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# econagents

econagents is a Python library that lets you use LLM agents in economic experiments. The framework connects LLM agents to game servers through WebSockets and provides a flexible architecture for designing, customizing, and running economic simulations.

## Installation

```shell
# Install from PyPI
pip install econagents

# Or install directly from GitHub
pip install git+https://github.com/iwanalabs/econagents.git
```

## Framework Components

econagents consists of four key components:

1. **Agent Roles**: Define player roles with customizable behaviors using a flexible prompt system.
2. **Game State**: Hierarchical state management with automatic event-driven updates.
3. **Agent Managers**: Manage agent connections to game servers and handle event processing.
4. **Game Runner**: Orchestrates experiments by gluing together the other components.

## Example Experiments

The repository includes three example games:

1. **`prisoner`**: An iterated Prisoner's Dilemma game with 5 rounds and 2 LLM agents.
2. **`ibex_tudelft/harberger`**: A Harberger Tax simulation with LLM agents.
3. **`ibex_tudelft/futarchy`**: A Futarchy simulation with LLM agents.

### Running the Prisoner's Dilemma game

The simplest game to run is a version of the repeated prisoner's dilemma game that runs on your local machine.

```shell
# Run the server
python examples/server/prisoner/server.py

# Run the experiment (in a separate terminal)
python examples/prisoner/run_game.py
```

Note: you still have to set up the connection to the agents.

## Key Features

- **Flexible Agent Customization**: Customize agent behavior with Jinja templates or custom Python methods
- **Event-Driven State Management**: Automatically update game state based on server events
- **Turn-Based and Continuous Action Support**: Handle both turn-based games and continuous action phases
- **LangChain Integration**: Built-in support for LangChain's agent capabilities

## Documentation

For detailed guides and API reference, visit [the documentation](https://econagents.readthedocs.io/en/latest/).
