Trace with OpenTelemetry

LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application. This guide covers both automatic instrumentation for LangChain applications and manual instrumentation for other frameworks. Learn how to trace your LLM applications using OpenTelemetry with LangSmith.

Update the LangSmith URL appropriately for self-hosted installations or organizations in the EU region in the requests below. For the EU region, use eu.api.smith.langchain.com.

Trace a LangChain application

If you’re using LangChain or LangGraph, use the built-in integration to trace your application:

Install the LangSmith package with OpenTelemetry support:
pip install "langsmith[otel]" pip install langchain
Requires Python SDK version langsmith>=0.3.18.

In your LangChain/LangGraph App, enable the OpenTelemetry integration by setting the LANGSMITH_OTEL_ENABLED environment variable:

LANGSMITH_OTEL_ENABLED=true
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_API_KEY=<your_langsmith_api_key>

Create a LangChain application with tracing. For example:

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Create a chain
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
model = ChatOpenAI()
chain = prompt | model

# Run the chain
result = chain.invoke({"topic": "programming"})
print(result.content)

View the traces in your LangSmith dashboard (example) once your application runs.

Trace a non-LangChain application

For non-LangChain applications or custom instrumentation, you can trace your application in LangSmith with a standard OpenTelemetry client:

Install the OpenTelemetry SDK, OpenTelemetry exporter packages, as well as the OpenAI package:
pip install openai pip install opentelemetry-sdk pip install opentelemetry-exporter-otlp
Setup environment variables for the endpoint, substitute your specific values:
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.smith.langchain.com/otel OTEL_EXPORTER_OTLP_HEADERS="x-api-key=<your langsmith api key>"
Depending on how your otel exporter is configured, you may need to append /v1/traces to the endpoint if you are only sending traces.

If you’re self-hosting LangSmith, replace the base endpoint with your LangSmith api endpoint and append /api/v1. For example: OTEL_EXPORTER_OTLP_ENDPOINT=https://ai-company.com/api/v1/otel
Optional: Specify a custom project name other than “default”:
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.smith.langchain.com/otel OTEL_EXPORTER_OTLP_HEADERS="x-api-key=<your langsmith api key>,Langsmith-Project=<project name>"

Log a trace. This code sets up an OTEL tracer and exporter that will send traces to LangSmith. It then calls OpenAI and sends the required OpenTelemetry attributes.

from openai import OpenAI
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
    BatchSpanProcessor,
)
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

otlp_exporter = OTLPSpanExporter(
    timeout=10,
)

trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(otlp_exporter)
)

tracer = trace.get_tracer(__name__)

def call_openai():
    model = "gpt-4o-mini"
    with tracer.start_as_current_span("call_open_ai") as span:
        span.set_attribute("langsmith.span.kind", "LLM")
        span.set_attribute("langsmith.metadata.user_id", "user_123")
        span.set_attribute("gen_ai.system", "OpenAI")
        span.set_attribute("gen_ai.request.model", model)
        span.set_attribute("llm.request.type", "chat")

        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "Write a haiku about recursion in programming."
            }
        ]

        for i, message in enumerate(messages):
            span.set_attribute(f"gen_ai.prompt.{i}.content", str(message["content"]))
            span.set_attribute(f"gen_ai.prompt.{i}.role", str(message["role"]))

        completion = client.chat.completions.create(
            model=model,
            messages=messages
        )

        span.set_attribute("gen_ai.response.model", completion.model)
        span.set_attribute("gen_ai.completion.0.content", str(completion.choices[0].message.content))
        span.set_attribute("gen_ai.completion.0.role", "assistant")
        span.set_attribute("gen_ai.usage.prompt_tokens", completion.usage.prompt_tokens)
        span.set_attribute("gen_ai.usage.completion_tokens", completion.usage.completion_tokens)
        span.set_attribute("gen_ai.usage.total_tokens", completion.usage.total_tokens)

        return completion.choices[0].message

if __name__ == "__main__":
    call_openai()

View the trace in your LangSmith dashboard (example).

Send traces to an alternate provider

While LangSmith is the default destination for OpenTelemetry traces, you can also configure OpenTelemetry to send traces to other observability platforms.

