reconmatch: offline transaction matching for people who reconcile for a living

reconmatch is a local-first transaction matching engine for accountants, bookkeepers, and controllers. Two CSVs in — books vs bank, invoices vs payments — a scored, auditable match report out. No account, no upload, no network call. Repo: github.com/SybilGambleyyu/reconmatch The unglamorous pain If you close books for a living, you already know the scene: two windows open, a bank CSV on the left, a general-ledger export on the right, and an afternoon disappearing into "which deposit is which invoice." Bank feeds help until they do not. The hard cases are ordinary: One deposit that covers three invoices Two payouts that sum to one sales batch on the books A check number in the memo on one side and a dedicated column on the other "ACH ACME CORP INV 1042" vs "Invoice payment ACME Corp" An orphan the feed never explained Enterprise close tools charge enterprise prices and want the data in their cloud. For a CPA firm or bookkeeper sitting on confidential client ledgers, "just upload the CSV" is often a non-starter. Spreadsheet VLOOKUP falls over on partial payments and batch deposits. What reconmatch does Matching runs in deterministic phases so the same inputs always produce the same proposals: Exact / reference-strong — amount within tolerance, date in window, shared invoice/check/wire token Amount + date — numbers line up even when memos are noise Fuzzy description — token overlap plus sequence similarity (pure Python stdlib) Group 1:N and N:1 — one line equals the sum of several on the other side Every accepted match carries a score and human-readable reasons suitable for a workpaper. Unmatched lines stay unmatched — the tool does not invent a story for them. pip install git+https://github.com/SybilGambleyyu/reconmatch.git reconmatch books.csv bank.csv -o ./march-recon Enter fullscreen mode Exit fullscreen mode Outputs: plain-text report, matches CSV, unmatched CSV, and full JSON. Zero required third-party dependencies. Python 3.10+. Library use from reconmatch import MatchConfig, match_transactions from reconmatch.io import load_transactions_csv from reconmatch.models import Side books = load_transactions_csv("books.csv", side=Side.LEFT) bank = load_transactions_csv("bank.csv", side=Side.RIGHT) result = match_transactions(books, bank) print(result.summary()) Enter fullscreen mode Exit fullscreen mode Honest limits Group matching is bounded (default max five lines, tight date window) for realistic batches Description scoring is lexical, not semantic — clean noisy exports when you can This is a proposal engine. Material unmatched items still need a human It is not a GL, not a bank feed, and not a categorizer Why offline matters Privacy is not a slogan for this audience. Firms have engagement letters, peer review, and client trust. A matching step that never opens a socket is a feature. You can run it on an air-gapped close laptop if that is how the engagement is set up. Try it git clone https://github.com/SybilGambleyyu/reconmatch.git cd reconmatch pip install . reconmatch examples/books.csv examples/bank.csv -o ./out Enter fullscreen mode Exit fullscreen mode If you reconcile for clients and this saves even one painful afternoon a month, that is the product working. Issues with redacted CSV pairs are welcome — the matching rules should grow from real close work. MIT licensed. Built for people who still have to make two lists of money agree.