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Overview

The @arizeai/openinference-core package provides the foundational utilities for OpenInference JavaScript tracing:
  • OITracer: OpenTelemetry tracer wrapper with data masking
  • Context Attributes: Session ID, user ID, metadata, and tags propagation
  • Span Wrappers: withSpan, traceChain, traceAgent, traceTool
  • Decorators: @observe for class methods
  • Attribute Helpers: Functions to generate OpenInference-compliant attributes

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Installation

npm install --save @arizeai/openinference-core

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OITracer

OITracer wraps an OpenTelemetry tracer and can redact sensitive data before writing spans.

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Basic Usage

import { trace } from "@opentelemetry/api";
import { OITracer } from "@arizeai/openinference-core";

const tracer = new OITracer({
  tracer: trace.getTracer("my-service"),
  traceConfig: {
    hideInputs: true,
    hideOutputText: true,
    hideEmbeddingVectors: true,
    base64ImageMaxLength: 8000,
  },
});

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TraceConfig Options

interface TraceConfigOptions {
  hideInputs?: boolean;              // Hide all inputs
  hideOutputs?: boolean;             // Hide all outputs
  hideInputMessages?: boolean;       // Hide LLM input messages
  hideOutputMessages?: boolean;      // Hide LLM output messages
  hideInputText?: boolean;           // Hide input text content
  hideOutputText?: boolean;          // Hide output text content
  hideInputImages?: boolean;         // Hide input images
  hideEmbeddingVectors?: boolean;    // Hide embedding vectors
  hidePrompts?: boolean;             // Hide prompt templates
  base64ImageMaxLength?: number;     // Max base64 image length (default: 32000)
}

​
Environment Variables

Configure masking via environment variables: 
OPENINFERENCE_HIDE_INPUTS=true
OPENINFERENCE_HIDE_OUTPUTS=true
OPENINFERENCE_HIDE_INPUT_MESSAGES=true
OPENINFERENCE_HIDE_OUTPUT_MESSAGES=true
OPENINFERENCE_HIDE_INPUT_IMAGES=true
OPENINFERENCE_HIDE_INPUT_TEXT=true
OPENINFERENCE_HIDE_OUTPUT_TEXT=true
OPENINFERENCE_HIDE_EMBEDDING_VECTORS=true
OPENINFERENCE_BASE64_IMAGE_MAX_LENGTH=8000
OPENINFERENCE_HIDE_PROMPTS=true

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Context Attributes

Propagate request-level attributes across all spans in a context.

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Available Setters

import { context } from "@opentelemetry/api";
import {
  setSession,
  setUser,
  setMetadata,
  setTags,
  setPromptTemplate,
  setAttributes,
} from "@arizeai/openinference-core";

const enrichedContext = setAttributes(
  setPromptTemplate(
    setTags(
      setMetadata(
        setUser(
          setSession(context.active(), { sessionId: "sess-42" }),
          { userId: "user-7" }
        ),
        { tenant: "acme", environment: "prod" }
      ),
      ["support", "priority-high"]
    ),
    {
      template: "Answer using docs about {topic}",
      variables: { topic: "billing" },
      version: "v3",
    }
  ),
  { "app.request_id": "req-123" }
);

context.with(enrichedContext, async () => {
  // All spans here include propagated attributes
});

​
Context Setter Reference

FunctionParametersDescription
setSession{ sessionId: string }Set session ID
setUser{ userId: string }Set user ID
setMetadataRecord<string, unknown>Set metadata object
setTagsstring[]Set tags array
setPromptTemplate{ template, variables?, version? }Set prompt template info
setAttributesAttributesSet arbitrary attributes

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Retrieving Attributes

import { getAttributesFromContext } from "@arizeai/openinference-core";
import { context, trace } from "@opentelemetry/api";

const tracer = trace.getTracer("manual-tracer");
const span = tracer.startSpan("manual-span");
span.setAttributes(getAttributesFromContext(context.active()));
span.end();

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Span Wrappers

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withSpan

Wrap a function to automatically create spans: 
import { OpenInferenceSpanKind } from "@arizeai/openinference-semantic-conventions";
import { withSpan } from "@arizeai/openinference-core";

const retrieve = withSpan(
  async (query: string) => {
    return [`Document for ${query}`];
  },
  {
    name: "retrieve-documents",
    kind: OpenInferenceSpanKind.RETRIEVER,
  }
);

const docs = await retrieve("openinference");

