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.

Agents-Flex is a lightweight and elegant Java framework for Large Language Model (LLM) applications. It allows developers to define, parse and execute local methods efficiently. The framework supports local function definitions, parsing capabilities, callbacks through LLMs, and the execution of methods returning results. With minimal code, developers can harness the power of LLMs and integrate sophisticated functionalities into their applications.

Cyrano is an open-source Python framework and CLI for creating AI agents that orchestrate large language models and external tools through natural language prompts. Users can define custom tools (functions), configure memory and token limits, and handle callbacks. Cyrano handles parsing JSON responses from LLMs and executes specified tools in sequence. It emphasizes simplicity, modularity, and zero external dependencies, enabling developers to prototype chatbots, build automated workflows, and integrate AI capabilities into applications quickly.

llama-cpp-agent is an open-source C++ framework for running AI agents entirely offline. It leverages the llama.cpp inference engine to provide fast, low-latency interactions and supports a modular plugin system, configurable memory, and task execution. Developers can integrate custom tools, switch between different local LLM models, and build privacy-focused conversational assistants without external dependencies.

multiagent-golang provides a structured approach to building multi-agent systems in Go. It introduces an Agent abstraction where each agent can be equipped with various sensors to perceive its environment and actuators to take actions. Agents run concurrently using Go routines and communicate through dedicated messaging channels. The framework also includes an environment simulation layer to handle events, manage the agent lifecycle, and track state changes. Developers can easily extend or customize agent behaviors, configure simulation parameters, and integrate additional modules for logging or analytics. It streamlines the creation of scalable, concurrent simulations for research and prototyping.

Pocket AI is a portable AI accelerator equipped with the NVIDIA RTX A500 GPU. It is engineered to provide a perfect balance between power and performance, making it ideal for AI developers, data scientists, and tech enthusiasts. Its compact and lightweight design ensures easy portability, enabling users to carry powerful AI capabilities wherever they go. With seamless connectivity via USB PD, Pocket AI simplifies AI project execution on the go.