CommentGPT is an AI-powered tool designed to generate context-aware comments for social media posts. It uses advanced AI models to analyze the text, images, and existing comments to craft accurate responses. Users can select the comment type and language, and optionally add custom text for more personalized comments. It supports multi-language functionality including right-to-left languages like Hebrew and Arabic. This tool aims to provide engaging, polished comments in just a few clicks and works on all major social media platforms including Facebook, Instagram, Twitter, and LinkedIn.

The Context-First AGI Cognitive Context Engine (CCE) Prototype provides a robust toolkit for developers to implement context-aware AI agents. It leverages vector embeddings to store historical user interactions, enabling efficient retrieval of relevant context snippets. The engine automatically summarizes lengthy conversations to fit within LLM token limits, ensuring continuity and coherence in multi-turn dialogues. Developers can configure context prioritization strategies, manage memory lifecycles, and integrate custom retrieval pipelines. CCE supports modular plugin architectures for embedding providers and storage backends, offering flexibility for scaling across projects. With built-in APIs for storing, querying, and summarizing context, CCE streamlines the creation of personalized conversational applications, virtual assistants, and cognitive agents that require long-term memory retention.

gpt-func-calling is a developer toolkit that showcases OpenAI’s function calling feature, allowing chat-based AI to interact with external services. By defining function signatures in JSON, developers guide GPT-3.5/4 to recognize when to call a function, automatically format arguments, and handle the response in a structured manner. This streamlines integration with weather APIs, database queries, or custom business logic, ensuring consistent, reliable outputs without manual parsing.

IntelliConnect is a versatile AI agent framework that enables developers to build intelligent agents by connecting LLMs (e.g., GPT-4) with various external APIs and services. It supports multi-step reasoning, context-aware tool selection, and error handling, making it ideal for automating complex workflows such as customer support, data extraction from web or documents, scheduling, and more. Its plugin-based design allows easy extension, while built-in logging and observability help monitor agent performance and refine capabilities over time.

Modular LLM Architecture is designed to simplify the creation of customized LLM-driven applications through a composable, modular design. It provides core components such as memory modules for session state retention, tool interfaces for external API calls, prompt managers for template-based or dynamic prompt generation, and orchestration engines to control agent workflow. You can configure pipelines that chain together these modules, enabling complex behaviors like multi-step reasoning, context-aware responses, and integrated data retrieval. The framework supports multiple LLM backends, allowing you to switch or mix models, and offers extensibility points for adding new modules or custom logic. This architecture accelerates development by promoting reuse of components, while maintaining transparency and control over the agent’s behavior.