OpenYak Desktop Workbench
A local-first desktop agent for turning real files into structured work products with your chosen model boundary.
What does this agent do, and when should you use it?
OpenYak is a local-first desktop agent workspace that keeps files, conversations, memory, workflow state, and generated artifacts on the user’s device. It works with DOCX, XLSX, PPTX, PDF, CSV, local folders, and generated artifacts, presenting results in chat and an Artifact workspace. The desktop product uses Tauri v2, Rust, and Next.js 15, backed by a FastAPI agent runtime and SQLite storage. Inference can run locally through Rapid-MLX, Ollama, or a custom OpenAI-compatible endpoint, or directly against user-configured cloud providers such as OpenAI, Anthropic, and OpenRouter. Its Ultra mode lets a parent agent create 2–4 durable child sessions and synthesize their results into the original task.
After a user installs the desktop app, they can start a conversation and attach working files. OpenYak reads office documents, spreadsheets, slide decks, PDFs, CSVs, local folders, and project context; with user-controlled permissions, it can read, write, rename, organize, and automate files, then produce Markdown briefs, tables, diagrams, checklists, and other structured artifacts. The Next.js frontend supplies chat, settings, an artifact panel, and SSE streaming, while the FastAPI backend handles the agent engine, tool execution, LLM streaming, and storage. For complex work, Ultra lets the parent agent launch AgentRuns in durable child Sessions, follow their state, exchange input, and synthesize persisted outputs. Model traffic goes either to localhost services such as Rapid-MLX, Ollama, or a custom OpenAI-compatible server, or directly to the cloud provider configured by the user.
- A department manager with a dense DOCX memo can turn it into an executive brief, risks, owners, next actions, and a send-ready follow-up email.
- A finance professional reviewing a budget workbook can request budget-versus-actual variance, drivers, anomalies, and meeting talking points.
- A project lead reconciling a memo, budget, slide deck, and PDF can create one board brief artifact in a single thread.
- An operations user who needs continuity can move from analysis to a RACI, 30-day plan, agenda, and follow-up drafts without restating the context.
- A user with a multi-part task can enable Ultra so a parent agent distributes focused work to 2–4 child sessions and combines the outputs.
- A user away from their desk can enable remote access, scan a QR code, and send work to their computer through Cloudflare Tunnel.
What are this agent's strengths and limitations?
- Files, conversations, memory, artifacts, and workflow state are stored locally, and local-model requests can remain on the machine.
- It handles office documents, spreadsheets, slide decks, PDFs, CSVs, and local folders, then turns work into reusable Markdown briefs, tables, diagrams, and checklists.
- It offers multiple model paths: Rapid-MLX, Ollama, custom OpenAI-compatible endpoints, and a broad set of BYOK cloud providers.
- The runtime explicitly includes persisted run state, resumable SSE streams, cancellation, bounded retries, tool argument validation, and layered permissions.
- Ultra keeps durable child sessions, their status, and synthesized output inside the parent task rather than requiring a separate batch form.
- Offline operation requires the user to install and prepare Rapid-MLX, Ollama, or another local OpenAI-compatible service and download a model.
- When a cloud model is selected, prompts and relevant context are sent directly from the desktop to that provider; local storage does not make cloud inference offline.
- File-writing, renaming, organization, and automation are governed by user-controlled permissions, so available actions depend on the approvals granted.
- Remote access depends on Cloudflare Tunnel and token-based authentication, making it a network-dependent feature.
- The documented source-development path provides npm run dev:all but not a complete list of prerequisite runtimes or dependency-installation steps.
How do you install or deploy this agent?
Download the latest release for the target platform: macOS .dmg or .app, Windows x64 .exe installer, or Linux x64 .deb or .rpm. After installation, choose an inference location in Settings: Rapid-MLX on Apple Silicon macOS, Ollama on macOS, Windows, or Linux after downloading a model, or a custom OpenAI-compatible local endpoint. For cloud inference, provide your own provider API key; the ChatGPT option uses an existing Plus, Pro, Team, or Enterprise subscription when available. For source development, the only documented launch command is npm run dev:all, which starts the backend on port 8000 and frontend on port 3000; the supplied material does not document prerequisite Node.js, Rust, Python, or dependency-installation steps.
How do you use this agent?
Launch the app, start a new conversation, and attach a real file. Select a local Rapid-MLX or Ollama model, or a configured cloud provider, then ask for a deliverable such as three key takeaways, risks, owners, next actions, and a follow-up email based on the uploaded files. Review the chat response and the right-side Artifact panel, then continue in the same thread with requests for a RACI, plan, or revision. For complex work, enable Ultra and describe the goal normally; the parent agent decides whether to create child AgentRuns. For mobile access, enable remote access in Settings, scan the QR code, and connect through the mobile web client.
How does this agent compare with similar options?
The README positions OpenYak closer to a local agent workbench than web chat products such as ChatGPT or Claude.ai: it centers local files, artifacts, tool permissions, and long-running work tied to the user’s machine.