OpenResearch NEW

OpenResearch is an autoresearch platform that runs locally via a dashboard and CLI.

It assigns each research direction its own agent that runs in parallel within isolated worktrees.

Agents use existing tools and local compute while the platform can provision additional remote compute through a marketplace aggregating providers.

OpenResearch manages experiment lifecycles, including single-run reproduction, sweep optimization, scheduled runs, and ablation studies.

It captures run metadata and evaluation results to support reproducible workflows and track experiment progress.

The platform centralizes experiment orchestration, experiment tracking, and compute provisioning for iterative research workflows.

Key Features

• Local operation with dashboard and CLI
• Per-direction agents running in parallel within isolated worktrees
• Integrates existing tools and uses local compute; provisions remote compute via a provider marketplace
• Experiment lifecycle management (reproduction, sweep optimization, scheduled runs, ablation studies)
• Captures run metadata and evaluation results for experiment tracking

Use Cases

• Automate research workflows by running isolated parallel agents per research direction to orchestrate reproducible experiment lifecycles (scheduled runs, sweeps, ablations), track metadata and evaluations, and reproduce results across environments
• Optimize model performance with large-scale hyperparameter sweeps and ablation study automation using OpenResearch's parallel agents and on-demand local or marketplace compute, capturing config snapshots and evaluation metrics for efficient analysis
• Maintain organized, auditable research branches by provisioning isolated worktree environments, scheduling experiments locally or on remote marketplace compute, and sharing reproducible experiment metadata and dashboards with collaborators for publication and compliance

Who is it for?

• Machine learning researchers
• Data scientists
• Mlops engineers
• Research engineers
• Ai/ml engineers
• Academic researchers and phd students
• Research team leads and lab managers
• Experimentation managers and reproducibility officers
• Devops/cloud engineers managing compute
• Product teams running iterative ml experiments