Kun Workspace
A shared desktop and terminal workspace for taking local project tasks through reviewable, testable delivery.
Per-dimension scores and reasoning
Evidence shows: README declares local-first, data stored locally by default, and mentions tool approvals, sensitive-action confirmation, sandboxes; SECURITY.md provides vulnerability reporting channels. However, no specific permission model, approval flow details, or data flow diagrams are provided, and publisher identity is unverified. Deductions: least privilege, user confirmation, sensitive data handling are only asserted without implementation details; dependency security has only overrides and audit scripts, no audit results; external effects, rollback, source attribution are only partially documented or unclear.
Evidence shows: README and docs are consistent, package.json scripts are complete, test files demonstrate handling of edge cases (e.g., refusing uncertain operations). However, dependency availability is unverified, and no concrete examples of failure messages are provided. Deductions: self-consistency is good, but dependency availability relies only on package.json declarations, failure messages inferred from tests.
Evidence shows: README clearly identifies target users (developers, writers, designers, researchers) and multiple scenarios (coding, writing, design, research, automation), and states platform support (macOS, Windows, Linux). Capability boundaries are described in docs (e.g., Agent Graph does not expand permissions). However, trigger precision (e.g., commands, shortcuts) is not detailed in provided files. Deductions: audience and scenarios, capability boundaries, environment fit are well documented, but trigger precision evidence is insufficient.
Evidence shows: README provides installation steps, documentation links, FAQ, known limitations (e.g., experimental Agent Graph), license (PolyForm Noncommercial), and version info (package.json version 0.1.0). However, no explicit changelog, and maintenance responsibility only implied via contribution guide and CLA. Deductions: information architecture, install notes, examples and FAQ are sufficient, but naming stability, known limitations, versioning/changelog, maintenance responsibility are insufficiently evidenced.
Evidence shows: README describes outputs as verifiable results (code changes, test results, documents, etc.) and emphasizes value over regular chat boxes. However, cost-benefit is not quantified, and no real usage cases are provided. Deductions: output usability and marginal value are described, but cost-benefit lacks evidence.
Evidence shows: README claims are consistent with docs and test files (e.g., Agent Graph permissions, local data handling), and test files demonstrate verifiable behavior. However, cross-source corroboration is limited, and fact-inference separation is not explicit in docs. Deductions: claim traceability is good, but cross-source corroboration is insufficient, fact-inference separation only partially evident.
- Publisher identity is unverified; verify the source before downloading and installing.
- License is PolyForm Noncommercial; commercial use requires separate authorization. Confirm it fits your use case.
- Dependency security is only asserted via overrides and audit scripts; no actual audit results are provided. Consider running npm audit yourself.
- Agent Graph is marked experimental; use with caution in production.
What does this agent do, and when should you use it?
Kun is a local-first AI agent workspace for code, writing, design, research, and automation. Its Electron desktop GUI and terminal TUI connect to one local `kun serve` runtime and share threads, plans, approvals, and task history. The Code workspace supports local-project search and editing, command execution, diffs, tests, and review, while Write and Design can produce documents, exports, HTML prototypes, and design-system material. Its experimental Agent Graph lets a Lead Agent coordinate constrained subagents through dependency-based tasks and explicit handoff checks. It is aimed at people and teams that want local-workspace execution evidence while retaining a choice of model providers.
A user opens a local project in the GUI or TUI and submits a task; both interfaces use the same kun serve runtime. Kun can search and edit files, run Terminal commands, manage Plans and Todos, inspect Git / Worktree state, show inline Diffs and a Changes panel, and use /plan to break down a goal or /review to produce review findings. It accepts image and PDF inputs for research, while Write can export Markdown, HTML, PDF, DOCX, and editable PPTX; Design can create HTML prototypes and DESIGN_SYSTEM.md. Automation uses Schedule, Loop, Hook, MCP, Skills, Extensions, and a local runtime API. Agent Graph creates dependency graphs, dispatches subagents within the parent task's permissions, retains execution history, and supports pause, resume, retry, editing, and stopping.
- A developer fixing a cross-file issue in a local TypeScript project who needs code search, file edits, test execution, and a diff review in one task.
- A technical lead coordinating a verifiable multi-stage change through experimental Agent Graph nodes for research, implementation, and validation.
- A writer turning an outline, source material, or draft into a polished deliverable that can be exported as PDF, DOCX, or editable PPTX.
- A product designer exploring an interface from requirements or reference images, producing an HTML prototype and
DESIGN_SYSTEM.mdfor implementation. - A researcher extracting and organizing evidence from PDFs, images, and web leads into structured conclusions and continuing task context.
- An individual or team documenting a recurring workflow with Schedule, Loop, Hook, MCP, and Skills while keeping recoverable execution records.
What are this agent's strengths and limitations?
- The desktop GUI and terminal TUI share one
kun serveruntime, including threads, plans, approvals, usage, and background tasks. - It keeps local-project file work, Terminal execution, Git / Worktree state, diffs, tests, and
/reviewfindings within one task record. - It supports subscriptions, Coding Plans, Token Plans, APIs, OpenAI Chat Completions / Responses, Anthropic Messages-compatible services, and self-hosted models instead of a single provider.
- Agent Graph adds dependency scheduling, constrained subagents, evidence follow-up, and Lead Agent acceptance for complex tasks, with resumable history.
- Agent Graph is explicitly experimental; its coordination and acceptance flow is better suited to complex work, while Direct mode is faster for simple changes.
- Source use requires Node.js 22.19+, and practical use requires a supported subscription, API, or custom provider configuration.
- Local-first does not mean data never leaves the machine: prompts, attachments, and task context are sent to a selected cloud Provider when one is used.
- Media generation, higher-permission features, and available models depend on the version, operating system, Provider, model capability, and user authorization.
- The README identifies the license as PolyForm Noncommercial 1.0.0; commercial use, distribution, SaaS, hosting, resale, or commercial-product integration requires separate written authorization.
How do you install or deploy this agent?
Download the desktop release for macOS (Apple Silicon or Intel), Windows x64, or Linux x64; the desktop package includes the TUI. To run from source:
git clone https://github.com/KunAgent/Kun.git
cd Kun
npm ci
npm run devYou need Node.js 22.19+, npm, and at least one supported model subscription, API, or custom provider. For slower mainland-China network access, use: npm ci --registry=https://registry.npmmirror.com.
How do you use this agent?
At first launch, choose a UI language, sign in to a model subscription or configure an API key, Token Plan, or custom Provider; then open a local project or create a workspace and submit a scoped, verifiable task. Run kun from a project directory to start the bundled TUI, which connects automatically to the same local runtime as the GUI. For source development, use npm run dev:tui for the TUI and npm run typecheck, npm run lint, and npm run test for validation.
How does this agent compare with similar options?
Compared with a chat interface that only generates answers, Kun is designed to keep requirements, plans, file changes, tool results, tests, review evidence, and delivery in one continuous workflow; Direct mode remains the lighter option for simple tasks.
FAQ
Is Kun locked to one model provider?
Do the GUI and TUI create separate task histories?
kun serve runtime and can share threads, plans, approvals, usage, and background tasks.