers the code for the paper "Hamiltonian Neural Networks," which introduces a novel approach to modeling physical systems using neural networks. Unlike traditional neural networks, Hamiltonian Neural Networks (HNNs) are designed to learn and adhere to exact conservation laws, such as energy conservation, in an unsupervised fashion. The tool provides practical examples for various tasks, including modeling ideal mass-spring systems, pendulums (both ideal and real), two-body and three-body problems, and pixel observations of a pendulum. HNNs demonstrate faster training and better generalization compared to regular neural networks, with the added benefit of being perfectly reversible in time. This makes it particularly useful for researchers and developers working on physics-informed machine learning.

natbot is an open-source project designed to automate browser interactions using GPT-3. It allows users to control a web browser through AI commands, effectively turning natural language instructions into browser actions. The tool is hosted on GitHub, indicating a developer-centric approach and encouraging community contributions for its enhancement. While currently a foundational tool, the project roadmap includes improvements such as better prompt engineering, prompt chaining, enhanced DOM serialization, and the ability for the agent to manage multiple tabs. This makes natbot a valuable resource for developers looking to experiment with AI-driven browser automation and contribute to its evolution.

Ithaca is a pioneering deep neural network developed by Google DeepMind for the restoration, geographical, and chronological attribution of ancient Greek inscriptions. This open-source tool significantly enhances the historian's workflow by providing a collaborative, decision-support, and interpretable architecture. It achieves 62% accuracy in restoring damaged texts, and when used by historians, their performance leaps from 25% to 72%. Ithaca can also attribute inscriptions to their original location with 71% accuracy and date them with a distance of less than 30 years from ground-truth ranges, contributing to critical debates in Ancient History. The project includes an interactive online notebook and an offline library for advanced users.