OSU NLP Middleware is a lightweight framework built in Python that simplifies the development of AI agent systems. It provides a core agent loop that orchestrates interactions between natural language models and external tool functions defined as plugins. The framework supports popular LLM providers (OpenAI, Hugging Face, etc.), and enables developers to register custom tools for tasks like database queries, document retrieval, web search, mathematical computation, and RESTful API calls. Middleware manages conversation history, handles rate limits, and logs all interactions. It also offers configurable caching and retry policies for improved reliability, making it easy to build intelligent assistants, chatbots, and autonomous workflows with minimal boilerplate code.

Swarms operates as a Python-first framework and web-based interface, empowering users to configure individual agents with specific roles, memory management, and custom prompts. Users define agent interactions through a visual flow builder or YAML configuration, orchestrating complex decision trees, debates, and collaborative tasks. The platform supports plugin integration for data querying, knowledge base access, and third-party API calls. Upon deployment, Swarms provides real-time monitoring of agent activities, performance metrics, and logs. It scales horizontally using container orchestration tools, enabling large-scale AI simulations, robotic control architectures, or intelligent workflow automations. The open-source architecture ensures extensibility, community-driven enhancements, and self-hosting options for full data control.

Astro Agents provides a modular architecture for building AI agents in JavaScript and TypeScript. Developers can register custom tools for data lookup, integrate memory stores to preserve conversational context, and orchestrate multi-step reasoning workflows. It supports multiple LLM providers such as OpenAI and Hugging Face, and can be deployed as static sites or serverless functions. With built-in observability and extensible plugins, teams can prototype, test, and scale AI-driven assistants without heavy infrastructure overhead.