AIPE centralizes AI agent orchestration with pluggable modules for memory, planning, tool use, and multi-agent collaboration. Developers can define agent personas, incorporate context via vector stores, and integrate external APIs or databases. The framework offers a built-in web dashboard and CLI for testing prompts, monitoring agent state, and chaining tasks. AIPE supports multiple memory backends like Redis, SQLite, and in-memory stores. Its multi-agent setups allow assigning specialized roles—data extractor, analyst, summarizer—to tackle complex queries collaboratively. By abstracting prompt engineering, API wrappers, and error handling, AIPE speeds up deployment of AI-driven assistants for document QA, customer support and automated workflows.

The AWS Bedrock Multi-Agent Blueprint provides a modular framework to implement a multi-agent architecture on AWS Bedrock. It includes sample code for defining agent roles—planner, researcher, executor, and evaluator—that collaborate through shared message queues. Each agent can invoke different Bedrock models with custom prompts and pass intermediate outputs to subsequent agents. Built-in CloudWatch logging, error handling patterns, and support for synchronous or asynchronous execution demonstrate how to manage model selection, batch tasks, and end-to-end orchestration. Developers clone the repo, configure AWS IAM roles and Bedrock endpoints, then deploy via CloudFormation or CDK. The open-source design encourages extending roles, scaling agents across tasks, and integrating with S3, Lambda, and Step Functions.

The GA-based NQueen Solver uses a modular 2APL multi-agent architecture where each agent encodes a candidate N-Queen configuration. Agents evaluate their fitness by counting non-attacking queen pairs, then share high-fitness configurations with others. Genetic operators—selection, crossover, and mutation—are applied across the agent population to generate new candidate boards. Over successive iterations, agents collectively converge on valid N-Queen solutions. The framework is implemented in Java, supports parameter tuning for population size, crossover rate, mutation probability, and agent communication protocols, and outputs detailed logs and visualizations of the evolutionary process.

RoboCup Rescue Agent Simulation is an open-source framework that models urban disaster environments where multiple AI-driven agents collaborate to locate and rescue victims. It offers interfaces for navigation, mapping, communication, and sensor integration. Users can script custom agent strategies, run batch experiments, and visualize agent performance metrics. The platform supports scenario configuration, logging, and result analysis to accelerate research in multi-agent systems and disaster response algorithms.

VacuumWorld is an open-source simulation platform designed to facilitate the development and evaluation of multi-agent reinforcement learning algorithms. It provides grid-based environments where virtual vacuum cleaner agents operate to detect and remove dirt patches across customizable layouts. Users can adjust parameters such as grid size, dirt distribution, stochastic movement noise, and reward structures to model diverse scenarios. The framework includes built-in support for agent communication protocols, real-time visualization dashboards, and logging utilities for performance tracking. With simple Python APIs, researchers can quickly integrate their RL algorithms, compare cooperative or competitive strategies, and conduct reproducible experiments, making VacuumWorld ideal for academic research and teaching.