LLM Observability Node.js SDK Reference

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Overview

The LLM Observability SDK for Node.js enhances the observability of your JavaScript-based LLM applications. The SDK supports Node.js versions 16 and newer. For information about LLM Observability’s integration support, see Auto Instrumentation.

You can install and configure tracing of various operations such as workflows, tasks, and API calls with wrapped functions or traced blocks. You can also annotate these traces with metadata for deeper insights into the performance and behavior of your applications, supporting multiple LLM services or models from the same environment.

Setup

Prerequisites

The latest dd-trace package must be installed:

npm install dd-trace

LLM Observability requires a Datadog API key (see the instructions for creating an API key).

Command-line setup

Enable LLM Observability by running your application with NODE_OPTIONS="--import dd-race/initialize.mjs" and specifying the required environment variables.

Note: dd-trace/initialize.mjs automatically turns on all APM integrations.

DD_SITE= DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 \
DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME> NODE_OPTIONS="--import dd-trace/initialize.mjs" node <YOUR_APP_ENTRYPOINT>

DD_API_KEY: required - string
Your Datadog API key.
DD_SITE: required - string
The Datadog site to submit your LLM data. Your site is .
DD_LLMOBS_ENABLED: required - integer or string
Toggle to enable submitting data to LLM Observability. Should be set to 1 or true.
DD_LLMOBS_ML_APP: required - string
The name of your LLM application, service, or project, under which all traces and spans are grouped. This helps distinguish between different applications or experiments. See Application naming guidelines for allowed characters and other constraints. To override this value for a given root span, see Tracing multiple applications.
DD_LLMOBS_AGENTLESS_ENABLED: optional - integer or string - default: false
Only required if you are not using the Datadog Agent, in which case this should be set to 1 or true.

In-code setup

Enable LLM Observability programatically through the init() function instead of running with the dd-trace/initialize.mjs command. Note: Do not use this setup method with the dd-trace/initialize.mjs command.

const tracer = require('dd-trace').init({
  llmobs: {
    mlApp: "<YOUR_ML_APP_NAME>",
    agentlessEnabled: true,
  },
  site: "<YOUR_DATADOG_SITE>",
  env: "<YOUR_ENV>",
});

const llmobs = tracer.llmobs;

These options are set on the llmobs configuration:

mlApp: optional - string
The name of your LLM application, service, or project, under which all traces and spans are grouped. This helps distinguish between different applications or experiments. See Application naming guidelines for allowed characters and other constraints. To override this value for a given trace, see Tracing multiple applications. If not provided, this defaults to the value of DD_LLMOBS_ML_APP.
agentlessEnabled: optional - boolean - default: false
Only required if you are not using the Datadog Agent, in which case this should be set to true. This configures the dd-trace library to not send any data that requires the Datadog Agent. If not provided, this defaults to the value of DD_LLMOBS_AGENTLESS_ENABLED.

These options can be set on the general tracer configuration:

env: optional - string
The name of your application’s environment (examples: prod, pre-prod, staging). If not provided, this defaults to the value of DD_ENV.
service: optional - string
The name of the service used for your application. If not provided, this defaults to the value of DD_SERVICE.

DD_API_KEY and DD_SITE are read from environment variables for configuration, and cannot be configured programatically.

AWS Lambda setup

Use the llmobs.flush() function to flush all remaining spans from the tracer to LLM Observability at the end of the Lambda function.

Application naming guidelines

Your application name (the value of DD_LLMOBS_ML_APP) must be a lowercase Unicode string. It may contain the characters listed below:

Alphanumerics
Underscores
Minuses
Colons
Periods
Slashes

The name can be up to 193 characters long and may not contain contiguous or trailing underscores.

Tracing spans

To trace a span, use llmobs.wrap(options, function) as a function wrapper for the function you’d like to trace. For a list of available span kinds, see the Span Kinds documentation. For more granular tracing of operations within functions, see Tracing spans using inline methods.

Span Kinds

Span kinds are required, and are specified on the options object passed to the llmobs tracing functions (trace, wrap, and decorate). See the Span Kinds documentation for a list of supported span kinds.

Note: Spans with an invalid span kind are not submitted to LLM Observability.

Automatic function argument/output/name capturing

llmobs.wrap (along with llmobs.decorate for TypeScript) tries to automatically capture inputs, outputs, and the name of the function being traced. If you need to manually annotate a span, see Annotating a span. Inputs and outputs you annotate will override the automatic capturing. Additionally, to override the function name, pass the name property on the options object to the llmobs.wrap function:

function processMessage () {
  ... // user application logic
  return
}
processMessage = llmobs.wrap({ kind: 'workflow', name: 'differentFunctionName' }, processMessage)

LLM span

Note: If you are using any LLM providers or frameworks that are supported by Datadog’s LLM integrations, you do not need to manually start a LLM span to trace these operations.

