Trace LangChain applications (Python and JS/TS)

LangSmith integrates seamlessly with LangChain (Python and JavaScript), the popular open-source framework for building LLM applications.

Installation

Install the following for Python or JS (the code snippets use the OpenAI integration).

For a full list of packages available, see the LangChain docs.

pip

pip install langchain_openai

yarn add @langchain/openai @langchain/core

npm install @langchain/openai @langchain/core

pnpm add @langchain/openai @langchain/core

Quick start

1. Configure your environment

export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<your-api-key>
# This example uses OpenAI, but you can use any LLM provider of choice
export OPENAI_API_KEY=<your-openai-api-key>
# For LangSmith API keys linked to multiple workspaces, set the LANGSMITH_WORKSPACE_ID environment variable to specify which workspace to use.
export LANGSMITH_WORKSPACE_ID=<your-workspace-id>

If your account is in a region other than US (the default), also set LANGSMITH_ENDPOINT to the API URL for your region. Without this, your API key won’t be recognized and requests will fail to authenticate.

Region
GCP US
GCP EU
GCP APAC
AWS US

For example, EU accounts: export LANGSMITH_ENDPOINT="https://eu.api.smith.langchain.com".

If you are using LangChain.js with LangSmith and are not in a serverless environment, we also recommend setting the following explicitly to reduce latency:

export LANGCHAIN_CALLBACKS_BACKGROUND=true

If you are in a serverless environment, we recommend setting the reverse to allow tracing to finish before your function ends:

export LANGCHAIN_CALLBACKS_BACKGROUND=false

2. Log a trace

No extra code is needed to log a trace to LangSmith. Just run your LangChain code as you normally would.

Python

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Please respond to the user's request only based on the given context."),
    ("user", "Question: {question}\nContext: {context}")
])

model = ChatOpenAI(model="gpt-5.4-mini")
output_parser = StrOutputParser()
chain = prompt | model | output_parser

question = "Can you summarize this morning's meetings?"
context = "During this morning's meeting, we solved all world conflict."

chain.invoke({"question": question, "context": context})

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant. Please respond to the user's request only based on the given context."],
  ["user", "Question: {question}\nContext: {context}"],
]);

const model = new ChatOpenAI({ modelName: "gpt-5.4-mini" });
const outputParser = new StringOutputParser();
const chain = prompt.pipe(model).pipe(outputParser);

const question = "Can you summarize this morning's meetings?"
const context = "During this morning's meeting, we solved all world conflict."

await chain.invoke({ question: question, context: context });

3. View your trace

By default, the trace will be logged to the project with the name default. You can view an example of a trace logged using the above code publicly in LangSmith.

Trace selectively

The previous section showed how to trace all invocations of a LangChain runnables within your applications by setting a single environment variable. While this is a convenient way to get started, you may want to trace only specific invocations or parts of your application.

There are two ways to do this in Python: by manually passing in a LangChainTracer instance as a callback, or by using the tracing_context context manager.

In JS/TS, you can pass a LangChainTracer instance as a callback.

Python

# You can opt-in to specific invocations..
import langsmith as ls

with ls.tracing_context(enabled=True):
    chain.invoke({"question": "Am I using a callback?", "context": "I'm using a callback"})

# This will NOT be traced (assuming LANGSMITH_TRACING is not set)
chain.invoke({"question": "Am I being traced?", "context": "I'm not being traced"})

# This would not be traced, even if LANGSMITH_TRACING=true
with ls.tracing_context(enabled=False):
    chain.invoke({"question": "Am I being traced?", "context": "I'm not being traced"})

// You can configure a LangChainTracer instance to trace a specific invocation.
import { LangChainTracer } from "@langchain/core/tracers/tracer_langchain";

const tracer = new LangChainTracer();
await chain.invoke(
  {
    question: "Am I using a callback?",
    context: "I'm using a callback"
  },
  { callbacks: [tracer] }
);

Log to a specific project

Statically

As mentioned in the tracing conceptual guide LangSmith uses the concept of a Project to group traces. If left unspecified, the tracer project is set to default. You can set the LANGSMITH_PROJECT environment variable to configure a custom project name for an entire application run. This should be done before executing your application.

export LANGSMITH_PROJECT=my-project

The LANGSMITH_PROJECT flag is only supported in JS SDK versions >= 0.2.16, use LANGCHAIN_PROJECT instead if you are using an older version.

