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Evals

Live Evaluations

Live evaluations run scorers against real-time agent interactions. Attach scorers to agents during initialization to sample production traffic, enforce safety guardrails, and monitor conversation quality without running separate evaluation jobs.

Configuring Live Scorers​

Define scorers in the eval config when creating an agent:

import { Agent, VoltAgentObservability } from "@voltagent/core";
import { createModerationScorer } from "@voltagent/scorers";
import { openai } from "@ai-sdk/openai";

const observability = new VoltAgentObservability();

const agent = new Agent({
  name: "support-agent",
  instructions: "Answer customer questions about products.",
  model: openai("gpt-4o"),
  eval: {
    triggerSource: "production",
    environment: "prod-us-east",
    sampling: { type: "ratio", rate: 0.1 },
    scorers: {
      moderation: {
        scorer: createModerationScorer({
          model: openai("gpt-4o-mini"),
          threshold: 0.5,
        }),
      },
    },
  },
});

Scorers execute asynchronously after the agent response is generated. Scoring does not block the user-facing response.

Eval Configuration​

Required Fields​

None - all fields are optional. If no scorers are defined, evaluation is disabled.

Optional Fields​

triggerSource​

Tags the evaluation run with a trigger identifier. Use to distinguish between environments or traffic sources.

triggerSource: "production"; // live traffic
triggerSource: "staging"; // pre-production
triggerSource: "manual"; // manual testing

Default: "live" when unspecified.

environment​

Labels the evaluation with an environment tag. Appears in telemetry and VoltOps dashboards.

environment: "prod-us-east";
environment: "local-dev";

sampling​

Controls what percentage of interactions are scored. Use sampling to reduce latency and LLM costs on high-volume agents.

Ratio-based:

sampling: {
  type: "ratio",
  rate: 0.1,  // score 10% of interactions
}

Count-based:

sampling: {
  type: "count",
  rate: 100,  // score every 100th interaction
}

Always sample:

sampling: { type: "ratio", rate: 1 }  // 100%

When unspecified, sampling defaults to scoring every interaction (rate: 1).

Sampling decisions are made independently for each scorer. Set sampling at the eval level (applies to all scorers) or per-scorer to override.

scorers​

Map of scorer configurations. Each key identifies a scorer instance, and the value defines the scorer function and parameters.

scorers: {
  moderation: {
    scorer: createModerationScorer({ model, threshold: 0.5 }),
  },
  keyword: {
    scorer: keywordMatchScorer,
    params: { keyword: "refund" },
  },
}

redact​

Function to remove sensitive data from evaluation payloads before storage. Called synchronously before scoring.

redact: (payload) => ({
  ...payload,
  input: payload.input?.replace(/\b\d{4}-\d{4}-\d{4}-\d{4}\b/g, "[CARD]"),
  output: payload.output?.replace(/\b\d{4}-\d{4}-\d{4}-\d{4}\b/g, "[CARD]"),
});

The redacted payload is stored in observability but scoring uses the original unredacted version.

Scorer Configuration​

Each entry in the scorers map has this structure:

{
  scorer: LocalScorerDefinition | (() => Promise<LocalScorerDefinition>),
  params?: Record<string, unknown> | ((payload: AgentEvalContext) => Record<string, unknown>),
  sampling?: SamplingPolicy,
  id?: string,
  onResult?: (result: AgentEvalResult) => void | Promise<void>,
}

Fields​

scorer (required)​

The scoring function. Use prebuilt scorers from @voltagent/scorers or custom implementations via buildScorer.

Prebuilt scorer:

import { createModerationScorer } from "@voltagent/scorers";

scorer: createModerationScorer({ model, threshold: 0.5 });

Custom scorer:

import { buildScorer } from "@voltagent/core";

const customScorer = buildScorer({
  id: "length-check",
  type: "agent",
  label: "Response Length",
})
  .score(({ payload }) => {
    const length = payload.output?.length ?? 0;
    return { score: length > 50 ? 1 : 0 };
  })
  .build();

params​

Static or dynamic parameters passed to the scorer.

