I have a simple react implementation using the create_react_agent method. I am seeing that when checking traces in LangSmith, the LLM calls latency is being reported way higher in LangSmith than in the LLM logs. See example below:
The same LLM call is reported as 20.04 seconds in Langsmith, though in the LLM logs it is reported as 1.565 seconds.
Do you know what can be adding the extra 18 seconds?
This is my implementation:
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from typing import Annotated
from pydantic import BaseModel, Field
from .tools import rag_search_code, read_code_file, attempt_completion, list_files
from langchain_core.messages import AnyMessage
from langchain_core.runnables import RunnableConfig
from langgraph.prebuilt.chat_agent_executor import AgentState
# Model configuration
REACT_MODEL = "claude-sonnet-4-20250514"
# REACT_MODEL = "o3-mini"
# Initialize the language model
react_llm = init_chat_model(model=REACT_MODEL, temperature=0)
# Define the available tools
tools = [read_code_file, list_files, attempt_completion]
def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
project_folder_structure = config["configurable"].get(
"project_folder_structure", ""
)
# Define the React agent prompt
system_content = """Loren Ipsum
"""
user_content = f"""
Project Folder Structure:
{project_folder_structure}
"""
return [
{"role": "system", "content": system_content},
{"role": "user", "content": user_content},
] + state["messages"]
def create_react_agent_workflow():
"""
Create a React-style agent workflow for code analysis and assistance.
Args:
username: The username for project-specific operations
Returns:
A compiled LangGraph agent ready for execution
"""
# Create the React agent with the defined tools and prompt
agent = create_react_agent(model=react_llm, tools=tools, prompt=prompt)
return agent
async def run_react_agent_async(
query: str, username: str, project_folder_structure: str = "", config: dict = None
) -> dict:
"""
Run the React agent asynchronously with a given query.
Args:
query: The user's question or request
username: The username for project-specific operations
project_folder_structure: The project folder structure string
config: Additional configuration options
Returns:
The agent's response
"""
# Create the agent
agent = create_react_agent_workflow()
# Prepare the configuration
run_config = {
"configurable": {
"project_folder_structure": project_folder_structure,
"username": username,
**(config or {}),
}
}
# Execute the agent asynchronously
result = await agent.ainvoke(
{"messages": [{"role": "user", "content": query}]}, config=run_config
)
return result