Recall

A library you revisit across projects

Recall brings saved articles, videos, PDFs and personal notes into an ongoing knowledge library. It is a candidate for readers, researchers and self-directed learners who collect useful material across projects but struggle to find it again. The product connects related ideas and supports questions over saved sources.

Shortlist it when your main job is revisiting what you have learned. A recurring reading list, a research topic or material for your next article fits that pattern. If your priority is recording a live call, start with our meeting note-taker comparison. This profile covers Recall at recall.it, distinct from Microsoft Recall and the Recall.ai meeting API.

Inside Recall

What the workflow looks like

1. Save a source. Add reading, a video, a PDF or a personal note to the library. Use the browser extension when collecting material during normal browsing; check support for the source formats you actually use.
2. Organize and connect. Revisit the summary, tags and related ideas. Automatic organization and library chat are paid-plan considerations, so do not assume the free tier includes every part of the advertised workflow.
3. Ask and review. Ask a question across saved material, then open the originals to check the answer. The useful outcome is finding supporting information later, not simply accumulating more AI summaries.

Plans and the upgrade decision

The official pricing page lists these annual-equivalent prices. They are not month-to-month quotes, and paid usage remains subject to fair-use terms. Start free if the summary allowance covers your reading; consider Plus when organization and questions across the library become a recurring need. Max is a separate decision about model choice and bulk work.

Data, privacy and export

Recall's FAQ describes local-first storage with cloud backup and device sync, including Google Cloud infrastructure in Belgium. Local-first should not be read as a promise that every operation stays entirely offline. The vendor says saved content is not used for AI training.

The FAQ documents a zipped Markdown export under Settings. Before moving a large archive, export a small sample and inspect source links, attachments and connections in the files you receive. We have not tested an account or verified export completeness. The important question is whether the exported material remains useful in your next note-taking workflow.

Compare the fit

NotebookLM

Compare a focused research notebook with Recall's ongoing library. Use the same sources and check whether each answer can be traced back to them.

Obsidian

Compare the file control and manual organization you want. Check the extra setup needed for your preferred AI workflow rather than assuming the same features are built in.

Our AI knowledge-base guide compares these broader approaches. No comparative accuracy or retrieval benchmark is claimed here.

Before you commit

• Save one article, one PDF and one video on a subject you already know.
• Compare each summary against its original and ask a question spanning two sources.
• Repeat the search a week later: can you still find the information without remembering its title?
• Export a small set of notes before committing a larger archive or choosing an annual plan.

Review basis and sources

Public-source editorial review. Sources checked September 5, 2026; profile prepared September 6. This is an unpaid listing. We have not performed a hands-on product test or independently verified vendor privacy claims.

• Product overview and official illustration
• Plans and limits
• Storage, export and product identity

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