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GoldenMatch is the headline package of the Golden Suite. It finds duplicate records in seconds with no rules to write and no model to train. It combines fuzzy matching, probabilistic Fellegi-Sunter EM, LLM scoring, privacy-preserving record linkage, golden-record synthesis, and an introspective auto-configuration controller that beats hand-tuned baselines on standard benchmarks.

Quickstart

Dedupe, match, and write golden records.

Auto-config

How zero-config converges on a defensible config.

Backends and scale

Polars, DuckDB, chunked, and Ray.

CLI reference

Every command and the key flags.

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Install

pip install goldenmatch
Common extras: 
pip install goldenmatch[embeddings]   # sentence-transformers, FAISS
pip install goldenmatch[llm]          # Claude / OpenAI borderline scoring
pip install goldenmatch[duckdb]       # out-of-core backend
pip install goldenmatch[ray]          # distributed backend (50M+ rows)
pip install goldenmatch[postgres]     # Postgres sync
pip install goldenmatch[quality]      # GoldenCheck integration
pip install goldenmatch[transform]    # GoldenFlow integration
pip install goldenmatch[web]          # localhost browser workbench
pip install goldenmatch[mcp]          # MCP server for Claude Desktop

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Key features

  • 12+ scoring methods: exact, Jaro-Winkler, Levenshtein, token-sort, soundex, ensemble, embedding, record-embedding, dice, jaccard, surname IDF-weighted, and alias-aware.
  • Probabilistic matching: Fellegi-Sunter EM-trained m/u probabilities with automatic threshold estimation.
  • LLM scorer: scores borderline pairs with budget caps and graceful degradation.
  • 8+ blocking strategies: static, adaptive, sorted-neighborhood, multi-pass, ANN, canopy, and learned.
  • Golden records: five merge strategies with field-level provenance and quality-weighted survivorship.
  • PPRL: privacy-preserving record linkage across organizations (F1 0.924 on FEBRL4).
  • Learning Memory: persists steward corrections, unmerges, and LLM votes across runs.
  • Interactive TUI, REST API, MCP server (54 tools), A2A agent (31 skills), and a localhost web workbench.

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Benchmarks

Zero-config accuracy, quoted from the package README:
DatasetF1Note
DBLP-ACM (bibliographic)0.964Hand-tuned ceiling 0.918
Febrl3 (PII)0.944Zero-config
NCVR (voter records)0.972Zero-config
Febrl4 (PII, PPRL)0.924Bloom-filter PPRL
DQBench ER composite91.04No LLM (v1.12.0)

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A minimal example

import goldenmatch as gm

result = gm.dedupe("customers.csv", exact=["email"], fuzzy={"name": 0.85, "zip": 0.95})
result.golden.write_csv("deduped.csv")
Leave out exact and fuzzy entirely to let auto-config choose them.