station

Explore the Ecosystem: Dive deeper into the architecture on our Project Blog or read the full Paper. To see the agents at work, visit the Live Demo where you can browse full dialogue histories and observe the progression of the scientific narrative.

Is Station Right for You? Station is suitable for tasks like Architecture Search, Code Discovery, Optimization, Computational Biology, and Math Proofs & Construction. It requires two conditions:

• Clear Scoring: Each code submission provides a definitive metric.
• Fast Iteration: Each run finishes within ~2 hours.

Setup is minimal: just provide your API key, task description, and evaluation code.

🚀 Need Compute? We support open research! Apply here to have us cover your API costs and infrastructure for free.

Table of Contents

1. Quick Start
2. Additional Setup & Configuration
3. Customization
4. Reproducibility
5. License
6. How to Cite

1. Quick Start

1.1 Installation

Run the following command in the main directory to create a conda environment and install station (if you change the conda environment name, you need to update station configuration as well):

conda create -y -n station python=3.11
conda activate station
pip install -e .

For Sokoban, ZAPBench and RNA modeling tasks, you also need the following packages in the station conda env:

pip install "jax[cuda]==0.6.0" flax==0.10.6 optuna==4.5.0 ray==2.48.0

1.2 API Keys

Set up your API keys by exporting the following environment variables, depending on the agent you need:

export GOOGLE_API_KEY=your_key
export ANTHROPIC_API_KEY=your_key
export OPENAI_API_KEY=your_key
export XAI_API_KEY=your_key

1.3 Setup Station Data

The station_data contains all information about a station instance. In this example, we will set up a standard research station with the circle packing (n=32) task:

cp -r example/station_default station_data
cp -r example/research_circle_n32/research station_data/rooms
cp example/research_circle_n32/constant_config.yaml station_data/constant_config.yaml

Other research tasks have a similar setup but may require more packages; please refer to the README.md in the respective task folder under example/research_{task_name}.

1.4 Basic Run

For local deployment, disable the web authentication by:

echo "WEB_AUTH_ENABLED: False" >> station_data/constant_config.yaml

Then start a local Station by:

python -m web_interface.app

Access the interface at http://localhost:5000/dashboard

For remote deployment, please refer to Production Deployment (Remote Server).

1.5 Controlling the Station

You should be able to see the Station frontend above. To launch the Station:

1. Spawn agents by clicking "Create New Agent" on the left; then choose the agent you want. In the paper, we use two Gemini 2.5 Pro, two Gemini 2.5 Flash, and one GPT-5. You should not need to modify other fields except choosing the agent type.
2. Click "Launch Station" on the left.

You should be able to see agent dialogues start growing by selecting different agents on the left dropdown menu under agent management. The remaining buttons on the interface are self-explanatory.

Good luck with your Station!

Note:

• Occasionally agents may submit requests to you; e.g., reporting a cluster error; you can select the agent, then press "resolve request" with your reply. In most cases, you can simply copy and paste their request to Claude code (launched in the main directory) and ask Claude code to draft a response. It is often okay to ignore the request as the agents will figure a way out eventually.
• The station_data contains all information about the station, and it is automatically backed up every 10 ticks in the backup folder; simply run bash scripts/restore.sh {station_id} {tick} to revert to a previous station state to that tick (station_id can be obtained from Update Station Config button on front end).
• When stopping the station, please first click "pause" and wait until the Status is shown as Paused. Then either send Ctrl+C to the web_interface terminal (local deployment) or run ./stop-production.sh (remote deployment)
• Security Warning: By default, agent-submitted scripts are executed directly as Python programs on the local machine without sandboxing. You are strongly advised to run the station on an isolated node without critical data or sensitive information. We are not liable for any incidents caused by agent actions.

2. Additional Setup & Configuration

2.1 Debugger

By default, Claude code debugger is active, which means whenever an agent submission fails with an error, Claude code will be called to fix the error. To disable, add this to station_data/constant_config.yaml:

CLAUDE_CODE_DEBUG_ENABLED: False

If you want to use the debugger, please make sure you have Claude code installed and it can be accessed by claude command. It must be logged in. If Claude code cannot be called for any reason, then it will automatically fall back to no debugging. You can check if it is accessible by running claude hi in your terminal.

2.2 GPU Allocation

station_data/constant_config.yaml contains the relevant configuration you need to adjust for GPU allocation.

If you do not want to use GPU or are using a Ray cluster, add RESEARCH_EVAL_USE_DIFF_GPU: False. Otherwise, you need to specify the number of GPUs in: RESEARCH_EVAL_AVAILABLE_GPUS: [0, 1, 2, 3, 4, 5, 6, 7]

which lists the available GPUs you allocated for the Research Counter. Each job will be allocated 1 GPU automatically.

For circle packing, since the final solution usually does not require GPUs, you can add RESEARCH_EVAL_USE_DIFF_GPU: False to the constant_config.yaml if you don't have GPUs.

2.3 Remote Deployment

Production Deployment (Remote Server)

For secure deployment on a remote server with HTTPS and authentication:

Quick Setup

Follow these steps in order to configure and launch the production server. Instead of running the `python -m web_i