Codeless ONE is an intuitive no-code development platform powered by AI. It allows users to build enterprise-scale applications rapidly without needing any coding expertise. The platform supports rapid prototyping, facilitates intuitive development, and ensures streamline transitions from ideation to launch. It is designed to cater to various business needs, from project management to sales CRM, making it a versatile solution for enterprises looking to enhance their digital transformation processes.

This project demonstrates a comprehensive framework for creating retrieval-augmented AI agents using LlamaIndex. It guides developers through the entire workflow, starting with document ingestion and vector store creation, followed by defining a custom agent loop for contextual question-answering. Leveraging LlamaIndex's powerful indexing and retrieval capabilities, users can integrate any OpenAI-compatible language model, customize prompt templates, and manage conversation flows via a CLI interface. The modular architecture supports various data connectors, plugin extensions, and dynamic response customization, enabling rapid prototyping of enterprise-grade knowledge assistants, interactive chatbots, and research tools. This solution streamlines building domain-specific AI agents in Python, ensuring scalability, flexibility, and ease of integration.

Amoga is a cutting-edge, low-code work platform designed to enable businesses to build enterprise-grade applications up to 10 times faster than traditional methods. It offers a highly intuitive interface that simplifies information sharing, task assignment, and progress tracking. The platform is versatile enough to be adopted across various industries including healthcare, manufacturing, and more. By allowing users to create custom solutions or utilize pre-built applications, Amoga significantly enhances organizational productivity and efficiency, driving growth and seamless project execution.

Stellar Chat empowers teams to build conversational AI agents by providing a robust framework that abstracts LLM interactions, memory management, and tool integrations. At its core, it features an extensible pipeline that handles user input preprocessing, context enrichment through vector-based memory retrieval, and LLM invocation with configurable prompting strategies. Developers can plug in popular vector storage solutions like Pinecone, Weaviate, or FAISS, and integrate third-party APIs or custom plugins for tasks like web search, database queries, or enterprise application control. With support for streaming outputs and real-time feedback loops, Stellar Chat ensures responsive user experiences. It also includes starter templates and best-practice examples for customer support bots, knowledge search, and internal workflow automation. Deployed with Docker or Kubernetes, it scales to meet production demands while remaining fully open-source under the MIT license.