What Is OneBox?

OneBox is a free AI agent toolbox for Android, available on Google Play and the official site. According to the project’s GitHub repository, it is open-source under the Apache 2.0 license, meaning anyone can inspect, modify, or distribute the code. The app’s core idea is simple: you say what you need, and the built-in AI agent drives over 90 in-app tools to get it done. Examples include turning images into a PDF, logging a bill screenshot into a ledger, creating a to-do list, or looking up stock prices online.

The app also functions as a file viewer, supporting 124 unique file extensions across categories like images (24 formats), documents (11 formats), text and code (42 formats), audio (12 formats), video (13 formats), and archives (14 formats). This means small-business owners can open PDFs, Word documents, spreadsheets, and even code files without needing separate apps.

How the AI Agent Works for Small Businesses

The AI agent can invoke 90+ local tools—such as PDF processing, image editing, file management, and bookkeeping—and chain multi-step tasks across them. For a small business, this means you can automate workflows like converting a batch of receipts into a single PDF, then logging the total into a ledger, all in one conversation. The agent operates within the app’s own Android sandbox and declared permission set, so it never touches contacts, SMS, or call logs. Local tools run entirely on-device, and only the conversation with your chosen model goes online.

OneBox supports multiple mainstream LLMs from a single entry, with custom AI engines and prompt management. You bring your own API key—including free tiers like Gemini or OpenRouter’s free models—so the full assistant and agent are available at zero cost. Custom engines, endpoints, and tokens are fully open in the Google Play build and in builds from the repository.

Offline Capabilities: No Network, No Problem

OneBox includes on-device AI for chat, translation, and reading text from photos. For chat, you can download a local model (Gemma 3 1B or Qwen3 0.6B) inside the app, or import your own .litertlm file, and chat without network, account, or token cost. Inference runs entirely on-device via LiteRT-LM (available in Google Play and FOSS builds). Translation uses the Tencent Hy-MT2 1.8B model for 20 languages plus auto-detect, and camera input goes through on-device OCR, so photos are not uploaded. Reading text from photos uses a 26 MB PP-OCRv6 pack that is downloaded once and SHA-256 checked, supporting Chinese, English, Japanese, and common Latin scripts.

For small businesses, this means you can process sensitive documents—like customer invoices or internal notes—without sending data to the cloud. The app also has a picture-in-picture robot: when a long agent task runs, you can leave the app and the mini-window robot keeps you posted on progress, with one tap to return to the result.

What It Means for Your Business

OneBox replaces the need for multiple separate apps for scanning, bookkeeping, file conversion, and document viewing. Instead of juggling a PDF reader, a scanner, a note-taking app, and a spreadsheet viewer, you have one app that handles all of them. The AI agent reduces the learning curve: instead of memorizing steps or hunting for tools, you simply describe what you want.

The open-source nature means you can verify how data is processed locally and what never leaves the device. The app is free, with no subscription required if you use your own API key or offline models. This is especially useful for small businesses that want to keep costs low while automating repetitive tasks.

Takeaways

• OneBox bundles 90+ tools and an AI agent into one free Android app, reducing app clutter for small businesses.
• You can use your own API key or offline models, so there are no ongoing subscription costs.
• On-device AI for chat, translation, and OCR means sensitive data stays on your phone, not in the cloud.

What Is Muse Ember?

Meta Superintelligence Lab released a new AI model on Monday called Muse Ember. According to TechCrunch, it is the lab’s lightest and most cost-efficient AI model yet. Meta said Ember was “designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing.”

Ember is the fourth model released under Meta’s Muse brand, following the mid-weight Spark (released in April), Spark’s open source twin Glimmer (released in August), and the Wonder frontier model (released in October). The flagship consumer AI product, the Muse Agent, was released in September.

The Price That Changes the Math

At 10 cents per million input tokens, Ember is significantly cheaper than frontier competitors like Anthropic’s Haiku and OpenAI’s Luna, according to TechCrunch. It is also cost-competitive with open source competitors like DeepSeek V4.1 Flash and GLM 5.3 Flash.

