---
title: "@arizeai/openinference-core"
description: "OpenInference Core utilities for LLM tracing in TypeScript"
---

[![NPM Version](https://img.shields.io/npm/v/%40arizeai%2Fopeninference-core)](https://www.npmjs.com/package/@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.
</Note>

## Installation

```bash
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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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 |

```typescript
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:

```typescript
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.
</Note>

### Example: setSession

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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](https://github.com/Arize-ai/openinference/blob/main/spec/configuration.md) for more information.
</Note>

```typescript
import { OpenAIInstrumentation } from "@arizeai/openinference-instrumentation-openai";

const traceConfig = { hideInputs: true };

const instrumentation = new OpenAIInstrumentation({ traceConfig });
```

---

## Reference Documentation

<CardGroup cols={2}>
  <Card title="OpenInference JS Docs" icon="book" href="https://arize-ai.github.io/openinference/js/">
    Full API documentation and examples
  </Card>
  <Card title="npm" icon="arrow-up-right-from-square" href="https://www.npmjs.com/package/@arizeai/openinference-core">
    Package on npm
  </Card>
</CardGroup>
