TinyAgent provides a lightweight agent framework for orchestrating complex tasks with OpenAI GPT models. Developers install via pip, configure an API key, define tools or plugins, and leverage in-memory context to maintain multi-step conversations. TinyAgent supports chaining tasks, integrating external APIs, and persisting user or system memories. Its simple Pythonic API lets you prototype autonomous data analysis workflows, customer service chatbots, code generation assistants, or any use case requiring an intelligent, stateful agent. The library remains fully open-source, extensible, and platform-agnostic.

This repository provides a hands-on demonstration of two-way communication between agents built on the JADE framework. It includes example Java classes showing agent setup, FIPA-ACL compliant message creation, and asynchronous behavior handling. Developers can study Agent A sending a REQUEST, Agent B processing the request, and returning an INFORM message. The code illustrates registering agents with the Directory Facilitator, using cyclic and one-shot behaviors, applying message templates to filter messages, and logging conversation sequences. It’s an ideal starting point for prototyping multi-agent exchanges, custom protocols, or integrating JADE agents into larger distributed AI systems.

FastAPI Agents provides a robust service layer for developing LLM-based agents using the FastAPI web framework. It allows you to define agent behaviors with LangChain chains, tools, and memory systems. Each agent can be exposed as a standard REST endpoint, supporting asynchronous requests, streaming responses, and customizable payloads. Integration with vector stores enables retrieval-augmented generation for knowledge-driven applications. The framework includes built-in logging, monitoring hooks, and Docker support for containerized deployment. You can easily extend agents with new tools, middleware, and authentication. FastAPI Agents accelerates the production readiness of AI solutions, ensuring security, scalability, and maintainability of agent-based applications in enterprise and research settings.

AiChat offers a versatile toolkit for creating intelligent chat agents by providing role-based prompt management, memory handling, and streaming response capabilities. Users can set up multiple conversational roles, such as system, assistant, and user, to shape dialogue context and behavior. The framework supports plugin integrations for external APIs, data retrieval, or custom logic, enabling seamless extension of functionalities. AiChat's modular design allows easy swapping of language models and configuration of feedback loops to refine responses. Built-in memory features provide context persistence across sessions, while streaming API support delivers low-latency interactions. Developers benefit from clear documentation and sample projects to accelerate deployment of chatbots across web, desktop, or server environments.