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The experiments module runs tasks over dataset examples, records experiment runs in Phoenix, and can evaluate each run with either plain experiment evaluators or @arizeai/phoenix-evals evaluators.

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Relevant Source Files

  • src/experiments/runExperiment.ts for the task execution flow and return shape
  • src/experiments/helpers/getExperimentEvaluators.ts for evaluator normalization
  • src/experiments/helpers/fromPhoenixLLMEvaluator.ts for the phoenix-evals bridge
  • src/experiments/getExperimentRuns.ts for reading runs back after execution

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Two Common Patterns

Use asExperimentEvaluator() when your evaluation logic is plain TypeScript. Use @arizeai/phoenix-evals evaluators directly when you want model-backed judging.

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Code-Based Example

If you just want to compare task output against a reference answer or apply deterministic checks, use asExperimentEvaluator(): 
/* eslint-disable no-console */
import { createDataset } from "@arizeai/phoenix-client/datasets";
import {
  asExperimentEvaluator,
  runExperiment,
} from "@arizeai/phoenix-client/experiments";

async function main() {
  const { datasetId } = await createDataset({
    name: `simple-dataset-${Date.now()}`,
    description: "a simple dataset",
    examples: [
      {
        input: { name: "John" },
        output: { text: "Hello, John!" },
        metadata: {},
      },
      {
        input: { name: "Jane" },
        output: { text: "Hello, Jane!" },
        metadata: {},
      },
      {
        input: { name: "Bill" },
        output: { text: "Hello, Bill!" },
        metadata: {},
      },
    ],
  });

  const experiment = await runExperiment({
    dataset: { datasetId },
    task: async (example) => `hello ${example.input.name}`,
    evaluators: [
      asExperimentEvaluator({
        name: "matches",
        kind: "CODE",
        evaluate: async ({ output, expected }) => {
          const matches = output === expected?.text;
          return {
            label: matches ? "matches" : "does not match",
            score: matches ? 1 : 0,
            explanation: matches
              ? "output matches expected"
              : "output does not match expected",
            metadata: {},
          };
        },
      }),
      asExperimentEvaluator({
        name: "contains-hello",
        kind: "CODE",
        evaluate: async ({ output }) => {
          const matches =
            typeof output === "string" && output.includes("hello");
          return {
            label: matches ? "contains hello" : "does not contain hello",
            score: matches ? 1 : 0,
            explanation: matches
              ? "output contains hello"
              : "output does not contain hello",
            metadata: {},
          };
        },
      }),
    ],
  });

  console.table(experiment.runs);
  console.table(experiment.evaluationRuns);
}

main().catch(console.error);
This pattern is useful when:
  • you already know the exact correctness rule
  • you want fast, deterministic evaluation
  • you do not want to call another model during evaluation

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Model-Backed Example

If you want a model-backed experiment with automatic tracing and an LLM-as-a-judge evaluator, this is the core pattern: 
import { openai } from "@ai-sdk/openai";
import { createOrGetDataset } from "@arizeai/phoenix-client/datasets";
import { runExperiment } from "@arizeai/phoenix-client/experiments";
import type { ExperimentTask } from "@arizeai/phoenix-client/types/experiments";
import { createClassificationEvaluator } from "@arizeai/phoenix-evals";
import { generateText } from "ai";

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

const main = async () => {
  const answersQuestion = createClassificationEvaluator({
    name: "answersQuestion",
    model,
    promptTemplate:
      "Does the following answer the user's question: <question>{{input.question}}</question><answer>{{output}}</answer>",
    choices: {
      correct: 1,
      incorrect: 0,
    },
  });

  const dataset = await createOrGetDataset({
    name: "correctness-eval",
    description: "Evaluate the correctness of the model",
    examples: [
      {
        input: {
          question: "Is ArizeAI Phoenix Open-Source?",
          context: "ArizeAI Phoenix is Open-Source.",
        },
      },
      // ... more examples
    ],
  });

  const task: ExperimentTask = async (example) => {
    if (typeof example.input.question !== "string") {
      throw new Error("Invalid input: question must be a string");
    }
    if (typeof example.input.context !== "string") {
      throw new Error("Invalid input: context must be a string");
    }

    return generateText({
      model,
      experimental_telemetry: {
        isEnabled: true,
      },
      prompt: [
        {
          role: "system",
          content: `You answer questions based on this context: ${example.input.context}`,
        },
        {
          role: "user",
          content: example.input.question,
        },
      ],
    }).then((response) => {
      if (response.text) {
        return response.text;
      }
      throw new Error("Invalid response: text is required");
    });
  };

  const experiment = await runExperiment({
    experimentName: "answers-question-eval",
    experimentDescription:
      "Evaluate the ability of the model to answer questions based on the context",
    dataset,
    task,
    evaluators: [answersQuestion],
    repetitions: 3,
  });

  console.log(experiment.id);
  console.log(Object.values(experiment.runs).length);
  console.log(experiment.evaluationRuns?.length ?? 0);
};

main().catch(console.error);

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What This Example Shows

  • createOrGetDataset() creates or reuses the dataset the experiment will run against
  • task receives the full dataset example object
  • generateText() emits traces that Phoenix can attach to the experiment when telemetry is enabled
  • createClassificationEvaluator() from @arizeai/phoenix-evals can be passed directly to runExperiment()
  • runExperiment() records both task runs and evaluation runs in Phoenix

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Task Inputs

runExperiment() calls your task with the full dataset example, not just example.input. That means your task should usually read:
  • example.input for the task inputs
  • example.output for any reference answer
  • example.metadata for additional context
In the example above, the task validates example.input.question and example.input.context before generating a response.

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Evaluator Inputs

When an evaluator runs, it receives a normalized object with these fields:
FieldDescription
inputThe dataset example’s input object
outputThe task output for that run
expectedThe dataset example’s output object
metadataThe dataset example’s metadata object
This is why the createClassificationEvaluator() prompt can reference {{input.question}} and {{output}}. For code-based evaluators created with asExperimentEvaluator(), those same fields are available inside evaluate({ input, output, expected, metadata }).

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What runExperiment() Returns

The returned object includes the experiment metadata plus the task and evaluation results from the run.
  • experiment.id is the experiment ID in Phoenix
  • experiment.projectName is the Phoenix project that received the task traces
  • experiment.runs is a map of run IDs to task run objects
  • experiment.evaluationRuns contains evaluator results when evaluators are provided

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Follow-Up Helpers

Use these exports for follow-up workflows:
  • createExperiment
  • getExperiment
  • getExperimentInfo
  • getExperimentRuns
  • listExperiments
  • resumeExperiment
  • resumeEvaluation
  • deleteExperiment

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Tracing Behavior

runExperiment() can register a tracer provider for the task run so that task spans and evaluator spans show up in Phoenix during the experiment. This is why tasks that call the AI SDK can still emit traces to Phoenix when global tracing is enabled.

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Source Map

  • src/experiments/runExperiment.ts
  • src/experiments/createExperiment.ts
  • src/experiments/getExperiment.ts
  • src/experiments/getExperimentRuns.ts
  • src/experiments/helpers/getExperimentEvaluators.ts
  • src/experiments/helpers/fromPhoenixLLMEvaluator.ts
  • src/experiments/helpers/asExperimentEvaluator.ts