For a small business, that price matters. Agent-based automation only pays off when the model running it is cheap enough to work all day. Prices like this put practical automation within reach of businesses that couldn’t justify it a year ago.

How Ember Works in Practice

Given the emphasis on low cost and accuracy at scale, Ember is designed to pair with a larger model like Meta’s Muse Wonder in an agentic harness. As TechCrunch reports, in the case of a customer-support agent, you might see a heavier model establishing rules for how to handle a particular issue, then sending a lighter-weight model to apply the rules across tens of thousands of cases.

As Meta puts it in the announcement, “Muse Wonder plans, Muse Ember handles the volume.” This two-model approach means small businesses can use a powerful, expensive model only for planning and then use Ember for the repetitive, high-volume execution work.

Ember Handles More Than Text

With volume-heavy use cases in mind, Meta also trained Ember to work well across different types of media, including text, images, and audio. According to TechCrunch, the result makes the model surprisingly flexible in what kind of inputs it can handle.

As Meta puts it in the announcement, “agents can read screenshots, charts, and scanned documents, or work from voice memos, meeting recordings, and video without a separate model.” For a small business, this means one model can process customer emails, analyze screenshots of errors, and even listen to voicemails—all without needing separate AI tools.

What This Means for Small Businesses

Ember is built for the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. For a small business, that translates to automating customer support triage, processing invoices, extracting data from contracts, or routing inquiries to the right person.

The cost—10 cents per million input tokens—is low enough that a small business could run thousands of agent tasks per day for a few dollars. Combined with the ability to handle text, images, and audio, Ember makes it feasible to automate workflows that previously required multiple tools or manual effort.

As TechCrunch notes, Meta particularly emphasized Ember’s cost-per-task, using Artificial Analysis’s composite benchmark score to show Ember as cheaper and smarter than competitors. For small businesses watching every dollar, that combination of low cost and solid performance is the key to finally adopting AI agents.

Takeaways

• Muse Ember costs 10 cents per million input tokens, far below comparable models from Anthropic and OpenAI.
• It is designed for high-volume agent tasks like tool calls, coding, routing, extraction, and document parsing.
• Ember can process text, images, and audio, making it a versatile single model for many business automation needs.

What LobsterAI Does

LobsterAI is an open-source, desktop-grade AI agent built by NetEase Youdao. According to its GitHub repository, it is the first open-source desktop-grade agent from a major Chinese tech company. It is designed to get real work done — data analysis, slides, docs, video, and web research. Unlike most AI assistants that stop at an answer, LobsterAI is built to finish the job. It works where your work lives: point it at a folder and it reads spreadsheets, writes documents, runs scripts, and browses the web, asking before anything sensitive. You see the result, not just a reply.

The agent runs tools on your real desktop and can take commands from your phone via WeChat, Feishu, DingTalk, Telegram, and other messaging platforms. It is MIT-licensed, with sessions stored locally. You can sign in to use built-in models or bring your own API keys for OpenAI, Anthropic, Gemini, xAI, OpenRouter, Ollama, LM Studio, and any OpenAI-compatible endpoint.

Key Capabilities for Small Businesses

LobsterAI offers native Office editing: you can open Word, Excel, and PowerPoint files right in the app, edit by hand, or let the agent make changes live alongside you. It includes a built-in browser the agent can drive, with tabs, saved logins, and passkey support, for research and web workflows. The agent can also operate desktop apps on Windows x64 and macOS 13 or later.

You can install role-based kits for engineering, design, finance, sales, and more, pick individual skills, or connect MCP servers. Scheduled tasks let you turn any prompt into a recurring job: morning briefs, end-of-day wrap-ups, weekly reports, and health checks. The agent supports multiple agents with their own identity, model, skills, and working folder, and you can add ready-made experts from the Expert Plaza.

