Multiagent-Prediction-Reward is a research-oriented framework that integrates prediction models and reward distribution mechanisms for multi-agent reinforcement learning. It includes environment wrappers, neural modules for forecasting peer actions, and customizable reward routing logic that adapts to agent performance. The repository provides configuration files, example scripts, and evaluation dashboards to run experiments on cooperative tasks. Users can extend the code to test novel reward functions, integrate new environments, and benchmark against established multi-agent RL algorithms.

AgentMatch.AI leverages extensive data analysis to match you with the top-performing real estate agents in your area. Whether you're buying or selling, our platform analyzes thousands of agents and their performances to provide you with personalized recommendations. Our technology ensures you connect with the best professionals who can close transactions faster and achieve better prices, helping you navigate the complexities of real estate with ease.

CallZen is an advanced conversational intelligence tool designed to transform customer interactions. By transcribing and analyzing calls, chats, and meetings, CallZen identifies key moments, scores agent performance, and provides actionable insights. Features include sentiment analysis, automated compliance audits, and custom analytics. This empowers businesses to optimize agent performance, improve customer service, increase sales conversions, and ensure compliance through AI-driven insights.

Easy-Agent accelerates AI agent development by providing a modular framework that integrates LLMs with external tools, in-memory session tracking, and configurable action flows. Developers start by defining a set of tool wrappers that expose APIs or executables, then instantiate an agent with desired reasoning strategies—such as single-step, multi-step chain-of-thought, or custom prompts. The framework manages context, invokes tools dynamically based on model output, and tracks conversation history through session memory. It supports asynchronous execution for parallel tasks and solid error handling to ensure robust agent performance. By abstracting complex orchestration, Easy-Agent empowers teams to deploy intelligent assistants for use cases like automated research, customer support bots, data extraction pipelines, and scheduling assistants with minimal setup.