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withSpan Options

interface WithSpanOptions {
  name?: string;                          // Span name (defaults to function name)
  kind?: OpenInferenceSpanKind | SpanKind; // Span kind
  tracer?: Tracer;                        // Custom tracer
  processInput?: InputToAttributesFn;     // Custom input processor
  processOutput?: OutputToAttributesFn;   // Custom output processor
}

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Custom Input/Output Processing

import {
  getInputAttributes,
  getRetrieverAttributes,
  withSpan,
} from "@arizeai/openinference-core";

const retriever = withSpan(
  async (query: string) => [
    `Doc A for ${query}`,
    `Doc B for ${query}`,
  ],
  {
    name: "retriever",
    kind: "RETRIEVER",
    processInput: (query) => getInputAttributes(query),
    processOutput: (documents) =>
      getRetrieverAttributes({
        documents: documents.map((content, i) => ({
          id: `doc-${i}`,
          content,
        })),
      }),
  }
);

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Convenience Wrappers

These wrappers set the span kind automatically: 
import {
  traceChain,
  traceAgent,
  traceTool,
} from "@arizeai/openinference-core";

const tracedChain = traceChain(
  async (q: string) => `chain result: ${q}`,
  { name: "rag-chain" }
);

const tracedTool = traceTool(
  async (city: string) => ({ temp: 72, city }),
  { name: "weather-tool" }
);

const tracedAgent = traceAgent(
  async (q: string) => {
    const toolResult = await tracedTool("seattle");
    return tracedChain(`${q} (${toolResult.temp}F)`);
  },
  { name: "qa-agent" }
);

​
Decorators

The @observe decorator traces class methods (requires TypeScript 5+ standard decorators): 
import { OpenInferenceSpanKind } from "@arizeai/openinference-semantic-conventions";
import { observe } from "@arizeai/openinference-core";

class ChatService {
  @observe({ kind: OpenInferenceSpanKind.CHAIN })
  async runWorkflow(message: string) {
    return `processed: ${message}`;
  }

  @observe({ name: "llm-call", kind: OpenInferenceSpanKind.LLM })
  async callModel(prompt: string) {
    return `model output for: ${prompt}`;
  }
}

const service = new ChatService();
await service.runWorkflow("Hello");

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Decorator Options

interface ObserveOptions {
  name?: string;                          // Span name (defaults to method name)
  kind?: OpenInferenceSpanKind | SpanKind; // Span kind
  tracer?: Tracer;                        // Custom tracer
  processInput?: InputToAttributesFn;     // Custom input processor
  processOutput?: OutputToAttributesFn;   // Custom output processor
}

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Attribute Helpers

Generate OpenInference-compliant attributes:

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getLLMAttributes

import { getLLMAttributes } from "@arizeai/openinference-core";
import { trace } from "@opentelemetry/api";

const tracer = trace.getTracer("llm-service");

tracer.startActiveSpan("llm-inference", (span) => {
  span.setAttributes(
    getLLMAttributes({
      provider: "openai",
      modelName: "gpt-4o-mini",
      inputMessages: [
        { role: "user", content: "What is OpenInference?" },
      ],
      outputMessages: [
        { role: "assistant", content: "OpenInference is..." },
      ],
      tokenCount: { prompt: 12, completion: 44, total: 56 },
      invocationParameters: { temperature: 0.2 },
    })
  );
  span.end();
});

​
getLLMAttributes Parameters

interface LLMAttributesParams {
  provider?: string;                  // LLM provider (e.g., "openai")
  modelName?: string;                 // Model name (e.g., "gpt-4o-mini")
  inputMessages?: Message[];          // Input messages array
  outputMessages?: Message[];         // Output messages array
  tokenCount?: TokenCount;            // Token usage
  tools?: Tool[];                     // Available tools
  invocationParameters?: Record<string, unknown>; // Model parameters
}

interface Message {
  role: string;                       // "user", "assistant", "system"
  content: string;                    // Message content
  name?: string;                      // Optional name
  toolCalls?: ToolCall[];            // Tool calls in message
}

interface TokenCount {
  prompt?: number;                    // Prompt tokens
  completion?: number;                // Completion tokens
  total?: number;                     // Total tokens
}

