Structured Extraction Python Packages

pdfmux

PDF extraction that audits its own output — and certifies any other extractor's, catching pages they silently dropped. Verify signed manifests offline: free, MIT, no account. 0.903 on opendataloader-bench, #2 of 8 engines. 7-tool MCP server.

sibila

Extract structured data from local or remote LLM models

verifydoc

Trust layer for document→JSON extraction & AI agents: calibrated per-field confidence + source grounding + accept/review abstention on any OCR/VLM. Ships VerifyDocBench, a novel grounding-conditioned conformal method, and an MCP server.

ocrcontext

Intelligent document OCR and structured extraction. Turn PDFs and images into typed JSON with zero vendor lock-in.

analyxa

Multi-dimensional extraction engine for AI conversations.

confident-extract

Deterministic structured extraction from noisy LLM/OCR output. Zero LLM round-trips, microsecond latency, confidence score on every result. msgspec · Pydantic · dataclasses.

sacor

The Open Document Extraction Engine

summaryxtract

A new package is designed to facilitate structured, reliable extraction of key insights from user-provided texts about cultural topics. It accepts a text input, such as an article or discussion prompt

news-summizr

news-summizr extracts structured summaries from headlines, labeling key points like announcement, products, region for quick insight.