To trace an LLM span, specify the span kind as llm, and optionally specify the following arguments on the options object.

Arguments

modelName: optional - string - default: "custom"
The name of the invoked LLM.
name: optional - string
The name of the operation. If not provided, name defaults to the name of the traced function.
modelProvider: optional - string - default: "custom"
The name of the model provider.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

function llmCall () {
  const completion = ... // user application logic to invoke LLM
  return completion
}
llmCall = llmobs.wrap({ kind: 'llm', name: 'invokeLLM', modelName: 'claude', modelProvider: 'anthropic' }, llmCall)

Workflow span

To trace an LLM span, specify the span kind as workflow, and optionally specify the following arguments on the options object.

Arguments

name: optional - string
The name of the operation. If not provided, name defaults to the name of the traced function.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

function processMessage () {
  ... // user application logic
  return
}
processMessage = llmobs.wrap({ kind: 'workflow' }, processMessage)

Agent span

To trace an LLM span, specify the span kind as agent, and optionally specify the following arguments on the options object.

Arguments

name: optional - string
The name of the operation. If not provided, name defaults to the name of the traced function.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

function reactAgent () {
  ... // user application logic
  return
}
reactAgent = llmobs.wrap({ kind: 'agent' }, reactAgent)

Tool span

To trace an LLM span, specify the span kind as tool, and optionally specify the following arguments on the options object.

Arguments

name: optional - string
The name of the operation. If not provided, name defaults to the name of the traced function.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

function callWeatherApi () {
  ... // user application logic
  return
}
callWeatherApi = llmobs.wrap({ kind: 'tool' }, callWeatherApi)

Task span

To trace an LLM span, specify the span kind as task, and optionally specify the following arguments on the options object.

Arguments

name: optional - string
The name of the operation. If not provided, name defaults to the name of the traced function.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

function sanitizeInput () {
  ... // user application logic
  return
}
sanitizeInput = llmobs.wrap({ kind: 'task' }, sanitizeInput)

Embedding span

To trace an LLM span, specify the span kind as embedding, and optionally specify the following arguments on the options object.

Note: Annotating an embedding span’s input requires different formatting than other span types. See Annotating a span for more details on how to specify embedding inputs.

Arguments

modelName: optional - string - default: "custom"
The name of the invoked LLM.
name: optional - string
The name of the operation. If not provided, name is set to the name of the traced function.
modelProvider: optional - string - default: "custom"
The name of the model provider.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

function performEmbedding () {
  ... // user application logic
  return
}
performEmbedding = llmobs.wrap({ kind: 'embedding', modelName: 'text-embedding-3', modelProvider: 'openai' }, performEmbedding)

Retrieval span

To trace an LLM span, specify the span kind as retrieval, and optionally specify the following arguments on the options object.

Note: Annotating a retrieval span’s output requires different formatting than other span types. See Annotating a span for more details on how to specify retrieval outputs.

Arguments

name: optional - string
The name of the operation. If not provided, name defaults to the name of the traced function.
sessionId: optional - string
The ID of the underlying user session. See Tracking user sessions for more information.
mlApp: optional - string
The name of the ML application that the operation belongs to. See Tracing multiple applications for more information.

Example

The following also includes an example of annotating a span. See Annotating a span for more information.

function getRelevantDocs (question) {
  const contextDocuments = ... // user application logic
  llmobs.annotate({
    inputData: question,
    outputData: contextDocuments.map(doc => ({
      id: doc.id,
      score: doc.score,
      text: doc.text,
      name: doc.name
    }))
  })
  return
}
getRelevantDocs = llmobs.wrap({ kind: 'retrieval' }, getRelevantDocs)

Conditions for finishing a span for a wrapped function

llmobs.wrap extends the underlying behavior of tracer.wrap. The underlying span created when the function is called is finished under the following conditions:

If the function returns a Promise, then the span finishes when the promise is resolved or rejected.
If the function takes a callback as its last parameter, then the span finishes when that callback is called.
If t function doesn’t accept a callback and doesn’t return a Promise, then the span finishes at the end of the function execution.

