GoldenMatch

v1.2.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 4mo Data Pipelines & ETL

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✓ No known CVEs patched

Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution

+14 more

fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Summary

AI summary

Updates Strategy: exact_then_fuzzy, auto-detected, and production across a mixed release.

Full changelog

Autonomous ER Agent

GoldenMatch is now a discoverable AI agent. Other AI systems find it, invoke it, and get intelligent entity resolution with zero configuration.

What's New

Agent Intelligence -- Pass raw data, get deduplicated records with full reasoning:

from goldenmatch import AgentSession
session = AgentSession()
result = session.deduplicate("customers.csv")
# Strategy: exact_then_fuzzy (auto-detected)
# Clusters: 42, Match rate: 8.4%
# Review queue: 4 borderline pairs held for approval

A2A Protocol -- Discoverable by any A2A-compatible agent framework:

goldenmatch agent-serve --port 8200
# GET http://localhost:8200/.well-known/agent.json

Confidence-Gated Review Queue -- Auto-merge high confidence, hold borderline for review:

0.95: auto-merged
0.75-0.95: review queue
< 0.75: auto-rejected

Storage: memory (default), SQLite (persistent), Postgres (production)

10 New MCP Tools for Claude Desktop / Cursor / Windsurf

Demo: python examples/agent_demo.py

Install

pip install goldenmatch==1.2.0
pip install goldenmatch[agent]  # for A2A server

Full Changelog

https://github.com/benzsevern/goldenmatch/blob/main/CHANGELOG.md

View diff on GitHub

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Earlier breaking changes

v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.