Working with LangGraph

Automatically instrument LangChain chains and agents with LangDB—gain live traces, cost analytics, and latency insights through init().

LangDB provides seamless tracing and observability for LangChain-based applications.

Checkout: https://app.langdb.ai/sharing/threads/bfced28f-5966-4491-867e-fd3875fe3282

Installation

Install the LangDB client with LangChain support:

pip install pylangdb[langchain]

Quick Start

Export Environment Variables

export LANGDB_API_KEY="<your_langdb_api_key>"
export LANGDB_PROJECT_ID="<your_langdb_project_id>"

Initialize LangDB

Import and run the initialize before configuring your LangChain/LangGraph:

from pylangdb.langchain import init
# Initialise LangDB
init()

Define your Agent

# Your existing LangChain code works with proper configuration
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
import os 

api_base = "https://api.us-east-1.langdb.ai"
api_key = os.getenv("LANGDB_API_KEY")
project_id = os.getenv("LANGDB_PROJECT_ID")

# Default headers for API requests
default_headers: dict[str, str] = {
    "x-project-id": project_id
}

# Initialize OpenAI LLM with LangDB configuratio
llm = ChatOpenAI(
    model_name="gpt-4o",
    temperature=0.3,
    openai_api_base=api_base,
    openai_api_key=api_key,
    default_headers=default_headers,
)
result = llm.invoke([HumanMessage(content="Hello, LangDB!")])

Once LangDB is initialized, all calls to llm, intermediate steps, tool executions, and nested chains are automatically traced and linked under a single session.