Dynamically

This largely builds off of the previous section and allows you to set the project name for a specific LangChainTracer instance or as parameters to the tracing_context context manager in Python.

Python

# You can set the project name using the project_name parameter.
import langsmith as ls

with ls.tracing_context(project_name="My Project", enabled=True):
    chain.invoke({"question": "Am I using a context manager?", "context": "I'm using a context manager"})

// You can set the project name for a specific tracer instance:
import { LangChainTracer } from "@langchain/core/tracers/tracer_langchain";

const tracer = new LangChainTracer({ projectName: "My Project" });
await chain.invoke(
  {
    question: "Am I using a callback?",
    context: "I'm using a callback"
  },
  { callbacks: [tracer] }
);

Add metadata and tags to traces

You can annotate your traces with arbitrary metadata and tags by providing them in the RunnableConfig. This is useful for associating additional information with a trace, such as the environment in which it was executed, or the user who initiated it. For information on how to query traces and runs by metadata and tags, see Query traces (SDK)

When you attach metadata or tags to a runnable (either through the RunnableConfig or at runtime with invocation params), they are inherited by all child runnables of that runnable.

Python

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful AI."),
    ("user", "{input}")
])

# The tag "model-tag" and metadata {"model-key": "model-value"} will be attached to the ChatOpenAI run only
chat_model = ChatOpenAI().with_config({"tags": ["model-tag"], "metadata": {"model-key": "model-value"}})
output_parser = StrOutputParser()

# Tags and metadata can be configured with RunnableConfig
chain = (prompt | chat_model | output_parser).with_config({"tags": ["config-tag"], "metadata": {"config-key": "config-value"}})

# Tags and metadata can also be passed at runtime
chain.invoke({"input": "What is the meaning of life?"}, {"tags": ["invoke-tag"], "metadata": {"invoke-key": "invoke-value"}})

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const prompt = ChatPromptTemplate.fromMessages([
    ["system", "You are a helpful AI."],
    ["user", "{input}"]
])

// The tag "model-tag" and metadata {"model-key": "model-value"} will be attached to the ChatOpenAI run only
const model = new ChatOpenAI().withConfig({ tags: ["model-tag"], metadata: { "model-key": "model-value" } });
const outputParser = new StringOutputParser();

// Tags and metadata can be configured with RunnableConfig
const chain = (prompt.pipe(model).pipe(outputParser)).withConfig({"tags": ["config-tag"], "metadata": {"config-key": "top-level-value"}});

// Tags and metadata can also be passed at runtime
await chain.invoke({input: "What is the meaning of life?"}, {tags: ["invoke-tag"], metadata: {"invoke-key": "invoke-value"}})

Customize run name

You can customize the name of a given run when invoking or streaming your LangChain code by providing it in the Config. This name is used to identify the run in LangSmith and can be used to filter and group runs. The name is also used as the title of the run in the LangSmith UI. This can be done by setting a run_name in the RunnableConfig object at construction or by passing a run_name in the invocation parameters in JS/TS.

Python

# When tracing within LangChain, run names default to the class name of the traced object (e.g., 'ChatOpenAI').
configured_chain = chain.with_config({"run_name": "MyCustomChain"})
configured_chain.invoke({"input": "What is the meaning of life?"})

# You can also configure the run name at invocation time, like below
chain.invoke({"input": "What is the meaning of life?"}, {"run_name": "MyCustomChain"})

// When tracing within LangChain, run names default to the class name of the traced object (e.g., 'ChatOpenAI').
const configuredChain = chain.withConfig({ runName: "MyCustomChain" });
await configuredChain.invoke({ input: "What is the meaning of life?" });

// You can also configure the run name at invocation time, like below
await chain.invoke({ input: "What is the meaning of life?" }, {runName: "MyCustomChain"})

The run_name parameter only changes the name of the runnable you invoke (e.g., a chain, function). It does not rename the nested run automatically created when you invoke an LLM object like ChatOpenAI (gpt-5.4-mini). In the example, the enclosing run will appear in LangSmith as MyCustomChain, while the nested LLM run still shows the model’s default name.

To give the LLM run a more meaningful name, you can either:

• Wrap the model in another runnable and assign a run_name to that step.
• Use a tracing decorator or helper (e.g., @traceable in Python, or traceable from langsmith in JS/TS) to create a custom run around the model call.

