langchain/docs/use_cases/agent_simulations.md

Agent Simulations

Agent simulations involve interacting one of more agents with eachother. Agent simulations generally involve two main components:

• Long Term Memory
• Simulation Environment

Specific implementations of agent simulations (or parts of agent simulations) include

Simulations with Two Agents

• CAMEL: an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with each other.
• Two Player D&D: an example of how to use a generic simulator for two agents to implement a variant of the popular Dungeons & Dragons role playing game.

Simulations with Multiple Agents

• Multi-Player D&D: an example of how to use a generic dialogue simulator for multiple dialogue agents with a custom speaker-ordering, illustrated with a variant of the popular Dungeons & Dragons role playing game.
• Decentralized Speaker Selection: an example of how to implement a multi-agent dialogue without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks by outputting bids to speak. This example shows how to do this in the context of a fictitious presidential debate.
• Generative Agents: This notebook implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Park, et. al.