Intelligent Spark Agents - A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows

Aug 4, 2026

This paper presents a Spark-based modular LangGraph framework to strengthen machine learning workflows through scalability, visualization, and intelligent process optimization. The core innovation introduces Agent AI, combining Spark distributed computing with LangGraph workflow orchestration to automate data preprocessing, feature engineering, and model evaluation. Agents interact dynamically with data via Spark SQL and DataFrame agents, executing graph-structured tasks with real-time feedback for reliable decisions in distributed environments. The framework also leverages large language models through LangChain to enhance unstructured-data interaction and data-driven analysis, supported by experimental results showing improved efficiency, scalability, and accuracy.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

Motivation from big data and distributed computing

Modular workflow tool for easier ML development

Integration of Spark, agents, LangChain, and LangGraph

arXiv :2412 .0 1490v4 [ cs .AI] 6 Dec 2024 Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows Jialin Wang 1 and Zhihua Duan2 1 Executive Vice President,Ferret Relationship Intelligence Burlingame, CA 94010, USA [jialinwangspace@gmail.com](jialinwangspace@gmail.com)[ ](jialinwangspace@gmail.com)[https://www.linkedin.com/in/starspacenlp/](https://www.linkedin.com/in/starspacenlp/) 2 Intelligent Cloud Network Monitoring Department China Telecom Shanghai Company,Shanghai, China [duanzh.sh@chinatelecom.cn](duanzh.sh@chinatelecom.cn) Abstract. This paper presents a Spark-based modular LangGraph framework, designed to enhance machine learning workflows through scalability, visualization, and intelligent process optimization. At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark’s distributed computing capabilities and integrates with LangGraph for workflow orchestration. Agent AI facilitates the automation of data preprocessing, feature engineering, and model evaluation while dynamically interacting with data through Spark SQL and DataFrame agents. Through LangGraph’s graphstructured workflows, the agents execute complex tasks, adapt to new inputs, and provide real-time feedback, ensuring seamless decision-making and execution in distributed environments. This system simplifies machine learning processes by allowing users to visually design workflows, which are then converted into Spark-compatible code for high-performance execution. The framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data and enabling advanced data analysis. Experimental evaluations demonstrate significant improvements in process efficiency and scalability, as well as accurate data-driven decision-making in diverse application scenarios. This paper emphasizes the integration of Spark with intelligent agents and graph-based workflows to redefine the development and execution of machine learning tasks in big data environments, paving the way for scalable and user-friendly AI solutions. Keywords: Large Language Model · Agent · LangChain · LangGraph ChatGPT · ERNIE-4 · GLM-4 · Big Data · Machine learning · Apache Spark · Data analysis. 1 Introduction The development of information technology brings convenience to life and fastgrowing data. With the maturity of big data analysis technology represented by machine learning, big data has tremendous effect on social and economic life and provided a lot of help for business decision-making. For example, in the e-commerce industry, Taobao recommends suitable goods professionally to users after analyzing from large amounts of transaction data; in the advertising industry, online advertising predicts users’ preferences by tracking users’ clicks to improve users’ experience. However, the traditional business relational data management system has been unable to deal with the characteristics of big data including large capacity, diversity, and high-dimension . [1] In order to solve the problem of big data analysis, distributed computing has been widely used, among which the Apache Hadoop [2] is one of the widely used distributed systems in recent years. Hadoop adopts MapReduce as a rigorous computing framework. The emergence of Hadoop has promoted the popularity of large-scale data processing platforms. Spark [3], a big data architecture developed by AMPLab of the University of Berkeley, is also widely used. Spark integrates batch analysis, flow analysis, SQL processing, graph analysis, and machine learning applications. Compared with Hadoop, Spark is fast, flexible, and fault-tolerant, which is the ideal choice to run machine learning analysis programs. However, Spark is a tool for developers, which requires analysts to have certain computer skills and spend a lot of time creating, deploying and maintaining system

FAQs

What is the main contribution of the proposed framework?

It provides a Spark-based modular LangGraph framework centered on Agent AI to orchestrate scalable, visualizable machine learning workflows with intelligent optimization.

How do the agents support the machine learning workflow execution?

Agents automate preprocessing, feature engineering, and model evaluation, interact with data through Spark SQL and DataFrame agents, and run graph-structured tasks with real-time feedback.

How are large language models used in the framework?

Large language models are integrated via the LangChain ecosystem to improve interaction with unstructured data and enable advanced data analysis within the workflow.

Aug 4, 2026

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