How It Helps Small Business Owners

For a small business owner, LobsterAI can automate repetitive tasks like data analysis, report generation, and web research. Example prompts from the repository include: “I still track inventory and sales in Excel. Build a local inventory system that records purchases and sales, calculates stock and profit, and opens in my browser.” Or: “Use product-growth.xlsx to build a visual dashboard and summarize the main growth drivers.” You can also ask it to make a deck: “Research the AI agent market and turn the findings into a presentation.”

The agent can screen documents: “Turn the resumes in this folder into a screening sheet and shortlist the strongest candidates against the JD.” It can run scheduled work: “Every weekday at 9 AM, collect yesterday’s AI news and send me a concise digest.” All of this happens on your local machine, so your data stays with you.

Getting Started and Community

LobsterAI is available for download from the official website or as a release for macOS (Apple Silicon and Intel). The GitHub repository has over 6,200 stars and 978 forks, indicating an active community. It is built on OpenClaw and uses technologies like Electron, React, and TypeScript. The project is MIT-licensed, so you can use it freely and even modify it for your needs.

The agent includes a desktop companion — a floating orb for quick tasks, screenshots, and actions on selected text, such as translating or reading aloud. It also has durable memory files and optional scheduled “Dreaming” runs that consolidate what the agent learned, so preferences carry across sessions.

Takeaways

• LobsterAI is an open-source desktop agent that finishes tasks like data analysis, Office editing, and web research, not just answers questions.
• You can message it from your phone via Telegram, WeChat, or other platforms to start jobs while you’re away.
• It is MIT-licensed, runs locally, and supports built-in models or your own API keys.

What Is GerGit?

GerGit is a fast, native Git client for Linux, built in C# on .NET 10 with Avalonia. It is open-source under the GPL-3.0 license and inspired by the popular Fork client, though it is not affiliated with Fork or its developers. According to the project’s GitHub page, every Git operation runs the system git CLI, so it behaves exactly like the git you already use.

For small businesses, this means you get a visual interface for version control without changing how Git works under the hood. Your team can continue using the same Git commands and workflows they know, but through a graphical interface that reduces the need to memorize complex terminal syntax.

Key Features That Matter to Small Teams

GerGit includes a commit graph with search, branches, remotes, tags, stashes, worktrees, and submodules in a sidebar. It supports staging and unstaging whole files, hunks, or even single lines, and allows committing and amending with a single step. The diff view offers both inline and side-by-side modes with find functionality and image diff support.

For teams that deal with merge conflicts, GerGit provides a three-pane conflict resolver. Interactive rebase is supported with drag-and-drop reordering. It also integrates with GitHub and GitLab (including self-hosted instances) via personal access tokens, allowing clone from a project list, pull and merge requests, and notifications.

The tool includes a working-tree watcher that refreshes on file changes, a Quick Launch (Ctrl+P) feature, and repository settings with issue links. It supports light and dark themes that follow the OS, zoom, and single instance mode.

Installation and Requirements for Linux

GerGit is available for Ubuntu 26.04 (amd64) via a PPA. The installation command is: sudo add-apt-repository ppa:icaruzsoftware/gergit followed by sudo apt install gergit. It requires git 2.38 or newer, libsecret (and a keyring service such as GNOME Keyring or KWallet) for storing hosting tokens, and optionally libnotify-bin for desktop notifications, xdg-utils for opening folders and links, and util-linux setsid for live hook progress.

For small businesses that use Ubuntu or other Debian-based distributions, this means a straightforward installation process. The tool can also be built from source using the .NET 10 SDK.

Privacy and Network Use

GerGit has no telemetry. According to the project documentation, it uses the network only for git operations you start (fetch, pull, push, clone, and test connection), automatic background fetch (on by default every 10 minutes), submodule updates, GitHub and GitLab API calls when you sign in, and avatars from Gravatar, GitHub, or your GitLab server. All connections use HTTPS unless your GitLab server uses HTTP.

You can turn off avatar loading in Preferences under Integration. Hosting tokens are stored in the system keyring via libsecret. This gives small businesses control over their data and network usage, which is important for teams concerned about privacy.

Why GerGit Matters for Small Businesses

Small businesses often have limited development resources and cannot afford expensive Git GUI tools. GerGit is free and open-source, eliminating licensing costs. It reduces the learning curve for team members who are not comfortable with the command line, allowing them to contribute to version control tasks more easily.

By providing a visual interface for commit graphs, staging, diffs, rebasing, and conflict resolution, GerGit helps teams work faster and with fewer errors. The integration with GitHub and GitLab means teams can manage pull requests and notifications without leaving the tool. For Linux-based small businesses, this is a practical alternative to paid or cross-platform tools.

As noted by the project’s GitHub page, GerGit is inspired by Fork and aims to be a fast, native Git client for Linux. It is actively maintained with a roadmap, contributing guidelines, and a code of conduct.

Takeaways

• GerGit is a free, open-source Git GUI for Linux that eliminates licensing costs for small businesses.
• It provides a visual interface for commit graphs, staging, diffs, rebasing, and conflict resolution, reducing the need to memorize terminal commands.
• The tool integrates with GitHub and GitLab, supports background fetch, and has no telemetry, giving teams control over privacy and network use.

What Is Observer AI?

Observer AI is an open-source, local-first AI agent that watches your screen, camera, and audio and takes action when something happens. It runs on local models, so your data stays 100% private. The project is built by Roy Medina and is licensed under AGPLv3.

The agent works on a simple “When X then Y” principle. For example: when my training crashes, call me; when I’m out of the queue, SMS me; when I get distracted, WhatsApp me; when my Steam game downloads, Telegram me; when my overnight render fails, call me; when I haven’t made progress, WhatsApp me; when an appointment is available, send me a WhatsApp; when an item is back in stock, send me a Telegram.

Observer AI can be used through a web app (no install) at app.observer-ai.com, a desktop app for macOS, Windows, or Linux (recommended, ships with llama.cpp), or by bringing your own server with any OpenAI-compatible endpoint such as Ollama, llama.cpp, or vLLM.

How It Works for Small Businesses

For small-business owners, Observer AI replaces endless manual checks and missed notifications. Imagine a boutique clothing shop that gets an instant WhatsApp alert the moment a new shipment is logged in the inventory system, or a small print shop that receives a phone call the moment a large print job fails. Everything stays private and on-premise.

Each micro-agent runs in a loop: Sensors → Model → Code. Sensors include $SCREEN (screenshot for multimodal models), $CAMERA (camera frame), $MEMORY (stored text), $CLIPBOARD (clipboard contents), $MICROPHONE (microphone transcription), $SCREEN_AUDIO (transcription of a shared tab’s audio), and $ALL_AUDIO (mic and screen audio mixed, good for meetings). Audio is transcribed with Whisper via transformers.js.

The agent can send notifications through Discord, Telegram, WhatsApp, SMS, Email, Phone calls, or Pushover. It can also perform browser notifications, local sounds, video recording, and mobile app actions like locking the phone.

Key Features for Business Automation

Observer AI provides a variety of tools for memory and agents: getMemory, setMemory, appendMemory, getImageMemory, setImageMemory, appendImageMemory, startAgent, stopAgent, time, and sleep. These allow you to store and retrieve information across monitoring sessions.

For notifications, the agent supports sendDiscord, sendTelegram, sendEmail, sendPushover, call, sendWhatsapp, sendSms, and notify. Some channels (SMS, call, WhatsApp) require whitelisting. The agent also supports video recording with startClip, stopClip, markClip, and getVideo.

Decision models are available that don’t write text but answer typed questions with probabilities. You can ask yes/no questions or multiple questions at once using JSON prompts. This is useful for detecting specific conditions like “Is there a person in the room?” or “Which pet is visible?”

Privacy and Local-First Design

Observer AI is designed to be privacy-first. It runs entirely on your device, so your data never touches external servers. The desktop app ships with llama.cpp, so it runs any GGUF+mmproj model. You can also connect to local models via Ollama, llama.cpp, or vLLM.

