An OpenEnv benchmark testing the ability of AI agents to act as Site Reliability Engineers (SREs) by diagnosing and filtering raw production failure logs.

Deterministic evaluation environment for AI code reviewers covering bugs, security (OWASP), and architecture via FastAPI + OpenEnv.

AI-powered system for low-exposure route optimization using AQI, simulation, and intelligent decision-making

AI research environment that simulates the end-to-end scientific discovery process, enabling agents to analyze papers, generate hypotheses, design experiments, and validate results collaboratively

An OpenEnv-compliant reinforcement learning environment designed to train and evaluate AI agents on real-world SQL debugging, performance tuning, and schema design.

Gymnasium RL environment for AI-powered customer support triage — classify, prioritize, assign, and respond to emails under SLA pressure. Built for the Meta PyTorch Hackathon under the OpenEnv spec.

📧 Intelligent Agentic Workflow for Autonomous Enterprise Email Triage. Built with OpenEnv, featuring Chain-of-Thought reasoning and Self-Correcting agent logic for high-stakes corporate routing.

High-fidelity Reinforcement Learning environment for smart grids. Features a custom DC Power Flow physics solver and real-world AT&C telemetry to train AI in power distribution and fault isolation.

Adaptive Clinical Trial Design RL Environment

RunbookOps-caseop: Deterministic OpenEnv environment for SaaS incident triage, runbook-driven resolution, and agent evaluation.

A reinforcement learning agent that learns to intelligently shape electricity demand, reducing peak loads and optimizing energy consumption in real-time.

Chess OpenEnv Multi-Agent System

Agentic Reinforcement Learning Loop to make Scientific Discoveries on Mars

A production-grade OpenEnv environment for benchmarking RL agents on real-world data cleaning and schema engineering tasks.

PyTorch Training Optimization RL Environment

A rigorously formulated, non-stationary Partially Observable Markov Decision Process (POMDP) environment evaluating LLM crisis triage under real-world FEMA/ICS resource constraints. Features mathematical trajectory-aware reward shaping, NHPP disaster spawning, and 422→200 hallucination recovery.

Adaptive RL Reliability is an OpenEnv-compatible reinforcement learning environment for live-system autoscaling. It trains agents to make production capacity control decisions (scale down/hold/up) while protecting SLOs for latency, error rates, and CPU usage.

OpenEnv Framework based Multi-Agent Planning RL Environment - For Meta Pytorch OpenEnv Hackathon

Multi-zone disaster relief AI env for Meta PyTorch OpenEnv Hackathon. 4-stage pipeline: PyTorch ZoneScorerNet -> Triage -> Planner -> Action Agent. False SOS detection, cascading failures, airlift precision.

Deterministic AI evaluation environment for customer support agents with explainable scoring and real-world task simulation.