A jest plugin for evaluating human messages, agent or LLM-generated answers using LLM-based criteria matchers: LLM-Rubric, G-Eval, B-Eval. This package fits for testing agentic systems, chatbots, or any text, where needs to check correctness, relevance, and safety with advanced possibilities of AI.

Quick start​

npm install @eva-llm/llm-as-a-jest

{
  "setupFilesAfterEnv": ["@eva-llm/llm-as-a-jest/jest.setup"]
}

const SEC = 1000;

describe('llmAsJudge matcher', () => {

  it('should do smoke tests', async () => {
    const query = 'What is the capital of France?';
    const answer = 'Paris is the capital of France.';

    await expect(answer).gEval({
      criteria: 'answer should be factually correct',
    });

    await expect(answer).gEval({
      query,
      criteria: 'answer should be coherent to question',
    });

    await expect(answer).bEval({
      criteria: 'answer should be factually correct',
    });

    await expect(answer).bEval({
      query,
      criteria: 'answer should be coherent to question',
    });

    await expect(answer).llmRubric({
      criteria: 'answer should be factually correct',
    });
  }, 60 * SEC);
});

Matchers​

• llmRubric - Evaluates a text against a rubric using an LLM. Returns a reason, pass/fail, and normalized score.
• gEval - Evaluates a text or query-answer pair against criteria and derived steps using an LLM. Returns a reason and normalized score (0.0-1.0).
• bEval - Evaluates a text or query-answer pair against criteria and derived steps using an LLM, but with binary scoring (0 or 1). Returns a reason and a normalized score (0 or 1).

G-Eval vs B-Eval​

The divergence between G-Eval and B-Eval reveals a critical 'Judgement Gap':

• G-Eval (The Auditor): Scoring on a 0.0-1.0 scale allows the model to stay in a 'comfort zone', smoothing over internal contradictions.
• B-Eval (The Judge): A binary 0|1 choice forces Adjudication. This 'forced choice' triggers the Alignment Paradox, exposing the struggle between RLHF training and objective facts.

Conclusion: B-Eval is a superior stress-test for Epistemic Honesty. By stripping away the safety net of grey-zone scoring, it reveals exactly where logic breaks under the weight of normative priors.

More details in EVA-LLM Dark Teaming Manifesto.

Matcher Options​

GEvalOptions (for G-Eval and B-Eval):​

• query (string, optional): The question for answer if to evaluate query-answer pair.
• criteria (string | string[]): Criteria or rubric for evaluation. (required)
• threshold (number, optional): Pass threshold (default: pluginConfig.threshold).
• temperature (number, optional): LLM temperature (default: pluginConfig.temperature).
• provider (string, optional): LLM provider to use (default: pluginConfig.provider).
• model (string, optional): LLM model to use (default: pluginConfig.model).
• verbose (boolean, optional): If needs to show non-truncated query and answer in failed test error (default: pluginConfig.verbose).

LLMRubricOptions (for LLM-Rubric):​

• criteria (string | string[]): Criteria or rubric for evaluation. (required)
• threshold (number, optional): Pass threshold (default: pluginConfig.threshold).
• temperature (number, optional): LLM temperature (default: pluginConfig.temperature).
• provider (string, optional): LLM provider to use (default: pluginConfig.provider).
• model (string, optional): LLM model to use (default: pluginConfig.model).
• verbose (boolean, optional): If needs to show non-truncated query and answer in failed test error (default: pluginConfig.verbose).

Default Plugin Configuration​

You can override the default plugin configuration using the configure function. The defaults are:

{
  provider: 'openai',
  model: 'gpt-4.1-mini',
  threshold: 0.5,
  temperature: 0.0, // Recommended for judging
  verbose: false, // truncated query and answer in failed test error
}

Call configure({ ... }) in your setup to change these values globally for all matchers.

LLM Providers and Settings​

The following LLM providers are supported (via Vercel ai-sdk):

• OpenAI (openai)
• Anthropic (anthropic)
• Google (google)
• Mistral (mistral)
• Amazon Bedrock (bedrock)
• Azure (azure)
• DeepSeek (deepseek)
• Groq (groq)
• Perplexity (perplexity)
• xAI (xai)

Specify the provider name and model name in llmRubric, gEval, or bEval.

Note: Each provider integration is based on its respective ai-sdk package. Be sure to follow the provider's documentation for setup and authentication. Most providers require you to export an API key or token as an environment variable (e.g., export OPENAI_API_KEY=...).

More info about available providers and models in @eva-llm/eva-judge.

License​

MIT