# texera **Repository Path**: mirrors_apache/texera ## Basic Information - **Project Name**: texera - **Description**: Collaborative Machine-Learning-Centric Data Analytics Using Workflows - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-08-07 - **Last Updated**: 2026-05-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Apache Texera - Human-AI Collaborative Data Science Using Visual Workflows

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Apache Texera (Incubating) is an open-source platform for human-AI collaborative data science using visual workflows.

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Apache Texera (Incubating) is an open-source platform for human-AI collaborative data science using visual workflows. It enables human analysts to construct, execute, and refine data analysis tasks through an intuitive GUI, assisted by AI agents that understand natural-language instructions. Texera is well suited for a wide range of applications, including “AI for Science,” by making advanced AI and data science capabilities accessible to a broader community. It can run on a laptop for local use or be deployed in the cloud to support scalable processing of large datasets. The platform has the following key features: * Natural-language data science through AI agents * Intuitive GUI-based workflows for data science * Real-time collaboration for workflow editing and execution * Runtime debugging and interactive workflow execution * Language-agnostic workflow runtime, native support for Python and Java * Parallel backend engine for scalable big-data processing * Separation of compute and storage for flexible cloud deployment ![texera-screenshot](docs/system-screenshot.png) # Citation Please cite Texera as ``` @article{DBLP:journals/pvldb/WangHNKALLDL24, author = {Zuozhi Wang and Yicong Huang and Shengquan Ni and Avinash Kumar and Sadeem Alsudais and Xiaozhen Liu and Xinyuan Lin and Yunyan Ding and Chen Li}, title = {Texera: {A} System for Collaborative and Interactive Data Analytics Using Workflows}, journal = {Proc. {VLDB} Endow.}, volume = {17}, number = {11}, pages = {3580--3588}, year = {2024}, url = {https://www.vldb.org/pvldb/vol17/p3580-wang.pdf}, timestamp = {Thu, 19 Sep 2024 13:09:37 +0200}, biburl = {https://dblp.org/rec/journals/pvldb/WangHNKALLDL24.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```