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Bkper Agent

Draft transactions from receipts, invoices, statements, emails, and attachments with AI extraction inside Bkper.

Bkper Agent turns documents and short descriptions into draft transactions inside Bkper. Use it to reduce data entry while keeping each result as a draft until you post it.

This page is a usage guide. The canonical app overview and current configuration reference live at Bkper Agent.

When it runs

Bkper Agent is installed automatically on every Book. You do not start a session or connect a separate assistant. It runs from Book events such as:

  • file uploaded — extracts transactions from receipts, invoices, bills, statements, and CSVs;
  • transaction created — updates transactions from attached files and uses descriptions to suggest dates, amounts, Accounts, hashtags, and properties;
  • transaction posted or updated — learns from corrected drafts and improves future extraction patterns.

This is different from CLI Agent, which is an interactive terminal assistant you start yourself for local files, scripts, tests, and Bkper CLI commands. It is also different from MCP Server, which connects conversational assistants to Bkper through MCP tool calls.

For calculations, reports, reconciliations, or tax worksheets, use deterministic tools and reviewable scripts instead. See AI Fundamentals for Finance.

How documents become drafts

InputWhat Bkper Agent does
Receipt, invoice, or billOne transaction with extracted date, amount, description, and properties
Bank or credit card statementOne transaction per recognized statement row
CSV statementMultiple transactions from rows
File attached to an existing transactionThe existing transaction is updated and keeps the file
Email forwarded to a BookAttachments are parsed and kept with the resulting drafts

Guide the Agent

Good results come from clear context and consistent corrections.

  1. Start from the right Account or Group. The Agent uses the selected context and historical transactions to infer the missing side of the movement.
  2. Use consistent descriptions and hashtags. Reusing tags such as #rent, #software, or #client_acme gives the Agent stronger signals for future account matching.
  3. Correct drafts before posting. Fix the date, amount, Accounts, description, hashtags, and properties. Posted corrections help the Agent improve future extraction.
  4. Prefer repeated corrections over one-off prompts. If a vendor, bank, or statement format repeats, your corrected posted transactions become useful training examples.
  5. Add explicit instructions only for stable patterns. If the same document type is repeatedly parsed wrong, add Account or Group instructions such as which column to use, which total to extract, or which property name to fill. Keep instructions factual and narrow.

For hashtag hygiene and account-matching signals, see Hashtags. For advanced prompt and parser properties, use the Bkper Agent app reference.

Before posting

Before posting an AI-created draft, check:

  • Date — matches the transaction, purchase, service, posting, or payment date you intend to record.
  • Amount — matches the source document or statement row.
  • From and To Accounts — represent the actual resource movement in Bkper’s from-to model.
  • Description and hashtags — are searchable and consistent with your Book.
  • Properties — include useful metadata, but not noisy values you do not report on.
  • Attachment — belongs to the transaction and remains available as evidence.
  • Duplicates — especially after importing statements or forwarding the same document twice.

If the draft is incomplete, keep it as a draft until the missing Account, date, amount, or evidence is resolved.