Mnemo: An Open-Source Learning Tool That Combines Notes, Flashcards, and Mind Maps

A Local-First, Open-Source Learning App

The market for learning tools is crowded, but genuinely integrated solutions that combine notes, flashcards, and mind maps in a single app — while committing to local storage and open-source principles — are rare. Mnemo, a project years in the making, recently released version 0.8.1 beta, staking its claim in a space dominated by tools like Notion, Anki, and Obsidian.

Mnemo's defining characteristic is its "local-first" approach: all data is stored on the user's own machine, with no account system and no tracking whatsoever. The only network activity the app performs is checking for updates — and even that can be disabled manually. For users who have long worried about their learning data being locked into cloud services or commercialized, this design offers a clear privacy guarantee.

Three Core Modules

Block Editor and Notes

Mnemo's note-taking system is built on a block editor architecture, supporting math formulas, code blocks, tables, images, and multi-column layouts, with direct PDF export. This "building block" editing experience aligns with the direction modern mainstream note-taking tools have taken, balancing structured organization with layout flexibility.

Spaced Repetition Flashcards with FSRS-6 Scheduling

The flashcard module is Mnemo's centerpiece. It uses the FSRS-6 scheduling algorithm, one of the more advanced spaced repetition engines in the memory science community today. Compared to the traditional SM-2 algorithm, it more accurately predicts the forgetting curve and optimizes review timing. The app also supports cloze cards and image occlusion cards — the latter being especially useful for visual memory tasks in fields like medicine, anatomy, and geography.

Notably, Mnemo supports two-way import and export with Anki while preserving learning progress. This means Anki's massive user base can migrate and try Mnemo at low cost without worrying about losing years of accumulated review data — a bridge that few competing tools can offer.

FSRS (Free Spaced Repetition Scheduler) was developed by researcher Jarrett Ye using machine learning methods. Its core breakthrough lies in modeling memory state with two variables — "retrievability" and "stability" — rather than relying solely on answer scores to adjust interval lengths as SM-2 does. SM-2 is the algorithm Anki has used by default for years: simple in logic but insensitive to individual differences, applying the same interval growth factor to every card for every user. FSRS, by contrast, dynamically estimates each card's forgetting rate based on a user's response history, finding a more precise review window between "not yet forgotten" and "already forgotten." FSRS-6 is the sixth iteration of this series, with significantly improved accuracy in predicting forgetting curves compared to earlier versions in benchmark testing. Anki has included FSRS as a built-in optional algorithm since version 23.10, making it the de facto new reference standard in the spaced repetition field.

Infinite Canvas Mind Maps

The third module is an infinite canvas-based mind mapping feature for organizing knowledge structures and conceptual relationships. Placing mind maps alongside notes and flashcards within the same app theoretically enables a closed-loop workflow of "input → organize → memorize."

An Open Approach to Data Portability

Mnemo takes a clear stance on data portability, supporting export to Markdown, PDF, Anki, CSV, PNG, and SVG formats. The developer states plainly that the goal is to ensure "your work is never locked inside the app."

This open approach is consistent with its Apache 2.0 open-source license. The app is built on .NET and React, with source code hosted on GitHub (onemnemo/mnemo). For technical users, open source means not only auditability and the ability to contribute, but also reduced risk of being unable to retrieve data if the project is ever abandoned.

Apache 2.0 is a permissive open-source license that allows users to freely use, modify, and distribute the code — including for commercial purposes — with the only requirement being that the original copyright notice is retained and modifications are noted. Unlike the GPL family of licenses, Apache 2.0 does not require derivative works to be released under the same license (i.e., it has no "copyleft" requirement), which reduces legal friction for commercial integration and secondary development. For end users, an Apache 2.0-licensed project means that even if the original developer stops maintaining it, the community can legally take over the code and continue releasing new versions — user data won't be stranded if the project shuts down. This aligns closely with Mnemo's overall emphasis on data sovereignty.

Platform Support and Beta-Stage Realities

Mnemo runs on Windows, macOS (Apple Silicon), and Linux. The developer is candid about the maturity level across platforms: the Windows version has seen months of daily use and is relatively stable, while macOS and Linux builds have had less testing time and users should expect some rough edges.

One detail worth noting: the Windows build has not yet been code-signed, so SmartScreen will display a security warning on first launch. The macOS version, however, has been notarized. These are common hurdles for independent open-source projects at the distribution stage — they don't affect the software's usability, but they do add friction for non-technical users.

Roadmap and Positioning

According to the developer's published plans, the next priorities are optional sync functionality (supporting both official hosting and self-hosting) and a mobile app. Sync and mobile are precisely the most obvious gaps in the current version — reviewing flashcards on a phone is nearly a necessity for most learners, and the absence of this feature limits Mnemo's appeal to mainstream users.

Overall, Mnemo's value proposition is clear: a local, open-source, tracking-free desktop app that integrates a learning workflow typically spread across multiple tools. It delivers on privacy, data openness, and Anki compatibility, making it a good fit for technically minded learners who prioritize data sovereignty. But as a beta product, its cross-platform stability issues and the absence of sync and mobile support mean it's currently better suited for early adopters than as a full replacement for existing workflows. For anyone following the open-source learning tool ecosystem, this is a project worth watching.