---
title: LangGraph Framework Enables Construction of ReAct Agents
url: https://www.dataloco.com/en/langgraph-framework-enables-construction-of-react-agents
published: 2026-09-15T06:12:57+00:00
language: en
section: Developers
source: https://machinelearningmastery.com/building-react-agents-with-langgraph-a-beginners-guide/
organizations: LangGraph, LangChain, OpenAI
publisher: Dataloco
---

# LangGraph Framework Enables Construction of ReAct Agents

LangGraph is a framework built on top of LangChain that allows for the definition of agent workflows as graphs. These graphs consist of nodes, which represent steps in a process, and edges, which define the paths and flow of information between those steps. This structure supports complex flows, including conditional branching and loops.

One common application of this framework is the implementation of the ReAct pattern, which stands for Reasoning and Acting. This pattern creates a cycle where an agent thinks through a problem and takes actions to solve it. The cycle repeats until the agent has gathered sufficient information to answer a user question.

Agents built with this pattern require a state object that flows through the graph nodes. This object acts as shared memory to accumulate information. Nodes read the current state and add their own contributions before passing the state forward. In a ReAct agent, tools are functions that perform actions, such as calling APIs, querying databases, or searching the web.

Workflows can be implemented using hardcoded logic or driven by a large language model. A hardcoded version uses fixed rules to decide the next action, while an LLM driven agent uses a model such as GPT-4o from OpenAI to serve as a reasoning engine. The LLM can dynamically decide what information to gather and how to process queries, making the agent more flexible than a rigid script.

Because LangGraph separates the workflow structure from the intelligence driving it, developers can swap components while keeping the graph topology intact. This allows for the extension of agents through the addition of calculators, real web search tools, or the creation of multi agent systems where several ReAct agents collaborate.