Override model name in traces

When tracing LangChain model calls, LangSmith automatically captures the model identifier used in the API call. However, you may want to display a different, more descriptive name in traces for organizational purposes or to distinguish between different model configurations. You can do this by passing the ls_model_name metadata parameter when constructing or configuring your LangChain model.

This is particularly useful when:

• Working with self-hosted or local models where the model ID might not be descriptive.
• Using the same model with different configurations and wanting to distinguish them in traces.
• Creating aliases for models to make traces more readable for your team.
• Standardizing model names across different deployment environments.

Python

from langchain_openai import ChatOpenAI
from langchain_ollama import ChatOllama

# Override model name for a local model
llm = ChatOllama(
    model="llama2:13b-chat",  # Actual model ID
    metadata={"ls_model_name": "llama2-13b-production"}  # Name shown in LangSmith
)

# Or with OpenAI to distinguish configurations
llm_creative = ChatOpenAI(
    model="gpt-5.5",
    temperature=0.9,
    metadata={"ls_model_name": "gpt-5.4-creative"}
)

llm_factual = ChatOpenAI(
    model="gpt-5.5",
    temperature=0.1,
    metadata={"ls_model_name": "gpt-5.4-factual"}
)

# The metadata is inherited when the model is used in a chain
result = llm.invoke("What is the meaning of life?")

import { ChatOpenAI } from "@langchain/openai";
import { ChatOllama } from "@langchain/ollama";

// Override model name for a local model
const llm = new ChatOllama({
  model: "llama2:13b-chat",  // Actual model ID
  metadata: { ls_model_name: "llama2-13b-production" }  // Name shown in LangSmith
});

// Or with OpenAI to distinguish configurations
const llmCreative = new ChatOpenAI({
  modelName: "gpt-5.5",
  temperature: 0.9,
  metadata: { ls_model_name: "gpt-5.4-creative" }
});

const llmFactual = new ChatOpenAI({
  modelName: "gpt-5.5",
  temperature: 0.1,
  metadata: { ls_model_name: "gpt-5.4-factual" }
});

// The metadata is inherited when the model is used in a chain
const result = await llm.invoke("What is the meaning of life?");

When you pass ls_model_name in the model’s metadata, this name will appear in the LangSmith UI for all traces involving that model instance. This works for any LangChain chat model or LLM and is inherited by all runs that use the model, including when it’s part of a chain.

The ls_model_name metadata parameter is also used for cost tracking. When combined with the ls_provider parameter, LangSmith can automatically calculate costs for custom or self-hosted models. For more information about all available metadata parameters, see the metadata parameters reference.

Customize run ID

You can customize the ID of a given run when invoking or streaming your LangChain code by providing it in the Config. This ID is used to uniquely identify the run in LangSmith and can be used to query specific runs. The ID can be useful for linking runs across different systems or for implementing custom tracking logic. This can be done by setting a run_id in the RunnableConfig object at construction or by passing a run_id in the invocation parameters.

This feature is not currently supported directly for LLM objects.

Python

import uuid

my_uuid = uuid.uuid4()

# You can configure the run ID at invocation time:
chain.invoke({"input": "What is the meaning of life?"}, {"run_id": my_uuid})

const myUuid = crypto.randomUUID();

// You can configure the run ID at invocation time, like below
await chain.invoke({ input: "What is the meaning of life?" }, { runId: myUuid });

Note that if you do this at the root of a trace (i.e., the top-level run, that run ID will be used as the trace_id).

Access run (span) ID for LangChain invocations

When you invoke a LangChain object, you can manually specify the run ID of the invocation. This run ID can be used to query the run in LangSmith.

In JS/TS, you can use a RunCollectorCallbackHandler instance to access the run ID.

Python

import uuid

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Please respond to the user's request only based on the given context."),
    ("user", "Question: {question}\n\nContext: {context}")
])
model = ChatOpenAI(model="gpt-5.4-mini")
output_parser = StrOutputParser()

chain = prompt | model | output_parser

question = "Can you summarize this morning's meetings?"
context = "During this morning's meeting, we solved all world conflict."
my_uuid = uuid.uuid4()
result = chain.invoke({"question": question, "context": context}, {"run_id": my_uuid})
print(my_uuid)