The project is open-source under AGPLv3, meaning you can inspect, modify, and self-host the code. The GitHub repository has 1,800+ stars and 153 forks, indicating active community interest. The project also has a Discord community and a subreddit r/ObserverAI for sharing agents, asking questions, and reporting bugs.

Getting Started

To get started, you can use the web app with no installation at app.observer-ai.com and download a model to run it in your browser. For a more robust experience, download the desktop app for macOS, Windows, or Linux. It ships with llama.cpp, so it runs any GGUF+mmproj model.

If you have your own server, you can set a Custom Model Server URL to any OpenAI-compatible endpoint. Then describe what you want Observer to watch, or build an agent yourself from the Micro Agents tab. The CLI tool is also available: run ‘observe sleep 5’ and Observer calls you when the command exits.

Observer AI is built by Roy Medina and is available on GitHub at github.com/Roy3838/Observer. The project is licensed under AGPLv3 and is free to use, modify, and distribute.

Takeaways

• Observer AI is an open-source, local-first AI agent that monitors screen, camera, and audio and triggers actions when conditions are met.
• It runs entirely on your device, keeping your data 100% private and secure.
• Small businesses can automate monitoring tasks like inventory updates, job failures, and appointment availability without manual checks.

What Are Apple’s Intelligence Usage Packs?

Apple has started selling $50 Intelligence Usage Packs that give users continued access to certain AI features after they hit a free usage cap. The first app to offer this option is Logic Pro, where users can buy more usage for a one-time purchase price of $49.99, as reported by The Verge.

These packs are designed for features that rely on cloud-based AI models from OpenAI or Google Gemini. According to Apple’s support article, the packs can only be used by the Apple Account that purchased them—Family Sharing is not supported. Any unused additional usage will roll over from month to month but expires 12 months after purchase.

For Logic Pro, the usage limit is ‘impacted by the complexity of conversations you have with the assistant,’ Apple explains. Users who run out of access can find the option to buy an Intelligence Usage Pack in the Intelligence Features Show Usage section of the app’s main menu.

Which Apps Will Be Affected?

Currently, only Logic Pro has the option to buy more usage, but Apple is expected to add the packs to other Creator Studio apps this month. According to The Verge, these include Pixelmator Pro, Final Cut Pro, Motion, plus Pages, Keynote, and Numbers—apps that are getting access to Apple’s AI chatbot.

The baseline Apple Intelligence tools remain free across devices, as noted by 9to5Mac. However, this marks Apple’s first concrete step toward charging for expanded AI usage. For small businesses that use these tools regularly, the shift means that heavy reliance on cloud-based AI features could lead to recurring costs.

How the Usage Caps Work

Apple’s support page, as reported by Engadget, explains that users with active Apple Creator Studio subscriptions in the US, UK, Canada, Brazil, France, Germany, India, and Mexico can buy intelligence usage packs to keep using AI features after they reach their monthly usage limit.

The initial allowance in a Creator Studio subscription can generate up to 50 images, 250 shapes, 50 presentations of 8 to 10 slides each, or presenter notes for 700 slides, according to AppleInsider. Once those limits are reached, the $49.99 pack provides additional capacity.

This tiered system means small businesses that produce a high volume of content—such as marketing materials, presentations, or video projects—may hit the free cap quickly and need to purchase packs regularly.

What This Means for Small Business Owners

For small businesses using Apple’s Creator Studio apps, the introduction of usage packs introduces a new variable in budgeting. If your workflow depends on AI features like generating images, shapes, or presentations, you may need to factor in $50 per pack when you exceed the monthly limit.

The packs are not shareable across accounts, so each team member using their own Apple ID would need their own pack. This could multiply costs for small teams. Additionally, the 12-month expiration on unused rollover usage means you can’t stockpile capacity indefinitely.

As noted in the Launch AI Works Insights post, small businesses should ‘know what you’re paying for before you build your workflow around it.’ Understanding the usage limits and costs upfront can help avoid surprises and ensure that AI tools remain a cost-effective part of your operations.

