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.

AI Web Scraper automates the process of collecting and structuring data from websites by combining a headless browser for rendering JavaScript with powerful AI-driven parsing. Users supply a URL and optional custom prompts, and the tool fetches the page, renders dynamic content, and feeds the result into a large language model to extract tables, lists, metadata, summaries, or any user-defined information. Output is provided in clean JSON, ready for downstream processing or integration into data pipelines.