The following example demonstrates the second condition, where the last argument is a callback:

Example

const express = require('express')
const app = express()

function myAgentMiddleware (req, res, next) {
  const err = ... // user application logic
  // the span for this function is finished when `next` is called
  next(err)
}
myAgentMiddleware = llmobs.wrap({ kind: 'agent' }, myAgentMiddleware)

app.use(myAgentMiddleware)

If the application does not use the callback function, it is recommended to use an inline traced block instead. See Tracing spans using inline methods for more information.

const express = require('express')
const app = express()

function myAgentMiddleware (req, res) {
  // the `next` callback is not being used here
  return llmobs.trace({ kind: 'agent', name: 'myAgentMiddleware' }, () => {
    return res.status(200).send('Hello World!')
  })
}

app.use(myAgentMiddleware)

Tracking user sessions

Session tracking allows you to associate multiple interactions with a given user. When starting a root span for a new trace or span in a new process, specify the sessionId argument with the string ID of the underlying user session:

function processMessage() {
    ... # user application logic
    return
}
processMessage = llmobs.wrap({ kind: 'workflow', sessionId: "<SESSION_ID>" }, processMessage)

Annotating a span

The SDK provides the method llmobs.annotate() to annotate spans with inputs, outputs, and metadata.

Arguments

The LLMObs.annotate() method accepts the following arguments:

span: optional - Span - default: the current active span
The span to annotate. If span is not provided (as when using function wrappers), the SDK annotates the current active span.
annotationOptions: required - object
An object of different types of data to annotate the span with.

The annotationOptions object can contain the following:

inputData: optional - JSON serializable type or list of objects
Either a JSON serializable type (for non-LLM spans) or a list of dictionaries with this format: {role: "...", content: "..."} (for LLM spans). Note: Embedding spans are a special case and require a string or an object (or a list of objects) with this format: {text: "..."}.
outputData: optional - JSON serializable type or list of objects
Either a JSON serializable type (for non-LLM spans) or a list of objects with this format: {role: "...", content: "..."} (for LLM spans). Note: Retrieval spans are a special case and require a string or an object (or a list of objects) with this format: {text: "...", name: "...", score: number, id: "..."}.
metadata: optional - object
An object of JSON serializable key-value pairs that users can add as metadata information relevant to the input or output operation described by the span (model_temperature, max_tokens, top_k, etc.).
metrics: optional - object
An object of JSON serializable keys and numeric values that users can add as metrics relevant to the operation described by the span (input_tokens, output_tokens, total_tokens, etc.).
tags: optional - object
An object of JSON serializable key-value pairs that users can add as tags regarding the span’s context (session, environment, system, versioning, etc.). For more information about tags, see Getting Started with Tags.

Example

function llmCall (prompt) {
  const completion = ... // user application logic to invoke LLM
  llmobs.annotate({
    inputData: [{ role: "user", content: "Hello world!" }],
    outputData: [{ role: "assistant", content: "How can I help?" }],
    metadata: { temperature: 0, max_tokens: 200 },
    metrics: { input_tokens: 4, output_tokens: 6, total_tokens: 10 },
    tags: { host: "host_name" }
  })
  return completion
}
llmCall = llmobs.wrap({ kind:'llm', modelName: 'modelName', modelProvider: 'modelProvider' }, llmCall)

function extractData (document) {
  const resp = llmCall(document)
  llmobs.annotate({
    inputData: document,
    outputData: resp,
    tags: { host: "host_name" }
  })
  return resp
}
extractData = llmobs.wrap({ kind: 'workflow' }, extractData)

function performEmbedding () {
  ... // user application logic
  llmobs.annotate(
    undefined, { // this can be set to undefined or left out entirely
      inputData: { text: "Hello world!" },
      outputData: [0.0023064255, -0.009327292, ...],
      metrics: { input_tokens: 4 },
      tags: { host: "host_name" }
    }
  )
}
performEmbedding = llmobs.wrap({ kind: 'embedding', modelName: 'text-embedding-3', modelProvider: 'openai' }, performEmbedding)

function similaritySearch () {
  ... // user application logic
  llmobs.annotate(undefined, {
    inputData: "Hello world!",
    outputData: [{ text: "Hello world is ...", name: "Hello, World! program", id: "document_id", score: 0.9893 }],
    tags: { host: "host_name" }
  })
  return
}
similaritySearch = llmobs.wrap({ kind: 'retrieval', name: 'getRelevantDocs' }, similaritySearch)

Evaluations

The LLM Observability SDK provides the methods llmobs.exportSpan() and llmobs.submitEvaluation() to help your traced LLM application submit evaluations to LLM Observability.

Exporting a span

llmobs.exportSpan() can be used to extract the span context from a span. You’ll need to use this method to associate your evaluation with the corresponding span.

Arguments

The llmobs.exportSpan() method accepts the following argument:

span: optional - Span
The span to extract the span context (span and trace IDs) from. If not provided (as when using function wrappers), the SDK exports the current active span.

