Cannonade

Local-First LLM Testing

Run one test suite across many models

Create your test cases once, run them against every AI model you're evaluating, and compare their pass rate, score, cost, and speed.

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  • Windows, macOS, Linux
  • Free and open source
  • Runs on your machine
Cannonade test run details: three models scored against an LLM rubric suite

How it works

What a run looks like

Add a provider

Point Cannonade at a local server or a cloud gateway.

Write the cases

A case is an input, an optional system prompt, and the evaluators that score the answer.

Start a run

Pick as many models as you want.

Check the result

See what each model answered, what it scored, and how fast it was.

Features

What is in the app

Run results

Results arrive while the run is still going, and stay on disk once it finishes.

  • Pass rate and score per model
  • Tokens, speed, and cost
A finished test case in a run: system prompt, model output, evaluator score, and token stats

Playground

A chat view for trying a model before writing cases for it.

  • Any configured model
  • Sampling parameters per message
  • Stats on every response
The playground chat view with a local model selected and response stats above the reply

Prompt library

System prompts live in one place, so one edit reaches every case using them.

  • Shared by suites and the playground
  • Versioned
The prompt library listing shared system prompts

Model management

Handle local models without switching to another app.

  • Load, unload or delete them
The models view listing downloaded local models

Providers

Bring the models you already run

A provider is whatever serves the models: a local server on your machine or network, or a cloud gateway behind an API key. Add one and its models appear in the app. A run targets a single provider and any number of its models.

Download

Get Cannonade

Free and open source.

Windows.exe installerWindows 10 and later, x64DownloadmacOS.dmgApple silicon and IntelDownloadLinux.AppImagex64, no install requiredDownload

All builds are published on GitHub Releases.