BeeAI Framework Py Starter is an open-source Python project designed to bootstrap AI agent creation. It includes core modules for agent orchestration, a plugin system to extend functionality, and adapters for connecting to popular LLM APIs. Developers can define tasks, manage conversational memory, and integrate external tools through simple configuration files. The framework emphasizes modularity and ease of use, enabling rapid prototyping of chatbots, automated assistants, and data-processing agents without boilerplate code.

Java-Action-Datetime is an add-on module for the LightJason multi-agent system framework, designed to handle all temporal operations within your agents. It provides actions to retrieve the current timestamp, parse date/time strings into Java temporal objects, apply custom formatting patterns, perform arithmetic operations such as adding or subtracting durations, compute differences between datetimes, and convert between timezones. These actions seamlessly integrate into LightJason agent code, reducing boilerplate and enabling reliable, consistent temporal reasoning across distributed agent deployments.

Codebuddy is a versatile AI Assistant designed to integrate seamlessly into developers' existing workflows via a simple CLI interface. By connecting to OpenAI's API, it can generate syntactically correct code snippets in languages such as Python, JavaScript, and Java based on natural language prompts. It also analyzes existing code to suggest refactorings, identify bugs, and optimize performance. With its built-in unit test generation, developers can quickly create test cases without manual boilerplate coding. Codebuddy can draft commit messages, document functions, and explain complex code blocks in plain English. Its context-aware suggestions ensure that recommendations align with the project's style and dependencies, significantly reducing development time and enhancing code quality.

Dev-Agent is an open-source AI agent framework that empowers developers to rapidly build and deploy autonomous agents. It combines a modular plugin architecture with easy-to-configure tool invocation, including HTTP endpoints, database queries, and custom scripts. Agents can leverage a persistent memory layer to reference past interactions, and orchestrate multi-step reasoning flows for complex tasks. With built-in support for OpenAI GPT models, users define agent behavior via simple JSON or YAML specs. The CLI tool manages authentication, session state, and logging. Whether creating customer support bots, data retrieval assistants, or automated CI/CD helpers, Dev-Agent reduces development overhead and enables seamless extension through community-driven plugins, offering flexibility and scalability for diverse AI-driven applications.

LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.

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.