ChatGPT Prompt Automation Queue is a Chrome extension designed to automate your ChatGPT workflows. It allows you to save and reuse sequences of prompts to ChatGPT, sending them one by one automatically. This extension supports multiple GPT versions and works on all operating systems through Chrome. Perfect for bloggers, researchers, content creators, and developers, it helps in automating common tasks, making your work more efficient and time-saving.

Deep Research Agent leverages OpenAI's GPT models to perform advanced document retrieval and analysis. Users configure data sources (e.g., PubMed, arXiv), define queries, and receive digestible summaries highlighting methods, results, and key arguments. It supports multi-document comparison, citation extraction, and interactive Q&A sessions. Modular architecture allows integration of custom connectors, NLP pipelines, and export formats like markdown or JSON. With built-in scheduling, it can periodically update literature reviews, detect new research trends, and generate reports. Ideal for research teams, academics, and industry analysts seeking to reduce manual reading time and improve insight discovery in vast scientific corpora.

Agentic Kernel offers a decoupled architecture for constructing AI agents by composing reusable components. Developers can define planning pipelines to break down goals, configure short-term and long-term memory stores using embeddings or file-based backends, and register external tools or APIs for action execution. The framework supports dynamic tool selection, agent reflection cycles, and built-in scheduling to manage agent workflows. Its pluggable design accommodates any LLM provider and custom components, enabling use cases such as conversational assistants, automated research agents, and data-processing bots. With transparent logging, state management, and easy integration, Agentic Kernel accelerates development while ensuring maintainability and scalability in AI-driven applications.

Autonomais is a modular AI agent framework designed for full autonomy in task planning and execution. It integrates large language models to generate plans, orchestrates actions via a customizable pipeline, and stores context in memory modules for coherent multi-step reasoning. Developers can plug in external tools like web scrapers, databases, and APIs, define custom action handlers, and fine-tune agent behavior through configurable skills. The framework supports logging, error handling, and step-by-step debugging, ensuring reliable automation of research tasks, data analysis, and web interactions. With its extensible plugin architecture, Autonomais enables rapid development of specialized agents capable of complex decision-making and dynamic tool usage.

Multi-Agent-LLM is designed to streamline the orchestration of multiple AI agents powered by large language models. Users can define individual agents with unique personas, memory storage, and integrated external tools or APIs. A central AgentManager handles communication loops, allowing agents to exchange messages in a shared environment and collaboratively advance towards complex objectives. The framework supports swapping LLM providers (e.g., OpenAI, Hugging Face), flexible prompt templates, conversation histories, and step-by-step tool contexts. Developers benefit from built-in utilities for logging, error handling, and dynamic agent spawning, enabling scalable automation of multi-step workflows, research tasks, and decision-making pipelines.