Practical Steps to Manage Costs

First, review your current usage of AI features in apps like Logic Pro or any Creator Studio app you use. Check the Intelligence Features Show Usage section to see how close you are to the free cap. This will give you a baseline for how often you might need to buy a pack.

Second, consider whether you can reduce reliance on cloud-based AI features by using local alternatives or optimizing your prompts to stay within the free allowance. For example, if you generate many images, batch them efficiently to avoid hitting the cap prematurely.

Finally, budget for potential pack purchases if your business relies heavily on these tools. The $49.99 price is a one-time purchase per pack, but if you need multiple packs per month, the cost can add up. Monitor your usage trends to predict when you’ll need to buy more.

Takeaways

• Apple’s $49.99 Intelligence Usage Packs are now available for Logic Pro, with plans to expand to other Creator Studio apps like Final Cut Pro and Pages.
• The packs are tied to a single Apple Account, not shareable via Family Sharing, and unused usage expires after 12 months.
• Small businesses should audit their AI feature usage and budget for potential additional costs if they regularly exceed the free monthly limits.

Why Telegram’s native search falls short for growing teams

Telegram is popular for internal communication, but its built‑in search works best for short, recent threads. When a channel or group accumulates thousands of messages, finding a single reference can require endless scrolling. The platform’s filters—such as date ranges or media type—help a bit, but they still rely on Telegram’s own index, which does not handle Chinese or other languages that lack spaces between words. For small businesses that rely on Telegram for project updates, client requests, or legal records, the time spent hunting for a specific message adds up and can delay decisions.

How telegram‑search creates a local, searchable archive

The open‑source telegram‑search project (hosted on GitHub) lets a user export their chat history and build a local index. After a one‑time authentication, the tool pulls messages via Telegram’s export API and tokenizes them on the user’s machine. Tokenization works for languages that do not use spaces, such as Chinese, so word‑level hits are possible. The index supports fuzzy matching, which tolerates misspellings, and vector‑based semantic search, allowing queries to match the meaning of a sentence rather than exact keywords. Users can run the CLI (tgs) to list chats, sync historical data, and issue search commands. The daemon mode can keep the index up‑to‑date by listening for new messages in real time. Exported data can also be saved as monthly JSONL files with SHA‑256 checksums for verification.

Practical benefits for small‑business workflows

For a small business, the main advantage is speed. Instead of opening a chat and scrolling through months of messages, a team member can type a phrase into the terminal and get the exact message, its timestamp, and the surrounding context in seconds. This reduces the time spent on customer support follow‑ups, contract negotiations, or internal audits. Because the data stays on the user’s computer, privacy is maintained—no third‑party cloud service stores the content unless the business chooses to deploy the optional web UI. The tool also offers AI‑assisted features such as unread‑message summaries and RAG (retrieval‑augmented generation) queries, which can be invoked by an AI agent after the user has logged in once. Finally, the ability to export month‑by‑month JSONL files makes it easy to archive compliance‑required records without relying on Telegram’s retention policies.

Getting started and what to watch for

To try the solution, install the CLI via Homebrew (macOS/Linux) using the repository’s tap, then run the login command. After authentication, the user can sync a specific chat or the entire history. The daemon can be started as a background service so new messages are indexed automatically. Small teams should consider a dedicated profile per account if multiple users need separate archives. Optional environment variables let you configure a custom Telegram API ID/Hash, a proxy, or a remote token for read‑only cloud access. If you need to search linked web content, the tool can integrate with Firecrawl to fetch and index page text, but this feature must be enabled manually. Because the software is released under the AGPL‑3.0 license, any modifications that are shared publicly must also be open source.

Takeaways

• Local export and indexing give instant, privacy‑preserving search across all Telegram messages.
• Fuzzy and vector‑based queries handle misspellings and semantic searches, useful for multilingual teams.
• The CLI can run as a daemon to keep the index current, and exported JSONL files support compliance archiving.

What Is Open Duet?

