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

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)

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)

from ddtrace.llmobs.decorators import llm

@llm(model_name="claude", name="invoke_llm", model_provider="anthropic")
def llm_call():
    completion = ... # user application logic to invoke LLM
    return completion

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

from ddtrace.llmobs.decorators import workflow

@workflow
def process_message():
    ... # user application logic
    return

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

from ddtrace.llmobs.decorators import agent

@agent
def react_agent():
    ... # user application logic
    return

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

from ddtrace.llmobs.decorators import tool

@tool
def call_weather_api():
    ... # user application logic
    return

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

from ddtrace.llmobs.decorators import task

@task
def sanitize_input():
    ... # user application logic
    return

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

from ddtrace.llmobs.decorators import embedding

@embedding(model_name="text-embedding-3", model_provider="openai")
def perform_embedding():
    ... # user application logic
    return

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

from ddtrace.llmobs.decorators import retrieval

@retrieval
def get_relevant_docs(question):
    context_documents = ... # user application logic
    LLMObs.annotate(
        input_data=question,
        output_data = [
            {"id": doc.id, "score": doc.score, "text": doc.text, "name": doc.name} for doc in context_documents
        ]
    )
    return

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)

from ddtrace.llmobs.decorators import workflow

@workflow(session_id="<SESSION_ID>")
def process_user_message():
    LLMObs.annotate(
        ...
        tags = {"user_handle": "poodle@dog.com", "user_id": "1234", "user_name": "poodle"}
    )
    return

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

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs.decorators import embedding, llm, retrieval, workflow

@llm(model_name="model_name", model_provider="model_provider")
def llm_call(prompt):
    resp = ... # llm call here
    LLMObs.annotate(
        span=None,
        input_data=[{"role": "user", "content": "Hello world!"}],
        output_data=[{"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 resp

@workflow
def extract_data(document):
    resp = llm_call(document)
    LLMObs.annotate(
        input_data=document,
        output_data=resp,
        tags={"host": "host_name"},
    )
    return resp

@embedding(model_name="text-embedding-3", model_provider="openai")
def perform_embedding():
    ... # user application logic
    LLMObs.annotate(
        span=None,
        input_data={"text": "Hello world!"},
        output_data=[0.0023064255, -0.009327292, ...],
        metrics={"input_tokens": 4},
        tags={"host": "host_name"},
    )
    return

@retrieval(name="get_relevant_docs")
def similarity_search():
    ... # user application logic
    LLMObs.annotate(
        span=None,
        input_data="Hello world!",
        output_data=[{"text": "Hello world is ...", "name": "Hello, World! program", "id": "document_id", "score": 0.9893}],
        tags={"host": "host_name"},
    )
    return

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)

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs.decorators import workflow

@workflow
def rag_workflow(user_question):
    context_str = retrieve_documents(user_question).join(" ")

    with LLMObs.annotation_context(
        prompt = Prompt(
            variables = {
                "question": user_question,
                "context": context_str,
            },
            template = "Please answer the..."
        ),
        tags = {
            "retrieval_strategy": "semantic_similarity"
        },
        name = "augmented_generation"
    ):
        completion = openai_client.chat.completions.create(...)
    return completion.choices[0].message.content

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs.decorators import llm

@llm(model_name="claude", name="invoke_llm", model_provider="anthropic")
def llm_call():
    completion = ... # user application logic to invoke LLM
    span_context = LLMObs.export_span(span=None)
    return completion

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)

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs.decorators import llm

@llm(model_name="claude", name="invoke_llm", model_provider="anthropic")
def llm_call():
    completion = ... # user application logic to invoke LLM

    # joining an evaluation to a span via a tag key-value pair
    msg_id = get_msg_id()
    LLMObs.annotate(
        tags = {'msg_id': msg_id}
    )

    LLMObs.submit_evaluation_for(
        span_with_tag_value = {
            "tag_key": "msg_id",
            "tag_value": msg_id
        },
        ml_app = "chatbot",
        label="harmfulness",
        metric_type="score",
        value=10,
        tags={"evaluation_provider": "ragas"},
    )

    # joining an evaluation to a span via span ID and trace ID
    span_context = LLMObs.export_span(span=None)
    LLMObs.submit_evaluation(
        span = span_context,
        ml_app = "chatbot",
        label="harmfulness",
        metric_type="score",
        value=10,
        tags={"evaluation_provider": "ragas"},
    )
    return completion

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)

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs import LLMObsSpan

def redact_processor(span: LLMObsSpan) -> LLMObsSpan:
    if span.get_tag("no_output") == "true":
        for message in span.output:
            message["content"] = ""
    return span


# If using LLMObs.enable()
LLMObs.enable(
  ...
  span_processor=redact_processor,
)
# else when using `ddtrace-run`
LLMObs.register_processor(redact_processor)

with LLMObs.llm("invoke_llm_with_no_output"):
    LLMObs.annotate(tags={"no_output": "true"})

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs import LLMObsSpan

def redact_processor(span: LLMObsSpan) -> LLMObsSpan:
    if span.get_tag("no_input") == "true":
        for message in span.input:
            message["content"] = ""
    return span

LLMObs.register_processor(redact_processor)


def call_openai():
    with LLMObs.annotation_context(tags={"no_input": "true"}):
        # make call to openai
        ...

from ddtrace.llmobs import LLMObs

def process_message():
    with LLMObs.workflow(name="process_message", session_id="<SESSION_ID>", ml_app="<ML_APP>") as workflow_span:
        ... # user application logic
    return

from ddtrace.llmobs import LLMObs

def process_message():
    workflow_span = LLMObs.workflow(name="process_message")
    ... # user application logic
    separate_task(workflow_span)
    return

def separate_task(workflow_span):
    ... # user application logic
    workflow_span.finish()
    return

from ddtrace.llmobs.decorators import workflow

@workflow(name="process_message", ml_app="<NON_DEFAULT_ML_APP_NAME>")
def process_message():
    ... # user application logic
    return

from ddtrace import patch
patch(<INTEGRATION_NAME>=True)

from ddtrace.llmobs import LLMObs
from ddtrace.llmobs.decorators import workflow

@workflow
def client_send_request():
    request_headers = {}
    request_headers = LLMObs.inject_distributed_headers(request_headers)
    send_request("<method>", request_headers)  # arbitrary HTTP call

from ddtrace.llmobs import LLMObs

def server_process_request(request):
    LLMObs.activate_distributed_headers(request.headers)
    with LLMObs.task(name="process_request") as span:
        pass  # arbitrary server work

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

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
  })
}

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
}

// 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
  }
}

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

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