Skip to main content
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.

​
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
  • src/types/experiments.ts for EvaluatorParams including traceId
  • src/spans/getSpans.ts for fetching spans by trace ID and span kind

​
Two Common Patterns

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

​
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

​
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);

​
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

​
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.

​
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
traceIdThe OpenTelemetry trace ID of the task run (optional, string | null)
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, traceId }).

​
Trace-Based Evaluation

Each task run captures an OpenTelemetry trace ID. Evaluators can use traceId to fetch the task’s spans from Phoenix and evaluate the execution trajectory — for example, verifying that specific tool calls were made or inspecting intermediate steps. This pattern works best with evaluateExperiment() as a separate step after runExperiment(), so that all task spans are ingested into Phoenix before the evaluator queries them. 
import { traceTool } from "@arizeai/openinference-core";
import { createClient } from "@arizeai/phoenix-client";
import { createDataset } from "@arizeai/phoenix-client/datasets";
import {
  asExperimentEvaluator,
  evaluateExperiment,
  runExperiment,
} from "@arizeai/phoenix-client/experiments";
import { getSpans } from "@arizeai/phoenix-client/spans";

const client = createClient();

const { datasetId } = await createDataset({
  client,
  name: "tool-call-dataset",
  description: "Questions that require tool use",
  examples: [
    {
      input: { question: "What is the weather in San Francisco?" },
      output: { expectedTool: "getWeather" },
      metadata: {},
    },
  ],
});

// Step 1: Run the experiment with traced tool calls
const experiment = await runExperiment({
  client,
  dataset: { datasetId },
  setGlobalTracerProvider: true,
  task: async (example) => {
    // traceTool wraps a function with a TOOL span
    const getWeather = traceTool(
      ({ location }: { location: string }) => ({
        location,
        temperature: 72,
        condition: "sunny",
      }),
      { name: "getWeather" }
    );

    const city = (example.input.question as string).match(/in (.+)\?/)?.[1];
    const result = getWeather({ location: city ?? "Unknown" });
    return `The weather in ${result.location} is ${result.temperature}F.`;
  },
});

const projectName = experiment.projectName!;

// Step 2: Evaluate using traceId to inspect the task's spans
const evaluated = await evaluateExperiment({
  client,
  experiment,
  evaluators: [
    asExperimentEvaluator({
      name: "has-expected-tool-call",
      kind: "CODE",
      evaluate: async ({ traceId, expected }) => {
        if (!traceId) {
          return { label: "no trace", score: 0 };
        }

        // Fetch TOOL spans from this task's trace
        const { spans: toolSpans } = await getSpans({
          client,
          project: { projectName },
          traceIds: [traceId],
          spanKind: "TOOL",
        });

        const expectedTool = (expected as { expectedTool?: string })
          ?.expectedTool;
        const toolNames = toolSpans.map((s) => s.name);
        const found = toolNames.some((name) => name.includes(expectedTool!));

        return {
          label: found ? "tool called" : "no tool call",
          score: found ? 1 : 0,
          explanation: found
            ? `Found: ${toolNames.join(", ")}`
            : `Expected "${expectedTool}" but found none`,
        };
      },
    }),
  ],
});
Key points:
  • Use setGlobalTracerProvider: true on runExperiment() so that child spans from traceTool or other OTel instrumentation land in the same trace as the task
  • Use evaluateExperiment() as a separate step so spans are ingested before querying
  • Use getSpans() with traceIds and spanKind filters to fetch specific spans from the task trace
  • traceId is null in dry-run mode since no real traces are recorded

​
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

​
Follow-Up Helpers

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

​
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.

​
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