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getEmbeddingAttributes

import { getEmbeddingAttributes } from "@arizeai/openinference-core";

span.setAttributes(
  getEmbeddingAttributes({
    modelName: "text-embedding-3-small",
    embeddings: [
      { text: "First text", vector: [0.1, 0.2, 0.3] },
      { text: "Second text", vector: [0.4, 0.5, 0.6] },
    ],
  })
);

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getRetrieverAttributes

import { getRetrieverAttributes } from "@arizeai/openinference-core";

span.setAttributes(
  getRetrieverAttributes({
    documents: [
      { id: "doc-1", content: "Document content", score: 0.95 },
      { id: "doc-2", content: "Another document", score: 0.87 },
    ],
  })
);

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getToolAttributes

import { getToolAttributes } from "@arizeai/openinference-core";

span.setAttributes(
  getToolAttributes({
    name: "get_weather",
    description: "Get current weather for a city",
    parameters: { city: "Seattle", units: "fahrenheit" },
  })
);

​
getInputAttributes / getOutputAttributes

import {
  getInputAttributes,
  getOutputAttributes,
} from "@arizeai/openinference-core";
import { MimeType } from "@arizeai/openinference-semantic-conventions";

// String input
span.setAttributes(getInputAttributes("What is OpenInference?"));

// Structured input
span.setAttributes(
  getInputAttributes({
    value: JSON.stringify({ query: "search term" }),
    mimeType: MimeType.JSON,
  })
);

// Output
span.setAttributes(
  getOutputAttributes("OpenInference is a framework...")
);

​
getMetadataAttributes

import { getMetadataAttributes } from "@arizeai/openinference-core";

span.setAttributes(
  getMetadataAttributes({
    tenant: "acme",
    environment: "production",
    version: "1.2.3",
  })
);

​
Utility Functions

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withSafety

Wrap a function and return null on error: 
import { withSafety } from "@arizeai/openinference-core";

const safeParser = withSafety({
  fn: (json: string) => JSON.parse(json),
  onError: (error) => console.error("Parse failed:", error),
});

const result = safeParser("invalid json"); // Returns null instead of throwing

​
safelyJSONStringify / safelyJSONParse

import {
  safelyJSONStringify,
  safelyJSONParse,
} from "@arizeai/openinference-core";

const json = safelyJSONStringify({ key: "value" }); // Returns string or undefined
const obj = safelyJSONParse(json); // Returns object or undefined

​
Complete Example

import { trace, context } from "@opentelemetry/api";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import { ConsoleSpanExporter, SimpleSpanProcessor } from "@opentelemetry/sdk-trace-base";
import { OpenInferenceSpanKind } from "@arizeai/openinference-semantic-conventions";
import {
  OITracer,
  setSession,
  setUser,
  withSpan,
  getLLMAttributes,
} from "@arizeai/openinference-core";

// Setup provider
const provider = new NodeTracerProvider();
provider.addSpanProcessor(new SimpleSpanProcessor(new ConsoleSpanExporter()));
provider.register();

// Create OITracer with data masking
const oiTracer = new OITracer({
  tracer: trace.getTracer("my-app"),
  traceConfig: {
    hideInputImages: true,
    base64ImageMaxLength: 8000,
  },
});

// Define traced functions
const callLLM = withSpan(
  async (prompt: string) => {
    // Simulate LLM call
    return `Response to: ${prompt}`;
  },
  {
    name: "llm-call",
    kind: OpenInferenceSpanKind.LLM,
    tracer: oiTracer,
    processOutput: (output) => 
      getLLMAttributes({
        modelName: "gpt-4o-mini",
        outputMessages: [{ role: "assistant", content: output }],
      }),
  }
);

const processRequest = withSpan(
  async (query: string) => {
    return callLLM(query);
  },
  {
    name: "process-request",
    kind: OpenInferenceSpanKind.CHAIN,
  }
);

// Execute with context
const enrichedContext = setUser(
  setSession(context.active(), { sessionId: "sess-123" }),
  { userId: "user-456" }
);

context.with(enrichedContext, async () => {
  const result = await processRequest("What is OpenInference?");
  console.log(result);
});

​
Next Steps

Semantic Conventions

Learn about OpenInference semantic conventions

Instrumentations

Auto-instrument LLM frameworks

Examples

View complete examples

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