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Machine Learning in Linux: Reor – AI note-taking app is a local-first desktop knowledge-management application, not a Linux machine-learning framework. Reor uses local language models, embeddings, semantic search, and retrieval-augmented generation (RAG) to work with Markdown notes. As of August 18, 2026, GitHub marks Reor’s repository archived, so the app is interesting for privacy-focused experimentation but carries significant maintenance and compatibility risk.
Reor is designed for people who want an Obsidian-like Markdown workspace with local AI features. Reor can connect related notes, search by meaning rather than only exact words, answer questions over a note collection, and assist with drafting, summarizing, and rewriting. The local workflow is primarily built around Ollama, although the project also documents OpenAI-compatible APIs.
Key takeaways
- Reor is a Linux desktop note-taking and personal knowledge-management app built around local AI, Markdown notes, embeddings, semantic search, and RAG.
- GitHub marked the Reor repository archived and read-only on March 7, 2026, making long-term compatibility with current Linux systems, Ollama releases, and models uncertain.
- Reor can support an offline workflow when local models are configured, but using a remote OpenAI-compatible API can send prompts or note content away from the computer.
- The latest visible GitHub release is labeled v-0.2.32 and lists a BlockNote editor, image and video support, hybrid search, and a similar-notes sidebar; the unusual release chronology should be checked before installation.
- Reor is best treated as a local-RAG experiment or niche privacy tool, not the safest default for a new long-term knowledge base that requires active support, synchronization, or a mature plugin ecosystem.
What is Reor?
Reor is a desktop note-taking and personal knowledge-management application that stores notes locally and adds AI-assisted retrieval and writing features. The project describes support for macOS, Linux, and Windows, with an Obsidian-like Markdown workflow and a user-selected directory for the note collection. Its source repository identifies Ollama, Transformers.js, and LanceDB among the technologies used by the application. The project’s stated purpose and architecture are documented in the Reor GitHub repository.
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The distinction matters. Reor does not appear to train a new model on your notes. Reor is better understood as a local AI interface over a personal Markdown corpus: the human writes and maintains the source material, while the retrieval system finds relevant passages and the language model summarizes or transforms them.
Is Reor still worth using in 2026?
Reor is worth trying if local semantic search and private, desktop-based AI matter more to you than project longevity. Reor is a risky choice for a new primary knowledge base if you need active maintenance, guaranteed security updates, official support, reliable synchronization, or predictable compatibility with current model runtimes.
Best for: technically capable Linux users experimenting with local RAG over Markdown notes.
Main strength: local semantic organization, related-note discovery, and question answering over a personal corpus.
Main weakness: the GitHub repository is archived and read-only, creating maintenance and model-compatibility risk.
Use Reor if: you can troubleshoot local models, keep backups, and accept that an archived application may stop working with newer dependencies.
Approach cautiously if: your notes are mission-critical or your workflow depends on mobile apps, cloud collaboration, plugins, publishing, or vendor support.
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GitHub shows the repository as archived on March 7, 2026. The Reor releases page currently displays v-0.2.32 as the latest visible release, with features including a BlockNote-based editor, image and video support, hybrid search, and a restored similar-notes sidebar. The archive date and displayed release chronology are unusual, so inspect the release assets, commit history, and repository status before installing rather than assuming that Reor is actively maintained.
Reor is licensed under AGPL-3.0, as shown by the project’s license file. The public license and source availability make inspection or forking possible, but those properties do not provide a support team, a security-update guarantee, or compatibility with future Linux and Ollama versions.
How does Reor’s machine-learning pipeline work?
Reor’s machine-learning pipeline converts note text into searchable representations, retrieves relevant passages, and gives those passages to a language model for generation. The pipeline combines embeddings, vector similarity search, retrieval, and language generation rather than relying on a model to remember your entire note collection.
- Write or import notes. You create Markdown notes or place Markdown files in Reor’s selected notes directory.
- Split the content. Reor divides note content into smaller chunks so individual passages can be indexed and retrieved.
- Create embeddings. An embedding model converts each chunk into a numerical vector. Similar meanings tend to produce vectors that are closer together, although similarity is not the same as factual correctness.
