Thousand Token Wood: shipping a multi-agent economy on a 3B model

New

Multi-agent economy simulation running on a 3B language model.

8.8 (63.944 score)

open-source

Overview

Thousand Token Wood demonstrates how to build a functioning multi-agent economy using only a 3 billion parameter language model. It's designed for researchers and developers exploring agent-based systems and economic simulations at scale with limited computational resources. The project shows that sophisticated multi-agent interactions don't require massive models.

Pros

Runs complete multi-agent economy on efficient 3B model
Open-source implementation available for community use and extension
Demonstrates feasibility of agent systems with smaller models
Includes hackathon-ready code for rapid experimentation

✕ Cons

Limited documentation beyond initial blog post and code
Requires technical expertise to set up and modify
Not designed as production-ready commercial service

Key Features

Multi-agent economy simulation

3B parameter language model

Open-source codebase

Agent interaction framework

Economic system modeling

Use Cases

Researchers studying agent-based economic systems efficientlyML engineers exploring multi-agent coordination at scaleHackathon participants building agent economy demosDevelopers prototyping agent systems with resource constraints

Best For

AI ResearchersMachine Learning EngineersHackathon ParticipantsEconomics ModelersOpen-Source Contributors

Frequently Asked Questions

What does Thousand Token Wood cost?▾

Thousand Token Wood is open-source, so there is no licensing fee. You can deploy it yourself on your own infrastructure or hardware.

How difficult is it to set up and learn?▾

Setup difficulty depends on your technical background. The hackathon-ready codebase is designed for rapid experimentation, but you'll need familiarity with Python, language models, and agent frameworks to customize it effectively.

Can it integrate with other tools or APIs?▾

As an open-source project, the codebase can be modified and integrated with external systems, though pre-built integrations depend on community contributions. Check the repository for available extensions and plugins.

What's the main limitation of this tool?▾

The 3B model size, while efficient, has reduced reasoning capacity compared to larger models, which may limit the complexity and realism of economic simulations in certain scenarios.

What's the ideal use case?▾

Ideal for researchers, educators, and developers exploring multi-agent systems, economic modeling, and AI feasibility studies on resource-constrained hardware without the cost of larger language models.