In langsmith Python SDK version ≥ 0.4.1, setting LANGSMITH_OTEL_ENABLED=true will by default send traces to both LangSmith and your instrumented OTEL endpoint. No extra code is needed for fan-out.

Use environment variables for global configuration

By default, the LangSmith OpenTelemetry exporter will send data to the LangSmith API OTEL endpoint, but this can be customized by setting standard OTEL environment variables:

OTEL_EXPORTER_OTLP_ENDPOINT: Override the endpoint URL
OTEL_EXPORTER_OTLP_HEADERS: Add custom headers (LangSmith API keys and Project are added automatically)
OTEL_SERVICE_NAME: Set a custom service name (defaults to "langsmith")

LangSmith uses the HTTP trace exporter by default. If you’d like to use your own tracing provider, you can either:

Set the OTEL environment variables as shown above, or
Set a global trace provider before initializing LangChain components, which LangSmith will detect and use instead of creating its own.

Configure alternate OTLP endpoints

To send traces to a different provider, configure the OTLP exporter with your provider’s endpoint:

import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Set environment variables for LangChain
os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"

# Configure the OTLP exporter for your custom endpoint
provider = TracerProvider()
otlp_exporter = OTLPSpanExporter(
    # Change to your provider's endpoint
    endpoint="https://otel.your-provider.com/v1/traces",
    # Add any required headers for authentication
    headers={"api-key": "your-api-key"}
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

# Create and run a LangChain application
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
model = ChatOpenAI()
chain = prompt | model
result = chain.invoke({"topic": "programming"})
print(result.content)

To disable hybrid tracing behavior after 0.4.1 and send traces only to your OTEL endpoint and exclude sending to LangSmith, add an additional env var:LANGSMITH_OTEL_ONLY = “true”

Supported OpenTelemetry attribute and event mapping

When sending traces to LangSmith via OpenTelemetry, the following attributes are mapped to LangSmith fields:

Core LangSmith attributes

OpenTelemetry attribute	LangSmith field	Notes
`langsmith.trace.name`	Run name	Overrides the span name for the run
`langsmith.span.kind`	Run type	Values: `llm`, `chain`, `tool`, `retriever`, `embedding`, `prompt`, `parser`
`langsmith.trace.session_id`	Session ID	Session identifier for related traces
`langsmith.trace.session_name`	Session name	Name of the session
`langsmith.span.tags`	Tags	Custom tags attached to the span (comma-separated)
`langsmith.metadata.{key}`	`metadata.{key}`	Custom metadata with langsmith prefix

GenAI standard attributes

OpenTelemetry attribute	LangSmith field	Notes
`gen_ai.system`	`metadata.ls_provider`	The GenAI system (e.g., “openai”, “anthropic”)
`gen_ai.operation.name`	Run type	Maps “chat”/“completion” to “llm”, “embedding” to “embedding”
`gen_ai.prompt`	`inputs`	The input prompt sent to the model
`gen_ai.completion`	`outputs`	The output generated by the model
`gen_ai.prompt.{n}.role`	`inputs.messages[n].role`	Role for the nth input message
`gen_ai.prompt.{n}.content`	`inputs.messages[n].content`	Content for the nth input message
`gen_ai.prompt.{n}.message.role`	`inputs.messages[n].role`	Alternative format for role
`gen_ai.prompt.{n}.message.content`	`inputs.messages[n].content`	Alternative format for content
`gen_ai.completion.{n}.role`	`outputs.messages[n].role`	Role for the nth output message
`gen_ai.completion.{n}.content`	`outputs.messages[n].content`	Content for the nth output message
`gen_ai.completion.{n}.message.role`	`outputs.messages[n].role`	Alternative format for role
`gen_ai.completion.{n}.message.content`	`outputs.messages[n].content`	Alternative format for content
`gen_ai.tool.name`	`invocation_params.tool_name`	Tool name, also sets run type to “tool”

