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Basic Information

GPTeam is an open-source project that creates and runs a simulated world of collaborative AI agents powered primarily by GPT models. The repository provides code and configuration to spawn multiple agents that communicate, move between locations, and work together to accomplish predefined goals. It is intended for exploring multi-agent productivity, agent memory and reflection, and emergent coordination using large language models. The project runs locally and requires API keys configured in an environment file. Users can launch the simulation with provided CLI commands, view per-agent state files in the agents/ folder, and customize the world via a config.json. The README also points to a web app, a video demo, and a detailed architecture blog post for additional context.

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Features
The repository implements a multi-agent simulation where each agent has its own memory and can communicate and collaborate. Agent memory and reflection are explicitly implemented and inspired by academic research. The world is configurable through a JSON file allowing changes to agents and locations. The project supports multiple LLM backends and modes, including GPT-4, a cheaper gpt-3.5-turbo option for running in "turbo" mode, optional Anthropic Claude support, and integration with the Window extension. Tooling includes setup and runtime commands (python setup.py, poetry run world, poetry run db-reset) and a Discord integration with separate setup docs. Runtime agent state is exposed as text files in an agents/ directory for inspection. The project is MIT licensed.
Use Cases
GPTeam helps developers and researchers prototype and study multi-agent systems using contemporary LLMs. It provides a hands-on environment to observe how separate agents with memory and communication coordinate on tasks and how different model backends affect behavior. The configurable world and simple CLI let users iterate on agent compositions, locations, and goals, and reset the database for fresh runs. Optional integrations such as Discord and Window allow natural interaction channels and alternative model access. The project is useful for experimenting with agent architectures, testing memory and reflection strategies inspired by research, demonstrating emergent collaboration in a controlled simulation, and producing reproducible demos or teaching materials.

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