Example

function llmCall () {
  const completion = ... // user application logic to invoke LLM
  const spanContext = llmobs.exportSpan()
  return completion
}
llmCall = llmobs.wrap({ kind: 'llm', name: 'invokeLLM', modelName: 'claude', modelProvider: 'anthropic' }, llmCall)

Submit evaluations

llmobs.submitEvaluation() can be used to submit your custom evaluation associated with a given span.

Arguments

The llmobs.submitEvaluation() method accepts the following arguments:

span_context: required - dictionary
The span context to associate the evaluation with. This should be the output of LLMObs.export_span().
evaluationOptions: required - object
An object of the evaluation data.

The evaluationOptions object can contain the following:

label: required - string
The name of the evaluation.
metricType: required - string
The type of the evaluation. Must be one of “categorical” or “score”.
value: required - string or numeric type
The value of the evaluation. Must be a string (for categorical metric_type) or number (for score metric_type).
tags: optional - dictionary
A dictionary of string key-value pairs that users can add as tags regarding the evaluation. For more information about tags, see Getting Started with Tags.

Example

function llmCall () {
  const completion = ... // user application logic to invoke LLM
  const spanContext = llmobs.exportSpan()
  llmobs.submitEvaluation(spanContext, {
    label: "harmfulness",
    metricType: "score",
    value: 10,
    tags: { evaluationProvider: "ragas" }
  })
  return completion
}
llmCall = llmobs.wrap({ kind: 'llm', name: 'invokeLLM', modelName: 'claude', modelProvider: 'anthropic' }, llmCall)

Advanced tracing

Tracing spans using inline methods

The llmobs SDK provides a corresponding inline method to automatically trace the operation a given code block entails. These methods have the same argument signature as their function wrapper counterparts, with the addition that name is required, as the name cannot be inferred from an anonymous callback. This method will finish the span under the following conditions:

If the function returns a Promise, then the span finishes when the promise is resolved or rejected.
If the function takes a callback as its last parameter, then the span finishes when that callback is called.
If the function doesn’t accept a callback and doesn’t return a Promise, then the span finishes at the end of the function execution.

Example without a callback

function processMessage () {
  return llmobs.trace({ kind: 'workflow', name: 'processMessage', sessionId: '<SESSION_ID>', mlApp: '<ML_APP>' }, workflowSpan => {
    ... // user application logic
    return
  })
}

Example with a callback

function processMessage () {
  return llmobs.trace({ kind: 'workflow', name: 'processMessage', sessionId: '<SESSION_ID>', mlApp: '<ML_APP>' }, (workflowSpan, cb) => {
    ... // user application logic
    let maybeError = ...
    cb(maybeError) // the span will finish here, and tag the error if it is not null or undefined
    return
  })
}

The return type of this function matches the return type of the traced function:

function processMessage () {
  const result = llmobs.trace({ kind: 'workflow', name: 'processMessage', sessionId: '<SESSION_ID>', mlApp: '<ML_APP>' }, workflowSpan => {
    ... // user application logic
    return 'hello world'
  })

  console.log(result) // 'hello world'
  return result
}

Function decorators in TypeScript

The Node.js LLM Observability SDK offers an llmobs.decorate function which serves as a function decorator for TypeScript applications. This functions tracing behavior is the same as llmobs.wrap.

Example

// index.ts
import tracer from 'dd-trace';
tracer.init({
  llmobs: {
    mlApp: "<YOUR_ML_APP_NAME>",
  },
});

const { llmobs } = tracer;

class MyAgent {
  @llmobs.decorate({ kind: 'agent' })
  async runChain () {
    ... // user application logic
    return
  }
}

Force flushing in serverless environments

llmobs.flush() is a blocking function that submits all buffered LLM Observability data to the Datadog backend. This can be useful in serverless environments to prevent an application from exiting until all LLM Observability traces are submitted.

Tracing multiple applications

The SDK supports tracing multiple LLM applications from the same service.

You can configure an environment variable DD_LLMOBS_ML_APP to the name of your LLM application, which all generated spans are grouped into by default.

To override this configuration and use a different LLM application name for a given root span, pass the mlApp argument with the string name of the underlying LLM application when starting a root span for a new trace or a span in a new process.

function processMessage () {
  ... // user application logic
  return
}
processMessage = llmobs.wrap({ kind: 'workflow', name: 'processMessage', mlApp: '<NON_DEFAULT_ML_APP_NAME>' }, processMessage)

Distributed tracing

The SDK supports tracing across distributed services or hosts. Distributed tracing works by propagating span information across web requests.

The dd-trace library provides out-of-the-box integrations that support distributed tracing for popular web frameworks. Requiring the tracer automatically enables these integrations, but you can disable them optionally with:

const tracer = require('dd-trace').init({
  llmobs: { ... },
})
tracer.use('http', false) // disable the http integration