GoldenMatch overview

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The healing loop

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Install

pip install goldenmatch

npm install goldenmatch

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: eight 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 (78 tools), A2A agent, and a localhost web workbench.

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Benchmarks

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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")