Backgrind vs OpenAI Evals: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Backgrind and OpenAI Evals — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Backgrind
Backgrind
Always-on-top desktop overlay for macOS and Windows that runs your AI coding agent and pings you only when it needs approval or input.
Key features
- Always-On-Top Overlay: Floats your coding agent over any app, editor, browser or fullscreen game so it stays in view.
- Bring Your Own Agent: Works as a thin frontend over Claude Code, Cursor or a Backgrind-hosted model using your existing login and history.
- Attention-Only Alerts: Stays quiet while the agent works and flashes or chimes only when it needs approval or input.
- Inline Approvals: Surfaces command-run and dependency-install requests so you can approve or reject them in place.
- Customizable Window: Drag, stretch, recolor and fade the floating window to fit your workspace.
- Cross-Platform: Available for both macOS and Windows.
Best for
- Background Coding: Kick off a refactor or build and keep working elsewhere until the agent needs you.
- Supervising Multiple Agents: Keep several agent sessions visible in floating windows at once.
- Vibe Coding: Let casual builders run an agent without learning a full IDE workflow.
- Long-Running Tasks: Monitor test runs and multi-step builds without staring at a terminal.
- Approval Gating: Review and authorize potentially risky commands before they execute.
OpenAI Evals
OpenAI
Open-source framework and registry for creating, running, and comparing evaluations of large language models and LLM systems.
Key features
- Registry of Benchmarks: A curated, open registry of existing evals and benchmarks for common LLM tasks, enabling quick comparison across models and tasks.
- Custom & Private Evals: Author and run custom evals using your own datasets and grading logic; private evals let teams evaluate proprietary workflows without exposing data publicly.
- Grader Framework: Build rubric-driven automated graders, model-based graders, or human-in-the-loop grading pipelines to produce consistent, repeatable scoring.
- CLI/SDK & API Integration: Python-first SDK and CLI that integrate with the OpenAI API, support threaded execution, detailed logs, and programmatic control for batch runs.
- Continuous Evaluation (CE): Integrate evals into development workflows to run on changes, detect regressions, and track performance over time across model versions.
- Detailed Reporting & Metrics: Produces sample-level logs, aggregated counts and metrics, and final reports that summarize correctness, rubric scores, and other custom metrics.
- Extensibility & Reproducibility: Templates and examples in the repository make it straightforward to extend eval types (e.g., classification, generation, instruction following) and reproduce results.