LangGraph is a powerful Python framework that allows developers to build stateful, multi-agent applications using graphs. It is built on top of LangChain and enables dynamic control flow, making it suitable for complex LLM-based systems such as multi-step workflows, agent collaboration, and long-running conversations.
1. Introduction to LangGraph
LangGraph is an innovative framework built on top of LangChain that enables developers to construct LLM-powered applications using a graph-based architecture. In this architecture, each node represents a function (such as an LLM, a custom tool, or a decision step), and edges define how control and data flow from one step to the next.
This model supports complex, non-linear logic such as conditionals, retries, parallel executions, and loops—making it ideal for designing sophisticated workflows that are difficult to achieve with traditional linear pipelines.
LangGraph is especially useful when building:
- Multi-agent workflows: Coordinate multiple LLMs or agents that interact with one another to complete a task.
- Custom toolchains for LLM agents: Connect multiple tools and functions to extend LLM capabilities with domain-specific processing.
- Interactive chat systems with memory: Maintain context across turns using built-in memory and state mechanisms.
- Branching conversational flows: Dynamically change the flow of conversation based on inputs, context, or logic.
1.1 Key Features
LangGraph provides a rich set of features designed to simplify the development of complex LLM applications. These features help developers modularize their logic, manage state, and construct dynamic execution flows efficiently.
- Composable Nodes: Define each step of your workflow as a reusable Python function, LangChain chain, or subgraph.
- Graph-based Workflow: Represent logic visually or programmatically using a directed graph, enhancing clarity and control.
- Memory & State Management: Store and access variables, history, and state across different nodes using LangChain-compatible memory modules.
- Dynamic Routing: Determine which node to execute next based on the results of previous steps, user inputs, or decision rules.
- Asynchronous Execution: Supports async functions, enabling efficient I/O operations and parallel task handling.
1.2 Benefits
LangGraph offers several advantages that make it a compelling choice for developers working on advanced LLM applications. Its modular nature, compatibility with LangChain, and support for complex logic make it suitable for production-grade systems.
- Highly modular and extensible: Easily add, replace, or reuse components across different applications or flows.
- Clear execution model with graph visualization: Improves debugging and comprehension by making control flow explicit.
- Seamless LangChain integration: Directly utilizes LangChain’s tools, memory, agents, and chains.
- Supports complex use cases out-of-the-box: Designed for workflows requiring loops, branching logic, multiple agents, and long-term memory.
1.3 Limitations
While LangGraph is powerful, it also comes with certain trade-offs. It’s important to understand the framework’s current constraints before deciding to use it in production or for smaller projects.
- Still evolving: As a relatively new project, the community, support, and documentation may not be as mature as other frameworks.
- Requires understanding of graph and async concepts: Developers need to be familiar with asynchronous programming and graph-based design principles to use LangGraph effectively.
- May be overkill for simple workflows: For linear or single-agent flows, simpler frameworks or plain LangChain chains might be more appropriate.
2. Code Example
Let’s build a simple LangGraph app where an AI assistant asks a question, processes the response, and either continues the loop or exits based on user input.
2.1 Installation
To get started with LangGraph, you need to install a few essential Python packages. This includes the langgraph library itself, as well as langchain for LLM interaction and openai to connect with OpenAI’s models. Use the following pip command to install all dependencies:
pip install langgraph langchain openai
2.2 Python Code
Once the required libraries are installed, you can begin writing your LangGraph application. The example below imports necessary modules, sets up a state structure, defines functions as graph nodes, and constructs a simple workflow using LangGraph’s stateful graph-based design. This structure makes it easy to model conditional logic and iterative execution in LLM-powered applications.
# Author: Yatin Batra
# Filename: main.py
from langchain.chat_models import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing import TypedDict, Literal
import os
# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "your-api-key"
# Define a simple state dictionary
class ChatState(TypedDict):
message: str
action: Literal["continue", "exit"]
# Define a node function to ask a question
def ask_question(state: ChatState) -> ChatState:
llm = ChatOpenAI(temperature=0)
response = llm.predict_messages([("system", "You are a helpful assistant."),
("user", "What's the capital of France?")])
return {"message": response.content, "action": "continue"}
# Define a node function to check exit condition
def check_exit(state: ChatState) -> str:
if "paris" in state["message"].lower():
return END
return "ask_question"
# Build the graph
graph = StateGraph(ChatState)
graph.add_node("ask_question", ask_question)
graph.set_entry_point("ask_question")
graph.add_conditional_edges("ask_question", check_exit)
graph.compile()
# Execute the graph
app = graph.compile()
final_state = app.invoke({"message": "", "action": "continue"})
print("Final state:", final_state)
2.2.1 Code Explanation
This code demonstrates a basic LangGraph application that integrates OpenAI’s Chat model using LangChain. It starts by importing required modules such as ChatOpenAI for language model interaction, StateGraph and END for graph construction, and standard Python utilities like TypedDict, Literal, and os. The OpenAI API key is set using os.environ. A custom state type ChatState is defined using TypedDict with two fields: message (a string) and action (a literal value that can be “continue” or “exit”). The ask_question function is defined as a node that calls the OpenAI LLM with a system prompt (“You are a helpful assistant.”) and a user query (“What’s the capital of France?”), returning the model’s response along with an action to continue. The check_exit function checks whether the model’s response includes the word “Paris” (case-insensitive); if so, it returns END to halt the workflow, otherwise, it loops back to ask_question. A StateGraph object is initialized with ChatState as the schema, and nodes are added and wired with conditional logic. The graph is compiled into an executable app, and app.invoke is called with an initial empty message and a “continue” action. The final output state, which contains the assistant’s response, is printed. This example shows how LangGraph helps build modular, stateful, and logic-driven workflows with LLMs using a directed graph model.
2.2.2 Code Output
After executing the LangGraph workflow, the final output state is displayed. This output shows the assistant’s response to the query along with the action indicating the workflow’s status. In this example, the message confirms that the capital of France is Paris, and the action remains “continue,” signaling that the workflow could proceed if additional steps were defined.
Final state: {'message': 'The capital of France is Paris.', 'action': 'continue'}
3. Conclusion
LangGraph is a promising tool that brings structure and statefulness to LLM-powered applications. Its graph-based model enables developers to create robust, maintainable AI workflows. Whether you’re orchestrating multiple agents, building advanced chatbots, or managing toolchains, LangGraph offers a clean and scalable solution to complex logic.
