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Environments

Synthetic Environments / Long Horizon Tasks / Digital Control Agents

Motivation

  • We're building Environments to have key desiderata for long-horizon language agents
  • Snapshotting and reproducibility
  • Statefulness as a first-class citizen
  • Consistent abstractions for engine interaction and state reads across settings
  • Environment observability and tracing
  • HTTP access for simplified training and evaluation
  • Strong data abstractions to enable easily-configurable filtering and curriculum learning
  • Simple abstractions for agent-originated tool edits, etc.

User Guide

Note - this repo is under extremely active development. Hic sunt dracones, if not contributing it may be more useful as a reference or development resource than as core code for your production systems.

Supported Environments

[] Sokoban (maturing, not active dev) - This environment is not of our own making! Please credit the great researchers at Deepmind and Ragen, among others. - Checkout test_synth_react.py for a hello world example! [] Hendryks Math [] [] Crafter-Classic (maturing) [] EnronBench (active dev) [] SWE-Bench (active dev - do not attempt) [] NMMO (active dev - do not attempt)

...

Development

To use the Astral suite of devtools: package management: uv sync, uv add, uv remove linting: ruff format . type(hint) checking: uvx ty check

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