- Store the vectors. Reor uses a local vector database as part of its documented technology stack, allowing the application to search the indexed representations.
- Retrieve relevant content. A question or writing context is compared with the indexed vectors. Reor selects note chunks that appear relevant.
- Generate a response. In question-and-answer mode, retrieved context is supplied to a language model, which formulates an answer based on that context.
- Support the editor. In editor mode, related notes can be surfaced while you write, helping you discover connections without manually opening every file.
The practical model is therefore human author + local retrieval system + language-model generator. Reor can help navigate and summarize a corpus, but Reor cannot guarantee that retrieved notes are complete, current, consistent, or correctly interpreted.
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Markdown notes
↓
text chunks → embedding model → local vectors/index
↓ ↑
question or editor context → similarity retrieval
↓
relevant note passages
↓
local LLM responseSemantic search can find a note about “sleep quality” when a query uses “rest and recovery,” but the result depends on the embedding model and the wording and quality of the notes. Hybrid search in the latest visible release combines semantic-style retrieval with another search approach, but hybrid search still does not establish that a retrieved passage is true.
Which AI features does Reor provide?
Reor’s documented feature set centers on using AI inside a personal Markdown knowledge base rather than providing a general chat assistant detached from your notes.
| Feature | What it does | Important qualification |
|---|---|---|
| Related-note suggestions | Surfaces notes that appear semantically connected to the current content. | Results depend on indexing, note quality, and the embedding model. |
| Semantic search | Searches by meaning instead of requiring an exact keyword match. | Semantic similarity can miss relevant notes or connect unrelated passages. |
| Hybrid search | Combines search approaches in the latest visible release. | Listed for v-0.2.32; verify the installed build and release assets. |
| Questions over notes | Uses RAG to retrieve note context before generating an answer. | Generated answers can be incomplete, outdated, or unsupported. |
| Editor assistance | Supports drafting, summarizing, and rewriting within the editor. | Output remains generated text that requires review. |
| Note tools | Later releases describe search, create, edit, delete, and list operations. | Check the exact build before relying on tool-enabled workflows. |
| Flashcards | Project descriptions and earlier coverage describe AI-generated flashcards. | Treat this as a documented or previously advertised capability, not a guarantee for every current build. |
| Media support | The latest visible release lists image and video support. | Media handling does not mean every format or embedded workflow is fully compatible. |
Reor should not be described as an autonomous “second brain.” Reor’s output depends on whether notes were indexed, how the notes are structured, how well the embedding model represents the subject, and whether the language model can follow the retrieved context.
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Is Reor private and offline?
Reor can support a local and offline note-taking workflow when notes, embedding models, and language models are kept on the machine and Reor is configured to use a local Ollama service. Reor is not automatically offline in every configuration: the project also documents OpenAI-compatible APIs, and a remote endpoint changes where prompts and note content are processed.
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|---|---|
| Local notes + local Ollama model + local embeddings | The primary note and inference workflow can remain on the computer, subject to downloads, updates, and other configured services. |
| Local notes + remote OpenAI-compatible API | Prompts, retrieved passages, or other note content may leave the computer and be processed by the remote provider. |
| Local models with initial downloads | Normal offline use may be possible after model files and application assets are downloaded, but first-run setup generally needs access to those files. |
Before adding sensitive notes, inspect Reor’s provider settings and confirm whether the endpoint is local or remote. A local model reduces cloud exposure but does not make the computer inherently secure. Embeddings and vector indexes can still reveal information about a note collection to anyone who can read the files. Use operating-system permissions, encrypted storage where appropriate, protected backups, and separate user accounts on shared machines.
How do you install Reor on Linux?
Reor’s documented Linux installation path is to download the application from the project website or GitHub releases, install the package like a normal desktop application, choose a notes directory, and configure a local model. The supplied project material does not establish one universal current AppImage filename, Debian package filename, Flatpak identifier, Snap package, or command-line installer.
That limitation is especially important because the repository is archived. Open the current Reor release assets and choose the package that matches your Linux architecture and packaging preferences. Do not copy an installation command from an old guide unless the command refers to the exact asset you have verified. The release asset—not a generic claim that “Linux is supported”—is the authoritative source for the package format and architecture available for a particular build.
