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This paper introduces persistent discovery context, a lightweight memory layer for data-centric agents that stores prior intent-to-object mappings to enhance retrieval quality across tasks, demonstrating improvements in structured data environments.
This tutorial from freeCodeCamp explains how to design schemas, validate outputs, and handle failures to reliably extract structured data from LLMs, covering techniques like constrained outputs, retry loops, and streaming.
LlamaParse has introduced native agentic spreadsheet extraction using a tuned model and harness for schema-guided extraction, enabling conversion of dense sheets like balance sheets into clean structured fields.
HyperExtract is an LLM-powered framework that converts unstructured documents into structured knowledge graphs, hypergraphs, and lists, simplifying knowledge extraction and management.
This paper introduces a structured approach to extracting persona information for LLM-based digital twins, showing that structured representations improve predictive accuracy over raw transcripts. An automatic pipeline is proposed to adapt structures to different tasks.
LlamaParse now handles revision tracking in documents, providing clean markdown of the final state and structured data for edits, deletions, and comments, addressing issues where parsers misinterpret tracked changes.
AnakinScraper OSS is an open-source web scraping API designed for AI applications, converting websites to clean Markdown or structured JSON for use in RAG pipelines and AI agents.
HERMES is a scalable multi-agent framework for extracting structured knowledge from ultra-long scientific documents in geoscience, achieving high accuracy and sixfold efficiency improvement over manual methods.
LiteParse now supports extracting structured data from PDFs—form fields, checkbox states, annotations, images, vector graphics, and word-level bounding boxes—without a vision model, plus complexity signals to route harder pages to tools like LlamaParse.
NLWeb is an open-source collection of protocols and Python tools that allows developers to add natural-language interfaces to websites without rebuilding the content layer, using existing Schema.org or RSS data.
An article explaining how to consume and use microformats 2 data on personal websites, covering parser selection, fetching considerations, and data storage strategies.
The article argues that in agent-to-agent communication, authentication alone is insufficient for authorization; instead, structured, inspectable claims about intent, identity, and authority are needed, with the human remaining the final authority.
GitHub Issue Fields are now generally available, enabling users to add structured metadata such as priority, effort, and dates to issues across repositories.
Context.dev is a YC-backed API that allows developers and AI agents to scrape, crawl, and extract structured data from any website, with features like markdown, HTML, sitemaps, screenshots, and brand intelligence, aiming to simplify web data integration.
The Marble Curriculum, a comprehensive open-source dataset covering 1,590 primary school concepts with 3,221 connections across 8 subjects, has been released to enable building learning paths and AI-driven educational tools.
A practical solution using AnySearch to enable local AI agents to efficiently query multiple specialized sources (CVEs, SEC filings) and return structured JSON/Markdown, avoiding rate limits and broken SDKs.
Object Aligner is an open-source Python library that deterministically scores two JSON objects by recursively aligning their trees, using Hungarian algorithm for unordered collections and sequence alignment for ordered ones. It introduces referential alignment for graphs/hypergraphs and can be used as a reward function in LLM prompt optimization.
Introduces Evolutionary Feature Engineering (EFE), a framework that uses LLM-based evolution to automatically discover preprocessing transformations for structured data, improving time-series forecasting and tabular prediction accuracy while preserving interpretability.
SchemaRAG is a retrieval-augmented generation framework that dynamically reduces the output schema space for LLM-driven structured information extraction, achieving improved performance and efficiency on healthcare and e-commerce datasets.
Presents a systematic methodology for converting Hindi WordNet into 1.25 million instruction-response pairs to fine-tune a 12B-parameter language model using LoRA, demonstrating improved pedagogical effectiveness for specialized conversational systems in low-resource languages.