litmus4ai

litmus4ai / litmus

Public

⚡ Unit tests for AI. Test prompts, compare models, save money.

ai cli cost-optimization developer-tools evaluation
43
0
100% credibility
Found Apr 12, 2026 at 43 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
TypeScript
AI Summary

Litmus is a testing platform for AI prompts that lets users run checks across models, evaluate against datasets, generate test data, project costs, and view results in a dashboard.

How It Works

1
🔍 Discover Litmus

You hear about Litmus, a friendly tool that helps test your AI assistants to make sure they work right and save money.

2
📥 Set it up quickly

Download and prepare Litmus on your computer with a simple setup that feels effortless.

3
🔗 Connect AI brains

Link your favorite AI services like chatting helpers so Litmus can talk to them.

4
✏️ Write easy checks

Describe what you want your AI to do and add simple rules like 'must include this word' or 'stay under budget'.

5
🚀 Run tests and wow!

Hit go and watch colorful reports show which AIs pass, how fast they are, and smart tips to pick the best one.

6
📊 Check your dashboard

Open the web view anytime to see test history, compare costs, and generate more test examples.

🎉 AI ready to shine

Now your AI features are reliable, speedy, and cheap – deploy with total confidence!

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 43 to 43 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is litmus?

Litmus brings unit tests to AI prompts, letting you define YAML configs with prompts, inputs, and assertions like JSON validity, text contains, or cost/latency limits. Run tests across OpenAI, Anthropic, Google, and Hugging Face models via a Python CLI—get pass/fail results, token counts, and costs in tables or CI-friendly output. Optional Supabase storage and React dashboard track github unit test results over time, with commands like `litmus run`, `litmus eval`, `litmus cost`, and `litmus generate` for datasets.

Why is it gaining traction?

It stands out with built-in model comparison, cost projections showing monthly savings (e.g., switch to free HF models), and AI-generated datasets for evals—far beyond basic prompt testers. Developers hook on litmus test github automation for CI/CD, where github unit test coverage reports highlight regressions, latency spikes, or budget overruns. The litmus meaning here is quick reliability checks, like litmus paper for prompts.

Who should use this?

AI backend engineers validating LLM features before prod, teams doing github unit test automation on prompt changes, or fintech/health devs needing structured JSON outputs with cost guards. Ideal for those tired of manual model evals, wanting litmus edge github tools for edge cases like multilingual inputs or empty data.

Verdict

Early project at 43 stars and 1.0% credibility score—solid demos and init command get you testing fast, but lacks broad adoption and deep test coverage reports. Grab it for prototyping AI pipelines; scale once mature.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.