- Back up any existing notes before testing Reor.
- Inspect the latest visible release and download the Linux asset appropriate for your system.
- Install or launch that asset according to its actual format.
- On first launch, select or create the directory where Reor will store notes.
- Configure Ollama or another supported provider in Reor’s settings.
- Record the Reor version and the model-runtime version so the setup can be reproduced or rolled back.
The repository also documents a source-build route:
git clone https://github.com/reorproject/reor.git
cd reor
npm install
npm run dev
npm run buildThose commands build the source repository and are not necessarily the recommended route for ordinary Linux users. Because an archived project’s dependencies can age, the commands may require dependency changes or fail on a current Node.js and npm environment.
How do you install Ollama for Reor?
Ollama is the local model runtime that Reor documents most prominently. Ollama’s official Linux installation command is:
curl -fsSL https://ollama.com/install.sh | shThe command is documented in the official Ollama repository, and Ollama also provides manual Linux installation instructions. After installation, check that the executable is available:
ollama --versionOllama’s official README currently demonstrates running a model with:
ollama run gemma4The gemma4 command is an Ollama example, not a guarantee that Gemma 4 is the best or fully compatible Reor configuration. Reor may require a model that works for the specific generation and embedding operations used by the installed build. Test the model in Ollama first, then confirm that Reor can use the model for the feature you need.
If you prefer a graphical local-model manager, LM Studio’s Linux documentation describes support for x64 Linux PCs and local model workflows. LM Studio can be a useful companion or alternative runtime for users who prefer model discovery and a GUI, but Reor’s documented local integration is Ollama, so compatibility with a particular Reor build must be verified rather than assumed.
How do you select a model inside Reor?
Reor documents the settings path Settings → Add New Local LLM, where the user enters the model name to download or configure. The exact label may vary by build because the repository is archived and user-interface labels can differ between releases.
Keep two jobs separate when configuring local AI:
- Generation model: produces answers, summaries, drafts, rewrites, and other language output.
- Embedding model: converts note chunks and queries into vectors for semantic retrieval.
A model that generates fluent text is not automatically suitable as an embedding model, and an embedding model is not a conversational assistant. Verify what the current Reor build expects before choosing a model. Avoid treating an arbitrary Ollama model name as universally compatible.
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Allow time for the first indexing pass. Reor needs to process the note corpus and create embeddings before semantic search and related-note features can be useful. Model files and vector indexes also consume disk space, so check available storage before importing a large vault.
How do you import existing Markdown notes?
Reor works within one filesystem directory selected during first-run setup. To bring notes from Obsidian, Logseq, or another Markdown application, the documented approach is to copy Markdown files into that directory manually rather than relying on a mature one-click integration.
- Make a complete backup of the original vault or note collection.
- Create a separate test directory for Reor.
- Copy the notes into the test directory; do not move the originals.
- Inspect YAML frontmatter, wiki links, relative links, attachments, embedded media, HTML blocks, tables, callouts, filenames, and non-ASCII characters.
- Let Reor index the test corpus.
- Test search, related notes, and questions against several known notes.
- Only switch to the main collection after confirming that the migration is acceptable.
The project warns that existing Markdown frontmatter may not parse correctly. The project also describes integrations as future work rather than as a mature import system. A copied test vault and a rollback plan are therefore essential for serious migrations.
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What Linux hardware does Reor need?
Reor’s local workflow can be demanding because indexing and answering may involve both an embedding model and a language model. The actual experience depends on the model, quantization, note-corpus size, CPU, GPU, driver stack, available RAM and VRAM, storage speed, thermals, and whether the computer is on battery power.
| Hardware situation | Likely trade-off | Practical starting point |
|---|---|---|
| CPU-only laptop | Lower setup complexity but slower indexing and generation, with potentially high CPU use. | Use a smaller model, test a small corpus, and expect longer responses. |
| Integrated graphics | Shared system memory and limited acceleration may make larger models uncomfortable. | Monitor RAM and system responsiveness; avoid assuming automatic GPU use. |
| Dedicated GPU | Can improve local inference in compatible configurations, but drivers and model-runtime support matter. | Confirm that the runtime actually uses the GPU instead of relying on the presence of a GPU. |
| Large note collection | More chunks mean longer indexing and a larger local index. | Index a copy or smaller subset first, then expand in stages. |
| Battery-powered laptop | Inference and indexing can increase power use, heat, and fan noise. | Run heavy indexing on AC power and close other CPU- or GPU-intensive applications. |
Release notes mention GPU-driver support for local LLMs on Linux and Windows, but that statement does not establish equal acceleration for NVIDIA, AMD, and Intel hardware or identify driver versions. Reor should not be described as automatically using every Linux GPU.
