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This page gets you from install to a deduplicated file in a couple of minutes. For a deeper tour of the headline package, see the GoldenMatch quickstart.

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Install

pip install goldenmatch

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Deduplicate a file

The fastest path is zero-config. GoldenMatch detects column types, assigns scorers, picks a blocking strategy, and writes golden records.
CLI
goldenmatch dedupe customers.csv
The same in Python: 
import goldenmatch as gm

result = gm.dedupe("customers.csv")
print(result)  # DedupeResult(records=5000, clusters=847, match_rate=12.0%)
result.golden.write_csv("deduped.csv")
And in TypeScript: 
import { dedupe } from "goldenmatch";

const rows = [
  { id: 1, name: "John Smith", email: "john@example.com", zip: "12345" },
  { id: 2, name: "Jon Smith",  email: "john@example.com", zip: "12345" },
  { id: 3, name: "Jane Doe",   email: "jane@example.com", zip: "54321" },
];

const result = dedupe(rows, { fuzzy: { name: 0.85 }, blocking: ["zip"], threshold: 0.85 });
console.log(result.stats);

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Run the whole pipeline

To profile, standardize, and deduplicate in one call, use GoldenPipe. It runs GoldenCheck, conditionally routes through GoldenFlow, then deduplicates with GoldenMatch. 
pip install goldenpipe

import goldenpipe as gp

result = gp.run("customers.csv")
print(result.status)     # "success"
print(result.reasoning)  # why each stage ran or was skipped

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Optional extras

GoldenMatch ships a single core install plus opt-in extras: 
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[web]          # localhost browser workbench
pip install goldenmatch[mcp]          # MCP server for Claude Desktop
Run the interactive setup wizard to configure GPU, API keys, and database connections: 
goldenmatch setup
Try it on bundled sample data first with goldenmatch demo.

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Next steps

Auto-config

How zero-config converges on a defensible config.

Backends and scale

Polars, DuckDB, chunked, and Ray.