@agentskit/eval

Measure agent quality with numbers, not vibes — ship with confidence.

Why

• Replace "it seemed to work" with real metrics — accuracy, per-case latency, token cost, and pass/fail for every test case in a single result object
• CI/CD ready — exit codes reflect suite results; gate deployments on accuracy thresholds so regressions never reach production
• Flexible assertions — exact string matching, includes for LLM verbosity, or full control with a custom (result) => boolean function per case

Install

npm install @agentskit/eval
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Quick example

import { runEval } from '@agentskit/eval'
import { createRuntime } from '@agentskit/runtime'
import { anthropic } from '@agentskit/adapters'

const runtime = createRuntime({
  adapter: anthropic({ apiKey: process.env.ANTHROPIC_API_KEY, model: 'claude-sonnet-4-6' }),
})

const result = await runEval({
  agent: async (input) => {
    const r = await runtime.run(input)
    return r.content
  },
  suite: {
    name: 'qa-baseline',
    cases: [
      { input: 'What is 2+2?', expected: '4' },
      { input: 'Capital of France?', expected: 'Paris' },
      { input: 'Is TypeScript a superset of JavaScript?', expected: (r) => r.toLowerCase().includes('yes') },
    ],
  },
})

console.log(`Accuracy: ${(result.accuracy * 100).toFixed(1)}%`)
console.log(`Passed: ${result.passed}/${result.totalCases}`)
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Next steps

• Point the agent closure at any async boundary: createRuntime, a custom @agentskit/core controller, or a thin HTTP wrapper
• Run suites in CI and gate merges on result.accuracy — pair with @agentskit/observability to debug failures

Ecosystem

Docs

Full documentation