Static:

params: {
  keyword: "refund",
  threshold: 0.8,
}

Dynamic:

params: (payload) => ({
  keyword: extractKeyword(payload.input),
  threshold: 0.8,
});

Dynamic params are resolved before each scorer invocation.

sampling​

Override the global sampling policy for this scorer.

sampling: { type: "ratio", rate: 0.05 }  // 5% for this scorer only

id​

Override the scorer's default ID. Useful when using the same scorer multiple times with different params.

scorers: {
  keywordRefund: {
    scorer: keywordScorer,
    id: "keyword-refund",
    params: { keyword: "refund" },
  },
  keywordReturn: {
    scorer: keywordScorer,
    id: "keyword-return",
    params: { keyword: "return" },
  },
}

onResult​

Callback invoked after scoring completes. Use for custom logging, alerting, or side effects.

onResult: async (result) => {
  if (result.score !== null && result.score < 0.5) {
    await alertingService.send({
      message: `Low score: ${result.scorerName} = ${result.score}`,
    });
  }
};

Scorer Context​

Scorers receive an AgentEvalContext object with these properties:

interface AgentEvalContext {
  agentId: string;
  agentName: string;
  operationId: string;
  operationType: "generateText" | "streamText" | string;
  input: string | null; // normalized string
  output: string | null; // normalized string
  rawInput: unknown; // original input value
  rawOutput: unknown; // original output value
  userId?: string;
  conversationId?: string;
  traceId: string;
  spanId: string;
  timestamp: string;
  metadata?: Record<string, unknown>;
  rawPayload: AgentEvalPayload;
}

Use input and output for text-based scorers. Access rawInput and rawOutput for structured data.

Building Custom Scorers​

Use buildScorer to create scorers with custom logic:

import { buildScorer } from "@voltagent/core";

const lengthScorer = buildScorer({
  id: "response-length",
  type: "agent",
  label: "Response Length Check",
})
  .score(({ payload, params }) => {
    const minLength = (params.minLength as number) ?? 50;
    const length = payload.output?.length ?? 0;
    return {
      score: length >= minLength ? 1 : 0,
      metadata: { actualLength: length, minLength },
    };
  })
  .reason(({ score, params }) => {
    const minLength = (params.minLength as number) ?? 50;
    return {
      reason:
        score >= 1
          ? `Response meets minimum length of ${minLength} characters.`
          : `Response is shorter than ${minLength} characters.`,
    };
  })
  .build();

Builder Methods​

.score(fn)​

Defines the scoring function. Return { score, metadata? } or just the numeric score.

.score(({ payload, params, results }) => {
  const match = payload.output?.includes(params.keyword);
  return {
    score: match ? 1 : 0,
    metadata: { keyword: params.keyword, matched: match },
  };
})

Context properties:

  • payload - AgentEvalContext with input/output
  • params - Resolved parameters
  • results - Shared results object for multi-stage scoring

.reason(fn) (optional)​

Generates human-readable explanations. Return { reason: string }.

.reason(({ score, params }) => ({
  reason: score >= 1 ? "Match found" : "No match",
}))

.build()​

Returns the LocalScorerDefinition object.

LLM Judge Scorers​

Use AI SDK's generateObject to build LLM-based evaluators:

import { buildScorer } from "@voltagent/core";
import { openai } from "@ai-sdk/openai";
import { generateObject } from "ai";
import { z } from "zod";

const JUDGE_SCHEMA = z.object({
  score: z.number().min(0).max(1).describe("Score from 0 to 1"),
  reason: z.string().describe("Detailed explanation"),
});

const helpfulnessScorer = buildScorer({
  id: "helpfulness",
  label: "Helpfulness Judge",
})
  .score(async ({ payload }) => {
    const prompt = `Rate the response for clarity and helpfulness.

User Input: ${payload.input}
Assistant Response: ${payload.output}

Provide a score from 0 to 1 with an explanation.`;

    const response = await generateObject({
      model: openai("gpt-4o-mini"),
      schema: JUDGE_SCHEMA,
      prompt,
      maxTokens: 200,
    });

    return {
      score: response.object.score,
      metadata: {
        reason: response.object.reason,
      },
    };
  })
  .build();

The judge calls the LLM with a structured schema, ensuring consistent scoring output.

Prebuilt Scorers​

Moderation​

import { createModerationScorer } from "@voltagent/scorers";

createModerationScorer({
  model: openai("gpt-4o-mini"),
  threshold: 0.5, // fail if score < 0.5
});

Flags unsafe content (toxicity, bias, etc.) using LLM-based classification.