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { RunCollectorCallbackHandler } from "@langchain/core/tracers/run_collector";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant. Please respond to the user's request only based on the given context."],
  ["user", "Question: {question}\n\nContext: {context}"],
]);
const model = new ChatOpenAI({ modelName: "gpt-5.4-mini" });
const outputParser = new StringOutputParser();

const chain = prompt.pipe(model).pipe(outputParser);
const runCollector = new RunCollectorCallbackHandler();

const question = "Can you summarize this morning's meetings?"
const context = "During this morning's meeting, we solved all world conflict."
await chain.invoke(
    { question: question, context: context },
    { callbacks: [runCollector] }
);
const runId = runCollector.tracedRuns[0].id;
console.log(runId);

Ensure all traces are submitted before exiting

In LangChain Python, LangSmith’s tracing is done in a background thread to avoid obstructing your production application. This means that your process may end before all traces are successfully posted to LangSmith. This is especially prevalent in a serverless environment, where your VM may be terminated immediately once your chain or agent completes.

You can make callbacks synchronous by setting the LANGCHAIN_CALLBACKS_BACKGROUND environment variable to "false".

For both languages, LangChain exposes methods to wait for traces to be submitted before exiting your application. Below is an example:

Python

from langchain_openai import ChatOpenAI
from langchain_core.tracers.langchain import wait_for_all_tracers

llm = ChatOpenAI()

try:
  llm.invoke("Hello, World!")
finally:
  wait_for_all_tracers()

import { awaitAllCallbacks } from "@langchain/core/callbacks/promises";

try {
    const llm = new ChatOpenAI();
    const response = await llm.invoke("Hello, World!");
} catch (e) {
    // handle error
} finally {
    await awaitAllCallbacks();
}

Trace without setting environment variables

As mentioned in other guides, the following environment variables allow you to configure tracing enabled, the api endpoint, the api key, and the tracing project:

• LANGSMITH_TRACING
• LANGSMITH_API_KEY
• LANGSMITH_ENDPOINT
• LANGSMITH_PROJECT

However, in some environments, it is not possible to set environment variables. In these cases, you can set the tracing configuration programmatically.

This largely builds off of the previous section.

Python

import langsmith as ls

# You can create a client instance with an api key and api url
client = ls.Client(
    api_key="YOUR_API_KEY",  # This can be retrieved from a secrets manager
    api_url="https://api.smith.langchain.com",  # Self-hosted, GCP EU (`eu.api...`), GCP APAC (`apac.api...`), or AWS US (`aws.api...`) as needed
)

# You can pass the client and project_name to the tracing_context
with ls.tracing_context(client=client, project_name="test-no-env", enabled=True):
    chain.invoke({"question": "Am I using a callback?", "context": "I'm using a callback"})

import { LangChainTracer } from "@langchain/core/tracers/tracer_langchain";
import { Client } from "langsmith";

// You can create a client instance with an api key and api url
const client = new Client(
    {
        apiKey: "YOUR_API_KEY",
        apiUrl: "https://api.smith.langchain.com", // Self-hosted, GCP EU (`eu.api...`), GCP APAC (`apac.api...`), or AWS US (`aws.api...`) as needed
    }
);

// You can pass the client and project_name to the LangChainTracer instance
const tracer = new LangChainTracer({client, projectName: "test-no-env"});
await chain.invoke(
  {
    question: "Am I using a callback?",
    context: "I'm using a callback",
  },
  { callbacks: [tracer] }
);

Distributed tracing with LangChain (Python)

LangSmith supports distributed tracing with LangChain Python. This allows you to link runs (spans) across different services and applications. The principles are similar to the distributed tracing guide for the LangSmith SDK.

import langsmith
from langchain_core.runnables import chain
from langsmith.run_helpers import get_current_run_tree

# -- This code should be in a separate file or service --
@chain
def child_chain(inputs):
    return inputs["test"] + 1

def child_wrapper(x, headers):
    with langsmith.tracing_context(parent=headers):
        child_chain.invoke({"test": x})

# -- This code should be in a separate file or service --
@chain
def parent_chain(inputs):
    rt = get_current_run_tree()
    headers = rt.to_headers()
    # ... make a request to another service with the headers
    # The headers should be passed to the other service, eventually to the child_wrapper function

parent_chain.invoke({"test": 1})

Interoperability between LangChain (Python) and LangSmith SDK

If you are using LangChain for part of your application and the LangSmith SDK (see Custom instrumentation) for other parts, you can still trace the entire application seamlessly.