Before committing to a setup, check memory capacity, available disk space, Linux driver support, thermals, and the exact Ollama and Reor versions. Processor branding alone is a poor way to predict local-model performance.
What are Reor’s main limitations?
Archived project status
The archive status is the most important limitation in 2026. An archived repository can remain useful, but issues may go unanswered, dependencies may stop building, and changes in Ollama APIs or model conventions may break features without an upstream fix.
Retrieval is not understanding
Embeddings measure statistical similarity. Embeddings do not prove chronology, causality, authorial intent, or factual accuracy. Reor can retrieve a note that uses related language while missing the note that contains the decisive detail.
RAG can produce wrong answers
Reor can retrieve the wrong chunks, omit important notes, combine contradictory passages, misunderstand stale information, or generate a confident answer that the corpus does not support. Treat answers as navigation and summarization aids, not authoritative records.
Local does not mean lightweight
Local inference avoids dependence on a cloud model but can consume substantial system memory, graphics memory, disk space, CPU or GPU time, and battery power. Older laptops may become sluggish during indexing or generation.
Model compatibility can change
Ollama evolves independently of Reor. A newer model, API behavior, model name, or embedding endpoint may not work with an archived Reor build. Record exact versions and keep a working installer and model configuration when possible.
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Single-directory storage, frontmatter limitations, and uncertain handling of links and embedded media make a test import necessary for users coming from another Markdown application.
How do you troubleshoot Reor on Linux?
Reor cannot connect to Ollama
First check the runtime:
ollama --version
ollama run gemma4If Ollama cannot run a model directly, Reor is unlikely to work until the Ollama installation, service, model download, network access, or driver issue is resolved. If Ollama works independently, verify the provider address and model name in Reor, recognizing that settings labels may vary between builds.
A model download fails
Common causes include unavailable network access, an incorrect model name, insufficient disk space, an Ollama version that cannot run the requested model, incompatible architecture, or a model unsuitable for Reor’s expected task. Test the model in Ollama first and consult current Ollama documentation instead of relying on old Reor instructions.
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Search results are poor
Poor retrieval often means indexing has not finished, the embedding model is unsuitable, notes are too short or poorly structured, the query uses terminology absent from the corpus, or the index is stale or corrupted.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- Confirm that the notes are inside Reor’s selected directory.
- Wait for initial indexing to complete.
- Use exact keywords if the build provides keyword or hybrid search.
- Try several queries using different wording.
- Rebuild or regenerate the index only if the installed UI documents that option.
- Test a small, clean directory to separate corpus problems from application problems.
- Back up notes before deleting any local index or application data.
The application becomes slow
Try a smaller model, fewer concurrent tasks, closing other GPU- or CPU-heavy applications, running on AC power, reducing the initial corpus, indexing in stages, and checking whether the GPU driver is actually being used.
Imported notes look wrong
Keep the original vault intact and inspect YAML frontmatter, wiki links, relative links, attachments, embedded files, HTML blocks, tables, callouts, and non-ASCII filenames. Reor’s documented frontmatter warning makes a test directory more important than a direct migration.
Does Reor replace Obsidian or Logseq?
