Trace with LangGraph

LangSmith smoothly integrates with LangGraph (Python and JS) to help you trace agentic workflows, whether you’re using LangChain modules or other SDKs.

With LangChain

If you are using LangChain modules within LangGraph, you only need to set a few environment variables to enable tracing.

This guide will walk through a basic example. For more detailed information on configuration, see the Trace With LangChain guide.

1. Installation

Install the LangGraph library and the OpenAI integration for Python and JS (we use the OpenAI integration for the code snippets below).

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

pip

pip install langchain_openai langgraph

2. Configure your environment

Python

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 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

See this LangChain.js guide for more information.

3. Log a trace

Once you’ve set up your environment, you can call LangChain runnables as normal. LangSmith will infer the proper tracing config:

Python

from typing import Literal
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import ToolNode

@tool
def search(query: str):
    """Call to surf the web."""
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."

tools = [search]
tool_node = ToolNode(tools)

model = ChatOpenAI(model="gpt-4o", temperature=0).bind_tools(tools)

def should_continue(state: MessagesState) -> Literal["tools", "__end__"]:
    messages = state['messages']
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return "__end__"

def call_model(state: MessagesState):
    messages = state['messages']
    # Invoking `model` will automatically infer the correct tracing context
    response = model.invoke(messages)
    return {"messages": [response]}

workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge("__start__", "agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
)
workflow.add_edge("tools", 'agent')

app = workflow.compile()

final_state = app.invoke(
    {"messages": [HumanMessage(content="what is the weather in sf")]},
    config={"configurable": {"thread_id": 42}}
)

final_state["messages"][-1].content

An example trace from running the above code looks like this:

Without LangChain

If you are using other SDKs or custom functions within LangGraph, you will need to wrap or decorate them appropriately (with the @traceable decorator in Python or the traceable function in JS, or something like e.g. wrap_openai for SDKs). If you do so, LangSmith will automatically nest traces from those wrapped methods.

Here’s an example. You can also see this page for more information.

1. Installation

Install the LangGraph library and the OpenAI SDK for Python and JS (we use the OpenAI integration for the code snippets below).

pip

pip install openai langsmith langgraph

2. Configure your environment

Python

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>

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

See this LangChain.js guide for more information.

3. Log a trace

Once you’ve set up your environment, wrap or decorate the custom functions/SDKs you want to trace. LangSmith will then infer the proper tracing config:

Python

import json
import openai
import operator
from langsmith import traceable
from langsmith.wrappers import wrap_openai
from typing import Annotated, Literal, TypedDict
from langgraph.graph import StateGraph

class State(TypedDict):
    messages: Annotated[list, operator.add]

tool_schema = {
    "type": "function",
    "function": {
        "name": "search",
        "description": "Call to surf the web.",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"],
        },
    },
}

# Decorating the tool function will automatically trace it with the correct context
@traceable(run_type="tool", name="Search Tool")
def search(query: str):
    """Call to surf the web."""
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."

tools = [search]

def call_tools(state):
    function_name_to_function = {"search": search}
    messages = state["messages"]
    tool_call = messages[-1]["tool_calls"][0]
    function_name = tool_call["function"]["name"]
    function_arguments = tool_call["function"]["arguments"]
    arguments = json.loads(function_arguments)
    function_response = function_name_to_function[function_name](**arguments)
    tool_message = {
        "tool_call_id": tool_call["id"],
        "role": "tool",
        "name": function_name,
        "content": function_response,
    }
    return {"messages": [tool_message]}

wrapped_client = wrap_openai(openai.Client())

def should_continue(state: State) -> Literal["tools", "__end__"]:
    messages = state["messages"]
    last_message = messages[-1]
    if last_message["tool_calls"]:
        return "tools"
    return "__end__"

def call_model(state: State):
    messages = state["messages"]
    # Calling the wrapped client will automatically infer the correct tracing context
    response = wrapped_client.chat.completions.create(
        messages=messages, model="gpt-4o-mini", tools=[tool_schema]
    )
    raw_tool_calls = response.choices[0].message.tool_calls
    tool_calls = [tool_call.to_dict() for tool_call in raw_tool_calls] if raw_tool_calls else []
    response_message = {
        "role": "assistant",
        "content": response.choices[0].message.content,
        "tool_calls": tool_calls,
    }
    return {"messages": [response_message]}

workflow = StateGraph(State)
workflow.add_node("agent", call_model)
workflow.add_node("tools", call_tools)
workflow.add_edge("__start__", "agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
)
workflow.add_edge("tools", 'agent')

app = workflow.compile()

final_state = app.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)

final_state["messages"][-1]["content"]

An example trace from running the above code looks like this: