tax-doc-classifier

DocJev is an open-source Python library, CLI, and local app for classifying and splitting PDF, DOCX, and PPTX files with user-written natural-language rules. LiteParse extracts page text locally, then the hosted Jev decision engine assigns document categories or identifies boundaries between component documents; optional LlamaParse tiers provide cloud OCR for difficult inputs. Classification returns a category, probabilities, and review flags. Splitting returns ordered categories and contiguous page ranges, with optional export of each segment as a PDF and review reasons for uncertain boundaries. DOCX and PPTX processing requires LibreOffice, and inference is not offline because Jev is a hosted service. The project is distributed under the Apache-2.0 license.

PDF Inspector is an open-source Rust library by Firecrawl for classifying PDF documents and extracting position-aware text without OCR. It samples content streams to classify documents as text-based, scanned, image-based, or mixed, returns confidence scores and per-page OCR-routing information, and detects broken font encodings. Its extractor uses font information and X/Y coordinates to determine reading order, including multi-column and right-to-left layouts, and converts content to Markdown with headings, lists, code blocks, tables, formatting, links, and page breaks. Native Rust and CLI consumers can opt into selective OCR for pages that need it; the Python and Node.js bindings can run the same workflow, while browser WebAssembly provides local parsing without a server round trip. The project also supports CID font decoding and shares a single parsed document between classification and extraction.

opentax-engine is an open-source deterministic US tax calculator designed for AI agents and other applications. It accepts tax facts such as wages, filing status, and dependents, applies an encoded rule corpus, and returns cent-exact results with assumptions, legal citations, and a proof tree showing how each value was derived. If its rules cannot derive an answer, the project is designed to refuse rather than guess. The calculation can be selected for a date with an --as-of option, and the CLI accepts flags or JSON fact files; the videos also identify CLI, browser, and MCP integrations. The engine is implemented in TypeScript with no platform dependencies and can run in a self-contained browser HTML file containing the engine, rule corpus, and verifier. Proofs use canonical JSON, hashing, and a Merkle construction; the verifier independently re-derives the calculation and reports altered proof data, rule-corpus differences, or steps that do not match. The repository describes the project as AGPL-3.0 licensed, with commercial licenses available.

TaxHacker is a self-hosted AI accounting application for freelancers, independent developers, and small businesses. It processes receipt photos, invoice PDFs, bank statements, and other documents with a configurable LLM, extracting dates, merchants, amounts, taxes, product names, and line items into a structured, Excel-like database. It can categorize transactions, split invoice items into separate transactions, and use custom fields with user-defined prompts; supported model backends include OpenAI, Google Gemini, Mistral, and local OpenAI-compatible endpoints such as Ollama, LM Studio, vLLM, and LocalAI. The application detects document currencies and converts transactions using historical exchange rates from the transaction date, including cryptocurrency support, and provides filtering, multi-project organization, import/export, and custom categories. The repository describes the project as being in early development and advises using it at one's own risk.

typesafe-computer-use is an open-source macOS computer-use tool that drives a Mac toward a plain-English goal. It captures the frontmost window, reads text with OCR, collects labelled controls from the macOS accessibility tree, parses dates, and sends a structured list of possible actions to the TypeSafe decision model instead of sending screenshots to a frontier model for every step. The loop chooses actions such as clicking, scrolling, browser navigation, waiting, or pressing keys; a writing model is called only for free-text entry, proposed URLs, and the final answer, while passwords are never typed. It supports dry runs, confidence and step limits, annotated captures, detailed action and probability logs, and offline replay of saved screenshots. The repository targets macOS 14 or newer with Python 3.12 or newer, requires a TypeSafe API key, and is licensed under the MIT License.