Answer Correctness​

import { createAnswerCorrectnessScorer } from "@voltagent/scorers";

const scorer = createAnswerCorrectnessScorer({
  buildPayload: ({ payload, params }) => ({
    input: payload.input,
    output: payload.output,
    expected: params.expectedAnswer,
  }),
});

Evaluates factual accuracy. Requires expected in params. Users implement scoring logic.

Answer Relevancy​

import { createAnswerRelevancyScorer } from "@voltagent/scorers";

const scorer = createAnswerRelevancyScorer({
  strictness: 3,
  buildPayload: ({ payload, params }) => ({
    input: payload.input,
    output: payload.output,
    context: params.referenceContext,
  }),
});

Checks if the output addresses the input. Strictness controls evaluation level.

Keyword Match​

import { buildScorer } from "@voltagent/core";

const keywordScorer = buildScorer({
  id: "keyword-match",
  type: "agent",
})
  .score(({ payload, params }) => {
    const keyword = params.keyword as string;
    const matched = payload.output?.toLowerCase().includes(keyword.toLowerCase());
    return { score: matched ? 1 : 0 };
  })
  .build();

// Usage:
scorers: {
  keyword: {
    scorer: keywordScorer,
    params: { keyword: "refund" },
  },
}

VoltOps Integration​

When a VoltOps client is configured globally, live scorer results are forwarded automatically:

import VoltAgent, { Agent, VoltAgentObservability } from "@voltagent/core";
import { VoltOpsClient } from "@voltagent/sdk";

const voltOpsClient = new VoltOpsClient({
  publicKey: process.env.VOLTAGENT_PUBLIC_KEY,
  secretKey: process.env.VOLTAGENT_SECRET_KEY,
});

const observability = new VoltAgentObservability();

new VoltAgent({
  agents: { support: agent },
  observability,
  voltOpsClient, // enables automatic forwarding
});

The framework creates evaluation runs, registers scorers, appends results, and finalizes summaries. Each batch of scores (per agent interaction) becomes a separate run in VoltOps.

Sampling Strategies​

Ratio Sampling​

Sample a percentage of interactions:

sampling: { type: "ratio", rate: 0.1 }  // 10% of traffic

Use for high-volume agents where scoring every interaction is expensive.

Count Sampling​

Sample every Nth interaction:

sampling: { type: "count", rate: 100 }  // every 100th interaction

Use when you need predictable sampling intervals or rate-limiting.

Per-Scorer Sampling​

Override sampling for specific scorers:

eval: {
  sampling: { type: "ratio", rate: 1 },  // default: score all
  scorers: {
    moderation: {
      scorer: moderationScorer,
      sampling: { type: "ratio", rate: 1 },  // always run moderation
    },
    helpfulness: {
      scorer: helpfulnessScorer,
      sampling: { type: "ratio", rate: 0.05 },  // 5% for expensive LLM judge
    },
  },
}

Error Handling​

If a scorer throws an exception, the result is marked status: "error" and the error message is captured in errorMessage. Other scorers continue executing.

.score(({ payload, params }) => {
  if (!params.keyword) {
    throw new Error("keyword parameter is required");
  }
  // ...
})

The error appears in observability storage and VoltOps telemetry.

Best Practices​

Use Sampling for Expensive Scorers​

LLM judges and embedding-based scorers consume tokens and add latency. Sample aggressively:

sampling: { type: "ratio", rate: 0.05 }  // 5% for LLM judges

Combine Fast and Slow Scorers​

Run lightweight scorers (keyword match, length checks) on all interactions. Sample LLM judges at lower rates.

scorers: {
  keyword: {
    scorer: keywordScorer,
    sampling: { type: "ratio", rate: 1 },  // 100%
  },
  helpfulness: {
    scorer: helpfulnessScorer,
    sampling: { type: "ratio", rate: 0.1 },  // 10%
  },
}

Use Redaction for PII​

Strip sensitive data before storage:

redact: (payload) => ({
  ...payload,
  input: payload.input?.replace(/\b\d{3}-\d{2}-\d{4}\b/g, "[SSN]"),
  output: payload.output?.replace(/\b\d{3}-\d{2}-\d{4}\b/g, "[SSN]"),
});

Scorers receive unredacted data. Only storage and telemetry are redacted.

