Genworlds

Inspired by research from Stanford and Google on generative agents, GenWorlds provides the architectural primitives Worlds, Agents, Objects, Memories, and Brains to design and deploy sophisticated AI agent ecosystems with real-time, asynchronous communication between all participants.

The framework is available on GitHub and integrates natively with LangChain for agent capability composition.

At the core of GenWorlds is the concept of a 'World' a configurable interactive environment populated with Agents and Objects that can take actions and trigger events.

Developers define the rules, capabilities, and communication protocols of their World, then populate it with AI agents assigned specific objectives and behaviors. Agents interact asynchronously through events, enabling parallel execution of complex tasks without bottlenecks from sequential processing.

Dynamic memory management is a key capability of GenWorlds agents. Each agent can be equipped with long-term memory systems that allow it to store, recall, and learn from past interactions and outcomes.

This persistent memory enables agents to build knowledge over time, adapt their strategies based on experience, and maintain coherent behavior across extended task execution sessions. The memory architecture supports multiple storage backends for flexibility.

GenWorlds supports a variety of cognitive processing approaches for agents, including Tree of Thoughts, Chain of Thoughts, and AutoGPT-style reasoning allowing different agents within the same World to think in different ways, specialized to their designated roles.