Reads page by page under a budget
Tools force page reads under a character budget. A large lease or report does not flood the first turn with a fake full-coverage summary.
PageWise · Local document agent · macOS first
See what the agent read, page by page
A local PDF workbench: the model reads with page-level tools and budgets, while read-page markers, follow-along preview, and clickable citations leave a trail you can revisit.
Local PDF workbench
Ask about a page — see what was read
Open a PDF, ask a question, and watch the agent read specific pages under a budget. Citations and follow-along preview keep the source beside the answer.
Open or continue
Start with a document on disk
Open a PDF or drag it in. Recent files stay local so you can continue the same thread.
What you get in the workbench
What you get in the workbench
Text layer or vision — same tools
Use the PDF text layer when it exists; use vision when the page is image-heavy. The agent calls the same page tools either way.
The read trail becomes controls
Clickable page citations, pages-read chips, follow-along preview, and ask-about-this on a selection let you jump the preview to what the agent just used.
Workbench around the page
Preview with thumbnails, zoom, in-doc search, and outline or bookmark tree. One chat thread per document, saved locally, with Markdown export.
How a session runs
How a session runs
Open a document
Pick or drop a PDF or image. PageWise loads the preview and indexes text locally — path permission stays on your machine.
Ask, or select then ask
Type a question, or highlight text and ask about the selection. One thread stays with that document.
Watch the tool loop
The agent plans outline, search, and page reads inside step and read budgets, then writes a grounded answer on the last step — a search miss is not “does not exist.”
Follow and verify
Jump via page citations or follow-along preview so the preview tracks what was read. Decide what to trust with the source still in view.
Local by design
Local by design
Documents stay on disk
PageWise does not upload your PDFs or images to its own servers. Processing stays on your machine.
Only what the model needs leaves
Extracted text — and optional vision payloads you enable — go to the LLM you configure. Nothing else leaves without that setup.
Keys in the OS keychain
Credentials use macOS Keychain, Windows Credential Manager, or Linux Secret Service. Assistant and Scan (vision) can use different models, including Ollama.
FAQ
Can it run fully local with Ollama?
Yes. Point Assistant (and Scan if needed) at an OpenAI-compatible Ollama endpoint. Files stay on disk; only the payloads you enable go to that local model.
When should I enable OpenRouter web search?
Only when a question needs outside verification. It is opt-in per call and off by default, so ordinary document Q&A is not polluted by web results.
Do my files leave my computer?
No upload to a PageWise server. Only extracted text — and optional vision payloads — go to the model endpoint you configure.
How do scanned pages work?
When a text layer is thin or missing, indexing can use a vision / multimodal Scan model. Prefer a vision-capable model for image-heavy PDFs.
What platforms does it run on?
macOS is the release focus — download a DMG from GitHub Releases. The Tauri 2 app also builds for Windows and Linux; the project is MIT-licensed.