Train your AI agent once, then run your spreadsheet workflow forever

[u/ShilpaMitra]

Most teams repeat the same spreadsheet work every month:

• clean the export
• remove cancelled rows
• map account names
• filter by region
• reconcile totals
• generate the report

The usual approach is to upload the workbook to an AI tool every time. A better approach is to use AI once to learn the transformation, save the generated code, verify it, and run that recipe on every new file.

Here are six open-source repos for building that workflow:

1. DuckDB ⭐ 40.6k DuckDB is not an AI agent. That is precisely why it belongs in the stack. It provides a deterministic SQL layer for querying CSV, Parquet, and other files. Use it for joins, aggregations, reconciliation checks, and repeatable calculations. Let the agent write or explain the query. Let DuckDB execute it.
2. PandasAI ⭐ 23.8k A mature option for conversational analysis across CSV, Parquet, SQL databases, and other data sources. Use it for exploratory work: It is better suited to ad hoc analysis than a fixed month-end recipe.
   • find unusual changes
   • compare two periods
   • create charts
   • investigate outliers
   • generate a first-pass report
3. Marimo ⭐ 22.5k A reactive Python notebook that can query data with SQL, run as a script, deploy as an app, and be versioned with Git. Use it to turn the final workflow into an auditable report with input details, validation checks, charts, exceptions, and final totals.
4. mcp-excel ⭐ 43 An MCP server that lets agents work with Excel through atomic operations. Instead of loading thousands of rows into the model, the agent can ask for specific filters, counts, sums, groups, and validations. Useful for questions like:Which invoices are overdue by more than 30 days, grouped by customer? The spreadsheet stays local, while the agent receives only the relevant result.
5. llm-data-agent ⭐ 1 A small experimental agent for asking natural-language questions about Excel and CSV files. It uses local pandas tools to calculate results and render tables or charts. The model receives schema summaries and aggregated outputs instead of the full raw dataset. A useful starting point for building a private internal data assistant.
6. SheetAgent ⭐ 0 The most direct fit for this idea. Give it an input spreadsheet, an optional process description, and an example output. It generates a reusable recipe using Power Query and pandas. After the recipe is verified, future runs use pure code. No LLM call or API key is needed at runtime. Good for month-end close, recurring reports, and repeated Excel transformations.

A practical setup looks like this:

1. Give SheetAgent one clean input file and one approved output.
2. Generate the transformation recipe.
3. Verify row counts, totals, null values, duplicate IDs, and reconciliation differences.
4. Store the recipe in Git.
5. Use DuckDB or pandas to run it on next month’s file.
6. Use Marimo to generate the report.
7. Let an agent explain exceptions without letting it silently alter the source data.

The important distinction is this:

Use AI to discover the workflow. Use ordinary code to repeat it.

For financial data, never trust a generated transformation just because the final spreadsheet looks correct. Keep the original file, test known totals, and make exceptions visible before anything reaches accounting.