Reentry vs Recall: Which AI knowledge app fits your workflow?

The short answer is that Reentry is the stronger choice for turning everything you save into an active workspace. It captures sources directly where you are working, enriches them in place, organizes them across multiple useful views, and gives an agent tools to research, create, and rearrange the work itself.

Recall is the better fit in two important cases: you need a public product across web, Android, and browser extensions today, or scheduled spaced repetition is central to how you study. Recall also summarizes, tags, connects, chats with, and quizzes you on saved material. Reentry does those things too. Those shared capabilities are the baseline, not a reason to choose Recall over Reentry.

Reentry vs Recall at a glance

If you care most about...	Better fit	Why
Capturing a source with the fewest interruptions	Reentry	Paste or drop it onto a canvas, or share it through the iOS capture wrapper. The card enriches in place without redirecting you into a separate detail workflow.
Summaries, automatic labels, related items, and a knowledge graph	Reentry	Both products provide the basics. Reentry carries that organization into Library, Graph, search, canvases, Feed, Tutor, and agent context.
A visual workspace where saved context remains visible and actionable	Reentry	Sources, notes, and groups can become chat context and can be reorganized directly by the agent.
Researched mini-courses, lessons, knowledge checks, and progress	Reentry	Tutor can combine several saved sources with adjacent research and turn them into a structured course automatically.
Scheduled spaced repetition and serious retention practice	Recall	Its quiz system has review schedules, knowledge stages, several question formats, and shared challenges. Reentry does not yet have adaptive spaced repetition.
An agent that can rearrange and create inside the workspace	Reentry	The agent can work with canvases, groups, placements, labels, notes, and connected research tools.
Researching social platforms and audience discussion	Reentry	It can inspect posts, videos, ads, engagement, and comments, then place the findings into the workspace.
Full web, Android, and browser-extension clients today	Recall	Reentry is currently centered on Mac with an iOS capture wrapper.
Automatically generated feed entries and mini-courses	Reentry	Feed and Tutor provide proactive outputs beyond search, chat, and browser resurfacing.

A collection you revisit or a workspace that comes back to you

Recall is best understood as a polished capture, summary, library, chat, and study loop.

You save articles, YouTube videos, podcasts, PDFs, social content, bookmarks, or personal notes. Recall summarizes them, applies smart tags, finds related cards, and adds them to a searchable card grid and knowledge graph. Chat can use the knowledge base, the open web, or both. Quizzes turn saved material into review material.

Reentry includes that capture-to-understanding baseline, then gives the saved material somewhere to work.

The source lands on an infinite canvas alongside authored notes, drawings, groups, shapes, and other saved material. The canvas can be chaotic, deliberate, or both. Library, Graph, search, Feed, Tutor, and chat are additional views over the same items.

That changes the role of AI. Recall mainly helps you consume, retrieve, and review a collection. Reentry can also research a question across web and social platforms, inspect comments, create notes, place sources, build groups, organize a canvas, generate a course, schedule local research, and bring the result into view.

Capture and source understanding

Recall supports a broad set of sources. Its current documentation lists YouTube, TikTok, Vimeo, podcasts, webpages, PDFs, Google Docs and Slides, X, Reddit, LinkedIn, Instagram, bookmarks, and Markdown notes.

Saved content can receive concise or detailed AI summaries, timestamps where available, automatic tags, and related connections. Recall also stores personal notes in a block editor, so it is not limited to content created elsewhere.

Reentry makes external sources useful at capture time through a shorter flow. Paste or drop a webpage, PDF, image, video, YouTube link, Reddit thread, X post, TikTok, Instagram Reel, or social carousel onto the canvas and its native card appears immediately while enrichment continues in place. The iOS capture wrapper can send material to the Mac canvas without requiring the user to reopen it inside a separate card-detail screen.

Reentry extracts page metadata and full text, caches native media, preserves full video transcripts, analyzes the images in social carousels, reads visible engagement metrics, summarizes YouTube and Reddit audience discussion, generates summaries and labels, and indexes the result. The enrichment is not the destination. It becomes reusable context for the canvas, Library, Graph, Feed, Tutor, agent, API, and MCP server.

The summary is only one view of the item. Reentry keeps the full extracted source and transcript behind it, so a concise card does not erase the details the agent, Tutor, search, or user may need later. Recall currently distributes capture across more public clients and extensions. Reentry requires fewer steps once the source enters the workspace and preserves more of it as operational context.

Organization and visual thinking

Recall organizes knowledge through a card grid, tags, automatic connections, search, and a global graph. This is useful when the desired result is a conventional personal knowledge library with inferred relationships.

Reentry's canvas is not a graph visualization. It is an editable spatial surface.

Users can overlap and resize cards, write Markdown notes, draw, group material, add shapes and arrows, and place the same Library item on more than one canvas without duplicating its identity. Generated labels, related context, local search, a Library, a 3D graph, and non-destructive tidy views provide automatic organization without reducing everything to one card grid.

