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.

Windframe is an innovative tool for rapidly prototyping and building visually appealing web pages using Tailwind CSS. Its AI-enhanced visual builder features a range of pre-designed templates and blocks that make it easy to start any project. Users can import, edit, and customize HTML components directly within the editor, thereby enhancing productivity and reducing development time. Windframe supports drag-and-drop functionalities, AI-generated templates, and advanced organizational tools to streamline the design process.

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.

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.

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.

UI Code Agent listens to natural language prompts describing desired user interfaces and generates corresponding frontend code in React, Vue, or plain HTML/CSS. It integrates with OpenAI's API and LangChain for prompt processing, offers a live preview of generated components, and allows style customization. Developers can export code files or copy snippets directly into their projects. The agent runs as a web UI or CLI tool, enabling seamless integration into existing workflows. Its modular architecture supports plugins for additional frameworks and can be extended to incorporate company-specific design systems.

Code Agent combines planning, coding, testing, and debugging into a seamless pipeline. Users provide a project directory and a description of desired functionality. The agent then breaks down the task, generates code, executes tests, analyzes failures, and applies fixes in a loop until tests pass. It supports multiple programming languages, integrates with existing test suites, and commits changes automatically to version control. By automating repetitive tasks and error resolution, Code Agent accelerates prototyping and continuous integration.

JaCaMo provides a unified environment for designing and running multi-agent systems (MAS) by integrating three core components: the Jason agent programming language for BDI-based agents, CArtAgO for artifact-based environmental modeling, and Moise for specifying organizational structures and roles. Developers can write agent plans, define artifacts with operations, and organize groups of agents under normative frameworks. The platform includes tooling for simulation, debugging, and visualization of MAS interactions. With support for distributed execution, artifact repositories, and flexible messaging, JaCaMo enables rapid prototyping and research in areas like swarm intelligence, collaborative robotics, and distributed decision-making. Its modular design ensures scalability and extensibility across academic and industrial projects.

MARFT is a Python-based LLMs, enabling reproducible experiments and rapid prototyping of collaborative AI systems.