@arizeai/phoenix-client

This package provides a TypeScript client for the Arize Phoenix API. It is still under active development and is subject to change.

Installation

# or yarn, pnpm, bun, etc...
npm install @arizeai/phoenix-client
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Configuration

The client will automatically read environment variables from your environment, if available.

The following environment variables are used:

• PHOENIX_HOST - The base URL of the Phoenix API.
• PHOENIX_API_KEY - The API key to use for authentication.
• PHOENIX_CLIENT_HEADERS - Custom headers to add to all requests. A JSON stringified object.

PHOENIX_HOST='http://localhost:12345' PHOENIX_API_KEY='xxxxxx' pnpx tsx examples/list_datasets.ts
# emits the following request:
# GET http://localhost:12345/v1/datasets
# headers: {
#   "Authorization": "Bearer xxxxxx",
# }
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Alternatively, you can pass configuration options to the client directly, and they will be prioritized over environment variables and default values.

const phoenix = createClient({
  options: {
    baseUrl: "http://localhost:6006",
    headers: {
      Authorization: "Bearer xxxxxx",
    },
  },
});
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Prompts

@arizeai/phoenix-client provides a prompts export that exposes utilities for working with prompts for LLMs.

Creating a Prompt and push it to Phoenix

The createPrompt function can be used to create a prompt in Phoenix for version control and reuse.

import { createPrompt, promptVersion } from "@arizeai/phoenix-client/prompts";

const version = createPrompt({
  name: "my-prompt",
  description: "test-description",
  version: promptVersion({
    description: "version description here",
    modelProvider: "OPENAI",
    modelName: "gpt-3.5-turbo",
    template: [
      {
        role: "user",
        content: "{{ question }}",
      },
    ],
    invocationParameters: {
      temperature: 0.8,
    },
  }),
});
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Prompts that are pushed to Phoenix are versioned and can be tagged.

Pulling a Prompt from Phoenix

The getPrompt function can be used to pull a prompt from Phoenix based on some Prompt Identifier and returns it in the Phoenix SDK Prompt type.

import { getPrompt } from "@arizeai/phoenix-client/prompts";

const prompt = await getPrompt({ name: "my-prompt" });
// ^ you now have a strongly-typed prompt object, in the Phoenix SDK Prompt type

const promptByTag = await getPrompt({ tag: "production", name: "my-prompt" });
// ^ you can optionally specify a tag to filter by

const promptByVersionId = await getPrompt({
  versionId: "1234567890",
});
// ^ you can optionally specify a prompt version Id to filter by
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Using a Phoenix Prompt with an LLM Provider SDK

The toSDK helper function can be used to convert a Phoenix Prompt to the format expected by an LLM provider SDK. You can then use the LLM provider SDK as normal, with your prompt.

If your Prompt is saved in Phoenix as openai, you can use the toSDK function to convert the prompt to the format expected by OpenAI, or even Anthropic and Vercel AI SDK. We will do a best effort conversion to your LLM provider SDK of choice.

The following LLM provider SDKs are supported:

• Vercel AI SDK: ai ai
• OpenAI: openai openai
• Anthropic: anthropic @anthropic-ai/sdk

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { getPrompt, toSDK } from "@arizeai/phoenix-client/prompts";

const prompt = await getPrompt({ name: "my-prompt" });
const promptAsAI = toSDK({
  sdk: "ai",
  // ^ the SDK you want to convert the prompt to, supported SDKs are listed above
  variables: {
    "my-variable": "my-value",
  },
  // ^ you can format the prompt with variables, if the prompt has any variables in its template
  //   the format (Mustache, F-string, etc.) is specified in the Prompt itself
  prompt,
});
// ^ promptAsAI is now in the format expected by the Vercel AI SDK generateText function

const response = await generateText({
  model: openai(prompt.model_name),
  // ^ the model adapter provided by the Vercel AI SDK can be swapped out for any other model
  //   adapter supported by the Vercel AI SDK. Take care to use the correct model name for the
  //   LLM provider you are using.
  ...promptAsAI,
});
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REST Endpoints

The client provides a REST API for all endpoints defined in the Phoenix OpenAPI spec.