GenAI request parameters

OpenTelemetry attribute	LangSmith field	Notes
`gen_ai.request.model`	`invocation_params.model`	The model name used for the request
`gen_ai.response.model`	`invocation_params.model`	The model name returned in the response
`gen_ai.request.temperature`	`invocation_params.temperature`	Temperature setting
`gen_ai.request.top_p`	`invocation_params.top_p`	Top-p sampling setting
`gen_ai.request.max_tokens`	`invocation_params.max_tokens`	Maximum tokens setting
`gen_ai.request.frequency_penalty`	`invocation_params.frequency_penalty`	Frequency penalty setting
`gen_ai.request.presence_penalty`	`invocation_params.presence_penalty`	Presence penalty setting
`gen_ai.request.seed`	`invocation_params.seed`	Random seed used for generation
`gen_ai.request.stop_sequences`	`invocation_params.stop`	Sequences that stop generation
`gen_ai.request.top_k`	`invocation_params.top_k`	Top-k sampling parameter
`gen_ai.request.encoding_formats`	`invocation_params.encoding_formats`	Output encoding formats

GenAI usage metrics

OpenTelemetry attribute	LangSmith field	Notes
`gen_ai.usage.input_tokens`	`usage_metadata.input_tokens`	Number of input tokens used
`gen_ai.usage.output_tokens`	`usage_metadata.output_tokens`	Number of output tokens used
`gen_ai.usage.total_tokens`	`usage_metadata.total_tokens`	Total number of tokens used
`gen_ai.usage.prompt_tokens`	`usage_metadata.input_tokens`	Number of input tokens used (deprecated)
`gen_ai.usage.completion_tokens`	`usage_metadata.output_tokens`	Number of output tokens used (deprecated)

TraceLoop attributes

OpenTelemetry attribute	LangSmith field	Notes
`traceloop.entity.input`	`inputs`	Full input value from TraceLoop
`traceloop.entity.output`	`outputs`	Full output value from TraceLoop
`traceloop.entity.name`	Run name	Entity name from TraceLoop
`traceloop.span.kind`	Run type	Maps to LangSmith run types
`traceloop.llm.request.type`	Run type	”embedding” maps to “embedding”, others to “llm”
`traceloop.association.properties.{key}`	`metadata.{key}`	Custom metadata with traceloop prefix

OpenInference attributes

OpenTelemetry attribute	LangSmith field	Notes
`input.value`	`inputs`	Full input value, can be string or JSON
`output.value`	`outputs`	Full output value, can be string or JSON
`openinference.span.kind`	Run type	Maps various kinds to LangSmith run types
`llm.system`	`metadata.ls_provider`	LLM system provider
`llm.model_name`	`metadata.ls_model_name`	Model name from OpenInference
`tool.name`	Run name	Tool name when span kind is “TOOL”
`metadata`	`metadata.*`	JSON string of metadata to be merged

LLM attributes

OpenTelemetry attribute	LangSmith field	Notes
`llm.input_messages`	`inputs.messages`	Input messages
`llm.output_messages`	`outputs.messages`	Output messages
`llm.token_count.prompt`	`usage_metadata.input_tokens`	Prompt token count
`llm.token_count.completion`	`usage_metadata.output_tokens`	Completion token count
`llm.token_count.total`	`usage_metadata.total_tokens`	Total token count
`llm.usage.total_tokens`	`usage_metadata.total_tokens`	Alternative total token count
`llm.invocation_parameters`	`invocation_params.*`	JSON string of invocation parameters
`llm.presence_penalty`	`invocation_params.presence_penalty`	Presence penalty
`llm.frequency_penalty`	`invocation_params.frequency_penalty`	Frequency penalty
`llm.request.functions`	`invocation_params.functions`	Function definitions

Prompt template attributes

OpenTelemetry attribute	LangSmith field	Notes
`llm.prompt_template.variables`	Run type	Sets run type to “prompt”, used with input.value

Retriever attributes

OpenTelemetry attribute	LangSmith field	Notes
`retrieval.documents.{n}.document.content`	`outputs.documents[n].page_content`	Content of the nth retrieved document
`retrieval.documents.{n}.document.metadata`	`outputs.documents[n].metadata`	Metadata of the nth retrieved document (JSON)

