4GitHub Trending (AI/LLM filtered)·
Unstract: LLM-driven unstructured data extraction framework for API and ETL workflows
Unstract is an open-source Python framework by Zipstack that uses LLMs to extract structured data from unstructured documents, targeting API deployment and ETL pipeline use cases. The project has accumulated 7,087 GitHub stars with 61 added in a single day, indicating growing practitioner interest. It sits in the document-intelligence and data-pipeline tooling space.
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Hyper-Extract: LLM-powered extraction of graphs, hypergraphs, and spatio-temporal structures from text
Hyper-Extract is a Python library that uses LLMs to transform unstructured text into structured knowledge representations including graphs, hypergraphs, and spatio-temporal extractions via a single command interface. The project is trending on GitHub with 1,723 stars and 124 new stars today. It targets a practical gap in the LLM tooling ecosystem for structured knowledge extraction beyond simple key-value or flat-schema outputs.
MinerU: Document-to-LLM-Ready Markdown/JSON Conversion Tool
MinerU is an open-source Python tool by OpenDataLab that converts complex documents (PDFs, Office files) into structured markdown or JSON formats optimized for LLM and agentic workflows. The repository has accumulated 65,610 GitHub stars with 180 new stars today, indicating sustained community traction. It targets a common preprocessing bottleneck in RAG and agent pipelines.
STAGE pipeline generates source-grounded training data for text-to-JSON extraction
Researchers introduce STAGE (Spreadsheet-grounded Text-to-JSON Artifact GEneration), a data generation pipeline that uses LLMs to synthesize training data for structured extraction from long unstructured documents, validating outputs against underlying spreadsheets. Evaluated on STAGE-Eval, an 851-example benchmark, the pipeline substantially improves Qwen3-4B performance, raising exact match from 31.37% to 74.27% and value accuracy from 45.46% to 90.69%. The work targets a practical bottleneck in enterprise document processing: reliably converting financial filings and clinical records into machine-readable JSON.
Maxun: open-source no-code web scraping and AI data extraction platform gains traction on GitHub
Maxun is an open-source TypeScript platform for no-code web scraping, crawling, and AI-assisted data extraction that converts websites into structured APIs. The project has accumulated 16,135 GitHub stars with 88 new stars today, indicating active community interest. It is relevant to the AI/ML corpus as a data pipeline and extraction tooling layer for LLM workflows.
UltraX: Adaptive programmatic framework for large-scale LLM pre-training data refinement
UltraX is a function-calling data refinement framework for large-scale LLM pre-training corpora that extends prior rule-based and LLM-based approaches by introducing insertion alongside deletion and modification for fine-grained instance-level editing. The system builds a program-supervision pipeline using dataset-adaptive prompt optimization, Line Alignment Mapping, and Dynamic Context Replacement to convert raw text pairs into structured editing programs. Experiments show UltraX achieves the highest average performance across evaluated corpora and matches or surpasses baselines with fewer training tokens, suggesting improved data efficiency. The work addresses the diminishing returns of data scaling by focusing on data quality rather than quantity.
Crawl4AI: Open-Source LLM-Friendly Web Crawler & Scraper
Crawl4AI is an open-source Python library designed to make web crawling and scraping compatible with LLM pipelines. The project has accumulated over 66,500 GitHub stars with strong daily momentum (+216 today), indicating significant community adoption. It targets the data ingestion layer for AI agents and RAG systems that require structured web content.
OpenDCAI/DataFlow: LLM-based data preparation pipeline library gains traction on GitHub
DataFlow is an open-source Python library from OpenDCAI that provides LLM-based operators and pipelines for data preparation tasks. The repository has accumulated 6,971 stars with 139 added in a single day, indicating growing community interest. It targets the data curation and preprocessing workflow that underpins LLM training and fine-tuning pipelines.
LLM-powered automated extraction of 3.2M data points from 76,000 energy system studies
Researchers demonstrate automated extraction of quantitative techno-economic data from 76,000 energy system studies published since 2010, producing a FAIR database of 3.2 million structured data points and 20 million metadata entries. The system enables meta-analysis of the energy systems literature at scale, revealing where academic modeling assumptions diverge from empirical data. The work is a large-scale applied NLP/information-extraction deployment in a high-stakes scientific domain, with the resulting database made publicly accessible via an interactive dashboard.