SumizAI vs Ekorbia

What Ekorbia is

What Ekorbia is

Ekorbia is a native desktop Integrated Chat and Productivity Environment for local AI models powered by Ollama. Features include chat history, full-text search, prompt library, model comparison chat mode, chat overlay, ephemeral chat, file watches and more. Instead of using a simplified web interface for local models, a Mac user gets a full-fledged native application designed specifically for working with models running locally on their own hardware.

A free, open-source MIT-licensed model makes Ekorbia accessible to the entire local-AI enthusiast community with no financial barrier whatsoever, consistent with the philosophy of the open-weights and local-processing movement that Ollama itself represents. Being limited exclusively to Mac means concentrating development resources on depth of integration with one specific ecosystem rather than spreading across many platforms.

A rich feature set — from full-text chat-history search through a prompt library to a model comparison mode — lets Ekorbia compete in functionality with commercial cloud platforms, while offering full privacy and data control that comes from local processing.

A local-AI enthusiast using Ekorbia for daily work with several different Ollama models at once, using model comparison mode to assess which model performs best for which task, gradually accumulates valuable knowledge about the strengths and weaknesses of individual local models. Without saving those observations outside the app itself, it's hard to systematize that knowledge when choosing a model for a new project.

SumizAI complements Ekorbia exactly at that point: observations about the strengths and weaknesses of individual local AI models tested in Ekorbia can be saved as ai notes in a local Markdown file, building a personal guide to available Ollama models. An enthusiast testing dozens of different open-weights models ends up with ai notes forming condensed practical knowledge, far more useful than generic model rankings published online.

These tools complement each other: Ekorbia provides a full-fledged, native environment for working with local AI models on a Mac, while SumizAI ensures the conclusions from testing and comparing those models survive as ai notes independent of the app itself, ready to use for every subsequent project requiring the choice of the right model.

Combining both tools creates a coherent ecosystem of local, private AI processing: Ekorbia provides local model execution, and these notes in SumizAI provide local, lasting storage of the knowledge gained while working with those models, with no cloud dependency whatsoever at any stage of the process.

A development team building internal tools based on several different local Ollama models at once, using Ekorbia for daily work, especially benefits from keeping a shared, team-wide these notes documenting which model performs best for which type of internal tool task. A new engineer joining the team, given access to such a note gathered by predecessors, can pick the right model for their task right away, instead of wasting days repeating comparison tests the team already ran long ago. Such a shared these notes gradually becomes a living document, updated every time a new, promising model shows up in the Ollama ecosystem worth testing and adding to the collection.

That is our reading of the record. For AlternativeTo's own wording, see its page for Ekorbia — the facts above were read on 2026-08-16.

Ekorbia in depth

A desktop workspace for models that never leave the machine

Ekorbia is a native desktop application for talking to language models that run on your own hardware. The catalogue listing describes it as an integrated chat and productivity environment for local AI powered by Ollama, available on macOS, released under the MIT licence and free of charge. That summary is accurate but has been overtaken by the project itself: the current build ships a bundled llama-server sidecar, so Ollama is one option rather than a prerequisite, and installers exist for Windows and Linux as well. The repository sits at github.com/ekorbia/ekorbia-desktop, was last touched in late July 2026, and is small by any measure — a single star, no forks, no open issues, and no reviews or comments on AlternativeTo. Judge it on the code and the documentation, because there is no user base to ask.

How a conversation is organised

The chat surface is built around tabs, each holding its own independent history, which suits people who keep several unrelated threads alive at once. A comparison mode streams the same prompt to two or three models side by side, with tabs preserved across the comparison, so evaluating whether a smaller quantised model is good enough for a given task does not require running the prompt three times by hand. Ephemeral chats exist only in memory and vanish when the tab closes, leaving nothing on disk. Responses stream and can be stopped mid-flight; both user turns and assistant turns can be edited and retried, or regenerated outright. Searching old conversations uses SQLite's FTS5 index with BM25 ranking rather than a naive substring scan, which is the difference between a search box that is useful after a thousand messages and one that is not. Whole conversations export either to Markdown for a readable transcript or to JSON when nothing may be lost.

Getting material into the model

Attachments cover folders, PDFs, images, Markdown and plain text. Larger material is chunked and embedded locally using nomic-embed-text, giving retrieval-augmented answers without anything being uploaded; answers carry inline citations as source chips that expand to show relevance scores, and a watched folder is re-indexed incrementally rather than from scratch when its contents change. Images route automatically to a vision-capable model. On macOS a screenshot hotkey grabs the screen and attaches the capture to a fresh chat in one keystroke, though that path is not yet wired on Windows or Linux. Voice input runs through whisper.cpp on the device, Metal-accelerated on Apple silicon, covering ninety-nine languages with auto-detection and an optional translate-to-English toggle. A Spotlight-style overlay, bound to Command-Shift-Space on macOS and Alt-Space on Windows, opens a query box over whatever else is on screen; Linux does not have it yet.

Watches, memory and the prompt library

Two features push Ekorbia past being merely a chat window. The first is the watch system, which monitors folders, RSS and Atom feeds, and ordinary web pages in the background. Folder watches summarise files as they appear; feed watches pull article bodies; URL watches can either snapshot a page or run in diff mode to report what changed, with configurable polling intervals and CSS selectors to narrow the region of interest. New events raise native operating-system notifications and collect into a Today view that can itself be interrogated as a daily digest. The second is the memory file, a persistent Markdown document injected as system context into every query and deliberately kept read-only to the model, so an assistant cannot quietly rewrite its own standing instructions. Alongside these sits a prompt library of roughly three dozen built-ins stored as Markdown files with YAML front matter, invoked by typing a slash in the composer, with tags and five colour-coded favourites for filtering. Related chats, pinned files and prompts can be grouped into Spaces.

