@arizeai/openinference-core

This package provides OpenInference Core utilities for LLM Traces, including tracing helpers, decorators, and context attribute propagation.

Note

If you are tracing into Phoenix, @arizeai/phoenix-otel re-exports this package’s helpers, context utilities, attribute builders, and OITracer from a single import path alongside Phoenix registration. The wrappers resolve the default tracer when the wrapped function runs, which is useful for experiments and other workflows that swap providers.

Installation#

npm install @arizeai/openinference-core

Tracing Helpers#

This package provides convenient helpers to instrument your functions, agents, and LLM operations with OpenInference spans.

withSpan#

Wraps any function (sync or async) with OpenTelemetry tracing:

import { withSpan } from "@arizeai/openinference-core";import { OpenInferenceSpanKind } from "@arizeai/openinference-semantic-conventions";const processUserQuery = async (query: string) => {  const response = await fetch(`/api/process?q=${query}`);  return response.json();};const tracedProcess = withSpan(processUserQuery, {  name: "user-query-processor",  kind: OpenInferenceSpanKind.CHAIN,});

traceChain#

Convenience wrapper for tracing workflow sequences:

import { traceChain } from "@arizeai/openinference-core";const ragPipeline = async (question: string) => {  const documents = await retrieveDocuments(question);  const context = documents.map((d) => d.content).join("\n");  const answer = await generateAnswer(question, context);  return answer;};const tracedRag = traceChain(ragPipeline, { name: "rag-pipeline" });

traceAgent#

Convenience wrapper for tracing autonomous agents:

import { traceAgent } from "@arizeai/openinference-core";const simpleAgent = async (question: string) => {  const documents = await retrieveDocuments(question);  const analysis = await analyzeContext(question, documents);  return await executePlan(analysis);};const tracedAgent = traceAgent(simpleAgent, { name: "qa-agent" });

traceTool#

Convenience wrapper for tracing external tools:

import { traceTool } from "@arizeai/openinference-core";const weatherTool = async (city: string) => {  const response = await fetch(`https://api.weather.com/v1/${city}`);  return response.json();};const tracedWeatherTool = traceTool(weatherTool, { name: "weather-api" });

Additional Span-Kind Wrappers#

The remaining OpenInference span kinds each have a matching wrapper. Like traceChain/traceAgent/traceTool, each is a shorthand for withSpan(fn, { ...options, kind }) and accepts the same options minus kind. These wrappers are marked @experimental and may change in a future release.

Wrapper	Span kind	Use it for
`traceLLM`	`LLM`	Language-model invocations — chat/text completions and other inference calls
`traceRetriever`	`RETRIEVER`	Fetching documents from a knowledge base, vector store, or search index (RAG)
`traceReranker`	`RERANKER`	Reordering or scoring candidate documents by relevance
`traceEmbedding`	`EMBEDDING`	Converting text or data into vector representations
`traceGuardrail`	`GUARDRAIL`	Safety, validation, or policy checks (moderation, PII, compliance)
`traceEvaluator`	`EVALUATOR`	Scoring output quality — relevance, correctness, or LLM-as-a-judge
`tracePrompt`	`PROMPT`	Constructing, rendering, or templating a prompt before a model call

import {  traceEmbedding,  traceEvaluator,  traceGuardrail,  traceLLM,  tracePrompt,  traceReranker,  traceRetriever,} from "@arizeai/openinference-core";const retrieveDocuments = traceRetriever(  async (query: string) => vectorStore.similaritySearch(query, 5),  { name: "vector-search" });const evaluateAnswer = traceEvaluator(  async (question: string, answer: string) => judge.score({ question, answer }),  { name: "answer-evaluation" });

Decorators#

@observe#

Decorator for automatically tracing class methods:

import { observe } from "@arizeai/openinference-core";class ChatService {  @observe({ kind: "CHAIN" })  async processMessage(message: string) {    return `Processed: ${message}`;  }  @observe({ name: "llm-call", kind: "LLM" })  async callLLM(prompt: string) {    return await llmClient.generate(prompt);  }}

Customizing Spans#

The package offers utilities to track important application metadata using context attribute propagation:

Function	Description
`setSession`	Specify a session ID to track and group multi-turn conversations
`setUser`	Specify a user ID to track different conversations with a given user
`setMetadata`	Add custom metadata for operational needs
`setTags`	Add tags to filter spans on specific keywords
`setPromptTemplate`	Track prompt template used, with version and variables
`setAttributes`	Add multiple custom attributes at once

Note

All @arizeai/openinference auto instrumentation packages will pull attributes off of context and add them to spans.

