Building Stateful Multi-Agent Systems with LangGraph

Posted on February 18, 2025

Stateful, multi-agent applications are becoming increasingly relevant in AI-driven development. As large language models (LLMs) evolve, developers need tools that allow them to create structured, reliable workflows while also enabling dynamic agentic behavior. LangGraph is an emerging Python library designed to facilitate the construction of such systems. By integrating LangGraph with an LLM, developers can orchestrate workflows that are either strictly predefined or adaptive, allowing for greater flexibility in AI-driven applications.

Understanding LangGraph's Approach

LangGraph introduces a graph-based architecture that allows developers to define workflows as a set of nodes and edges, enabling controlled execution of tasks. Unlike traditional sequential logic, this graph-based approach makes it easier to visualize, debug, and modify AI-powered workflows. LangGraph supports two primary paradigms:

Workflows – These involve structured sequences where the logic is predefined. Tasks are executed in a controlled manner, making workflows ideal for enterprise applications that require reliability.

Agents – In this paradigm, LLMs dynamically determine their next steps. Agents can self-direct their execution paths based on responses, making them suitable for more autonomous applications like chatbots, research assistants, or multi-step automation systems.

LangGraph ensures smooth execution of both paradigms while allowing developers to incorporate persistence, streaming, and debugging tools to enhance system performance.

Getting Started with LangGraph

Before diving into an implementation, ensure you have LangGraph and LangChain installed:

bash

pip install langgraph langchain openai

Let’s start with a simple workflow example where an AI-powered agent receives a user query, processes it, and executes an appropriate function.

python

import langgraph
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage

# Initialize the LLM
llm = ChatOpenAI(model_name="gpt-4", temperature=0)

# Define a simple graph
graph = langgraph.Graph()

# Add a processing node
def process_message(messages):
    response = llm(messages)
    return response

graph.add_node("process_message", process_message)

# Connect nodes
graph.set_entry_point("process_message")
graph.set_exit_point("process_message")

# Run the workflow
messages = [HumanMessage(content="What is the capital of France?")]
response = graph.run(messages)

print(response)

This basic example sets up a LangGraph pipeline where a user query is processed using GPT-4, returning an AI-generated response.

Expanding to a Multi-Agent System

LangGraph’s real power emerges when multiple agents work together. Consider a system where one agent extracts keywords from a query and another searches for relevant information.

python
import langgraph
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(model_name="gpt-4", temperature=0)

graph = langgraph.Graph()

# Define keyword extraction node
def extract_keywords(messages):
    response = llm(messages + [HumanMessage(content="Extract keywords.")])
    return response

graph.add_node("extract_keywords", extract_keywords)

# Define search node
def search_information(messages):
    query = messages[-1].content
    return AIMessage(content=f"Searching for: {query}")

graph.add_node("search", search_information)

# Connect nodes
graph.add_edge("extract_keywords", "search")

graph.set_entry_point("extract_keywords")
graph.set_exit_point("search")

messages = [HumanMessage(content="Tell me about AI advancements in 2024.")]
response = graph.run(messages)

print(response)

This system first extracts keywords from the user query and then uses those keywords to simulate a search. It showcases how LangGraph enables multi-step AI workflows with modular logic.

Why LangGraph?

LangGraph offers significant advantages over traditional procedural execution:

Flexibility: Developers can easily modify workflows without rewriting large portions of code. State Management: LangGraph preserves context, making it ideal for multi-turn interactions. Debugging & Visualization: The graph structure allows developers to see execution flows and debug processes effectively.

Conclusion

LangGraph is a powerful tool for developers looking to build sophisticated AI workflows. Whether constructing structured automation or autonomous multi-agent systems, LangGraph provides a flexible, scalable, and stateful solution. As AI-driven applications become more complex, tools like LangGraph will be crucial in bridging the gap between structured logic and dynamic decision-making.

If you're building AI-powered applications and need a structured yet adaptable approach, LangGraph is worth exploring. Experiment with the examples above and start creating intelligent, stateful workflows today.