Orra.dev is a comprehensive AI agent creation platform designed to simplify the end-to-end lifecycle of intelligent assistants. By combining a visual workflow builder with seamless integrations to leading LLM providers and enterprise systems, Orra.dev allows teams to prototype conversation logic, refine agent behavior, and launch production-ready bots across multiple channels within minutes. Features include access to pre-built templates for FAQ bots, e-commerce assistants, and code review agents, along with customizable triggers, API connectors, and user role management. With built-in testing suites, collaborative versioning, and performance dashboards, organizations can iterate on agent responses, monitor user interactions, and optimize workflows based on real-time data, accelerating deployment and reducing maintenance overhead.

Python Coding Agent is an open-source command-line tool that uses GPT models to generate Python code based on text prompts, execute that code locally, and catch runtime errors. It provides instant feedback, allowing users to iteratively refine code, automate repetitive scripting tasks, prototype data analysis pipelines, and debug functions. By combining natural language understanding with real-time code execution, it bridges the gap between idea and implementation, speeding up development and learning.

SwiftAgent offers a robust toolkit for constructing intelligent agents by integrating OpenAI's models directly in Swift. Developers can declare custom actions and external tools, which agents invoke based on user queries. The framework maintains conversational memory, enabling agents to reference past interactions. It supports prompt templating and dynamic context injection, facilitating multi-turn dialogues and decision logic. SwiftAgent's async API works seamlessly with Swift concurrency, making it ideal for iOS, macOS, or server-side environments. By abstracting model calls, memory storage, and pipeline orchestration, SwiftAgent empowers teams to prototype and deploy conversational assistants, chatbots, or automation agents quickly within Swift projects.

SwiftSage leverages a large language model to interpret natural language descriptions and output fully functional SwiftUI views or Swift code modules. Users can request UI layouts, data models, or networking components, customize styling, and preview results in real-time. The tool supports iterative feedback, allowing developers and designers to refine code snippets until they meet project requirements. It streamlines prototyping, learning, and production stages of iOS app creation.

Xspiral is an AI-enhanced hybrid design and collaboration platform designed for creating stunning visual content. It merges powerful 2D and 3D design capabilities, enabling users to efficiently produce, manage, and share their designs in real-time. Whether you're a professional designer, a product manager, or a marketing expert, Xspiral facilitates intuitive workflows that streamline project collaboration. From rapid prototyping to animations, the platform empowers teams with the technology they need to deliver compelling visual graphics effortlessly.

This repository aggregates a comprehensive collection of multi-agent system components and distributed AI coordination techniques. It provides implementations of consensus algorithms, contract net negotiation protocols, auction-based task allocation, coalition formation strategies, and inter-agent communication frameworks. Users can leverage built-in simulation environments to model and test agent behaviors under varied network topologies, latency scenarios, and failure modes. The modular design allows developers and researchers to integrate, extend, or customize individual coordination modules for applications in robotics swarms, IoT device collaboration, smart grids, and distributed decision-making systems.

ASP-DALI provides a unified platform for defining and executing logic-based intelligent agents. Developers write ASP rules to represent agent knowledge and goals, while DALI constructs define event reactions and action executions. At runtime, an ASP solver computes answer sets that guide the agent’s decisions, enabling it to plan, react to incoming events, and adjust beliefs dynamically. The framework supports modular knowledge bases, facilitating incremental updates and clear separation between declarative rules and reactive behaviors. ASP-DALI is implemented in Prolog with interfaces to popular ASP solvers, simplifying integration and deployment across research and prototype scenarios.

KickstartDS is an open-source starter kit and next-gen UI development toolkit tailored for creating digital design systems. It features a low-code framework, comprehensive component library, and pattern library, enabling web development teams to establish consistent, brand-compliant web frontends efficiently. With KickstartDS, teams can quickly kickstart their design system projects, ensuring they adhere to best practices in UI and UX design.

Genie 2 is a revolutionary AI world modeling tool that uses an autoregressive latent diffusion model to generate fully playable, action-responsive 3D environments from a single image prompt. This technology supports realistic physics simulations, dynamic lighting, responsive object interactions, and complex character animations. The generated worlds can be manipulated in real-time, making Genie 2 an invaluable tool for rapid prototyping in game development, AI research, creative design workflows, and environment testing.

IoA provides a flexible architecture for defining, coordinating, and executing multiple AI agents in a unified workflow. Key components include a planner that decomposes high-level goals, an executor that dispatches tasks to specialized agents, and memory modules for context management. It supports integration with external APIs and toolkits, real-time monitoring, and customizable skill plugins. Developers can rapidly prototype autonomous assistants, customer support bots, and data processing pipelines by combining ready-made modules or extending them with custom logic.

Lightning AI is a comprehensive platform that integrates your favorite machine learning tools into a cohesive interface. It supports the entire AI development lifecycle, including data preparation, model training, scaling, and deployment. Designed by the creators of PyTorch Lightning, this platform provides robust capabilities for collaborative coding, seamless prototyping, scalable training, and effortless serving of AI models. The cloud-based interface ensures zero setup and a smooth user experience.

LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.

MASChat provides a flexible framework for orchestrating conversations among multiple AI agents powered by language models. Developers can define agents with specific roles—such as researcher, summarizer, or critic—and specify their prompts, permissions, and communication protocols. MASChat’s central manager handles message routing, ensures context preservation, and logs interactions for traceability. By coordinating specialized agents, MASChat decomposes complex tasks—like research, content creation, or data analysis—into parallel workflows, improving efficiency and insight. It integrates with OpenAI’s GPT APIs or local LLMs and allows plugin extensions for custom behaviors. MASChat is ideal for prototyping multi-agent strategies, simulating collaborative environments, and exploring emergent behaviors in AI systems.

OpenAssistant offers a comprehensive toolset for constructing and fine-tuning AI agents tailored to specific tasks. It includes data processing scripts to convert raw dialogue datasets into training formats, models for instruction-based learning, and utilities to monitor training progress. The framework’s plugin architecture allows seamless integration of external APIs for extended functionalities like knowledge retrieval and workflow automation. Users can evaluate agent performance using preconfigured benchmarks, visualize interactions through an intuitive web interface, and deploy production-ready endpoints with containerized deployments. Its extensible codebase supports multiple deep learning backends, enabling customization of model architectures and training strategies. By providing end-to-end support—from dataset preparation to deployment—OpenAssistant accelerates the development cycle of conversational AI solutions.

Rawr Agent is a modular, open-source Python framework that empowers developers to build autonomous AI agents by orchestrating complex workflows of LLM interactions. Leveraging LangChain under the hood, Rawr Agent lets you define task sequences either through YAML configurations or Python code, specifying tool integrations such as web APIs, database queries, and custom scripts. It includes memory components for storing conversational history and vector embeddings, caching mechanisms to optimize repeated calls, and robust logging and error handling to monitor agent behavior. Rawr Agent’s extensible architecture allows adding custom tools and adapters, making it suitable for tasks like automated research, data analysis, report generation, and interactive chatbots. With its simple API, teams can rapidly prototype and deploy intelligent agents for diverse applications.

The repository provides a step-by-step tutorial and reference implementation for creating a ReAct-based AI agent that uses DeepSeek for high-dimensional vector retrieval. It covers environment setup, dependency installation, and configuration of vector stores for custom data. The agent employs the ReAct pattern to combine reasoning traces with external knowledge searches, resulting in transparent and explainable responses. Users can extend the system by integrating additional document loaders, fine-tuning prompt templates, or swapping vector databases. This flexible framework enables developers and researchers to prototype powerful conversational agents that reason, retrieve, and interact seamlessly with various knowledge sources in a few lines of Python code.

AI Agents offers a structured toolkit for developers to build autonomous agents using large language models. It includes modules for integrating external APIs, managing conversational or long-term memory, orchestrating multi-step workflows, and chaining LLM calls. The framework provides templates for common agent types—data retrieval, question answering, and task automation—while allowing customization of prompts, tool definitions, and memory strategies. With asynchronous support, plugin architecture, and modular design, AI Agents enables scalable, maintainable, and extendable agentic applications.

Agents-Prompts is a comprehensive GitHub repository offering developers a structured collection of customizable prompt templates for building intelligent AI agents. These templates cover core functions such as memory management, dynamic instruction updates, multi-agent orchestration, decision-making logic, and API integration. Users can mix and match templates to define agent roles, tasks, and conversation flows, enabling rapid experimentation and prototyping. The repository also includes code samples for interfacing with major LLM services, examples for chaining agent actions, and guidelines for best practices when designing autonomous workflows. By leveraging these reusable prompt patterns, teams can accelerate development, maintain consistency across agents, and focus on higher-level application logic rather than low-level prompt engineering.

AgentVerse is designed to facilitate the creation of multi-agent architectures by offering a set of reusable modules and abstractions. Users can define unique agent classes with custom decision-making logic, establish communication channels for message passing, and simulate environmental conditions. The platform supports synchronous and asynchronous interactions among agents, enabling complex workflows such as negotiation, task delegation, and cooperative problem-solving. With integrated logging and monitoring, developers can trace agent actions and evaluate performance metrics. AgentVerse also includes templates for common use cases like autonomous exploration, trading simulations, and collaborative content generation. Its pluggable design allows seamless integration of external machine learning models, such as language models or reinforcement learning algorithms, providing flexibility for various AI-driven applications.

AI Reality is a platform that empowers users to build web AR prototypes seamlessly using AI technology. Harnessing the capabilities of Stable Diffusion and Open AI, the platform offers an intuitive and efficient way to generate augmented reality experiences. It caters to users ranging from beginners to experienced developers, making the creation of immersive digital experiences accessible and straightforward.