LangChain objects will be traced when invoked within a traceable function and be bound as a child run of the traceable function.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langsmith import traceable

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Please respond to the user's request only based on the given context."),
    ("user", "Question: {question}\nContext: {context}")
])

model = ChatOpenAI(model="gpt-5.4-mini")
output_parser = StrOutputParser()
chain = prompt | model | output_parser

# The above chain will be traced as a child run of the traceable function
@traceable(
    tags=["openai", "chat"],
    metadata={"foo": "bar"}
)
def invoke_runnnable(question, context):
    result = chain.invoke({"question": question, "context": context})
    return "The response is: " + result

invoke_runnnable("Can you summarize this morning's meetings?", "During this morning's meeting, we solved all world conflict.")

This will produce the following trace tree:

Interoperability between LangChain.JS and LangSmith SDK

Tracing LangChain objects inside traceable (JS only)

Starting with langchain@0.2.x, LangChain objects are traced automatically when used inside @traceable functions, inheriting the client, tags, metadata and project name of the traceable function.

For older versions of LangChain below 0.2.x, you will need to manually pass an instance LangChainTracer created from the tracing context found in @traceable.

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { getLangchainCallbacks } from "langsmith/langchain";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant. Please respond to the user's request only based on the given context.",
  ],
  ["user", "Question: {question}\nContext: {context}"],
]);

const model = new ChatOpenAI({ modelName: "gpt-5.4-mini" });
const outputParser = new StringOutputParser();
const chain = prompt.pipe(model).pipe(outputParser);

const main = traceable(
  async (input: { question: string; context: string }) => {
    const callbacks = await getLangchainCallbacks();
    const response = await chain.invoke(input, { callbacks });
    return response;
  },
  { name: "main" }
);

Tracing LangChain child runs via traceable / RunTree API (JS only)

We’re working on improving the interoperability between traceable and LangChain. The following limitations are present when using combining LangChain with traceable:

1. Mutating RunTree obtained from getCurrentRunTree() of the RunnableLambda context will result in a no-op.
2. It’s discouraged to traverse the RunTree obtained from RunnableLambda via getCurrentRunTree() as it may not contain all the RunTree nodes.
3. Different child runs may have the same execution_order and child_execution_order value. Thus in extreme circumstances, some runs may end up in a different order, depending on the start_time.

In some uses cases, you might want to run traceable functions as part of the RunnableSequence or trace child runs of LangChain run imperatively via the RunTree API. Starting with LangSmith 0.1.39 and @langchain/core 0.2.18, you can directly invoke traceable-wrapped functions within RunnableLambda.

import { traceable } from "langsmith/traceable";
import { RunnableLambda } from "@langchain/core/runnables";
import { RunnableConfig } from "@langchain/core/runnables";

const tracedChild = traceable((input: string) => `Child Run: ${input}`, {
  name: "Child Run",
});

const parrot = new RunnableLambda({
  func: async (input: { text: string }, config?: RunnableConfig) => {
    return await tracedChild(input.text);
  },
});

Alternatively, you can convert LangChain’s RunnableConfig to a equivalent RunTree object by using RunTree.fromRunnableConfig or pass the RunnableConfig as the first argument of traceable-wrapped function.

Traceable

import { traceable } from "langsmith/traceable";
import { RunnableLambda } from "@langchain/core/runnables";
import { RunnableConfig } from "@langchain/core/runnables";

const tracedChild = traceable((input: string) => `Child Run: ${input}`, {
  name: "Child Run",
});

const parrot = new RunnableLambda({
  func: async (input: { text: string }, config?: RunnableConfig) => {
    // Pass the config to existing traceable function
    await tracedChild(config, input.text);
    return input.text;
  },
});

import { RunTree } from "langsmith/run_trees";
import { RunnableLambda } from "@langchain/core/runnables";
import { RunnableConfig } from "@langchain/core/runnables";

const parrot = new RunnableLambda({
  func: async (input: { text: string }, config?: RunnableConfig) => {
    // create the RunTree from the RunnableConfig of the RunnableLambda
    const childRunTree = RunTree.fromRunnableConfig(config, {
      name: "Child Run",
    });

    childRunTree.inputs = { input: input.text };
    await childRunTree.postRun();

    childRunTree.outputs = { output: `Child Run: ${input.text}` };
    await childRunTree.patchRun();

    return input.text;
  },
});

If you prefer a video tutorial, check out the Alternative Ways to Trace video from the Introduction to LangSmith Course.

---

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.