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Choosing Your Stack: LangChain & LangGraph in Python vs. TypeScript/JavaScript?
- Authors
- Name
- Ali Ibrahim
Introduction
AI used to be a Python-first world. Most libraries, frameworks, and tutorials lived in that ecosystem. But today, AI is no longer locked to one language. The rise of major AI frameworks JavaScript/TypeScript versions like LangChain.js and LangGraph.js shows how serious the JavaScript ecosystem is becoming for building AI-powered applications.
If you’re a developer trying to decide between Python vs TypeScript/JavaScript for your next AI agent project, this post will break down everything you need to know before deciding. We’ll look at LangChain and LangGraph in both languages, compare maturity, deployment, and community—and end with some practical advice.
What is LangChain?
LangChain is a framework designed to simplify the process of building applications powered by large language models (LLMs). It provides tools for chaining together prompts, models, and tools to create complex workflows.
LangChain in Python
LangChain started in Python, and since day one, it was the go-to framework for building LLM applications. Below is a minimal example of how to use LangChain in Python:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = OpenAI()
chain = prompt | llm | StrOutputParser()
chain.invoke("your adjective here")
When invoked, this chain will pass the adjective to the prompt, which is then sent to the LLM to generate a joke. This is the starting point for many AI apps—wrapping an LLM with prompt templates and chains.
LangChain.js
For developers who live in the JavaScript/TypeScript world, LangChain.js brings the same concepts to Node.js and browser environments. It’s especially powerful if your app already runs on Vite, Next.js, or a Node.js backend.
Minimal Example:
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";
const prompt = ChatPromptTemplate.fromTemplate("Tell me a {adjective} joke");
const llm = new ChatOpenAI({ model: "gpt-4o-mini" });
const chain = prompt.pipe(llm);
const response = await chain.invoke({ adjective: "funny" });
console.log(response);
The API is nearly identical to Python, but you can run it in a TypeScript environment and integrate directly into your web stack.
While LangChain is excellent for linear workflows, many modern AI applications require more complex, stateful agents. This is where LangGraph comes in, and the choice between Python and JavaScript remains just as important.
What is LangGraph?
LangGraph is a low-level orchestration framework for building controllable agents.
While LangChain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures.While LangGraph can be used standalone, it also integrates seamlessly with any LangChain products, like LangSmith, LangGraph Platform.
LangGraph in Python
If you are building complex AI agents, LangGraph is the way to go. It solves one of the biggest limitations of chains: lack of state management and control flow. With LangGraph, you can design agents as graphs, where each node is a step and you control transitions explicitly.
Minimal Example:
from typing import Annotated
from langchain.chat_models import init_chat_model
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, add_messages]
graph_builder = StateGraph(State)
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
def chatbot(state: State):
return {"messages": [llm.invoke(state["messages"])]}
# The first argument is the unique node name
# The second argument is the function or object that will be called whenever
# the node is used.
graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()
This example shows how to build a chatbot using LangGraph, as you can see, we explicitly define the state and control flow. The StateGraph allows you to manage the agent's state, making it easy to build complex workflows.
LangGraph.js
LangGraph.js has similar principles as the Python version: explicit state graphs for agents.
Minimal Example:
import {
StateGraph,
MessagesAnnotation,
END,
START,
} from '@langchain/langgraph';
import { ChatOpenAI } from '@langchain/openai';
const stateGraph = new StateGraph(MessagesAnnotation);
const model = new ChatOpenAI({ model: "gpt-4o-mini" });
const chatbot = async (state: typeof MessagesAnnotation.State) => {
const response = await model.invoke(state.messages);
return { messages: [response] };
}
// The first argument is the unique node name
// The second argument is the function or object that will be called whenever
// the node is used.
stateGraph
.addNode('chatbot', chatbot)
.addEdge(START, 'chatbot')
.addEdge('chatbot', END);
const graph = stateGraph.compile();
The familiarity is clear. If you’re already building with TS/JS, it’s a smooth fit.
What are numbers saying?
To see the real-world adoption of both platforms, it's helpful to look at their usage statistics.
As of August 20, 2025, the JavaScript ecosystem shows significant traction on NPM. The core package, @langchain/core, sees over 1.2 million weekly downloads, while @langchain/langgraph adds nearly 400,000 on its own.
For Python, calculating a single figure is more complex due to its highly modular package ecosystem. However, as you noted, summing the downloads of its core components on https://pepy.tech/ reveals a larger, more established user base, with numbers easily exceeding several million weekly downloads.
These statistics perfectly frame the current landscape: Python's ecosystem is more mature and extensive , while the JavaScript community represents a serious, rapidly expanding frontier for AI development.
Community
The LangChain community is increasingly language-agnostic. Learning resources like academy.langchain.com and the interactive chat.langchain.com support both Python and TypeScript. The team's heavy investment in JavaScript is clear, as most major open-source agents are now released in both languages. Active hubs like the r/langchain subreddit and the official YouTube channel confirm that LangChain is a first-class citizen in both worlds.
Deployment
Deployment is often straightforward, as you can follow the existing process for your application, whether it's built on Next.js, NestJS, or a serverless platform.
For a more streamlined approach, the dedicated langgraph CLI handles the underlying infrastructure (like an API server and Redis) for you. This powerful tool is available for both Python and TypeScript, letting you focus solely on your agent's logic.
Which One to Choose?
- Choose Python if: You are a data scientist or machine learning engineer, or your project requires extensive data manipulation and integration with the broader Python ML ecosystem.
- Choose TypeScript/JavaScript if: You are a web developer or your project needs to run in a browser or on a serverless platform, and you prefer a single, unified codebase with the added benefit of type safety.
- And honestly—both ecosystems are moving fast. The bigger question is not which language but what are you building.
Conclusion
AI and AI agents will be everywhere. The choice of language matters less over time as coding tools and AI-assisted development blur the lines. What matters most is what you build.
That said, if you’re more comfortable in one ecosystem, stick with it. If you’re starting fresh, consider where you want your app to live.
For more insights and examples, check out the Agentailor Blog.
Getting Started Faster
If you’re leaning toward JS/TS and want to skip boilerplate, check out Agent Initializr. It scaffolds a production-ready LangGraph.js + NestJS backend, so you can focus on your agent logic instead of setup.