Tool attributes

OpenTelemetry attribute	LangSmith field	Notes
`tools`	`invocation_params.tools`	Array of tool definitions
`tool_arguments`	`invocation_params.tool_arguments`	Tool arguments as JSON or key-value pairs

Logfire attributes

OpenTelemetry attribute	LangSmith field	Notes
`prompt`	`inputs`	Logfire prompt input
`all_messages_events`	`outputs`	Logfire message events output
`events`	`inputs`/`outputs`	Logfire events array, splits input/choice events

OpenTelemetry event mapping

Event name	LangSmith field	Notes
`gen_ai.content.prompt`	`inputs`	Extracts prompt content from event attributes
`gen_ai.content.completion`	`outputs`	Extracts completion content from event attributes
`gen_ai.system.message`	`inputs.messages[]`	System message in conversation
`gen_ai.user.message`	`inputs.messages[]`	User message in conversation
`gen_ai.assistant.message`	`outputs.messages[]`	Assistant message in conversation
`gen_ai.tool.message`	`outputs.messages[]`	Tool response message
`gen_ai.choice`	`outputs`	Model choice/response with finish reason
`exception`	`status`, `error`	Sets status to “error” and extracts exception message/stacktrace

Event attribute extraction

For message events, the following attributes are extracted:

content → message content
role → message role
id → tool_call_id (for tool messages)
gen_ai.event.content → full message JSON

For choice events:

finish_reason → choice finish reason
message.content → choice message content
message.role → choice message role
tool_calls.{n}.id → tool call ID
tool_calls.{n}.function.name → tool function name
tool_calls.{n}.function.arguments → tool function arguments
tool_calls.{n}.type → tool call type

For exception events:

exception.message → error message
exception.stacktrace → error stacktrace (appended to message)

Implementation examples

Trace using the Traceloop SDK

The Traceloop SDK is an OpenTelemetry compatible SDK that covers a range of models, vector databases and frameworks. If there are integrations that you are interested in instrumenting that are covered by this SDK, you can use this SDK with OpenTelemetry to log traces to LangSmith. To see what integrations are supported by the Traceloop SDK, see the Traceloop SDK documentation. To get started, follow these steps:

Install the Traceloop SDK and OpenAI:

pip install traceloop-sdk
pip install openai

Configure your environment:

TRACELOOP_BASE_URL=https://api.smith.langchain.com/otel
TRACELOOP_HEADERS=x-api-key=<your_langsmith_api_key>

Optional: Specify a custom project name other than “default”:

TRACELOOP_HEADERS=x-api-key=<your_langsmith_api_key>,Langsmith-Project=<langsmith_project_name>

To use the SDK, you need to initialize it before logging traces:
```
from traceloop.sdk import Traceloop
Traceloop.init()
```

Log a trace:

import os
from openai import OpenAI
from traceloop.sdk import Traceloop

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

Traceloop.init()

completion = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "Write a haiku about recursion in programming."
        }
    ]
)

print(completion.choices[0].message)

View the trace in your LangSmith dashboard (example).

Trace using the Arize SDK

With the Arize SDK and OpenTelemetry, you can log traces from multiple other frameworks to LangSmith. Below is an example of tracing CrewAI to LangSmith, you can find a full list of supported frameworks here. To make this example work with other frameworks, you just need to change the instrumentor to match the framework.

Install the required packages:

pip install -qU arize-phoenix-otel openinference-instrumentation-crewai crewai crewai-tools

Set the following environment variables:

OPENAI_API_KEY=<your_openai_api_key>
SERPER_API_KEY=<your_serper_api_key>

Before running any application code, set up our instrumentor (you can replace this with any of the frameworks supported here):

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Add LangSmith API Key for tracing
LANGSMITH_API_KEY = "YOUR_API_KEY"
# Set the endpoint for OTEL collection
ENDPOINT = "https://api.smith.langchain.com/otel/v1/traces"
# Select the project to trace to
LANGSMITH_PROJECT = "YOUR_PROJECT_NAME"

# Create the OTLP exporter
otlp_exporter = OTLPSpanExporter(
    endpoint=ENDPOINT,
    headers={"x-api-key": LANGSMITH_API_KEY, "Langsmith-Project": LANGSMITH_PROJECT}
)