Endpoints are accessible via strongly-typed string literals and TypeScript auto-completion inside of the client object.

import { createClient } from "@arizeai/phoenix-client";

const phoenix = createClient();

// Get all datasets
const datasets = await phoenix.GET("/v1/datasets");

// Get specific prompt
const prompt = await phoenix.GET("/v1/prompts/{prompt_identifier}/latest", {
  params: {
    path: {
      prompt_identifier: "my-prompt",
    },
  },
});
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A comprehensive overview of the available endpoints and their parameters is available in the OpenAPI viewer within Phoenix, or in the Phoenix OpenAPI spec.

Datasets

The @arizeai/phoenix-client package allows you to create and manage datasets, which are collections of examples used for experiments and evaluation.

Creating a Dataset

You can create a dataset by providing a name, description, and an array of examples (each with input, output, and optional metadata).

import { createDataset } from "@arizeai/phoenix-client/datasets";

const { datasetId } = await createDataset({
  name: "questions",
  description: "a simple dataset of questions",
  examples: [
    {
      input: { question: "What is the capital of France" },
      output: { answer: "Paris" },
      metadata: {},
    },
    {
      input: { question: "What is the capital of the USA" },
      output: { answer: "Washington D.C." },
      metadata: {},
    },
  ],
});
// You can now use datasetId to run experiments or add more examples
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Experiments

The @arizeai/phoenix-client package provides an experiments API for running and evaluating tasks on datasets. This is useful for benchmarking models, evaluating outputs, and tracking experiment results in Phoenix.

Running an Experiment

To run an experiment, you typically:

1. Create a dataset (or use an existing one)
2. Define a task function to run on each example
3. Define one or more evaluators to score or label the outputs
4. Run the experiment and inspect the results

Below is a complete example:

import { createDataset } from "@arizeai/phoenix-client/datasets";
import {
  asExperimentEvaluator,
  runExperiment,
} from "@arizeai/phoenix-client/experiments";

// 1. Create a dataset
const { datasetId } = await createDataset({
  name: "names-dataset",
  description: "a simple dataset of names",
  examples: [
    {
      input: { name: "John" },
      output: { text: "Hello, John!" },
      metadata: {},
    },
    {
      input: { name: "Jane" },
      output: { text: "Hello, Jane!" },
      metadata: {},
    },
  ],
});

// 2. Define a task to run on each example
const task = async (example) => `hello ${example.input.name}`;

// 3. Define evaluators
const 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: {},
      };
    },
  }),
];

// 4. Run the experiment
const experiment = await runExperiment({
  dataset: { datasetId },
  task,
  evaluators,
});
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Hint: Tasks and evaluators are instrumented using OpenTelemetry. You can view detailed traces of experiment runs and evaluations directly in the Phoenix UI for debugging and performance analysis.

Traces

The @arizeai/phoenix-client package provides a traces export for retrieving trace data from Phoenix projects.

Fetching Traces

Use getTraces to retrieve traces with optional filtering, sorting, and pagination.

import { getTraces } from "@arizeai/phoenix-client/traces";

// Get the latest 10 traces
const result = await getTraces({
  project: { projectName: "my-project" },
  limit: 10,
});
console.log(result.data); // array of trace objects

// Filter by time range and include full span details
const detailed = await getTraces({
  project: { projectName: "my-project" },
  startTime: "2026-03-01T00:00:00Z",
  endTime: new Date(),
  includeSpans: true,
  sort: "latency_ms",
  order: "desc",
});

// Filter by session
const sessionTraces = await getTraces({
  project: { projectName: "my-project" },
  sessionId: "my-session-id",
});
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Pagination

Use the cursor from a previous result to fetch the next page:

import { getTraces } from "@arizeai/phoenix-client/traces";

let cursor: string | null = null;
const allTraces = [];

do {
  const result = await getTraces({
    project: { projectName: "my-project" },
    limit: 50,
    cursor,
  });
  allTraces.push(...result.data);
  cursor = result.nextCursor ?? null;
} while (cursor);
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Note: Requires Phoenix server >= 13.15.0.

Spans

The @arizeai/phoenix-client package provides a spans export for querying spans with powerful filtering.

