Agentic Shared Memory provides a robust solution for managing contextual data in AI-driven multi-agent environments. Leveraging vector embeddings and efficient data structures, it stores agent observations, decisions, and state transitions, enabling seamless context retrieval and update. Agents can query the shared memory to access past interactions or global knowledge, fostering coherent behavior and collaborative problem-solving. The library supports plug-and-play integration with popular AI frameworks like LangChain or custom agent orchestrators, offering customizable retention strategies, context windowing, and search functions. By abstracting memory management, developers can focus on agent logic while ensuring scalable, consistent memory handling across distributed or centralized deployments. This improves overall system performance, reduces redundant computations, and enhances agent intelligence over time.

GenWorlds is an AI development framework designed to facilitate the creation of multi-agent systems. Utilizing an event-based communication framework via websocket, it allows developers to set up interactive environments where autonomous agents can asynchronously interact with each other and their surroundings. These agents collaborate, plan actions, and execute complex tasks collectively, making GenWorlds a robust platform for creating scalable and flexible AI ecosystems.

Multi-Agent-LLM is designed to streamline the orchestration of multiple AI agents powered by large language models. Users can define individual agents with unique personas, memory storage, and integrated external tools or APIs. A central AgentManager handles communication loops, allowing agents to exchange messages in a shared environment and collaboratively advance towards complex objectives. The framework supports swapping LLM providers (e.g., OpenAI, Hugging Face), flexible prompt templates, conversation histories, and step-by-step tool contexts. Developers benefit from built-in utilities for logging, error handling, and dynamic agent spawning, enabling scalable automation of multi-step workflows, research tasks, and decision-making pipelines.