Automatic tags, connections, and a graph are therefore not a Recall advantage. Both products have them. Reentry is the stronger fit when that organization must also remain editable, visual, and available to an agent that can act on it.

Chat, web research, and agent actions

Recall can chat with the whole knowledge base, the web, or both. For a targeted conversation, users explicitly select cards or add notes, folders, and tags as context before moving into chat. Recall offers API and MCP access. Reentry also exposes its knowledge through an API and local MCP server, so external AI access is shared rather than a Recall advantage.

In Reentry, selecting the material is already the context step. Drag across the cards and notes on a canvas, then open chat. The agent is integrated with the application's own mutation and research tools. It can:

• search local items and full source text;
• search Reddit, YouTube, X, Instagram, TikTok, Hacker News, and Meta Ads;
• inspect result sets, full posts, video context, and comments;
• create Markdown notes, canvases, folders, groups, and labels;
• move or duplicate item placements without duplicating Library identity;
• focus the created or discovered result on the relevant canvas;
• discover packaged and user-provided skills.

Recall lets AI answer from the library. Reentry gives the agent a working environment it can change.

Recall's MCP access and bulk actions extend what can be done around the library, but its in-app assistant is not a canvas operator. Reentry's built-in agent can research beyond the collection and perform visible work inside it, which makes it the more capable in-app agent workflow.

Resurfacing and learning

Recall has a strong answer to deliberate memorization.

Its Augmented Browsing feature can highlight concepts related to saved material while you browse and lead back to exact mentions. It is currently a browser-extension feature, off by default, and documented as Beta. Its quiz system can generate multiple question formats, schedule reviews through spaced repetition, track knowledge stages, and share challenges.

For a student whose primary requirement is scheduled review and long-term retention, Recall has the more complete spaced-repetition system today. Reentry Tutor already creates multi-source courses with lessons, knowledge checks, explanations, and progress tracking, but adaptive spaced repetition is still planned.

Reentry uses different proactive surfaces:

• Feed automatically curates short entries from current and forgotten workspace material.
• Tutor can generate mini-courses from selected or automatically chosen topics, combining saved sources with researched context.
• The agent can introduce an older source into a current question even when the user did not name it.
• Related context and search reconnect items with notes, labels, groups, and other sources.

Recall is better at scheduling material you deliberately want to memorize. Reentry is broader: it makes old context participate in current work, generates new learning material, and can surface an angle the user did not know to request.

Local data and platform availability

Recall describes its architecture as local-first, with primary data and queries on the device plus cloud backup and sync. Its public feature-request tracker still contains active requests for full offline reading, search, and editing, so “local-first” should not be interpreted as proof that every workflow works offline.

Recall is available through the web, iOS, Android, Chrome, and Firefox. It is the clear choice when cross-platform access matters now.

Reentry's core canvases, notes, Library, search data, chats, jobs, full transcripts, enrichments, and cached assets are stored locally on the Mac. Its iOS wrapper sends captured material into the same enrichment and canvas workflow.

Choose Recall if...

• Spaced repetition and serious retention practice are primary goals.
• You need a full Android or web client plus browser extensions today.
• You prefer a conventional card grid and global graph over an editable canvas.
• You need a publicly available cross-platform product rather than joining a private beta.

Choose Reentry if...

• You want to paste, drop, or share something and let it enrich where you are already working.
• You want to save without filing, tagging, naming, or deciding why the source matters yet.
• You want useful material to return even when you cannot remember what to search for.
• Your material makes more sense when arranged visually.
• You want sources, personal notes, drawings, and research outputs on one editable canvas.
• You want an agent that can create, place, group, and reorganize material, not only answer from it.
• Your workflows include platform research, comments, competitor ads, and adjacent sources.
• You want saved material to return through an automatic feed and become researched courses with progress and knowledge checks.
• You want automatic labels, related context, search, Library, Graph, and canvas organization to work as one system.
• You want a local-first Mac workspace with an iOS capture wrapper.

Which one should you use?

Recall is a capable AI knowledge library, but much of its value still resolves into a summary, a chat, a graph, or a quiz. Reentry includes those foundations and gives saved knowledge somewhere to act. For people who want their collection to research, organize, teach, and move work forward, Reentry is the more ambitious and more useful choice.

Related comparisons

• Reentry vs Capacities: automatic memory or object-based notes?
• Reentry vs Obsidian: local toolkit or integrated workspace?
• Reentry vs Heptabase: which visual workspace fits?

Sources and current product references

• Recall pricing and current feature summary
• Recall supported content
• Recall chat overview
• Recall quiz and spaced repetition
• Recall tagging
• Recall knowledge graph
• Recall Augmented Browsing
• Recall privacy and local-first FAQ
• How Reentry approaches active memory