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Station

A Python-based open-world, multi-agent research environment that models a miniature scientific ecosystem.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Station — OpenViST project profile in Multi-Agent Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Station when the research task can be scored clearly and each run can finish in roughly two hours. The repository specifically identifies several scientific and technical task types as good fits.

Who it is for

  • Researchers studying autonomous scientific discovery
  • Mathematicians and other researchers interested in AI-assisted discovery
  • Technical users building self-hosted multi-agent research simulations
  • Teams exploring shared literature workflows for research agents

Documented uses

  • Run AI-agent research tasks with measurable scores
  • Explore architecture search, code discovery, optimization, computational biology, mathematical construction and data analysis
  • Inspect an agent research process through the project’s viewers
  • Build a self-hosted environment for collaborative research agents
Before you choose

Requirements & limits

The project is a public, Apache-2.0-licensed repository intended for self-hosted use. Its documented launch setup requires the OpenAI Codex CLI and API keys for selected model providers.

  • Whether the task has a clear scoring method
  • Whether a run can complete in roughly two hours
  • Whether the user can provide model-provider API keys
  • Whether the user can install and authenticate the OpenAI Codex CLI
  • Whether task-specific additional packages can be installed

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Station for a self-hosted research run

Create the Python environment, configure Codex and provider credentials, and prepare Station for initialization.

Before you start

  • A checkout of the Station repository.
  • Conda and Python 3.11.
  • The OpenAI Codex CLI, installed and authenticated for the same OS user.
  • API keys for the model providers you plan to use.
  • Additional task packages where required by the task README.

Steps

  1. From the repository root, create and activate a conda environment with Python 3.11.

  2. Install Station with pip install -e .

  3. Install the OpenAI Codex CLI, authenticate it, and verify it with codex --version. Set CODEX_BIN_PATH in .env if the executable is not on PATH.

  4. Export the provider API keys you plan to use, or configure keys, endpoints and proxies through More Tools > Set API Keys.

  5. Run ./deploy.sh with a secure password, then proceed to the documented initialization and run stage.

Expected outcome

The deployment configuration is prepared for initializing and running Station.

Guide limitations

  • Some tasks require additional packages listed in their task README.
  • The documented setup requires provider API keys and Codex authentication.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
30 September 2026
Original website title
dualverse-ai/station · Official homepage title · retained in its source language
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