Fetching Spans

Use getSpans to retrieve spans with filtering by kind, status, name, trace, and more.

import { getSpans } from "@arizeai/phoenix-client/spans";

// Get recent spans
const result = await getSpans({
  project: { projectName: "my-project" },
  limit: 100,
});

// Filter by span kind and status
const errorLLMSpans = await getSpans({
  project: { projectName: "my-project" },
  spanKind: "LLM",
  statusCode: "ERROR",
});

// Filter by name and trace
const spans = await getSpans({
  project: { projectName: "my-project" },
  name: "chat_completion",
  traceIds: ["trace-abc", "trace-def"],
});

// Root spans only
const rootSpans = await getSpans({
  project: { projectName: "my-project" },
  parentId: null,
});
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Span Annotations

The spans export also provides functions for managing span annotations — adding evaluations, feedback, and labels to spans.

Adding a Single Annotation

import { addSpanAnnotation } from "@arizeai/phoenix-client/spans";

const result = await addSpanAnnotation({
  spanAnnotation: {
    spanId: "f8b1c3a2d4e5f678",
    name: "correctness",
    label: "correct",
    score: 1.0,
    explanation: "The response accurately answered the question",
    annotatorKind: "HUMAN",
  },
  sync: true, // wait for the annotation ID to be returned
});
// result: { id: "annotation-id" } when sync: true, null when sync: false
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Logging Multiple Annotations

import { logSpanAnnotations } from "@arizeai/phoenix-client/spans";

const results = await logSpanAnnotations({
  spanAnnotations: [
    {
      spanId: "f8b1c3a2d4e5f678",
      name: "relevance",
      label: "relevant",
      score: 0.95,
      annotatorKind: "LLM",
    },
    {
      spanId: "a1b2c3d4e5f67890",
      name: "relevance",
      label: "irrelevant",
      score: 0.2,
      annotatorKind: "LLM",
    },
  ],
  sync: true,
});
// results: [{ id: "..." }, { id: "..." }]
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Fetching Span Annotations

import { getSpanAnnotations } from "@arizeai/phoenix-client/spans";

const result = await getSpanAnnotations({
  project: { projectName: "my-project" },
  spanIds: ["f8b1c3a2d4e5f678", "a1b2c3d4e5f67890"],
  includeAnnotationNames: ["relevance", "correctness"],
  limit: 100,
});
console.log(result.data); // array of span annotation objects
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Sessions

The @arizeai/phoenix-client package provides a sessions export for managing conversation sessions and their annotations.

Fetching Sessions

Use listSessions to list all sessions for a project, or getSession to retrieve a single session by ID.

import {
  listSessions,
  getSession,
  addSessionAnnotation,
} from "@arizeai/phoenix-client/sessions";

// List all sessions for a project
const sessions = await listSessions({
  project: "my-project",
});

for (const session of sessions) {
  console.log(
    `Session: ${session.sessionId}, Traces: ${session.traces.length}`
  );
}

// Get a single session by its ID
const session = await getSession({ sessionId: "my-session-id" });
console.log(session.traces); // array of trace summaries in the session
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Adding Session Annotations

await addSessionAnnotation({
  sessionAnnotation: {
    sessionId: "my-session-id",
    name: "user-satisfaction",
    label: "satisfied",
    score: 0.9,
    annotatorKind: "HUMAN",
  },
});
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Examples

To run examples, install dependencies using pnpm and run:

pnpm install
pnpx tsx examples/list_datasets.ts
# change the file name to run other examples
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Compatibility

This package utilizes openapi-ts to generate the types from the Phoenix OpenAPI spec.

Because of this, this package only works with the arize-phonix server 8.0.0 and above.

Compatibility Table:

Community

Join our community to connect with thousands of AI builders:

• 🌍 Join our Slack community.
• 📚 Read the Phoenix documentation.
• 💡 Ask questions and provide feedback in the #phoenix-support channel.
• 🌟 Leave a star on our GitHub.
• 🐞 Report bugs with GitHub Issues.
• 𝕏 Follow us on 𝕏.
• 🗺️ Check out our roadmap to see where we're heading next.