Reor does not replace Obsidian or Logseq for every user. Reor’s central advantage is integrated local AI retrieval, while Obsidian and Logseq are stronger choices for users who prioritize ecosystem maturity, established workflows, synchronization, extensions, or a specific editing style.
| Decision criterion | Reor | Obsidian | Logseq |
|---|---|---|---|
| Local Markdown workflow | Core design | Core design | Core design |
| Local AI and RAG | Central purpose | Usually added through plugins or external tools | Often added through extensions or external tools |
| Maintenance risk in 2026 | High because the repository is archived | Verify current status at publication | Verify current status at publication |
| Plugin and ecosystem maturity | Smaller and narrower | Larger ecosystem | Strong outline-oriented ecosystem and workflow |
| Best fit | Privacy-focused users willing to accept project risk | Users wanting a mature, extensible note system | Users who prefer outlines and linked blocks |
| Main concern | Long-term compatibility and support | AI may require additional configuration | Workflow may not suit document-oriented note takers |
This is a workflow comparison, not a performance benchmark. Users who need mobile applications, polished synchronization, extensive publishing, enterprise support, or a large plugin marketplace should investigate alternatives before placing their primary knowledge base in Reor.
For a graphical local-model experience, LM Studio’s official documentation describes Linux support and local model workflows. LM Studio may suit users who want model discovery and a GUI, while Ollama is the runtime Reor documents most directly. Neither alternative automatically solves Reor’s archived-project compatibility problem.
What is the safest way to evaluate Reor?
- Verify status first. Check the repository archive marker and the exact release asset before downloading.
- Use a disposable test directory. Copy a small, non-critical Markdown collection instead of opening your main vault first.
- Install and test Ollama independently. Run
ollama --versionand a model directly before troubleshooting Reor. - Configure local providers deliberately. Confirm that the endpoint is local if privacy is a requirement.
- Start small. Use a modest model and a small corpus while observing RAM, VRAM, disk, temperature, and battery impact.
- Test retrieval, not just chat. Ask questions whose answers you can verify in known notes and inspect whether the retrieved context is relevant.
- Check migration details. Test frontmatter, links, attachments, media, tables, and unusual filenames.
- Keep an exit plan. Preserve the original Markdown files, maintain backups, and record the Reor and Ollama versions that work together.
Final recommendation
Reor remains an unusually focused idea: a local-first Markdown knowledge base that uses embeddings, semantic retrieval, and local language models to help users find and work with related notes. That makes Reor appealing to Linux users who want private RAG without sending their personal knowledge base to a cloud service.
The archive marker changes the recommendation. As of August 18, 2026, Reor is better viewed as an interesting open-source local-AI experiment or niche privacy tool than as the safest foundation for a new, long-lived knowledge-management system. Try Reor on a copied note collection if you value local inference and can troubleshoot compatibility. Choose a more actively maintained application if support, synchronization, mobile access, ecosystem depth, or predictable updates matter more than integrated local RAG.
Frequently Asked Questions
Is Reor a Linux machine-learning framework?
No. Reor is a Linux desktop note-taking and personal knowledge-management application that uses existing language and embedding models for inference, retrieval, and writing assistance; Reor is not a Linux distribution, machine-learning library, or model-training environment.
Can Reor work completely offline?
Reor can support an offline workflow when notes, embedding models, and language models run locally through Ollama. Offline operation is configuration-dependent, and connecting an OpenAI-compatible remote API can send prompts or retrieved note content away from the computer.
Does Reor support every Ollama model?
No verified evidence shows that every Ollama model works with every Reor feature. Generation and embedding models have different roles, and an archived Reor build may not support newer model names, APIs, or embedding endpoints.
Can I import an Obsidian vault into Reor?
You can test-import Markdown notes by copying them into Reor’s selected notes directory, but the project warns that existing frontmatter may not parse correctly. Back up the original vault and test links, attachments, media, tables, callouts, and filenames before migrating.
Is Reor a good long-term knowledge-management choice in 2026?
Reor is a reasonable local-RAG experiment for technically capable users, but it is a risky primary knowledge base in 2026 because GitHub marks the repository archived and read-only. Users who need active maintenance, synchronization, mobile access, or official support should approach Reor cautiously.
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Bottom line: Reor demonstrates how machine learning in Linux can make a local Markdown collection searchable and interactive through embeddings, vector retrieval, and RAG. The privacy benefits are real only when local providers are used, and the practical cost is hardware demand plus an archived project. Test Reor on copied notes, record the exact versions that work, and do not make it your only copy of important knowledge.
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