Use Thresholds for Alerts​

Set thresholds and trigger alerts on failures:

scorers: {
  moderation: {
    scorer: createModerationScorer({ model, threshold: 0.7 }),
    onResult: async (result) => {
      if (result.score !== null && result.score < 0.7) {
        await alertingService.send({
          severity: "high",
          message: `Moderation failed: ${result.score}`,
        });
      }
    },
  },
}

Tag Environments​

Use environment to distinguish between deployments:

environment: process.env.NODE_ENV === "production" ? "prod" : "staging";

Filter telemetry by environment in VoltOps dashboards.

Examples​

Moderation + Keyword Matching​

import { Agent, VoltAgentObservability, buildScorer } from "@voltagent/core";
import { createModerationScorer } from "@voltagent/scorers";
import { openai } from "@ai-sdk/openai";

const moderationModel = openai("gpt-4o-mini");

const keywordScorer = buildScorer({
  id: "keyword-match",
  type: "agent",
})
  .score(({ payload, params }) => {
    const keyword = params.keyword as string;
    const matched = payload.output?.toLowerCase().includes(keyword.toLowerCase());
    return { score: matched ? 1 : 0, metadata: { keyword, matched } };
  })
  .build();

const agent = new Agent({
  name: "support",
  model: openai("gpt-4o"),
  eval: {
    triggerSource: "production",
    sampling: { type: "ratio", rate: 1 },
    scorers: {
      moderation: {
        scorer: createModerationScorer({ model: moderationModel, threshold: 0.5 }),
      },
      keyword: {
        scorer: keywordScorer,
        params: { keyword: "refund" },
      },
    },
  },
});

LLM Judge for Helpfulness​

import { Agent, buildScorer } from "@voltagent/core";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";

const HELPFULNESS_SCHEMA = z.object({
  score: z.number().min(0).max(1),
  reason: z.string(),
});

const helpfulnessScorer = buildScorer({
  id: "helpfulness",
  label: "Helpfulness",
})
  .score(async ({ payload }) => {
    const agent = new Agent({
      name: "helpfulness-judge",
      model: openai("gpt-4o-mini"),
      instructions: "You rate responses for helpfulness",
    });

    const prompt = `Rate the response for clarity, accuracy, and helpfulness.

User Input: ${payload.input}
Assistant Response: ${payload.output}

Provide a score from 0 to 1 with an explanation.`;

    const response = await agent.generateObject(prompt, HELPFULNESS_SCHEMA);

    const rawResults = (payload as any).results?.raw ?? {};
    rawResults.helpfulnessJudge = response.object;

    return {
      score: response.object.score,
      metadata: { reason: response.object.reason },
    };
  })
  .reason(({ results }) => {
    const judge = results.raw?.helpfulnessJudge as { reason?: string };
    return { reason: judge?.reason ?? "No explanation provided." };
  })
  .build();

const agent = new Agent({
  name: "support",
  model: openai("gpt-4o"),
  eval: {
    sampling: { type: "ratio", rate: 0.1 }, // 10% sampling
    scorers: {
      helpfulness: { scorer: helpfulnessScorer },
    },
  },
});

Multiple Scorers with Different Sampling​

const agent = new Agent({
  name: "support",
  model: openai("gpt-4o"),
  eval: {
    triggerSource: "production",
    environment: "prod-us-east",
    sampling: { type: "ratio", rate: 1 }, // default: score everything
    scorers: {
      moderation: {
        scorer: createModerationScorer({ model, threshold: 0.5 }),
        sampling: { type: "ratio", rate: 1 }, // always run
      },
      answerCorrectness: {
        scorer: createAnswerCorrectnessScorer(),
        sampling: { type: "ratio", rate: 0.05 }, // 5% (expensive)
        params: (payload) => ({
          expectedAnswer: lookupExpectedAnswer(payload.input),
        }),
      },
      keyword: {
        scorer: keywordScorer,
        params: { keyword: "refund" },
        sampling: { type: "ratio", rate: 1 }, // cheap, always run
      },
    },
  },
});

Combining Offline and Live Evaluations​

Use live evals for real-time monitoring and offline evals for regression testing:

  • Live: Sample 5-10% of production traffic with fast scorers (moderation, keyword match)
  • Offline: Run comprehensive LLM judges on curated datasets nightly

Both share the same scorer definitions. Move scorers between eval types as needed.

Next Steps​

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