Letting the model write files, carefully

Models advertising tool support can call a write_file function to save what they generate. The sandbox around it is described in some detail: output goes to a per-chat directory, and paths containing .., absolute prefixes, NUL bytes or symlink escapes are rejected, with the memory file explicitly out of reach. Generated files are tracked in a panel with version history and shortcuts to reveal or open them. Models without tool calling are not left out — each fenced code block gets its own save button as a fallback. Model capability is surfaced in the picker through TOOL and VISION badges, and the status bar reports whether a model is cold, warming or loaded, which is a small courtesy that removes a lot of guesswork about why a first reply is slow.

Stack, requirements and installation

The application is built on Tauri 2 with a Rust backend and a WebView front end that mixes plain JavaScript with React loaded through Babel-standalone; Markdown rendering uses marked, syntax highlighting uses highlight.js, and HTML is sanitised with DOMPurify. Storage is SQLite with FTS5. Inference comes from a bundled llama-server, from Ollama, or from any OpenAI-compatible endpoint. Every library, font and asset is vendored locally — Inter, JetBrains Mono and Instrument Serif among them — so the application boots with no network at all. End-to-end tests run under Playwright. Supported systems are macOS 12 Monterey and newer, Windows 10 build 1809 or Windows 11, and Linux distributions with WebKitGTK 4.1, with Ubuntu 22.04 and Fedora 39 named. Around eight gigabytes of RAM is suggested for the bundled models, and each model costs several gigabytes of disk. Builds are published as .dmg, .msi, .exe, .deb, .rpm and AppImage, or you can compile with cargo tauri build given Rust 1.80 or newer.

The rough edges worth knowing about

Nothing here is code-signed. On macOS that means clearing the quarantine attribute with xattr -dr com.apple.quarantine or using the right-click Open workaround; on Windows it means arguing with SmartScreen the first time. Signing is listed as a future intention rather than a shipped fact. Linux is the weakest platform: chat, attachments, watches, notifications, prompts and search all work, but the quick-query overlay and screenshot capture do not. The Windows gap is narrower — the overlay works, screenshot capture does not. Version numbering sits around 0.7 with roughly fifty-five commits on the main branch, which is honest about the maturity on offer. The privacy position, by contrast, is unusually concrete: the project states that chats, embeddings and saved files live only in the platform's application-data directory, that there is no sign-up, no API key, no usage tracking and no crash reporter calling home, and the source is public so the claim is checkable.

Who should look at it

Ekorbia fits someone on recent macOS hardware who wants local inference with real workflow scaffolding around it — background monitoring, retrieval over their own documents, a prompt catalogue and voice input — and who is unbothered by an unsigned binary and a young version number. It is a bad fit for teams, for anyone needing mobile access, and for anyone whose evaluation depends on a track record, because the project has effectively none yet. Its clearest advantage over the crowd of local chat front ends is that it does not stop at chat: the watch system and the memory file turn it into something closer to a personal monitoring desk. Anyone lining it up against hosted assistants such as SumizAI is really choosing between local hardware costs plus manual setup on one side, and a managed service on the other.

Product page: ekorbia.com

What SumizAI is

A note-taking application built around a conversation with an AI model. You ask a question, the answer streams back, and the answers worth keeping become Markdown notes — filed into a vault that is a folder on your own disk.

A vault is a directory holding base.md, a generated table of contents up to six levels deep, and a notes/ folder with one .md file per note. Before a note is written it is checked against the ones already there, so the fourth note about the same idea gets merged instead of added. Links between notes are ordinary Markdown links to files that exist.

The model is never ours: you bring your own API key to one of seven providers — Anthropic, OpenAI, Gemini, Groq, OpenRouter, a local Ollama or your own server — and pay that provider directly. A question sends the table of contents plus at most five relevant notes within a 24,000-character budget, and the app shows you which five it used.

Every answer kept this way becomes one of these ai notes, ready to search again months later without hunting back through old chats.

Side by side

Where they differ

• Ekorbia works with Ollama, and so does SumizAI: a local Ollama is one of its seven providers. You can keep the model on your own machine and still have the answers filed as Markdown notes rather than left in a chat window.
• Both mention AI. The difference is whose key it runs on: SumizAI never resells inference — you connect one of seven providers (Anthropic, OpenAI, Gemini, Groq, OpenRouter, a local Ollama or your own server) with your own API key and pay that provider directly. Check what Ekorbia does with the model bill before comparing prices.
• Ekorbia is open source (MIT), and SumizAI is not. If reading the source is what decides it for you, that is a real argument for Ekorbia. The guarantee SumizAI offers instead is structural rather than legal: the notes are Markdown files in a folder you chose, so they open in any editor whether or not the application is running.
• AlternativeTo's description of Ekorbia does not mention Markdown, so check what format your notes end up in before you fill it up. SumizAI writes standard .md files to a folder you picked, and an unpaid account can still export all of them.
• AlternativeTo lists no mobile version of Ekorbia. SumizAI runs on macOS, Windows, iOS and Android.
• Both search. SumizAI's search reads the whole note — title, summary, body and the original question and answer — rather than titles alone.

Which one to pick