# Set up the trace provider
provider = TracerProvider()
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)

# Now instrument CrewAI
from openinference.instrumentation.crewai import CrewAIInstrumentor
CrewAIInstrumentor().instrument(tracer_provider=provider)

Run a CrewAI workflow and the trace will automatically be logged to LangSmith:

from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool

search_tool = SerperDevTool()

# Define your agents with roles and goals
researcher = Agent(
    role='Senior Research Analyst',
    goal='Uncover cutting-edge developments in AI and data science',
    backstory="""You work at a leading tech think tank.
    Your expertise lies in identifying emerging trends.
    You have a knack for dissecting complex data and presenting actionable insights.""",
    verbose=True,
    allow_delegation=False,
    # You can pass an optional llm attribute specifying what model you wanna use.
    # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
    tools=[search_tool]
)

writer = Agent(
    role='Tech Content Strategist',
    goal='Craft compelling content on tech advancements',
    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
    You transform complex concepts into compelling narratives.""",
    verbose=True,
    allow_delegation=True
)

# Create tasks for your agents
task1 = Task(
    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
    Identify key trends, breakthrough technologies, and potential industry impacts.""",
    expected_output="Full analysis report in bullet points",
    agent=researcher
)

task2 = Task(
    description="""Using the insights provided, develop an engaging blog
    post that highlights the most significant AI advancements.
    Your post should be informative yet accessible, catering to a tech-savvy audience.
    Make it sound cool, avoid complex words so it doesn't sound like AI.""",
    expected_output="Full blog post of at least 4 paragraphs",
    agent=writer
)

# Instantiate your crew with a sequential process
crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2],
    verbose= False,
    process = Process.sequential
)

# Get your crew to work!
result = crew.kickoff()
print("######################")
print(result)

View the trace in your LangSmith dashboard (example).

Advanced configuration

Use OpenTelemetry Collector for fan-out

For more advanced scenarios, you can use the OpenTelemetry Collector to fan out your telemetry data to multiple destinations. This is a more scalable approach than configuring multiple exporters in your application code.

Install the OpenTelemetry Collector for your environment.

Create a configuration file (e.g., otel-collector-config.yaml) that exports to multiple destinations:

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  batch:

exporters:
  otlphttp/langsmith:
    endpoint: https://api.smith.langchain.com/otel/v1/traces
    headers:
      x-api-key: ${env:LANGSMITH_API_KEY}
      Langsmith-Project: my_project
  otlphttp/other_provider:
    endpoint: https://otel.your-provider.com/v1/traces
    headers:
      api-key: ${env:OTHER_PROVIDER_API_KEY}

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/langsmith, otlphttp/other_provider]

Configure your application to send to the collector:

import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Point to your local OpenTelemetry Collector
otlp_exporter = OTLPSpanExporter(
    endpoint="http://localhost:4318/v1/traces"
)
provider = TracerProvider()
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

# Set environment variables for LangChain
os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"

# Create and run a LangChain application
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
model = ChatOpenAI()
chain = prompt | model
result = chain.invoke({"topic": "programming"})
print(result.content)

This approach offers several advantages:

Centralized configuration for all your telemetry destinations
Reduced overhead in your application code
Better scalability and resilience
Ability to add or remove destinations without changing application code

Distributed tracing with LangChain and OpenTelemetry

Distributed tracing is essential when your LLM application spans multiple services or processes. OpenTelemetry’s context propagation capabilities ensure that traces remain connected across service boundaries.