Open Duet is an open-source, self-hosted alternative to paid AI coworker products like Claude Duet, OpenAI Dots, Meta Muse, and Grok Bot, according to the project’s GitHub repository. It is a workspace for creating named AI agents—each with its own instructions, memory, routines, and an isolated computer—and your approval before they act.

The project’s README explains that you can create agents and teams, give each agent a name, role, engine, workspace, memory, and approval settings. A lead can delegate work to specialists or coordinate agents in shared rooms. The platform keeps conversations, memory, routines, and approvals in one place.

You can run Open Duet as a desktop app on your own computer, or host the server yourself on a Linux server. The hosted web app is a separate product. In all cases, you bring your own AI engine and provider account—Open Duet does not include a model or model subscription.

How Small Businesses Can Use Open Duet

A local boutique uses Open Duet to auto-track inventory, generate purchase orders, and draft customer replies, eliminating endless spreadsheets, phone calls, and manual drafting, according to the Launch AI Works Insights dossier. The AI agents keep context, ask for approval before acting, and run on a routine you set, freeing you to focus on the shop floor.

You can create routines for scheduled, webhook, or manual runs. Each routine reviews results and answers requests for approval; a routine never approves an action by itself, as stated in the repository’s documentation. This means you can set up weekly inventory checks or daily email summaries without worrying about the AI making unauthorized changes.

The platform also supports connecting services such as GitHub, Gmail, and Google Calendar through your own Composio key, then granting access to selected Claude or Codex agents. Changes wait for your approval. You can also add individual tools from the MCP Marketplace.

Key Features That Matter for Small Business Owners

Open Duet offers several features designed to make AI coworkers practical for real work. The Today view gathers approvals, questions, routine outcomes, engine failures, and due tasks in one place, and can show read-only Gmail and calendar items. This helps you see what needs your attention without logging into multiple apps.

You can use a task board and calendar to assign work, schedule one-time runs, and review conversations and attempts. Agents can save notes, recall relevant notes and conversations, and search their history. You can review and edit what they remember.

The platform also lets you choose how much freedom agents have: Ask first, Accept edits, Approve for me (for Claude, Codex, and Cursor), or Plan only. You can review tool activity and approval decisions in the app. A phone app is also available to chat, stop an answer, allow or deny a tool call, or create and edit agents and rooms.

What You Need to Get Started

To run Open Duet, you need Node.js 22 or newer, uv, and at least one AI engine available on the same computer, according to the repository’s quick start guide. For a CLI engine, you install it and sign in before opening Open Duet. You can also configure an API-based engine in Settings → Engines. Docker is only needed for the optional isolated agent computers.

The desktop app runs from a source checkout. On first launch, you choose an engine, workspace, and approval mode. Then you create an agent with a name, a clear job, and a workspace, and start a chat. The project is in active development, and desktop releases, self-hosting, agent computers, and newer team features are still evolving. The repository advises reviewing known limitations before relying on them for important work.

Installers are experimental and unsigned. The assisted update flow offers a SHA-256-verified installer for you to open; it does not install updates automatically. The release-to-release update path has not been verified end to end.

Cost and Data Control

Open Duet is free and open-source under the Apache-2.0 license. You bring your own AI provider account and pay that provider for model usage. The hosted preview lets you use your own Boat key for sandbox compute. Paid plans use compute operated by Suparagent, so you do not need to supply a Boat key. In both cases, you bring your AI provider account and pay that provider for model usage.

Because Open Duet is self-hosted, your data stays on your own computer or server. The repository notes that paid AI coworker products keep your agents, memory, and credentials on someone else’s servers. Open Duet gives individuals and small teams the same idea in a form they can inspect and run themselves, with visible approval steps and local control.

Takeaways

• Open Duet is a free, open-source platform that lets you run AI coworkers on your own server or desktop, keeping your data private.
• Agents can handle tasks like inventory tracking, purchase orders, and email drafting, but always ask for your approval before acting.
• You bring your own AI engine and pay only for model usage; there is no subscription fee for the platform itself.