Hallucination Scorer

The createHallucinationScorer() function evaluates whether an LLM generates factually correct information by comparing its output against the provided context. This scorer measures hallucination by identifying direct contradictions between the context and the output.

Parameters

The createHallucinationScorer() function accepts a single options object with the following properties:

model:

LanguageModel

Configuration for the model used to evaluate hallucination.

options.scale:

number

= 1

Maximum score value.

options.context:

string[]

Static context strings to use as ground truth for hallucination detection.

options.getContext:

(params: GetContextParams) => string[] | Promise<string[]>

A hook to dynamically resolve context at runtime. Takes priority over static context. Useful for live scoring where context (like tool results) is only available when the scorer runs.

This function returns an instance of the MastraScorer class. The .run() method accepts the same input as other scorers (see the MastraScorer reference), but the return value includes LLM-specific fields as documented below.

GetContextParams

The getContext hook receives the following parameters:

run:

GetContextRun

The scorer run containing input, output, runId, requestContext, and tracingContext.

results:

Record<string, any>

Accumulated results from previous steps (e.g., preprocessStepResult with extracted claims).

score:

number

The computed score. Only present when called from the generateReason step.

step:

'analyze' | 'generateReason'

Which step is calling the hook. Useful for caching context between calls.

.run() Returns

runId:

string

The id of the run (optional).

preprocessStepResult:

object

Object with extracted claims: { claims: string[] }

preprocessPrompt:

string

The prompt sent to the LLM for the preprocess step (optional).

analyzeStepResult:

object

Object with verdicts: { verdicts: Array<{ statement: string, verdict: 'yes' | 'no', reason: string }> }

analyzePrompt:

string

The prompt sent to the LLM for the analyze step (optional).

score:

number

Hallucination score (0 to scale, default 0-1).

reason:

string

Detailed explanation of the score and identified contradictions.

generateReasonPrompt:

string

The prompt sent to the LLM for the generateReason step (optional).

Scoring Details

The scorer evaluates hallucination through contradiction detection and unsupported claim analysis.

Scoring Process

1. Analyzes factual content:
   • Extracts statements from context
   • Identifies numerical values and dates
   • Maps statement relationships
2. Analyzes output for hallucinations:
   • Compares against context statements
   • Marks direct conflicts as hallucinations
   • Identifies unsupported claims as hallucinations
   • Evaluates numerical accuracy
   • Considers approximation context
3. Calculates hallucination score:
   • Counts hallucinated statements (contradictions and unsupported claims)
   • Divides by total statements
   • Scales to configured range

Final score: (hallucinated_statements / total_statements) * scale

Important Considerations

• Claims not present in context are treated as hallucinations
• Subjective claims are hallucinations unless explicitly supported
• Speculative language ("might", "possibly") about facts IN context is allowed
• Speculative language about facts NOT in context is treated as hallucination
• Empty outputs result in zero hallucinations
• Numerical evaluation considers:
  • Scale-appropriate precision
  • Contextual approximations
  • Explicit precision indicators

Score interpretation

A hallucination score between 0 and 1:

• 0.0: No hallucination — all claims match the context.
• 0.3–0.4: Low hallucination — a few contradictions.
• 0.5–0.6: Mixed hallucination — several contradictions.
• 0.7–0.8: High hallucination — many contradictions.
• 0.9–1.0: Complete hallucination — most or all claims contradict the context.

Note: The score represents the degree of hallucination - lower scores indicate better factual alignment with the provided context

Examples

Static Context

Use static context when you have known ground truth to compare against:

src/example-static-context.ts

import { createHallucinationScorer } from "@mastra/evals/scorers/prebuilt";

const scorer = createHallucinationScorer({
  model: "openai/gpt-4o",
  options: {
    context: [
      "The first iPhone was announced on January 9, 2007.",
      "It was released on June 29, 2007.",
      "Steve Jobs introduced it at Macworld.",
    ],
  },
});

Dynamic Context with getContext

Use getContext for live scoring scenarios where context comes from tool results:

src/example-dynamic-context.ts

import { createHallucinationScorer } from "@mastra/evals/scorers/prebuilt";
import { extractToolResults } from "@mastra/evals/scorers";

const scorer = createHallucinationScorer({
  model: "openai/gpt-4o",
  options: {
    getContext: ({ run, step }) => {
      // Extract tool results as context
      const toolResults = extractToolResults(run.output);
      return toolResults.map((t) =>
        JSON.stringify({ tool: t.toolName, result: t.result })
      );
    },
  },
});

Live Scoring with Agent

Attach the scorer to an agent for live evaluation:

src/example-live-scoring.ts

import { Agent } from "@mastra/core/agent";
import { createHallucinationScorer } from "@mastra/evals/scorers/prebuilt";
import { extractToolResults } from "@mastra/evals/scorers";

const hallucinationScorer = createHallucinationScorer({
  model: "openai/gpt-4o",
  options: {
    getContext: ({ run }) => {
      const toolResults = extractToolResults(run.output);
      return toolResults.map((t) =>
        JSON.stringify({ tool: t.toolName, result: t.result })
      );
    },
  },
});

const agent = new Agent({
  name: "my-agent",
  model: "openai/gpt-4o",
  instructions: "You are a helpful assistant.",
  evals: {
    scorers: [hallucinationScorer],
  },
});

Batch Evaluation with runEvals

src/example-batch-evals.ts

import { runEvals } from "@mastra/core/evals";
import { createHallucinationScorer } from "@mastra/evals/scorers/prebuilt";
import { myAgent } from "./agent";

const scorer = createHallucinationScorer({
  model: "openai/gpt-4o",
  options: {
    context: ["Known fact 1", "Known fact 2"],
  },
});

const result = await runEvals({
  data: [
    { input: "Tell me about topic A" },
    { input: "Tell me about topic B" },
  ],
  scorers: [scorer],
  target: myAgent,
  onItemComplete: ({ scorerResults }) => {
    console.log({
      score: scorerResults[scorer.id].score,
      reason: scorerResults[scorer.id].reason,
    });
  },
});

console.log(result.scores);

For more details on runEvals, see the runEvals reference.

To add this scorer to an agent, see the Scorers overview guide.

Related

• Faithfulness Scorer
• Answer Relevancy Scorer