Entity Resolution Python Packages

recordlinkage

A powerful and modular toolkit for record linkage and duplicate detection in Python

4.6M 1K 153

splink

Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends

718K 2K 234

dedupe

:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

99K 4K 569

csvmatch

🔎 Finds fuzzy matches between CSV files

8K 191 21

anonlink

Python implementation of anonymous linkage using cryptographic linkage keys

7K 74 8

goldenmatch

🟡 Golden Suite — polyglot data-quality + entity-resolution toolkit. GoldenCheck profiles → GoldenFlow standardizes → GoldenMatch dedupes → GoldenPipe orchestrates. Zero-config defaults, 97% F1, MCP server per package + one master, multi-arch container images, drop-in Airflow DAGs.

5K 36 5

floof

Fuzzymatching made easy

5K 5 0

zingg

Scalable identity resolution, entity resolution, data mastering and deduplication using ML

4K 1K 165

pvrhino

On-device Speech-to-Intent engine powered by deep learning

4K 700 95

tablassert

Extract knowledge assertions from tabular data into NCATS Translator-compliant KGX NDJSON — declaratively, with entity resolution and quality control built in.

2K 5 0

etherealogic-aetheriaforge

Databricks-native intelligent data transformation engine — coherence-scored Bronze/Silver/Gold with entity resolution and temporal reconciliation in a single deployable product.

2K 1 0

pyjedai

An open-source library that leverages Python’s data science ecosystem to build powerful end-to-end Entity Resolution workflows.

2K 93 13

deduplipy

Python package for deduplication/entity resolution using active learning

2K 82 8

ads-and

NAND-based author name disambiguation for SAO/NASA ADS publication metadata

2K 1 0

pvrhinodemo

On-device Speech-to-Intent engine powered by deep learning

2K 700 95

easylink

A tool that allows users to build and run highly configurable record linkage/entity resolution pipelines.

1K 11 0

strwythura

Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. This produces a Streamlit app, with MLOps instrumentation.

1K 223 25