Example: setSession#

import { context } from "@opentelemetry/api";import { setSession } from "@arizeai/openinference-core";context.with(setSession(context.active(), { sessionId: "session-id" }), () => {  // Calls within this block will generate spans with the attributes:  // "session.id" = "session-id"});

Chaining Setters#

Each setter function returns a new active context, so they can be chained together:

import { context } from "@opentelemetry/api";import { setAttributes, setSession } from "@arizeai/openinference-core";context.with(  setAttributes(setSession(context.active(), { sessionId: "session-id" }), {    myAttribute: "test",  }),  () => {    // Calls within this block will generate spans with the attributes:    // "myAttribute" = "test"    // "session.id" = "session-id"  },);

Manual Span Context Propagation#

If you are creating spans manually and want to propagate context attributes, use the getAttributesFromContext utility:

import { getAttributesFromContext } from "@arizeai/openinference-core";import { context, trace } from "@opentelemetry/api";const contextAttributes = getAttributesFromContext(context.active());const tracer = trace.getTracer("example");const span = tracer.startSpan("example span");span.setAttributes(contextAttributes);span.end();

Attribute Helpers#

Generate properly formatted attributes for common LLM operations.

getLLMAttributes#

Generate attributes for LLM operations:

import { getLLMAttributes } from "@arizeai/openinference-core";import { trace } from "@opentelemetry/api";const tracer = trace.getTracer("llm-service");tracer.startActiveSpan("llm-inference", (span) => {  const attributes = getLLMAttributes({    provider: "openai",    modelName: "gpt-4",    inputMessages: [{ role: "user", content: "What is AI?" }],    outputMessages: [{ role: "assistant", content: "AI is..." }],    tokenCount: { prompt: 10, completion: 50, total: 60 },  });  span.setAttributes(attributes);  span.end();});

getEmbeddingAttributes#

Generate attributes for embedding operations:

import { getEmbeddingAttributes } from "@arizeai/openinference-core";import { trace } from "@opentelemetry/api";const tracer = trace.getTracer("embedding-service");tracer.startActiveSpan("generate-embeddings", (span) => {  const attributes = getEmbeddingAttributes({    modelName: "text-embedding-ada-002",    embeddings: [      { text: "The quick brown fox", vector: [0.1, 0.2, 0.3] },      { text: "jumps over the lazy dog", vector: [0.4, 0.5, 0.6] },    ],  });  span.setAttributes(attributes);  span.end();});

getRetrieverAttributes#

Generate attributes for document retrieval:

import { getRetrieverAttributes } from "@arizeai/openinference-core";import { trace } from "@opentelemetry/api";const tracer = trace.getTracer("retriever-service");async function retrieveDocuments(query: string) {  return tracer.startActiveSpan("retrieve-documents", async (span) => {    const documents = await vectorStore.similaritySearch(query, 5);    const attributes = getRetrieverAttributes({      documents: documents.map((doc) => ({        content: doc.pageContent,        id: doc.metadata.id,        score: doc.score,        metadata: doc.metadata,      })),    });    span.setAttributes(attributes);    span.end();    return documents;  });}

getToolAttributes#

Generate attributes for tool definitions:

import { getToolAttributes } from "@arizeai/openinference-core";import { trace } from "@opentelemetry/api";const tracer = trace.getTracer("tool-service");tracer.startActiveSpan("define-tool", (span) => {  const attributes = getToolAttributes({    name: "search_web",    description: "Search the web for information",    parameters: {      query: { type: "string", description: "The search query" },      maxResults: { type: "number", description: "Maximum results to return" },    },  });  span.setAttributes(attributes);  span.end();});

Trace Config#

Control settings like data privacy and payload sizes. You may want to keep sensitive information from being logged for security reasons, or limit the size of base64 encoded images.

Note

These values can also be controlled via environment variables. See the configuration spec for more information.

import { OpenAIInstrumentation } from "@arizeai/openinference-instrumentation-openai";const traceConfig = { hideInputs: true };const instrumentation = new OpenAIInstrumentation({ traceConfig });

Reference Documentation#

OpenInference JS Docs

Full API documentation and examples

npm

Package on npm