Evalship

Evalship is an automated quality control tool for LLM evals that monitors pull requests, catches regressions, and optimizes prompts and models.

Evalship acts as quality control for LLM evals by finding flaws in your evals during setup and putting them on autopilot. It watches every pull request, comments on what broke and why, and blocks merges when rules are broken. It also optimizes prompts and models by running variants to find cheaper or more accurate setups beyond chance. Teams can use it with any eval framework to run quality checks and receive weekly email reports. It records results from every eval run and detects untested AI calls.

Evalship at a glance

Best for: Software engineer
Runs on: Web, API
Pricing: Launch offer 60% off forever for the next 9 accounts; Start Pro at $19.60/mo; $24.50; $29.40
Released: October 2026
Category: AI research tool
Website: evalship.com

Key features

Finds flaws in LLM evals during setup
Comments on pull requests with failure details
Blocks pull requests that break rules
Optimizes prompts and models
Detects untested AI calls
Sends weekly email reports
Supports any eval framework

Use it for

Catching regressions in pull requests before they reach customers
Finding rules that no test case checks
Testing cheaper models for AI applications
Optimizing prompts to keep contract terms

Alternatives to Evalship

Evals Skills — It helps avoid common mistakes and pitfalls when building AI evaluation suites.
EvalCart — Test shopping agents with a fixed grocery catalog, tasks, and a deterministic scorer.
EvalScope — It combines model capability benchmarks with inference performance testing in one tool.
Rsi Eval — It supplies the evaluation boundary so methods can use existing benchmarks without reimplementing execution.
LMMs-Eval — Provides open research and tooling for multimodal model evaluation and benchmarks.
Lighteval — Evaluates LLMs across multiple backends with thousands of built-in tasks.
LLM Stats — Independent rankings updated continuously from live API metrics.
LLM Wiki Research — Compiles parallel agent research into source-backed markdown knowledge bases.
VLMEvalKit — Evaluate large vision-language models across numerous benchmarks with a single command.
AutoResearch — It provides a stateful, reproducible workflow for automated AI research.
Vegaml — Runs locally on your hardware with calibrated probabilities and conformal answer sets.

All Evalship alternatives