Multinear enables teams to ship reliable Generative AI applications that actually work. Our evaluation platform gives engineers and product managers the benchmarks, regression detection, and actionable insights they need to iterate fast while maintaining quality — turning AI's inherent unpredictability into controlled, measurable progress.

The Challenge: Generative AI outcomes are inherently probabilistic. Even minor changes — in prompts, LLM models, data, or logic — can introduce regressions and break your app unpredictably. Traditional testing falls short because it assumes deterministic outcomes, leaving teams frustrated and uncertain.

Multinear solves this by providing structured experimentation, clear benchmarking, and instant visibility into regressions. It shifts evaluation from vague metrics to concrete, business-centric tests, empowering your team to continuously deliver measurable improvements.

• Predictable outcomes
  Replace ambiguous metrics with clear, pass/fail criteria tied directly to your product’s real-world impact.
• Immediate regression detection
  Instantly spot regressions caused by changes to prompts, models, data, or business logic — no more guesswork.
• Rapid experimentation
  Quickly test and measure each change’s exact effect on reliability, accelerating your development cycle.
• Clear visibility into failures
  Know exactly why tests fail — prompt, model behavior, or data — allowing targeted, efficient fixes.
• Continuous improvement
  Maintain and evolve your baseline, confidently shipping measurable incremental improvements.

Multinear makes building reliable AI applications simple and systematic:

1. Define clear evaluations
   Specify precise, binary (pass/fail) tests aligned to real-world business goals.
2. Run structured experiments
   Systematically test changes in prompts, models, data, or logic with instant regression detection.
3. Iterate confidently
   Benchmark every iteration, immediately see improvements or regressions, and rapidly iterate to reliable solutions.

With Multinear, you'll spend less time guessing and debugging — and more time confidently shipping AI-driven solutions.

Follow these simple steps to get Multinear running quickly and easily:

Begin by installing the Multinear package from PyPI:

pip install multinear

Create your Multinear project and configuration structure:

multinear init

This command sets up a .multinear folder in your project directory, including essential configuration files and an SQLite database for experiment results.

Create a task runner file .multinear/task_runner.py. This file acts as the entry point for your AI application logic. It processes tasks defined in your evaluations and returns outputs for Multinear to assess.

• Why do I need it? Your task runner integrates Multinear directly with your AI application, ensuring experiments run consistently and reliably.

Define your evaluation criteria in .multinear/config.yaml. Here you'll specify your tasks, evaluation methods, and success criteria, ensuring your tests align directly with your desired business outcomes.

Start the Multinear web platform to run experiments and monitor progress visually:

multinear web

Visit http://127.0.0.1:8000 in your browser.

Prefer command line? You can do all the same tasks in CLI.

multinear run

Multinear provides detailed insights and instant visibility into your test outcomes, making it easy to understand and debug failures. Quickly detect regressions, visualize trends, and iterate confidently.

• Full Quickstart Guide – Get set up quickly.
• Defining Evaluations – Learn how to design great tests.
• Running Experiments – Run, benchmark, and iterate.
• Analyzing Results – Understand outcomes clearly.
• CLI Reference – Automate your workflow.
• Architecture & Contributing – Go deeper or contribute.

Multinear is released under the MIT License. Feel free to use, modify, and distribute this software per the terms of the license.