Context propagation in distributed tracing

In distributed systems, context propagation passes trace metadata between services so that related spans are linked to the same trace:

Trace ID: A unique identifier for the entire trace
Span ID: A unique identifier for the current span
Sampling Decision: Indicates whether this trace should be sampled

Set up distributed tracing with LangChain

To enable distributed tracing across multiple services:

import os
from opentelemetry import trace
from opentelemetry.propagate import inject, extract
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
import requests
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Set up OpenTelemetry trace provider
provider = TracerProvider()
otlp_exporter = OTLPSpanExporter(
    endpoint="https://api.smith.langchain.com/otel/v1/traces",
    headers={"x-api-key": os.getenv("LANGSMITH_API_KEY"), "Langsmith-Project": "my_project"}
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer(__name__)

# Service A: Create a span and propagate context to Service B
def service_a():
    with tracer.start_as_current_span("service_a_operation") as span:
        # Create a chain
        prompt = ChatPromptTemplate.from_template("Summarize: {text}")
        model = ChatOpenAI()
        chain = prompt | model

        # Run the chain
        result = chain.invoke({"text": "OpenTelemetry is an observability framework"})

        # Propagate context to Service B
        headers = {}
        inject(headers)  # Inject trace context into headers

        # Call Service B with the trace context
        response = requests.post(
            "http://service-b.example.com/process",
            headers=headers,
            json={"summary": result.content}
        )
        return response.json()

# Service B: Extract the context and continue the trace
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/process", methods=["POST"])
def service_b_endpoint():
    # Extract the trace context from the request headers
    context = extract(request.headers)
    with tracer.start_as_current_span("service_b_operation", context=context) as span:
        data = request.json
        summary = data.get("summary", "")

        # Process the summary with another LLM chain
        prompt = ChatPromptTemplate.from_template("Analyze the sentiment of: {text}")
        model = ChatOpenAI()
        chain = prompt | model
        result = chain.invoke({"text": summary})

        return jsonify({"analysis": result.content})

if __name__ == "__main__":
    app.run(port=5000)

Set up tracing

View traces

Monitoring

Automations

Human Feedback

Trace a RAG application

Trace a LangChain application

Trace a non-LangChain application

Send traces to an alternate provider

Use environment variables for global configuration

Configure alternate OTLP endpoints

Supported OpenTelemetry attribute and event mapping

Core LangSmith attributes

GenAI standard attributes

GenAI request parameters

GenAI usage metrics

TraceLoop attributes

OpenInference attributes

LLM attributes

Prompt template attributes

Retriever attributes

Tool attributes

Logfire attributes

OpenTelemetry event mapping

Event attribute extraction

Implementation examples

Trace using the Traceloop SDK

Trace using the Arize SDK

Advanced configuration

Use OpenTelemetry Collector for fan-out

Distributed tracing with LangChain and OpenTelemetry

Context propagation in distributed tracing

Set up distributed tracing with LangChain

Set up tracing

View traces

Monitoring

Automations

Human Feedback

Trace a RAG application

​Trace a LangChain application

​Trace a non-LangChain application

​Send traces to an alternate provider

​Use environment variables for global configuration

​Configure alternate OTLP endpoints

​Supported OpenTelemetry attribute and event mapping

​Core LangSmith attributes

​GenAI standard attributes

​GenAI request parameters

​GenAI usage metrics

​TraceLoop attributes

​OpenInference attributes

​LLM attributes

​Prompt template attributes

​Retriever attributes

​Tool attributes

​Logfire attributes

​OpenTelemetry event mapping

​Event attribute extraction

​Implementation examples

​Trace using the Traceloop SDK

​Trace using the Arize SDK

​Advanced configuration

​Use OpenTelemetry Collector for fan-out

​Distributed tracing with LangChain and OpenTelemetry

​Context propagation in distributed tracing

​Set up distributed tracing with LangChain

Trace a LangChain application

Trace a non-LangChain application

Send traces to an alternate provider

Use environment variables for global configuration

Configure alternate OTLP endpoints

Supported OpenTelemetry attribute and event mapping

Core LangSmith attributes

GenAI standard attributes

GenAI request parameters

GenAI usage metrics

TraceLoop attributes

OpenInference attributes

LLM attributes

Prompt template attributes

Retriever attributes

Tool attributes

Logfire attributes

OpenTelemetry event mapping

Event attribute extraction

Implementation examples

Trace using the Traceloop SDK

Trace using the Arize SDK

Advanced configuration

Use OpenTelemetry Collector for fan-out

Distributed tracing with LangChain and OpenTelemetry

Context propagation in distributed tracing

Set up distributed tracing with LangChain