pip install loopgym

🚀 The idea in one picture

flowchart TB
  SPEC["Your LSS YAML"]
  MAKE["loopgym.make(env_id)"]

  SIM["SimEnv<br/><i>deterministic · free · CI-safe</i>"]
  LIVE["LiveEnv<br/><i>real models · production eval</i>"]
  REPLAY["ReplayEnv<br/><i>LoopNet trajectories · zero API cost</i>"]

  SPEC --> MAKE
  MAKE --> SIM
  MAKE --> LIVE
  MAKE --> REPLAY

LSS declares the loop. LoopGym runs it. LoopBench scores it. Clean separation — like Gym vs. benchmark suites in reinforcement learning.

Run cost vs fidelity — and everything else you get

Pick the backend that matches your stage. SimEnv and ReplayEnv cost $0; LiveEnv uses real model spend when you need production truth.

The unlock: develop, test, benchmark, and regress before you burn tokens in prod.

⚡ Three backends, one line of code

import loopgym as lg

env = lg.make("loopbench/code-repair-v1")
obs = env.reset(task_id="cr-001")

while not env.done:
    action = your_agent.policy(obs)
    obs, reward, done, info = env.step(action)

🛠️ Try it in 60 seconds

pip install loopgym

python -c "
import loopgym as lg
env = lg.make('loopbench/code-repair-v1')
obs = env.reset(task_id='cr-001')
print('task:', obs.task_id, '| step:', obs.step)
"

Full quickstart:

git clone https://github.com/KanakMalpani/LoopGym.git && cd LoopGym
pip install -e ".[dev]"
python examples/quickstart.py
pytest tests/ -q

📈 Validate and reproduce

Ran a replay or SimEnv episode? Follow REPRODUCE.md and post on Discussion #10. Export trajectories via loopnet COMMUNITY-SUBMISSION.

🗺️ Environments (v0.1.3)

Bundled specs under envs/loopbench/ — validated against Loop Core Engineering in CI.

🎯 Who this is for

👁️ Observability

Trace loop iterations without raw chat logs (LTF 0.1):

pip install loopotel loopgym
python -c "
import loopgym as lg
from loopotel.integrations.loopgym import run_traced_episode
env = lg.make('loopbench/code-repair-v1')
result, trace = run_traced_episode(env, task_id='cr-001', seed=0, enabled=True)
print(result['success'], len(trace['spans']), 'spans')
"

Full stack walkthrough: LoopNet end-to-end tutorial.

⚙️ Ecosystem

Stack map: ECOSYSTEM.md

📝 Citation

@software{loopgym2026,
  title={LoopGym: OpenAI Gym for LSS-Defined Agent Loops},
  author={Malpani, Kanak},
  year={2026},
  url={https://pypi.org/project/loopgym/}
}