Personal assistants · AI agent
Kun: review, pricing, security and alternatives
Kun is an open-source AI agent published by KunAgent, in the personal assistants family. A local-first AI agent workspace for coding, writing, design, research, and automation, with desktop and terminal interfaces. Its repository has 6,315 stars and its last commit is from October 17, 2026.
| Adoption (stars) | 22/40 | |
|---|---|---|
| Activity (commits, releases) | 30/30 | |
| Framework (licence, control, models) | 10/30 |
What Kun does
Kun belongs to the personal assistants family: general-purpose agents, often self-hosted and driven from a messaging app, that act for you on your files, accounts and services. On our grid it ranks #28 of the 46 projects of this family listed in the directory.
You use it from: desktop app and terminal (CLI). Usable models: DeepSeek. It can run on your own machine or server.
Who is Kun for?
It targets individuals and freelancers who want an assistant able to act for them day to day, often from a messaging app. Typical tasks people give it:
- sort e-mail and draft replies
- manage a calendar and reminders
- look up information and summarise it
- run small recurring tasks
It is a good candidate if you are comfortable in a terminal and your data must stay on your machines.
Access and human control
Its documentation does not detail the access granted to the agent: check the settings before connecting it to your accounts.
We could not establish whether it asks for human approval before sensitive actions: test it first on an unimportant account or folder.
Kun pricing
The code is public but the repository declares no licence: reuse is not formally allowed. The real cost comes from model calls: each loop step sends the whole context back to the model, and a task of several dozen steps can cost far more than a single question. The LLM price calculator estimates it for the chosen model.
Getting started with Kun
- Install it: the project ships as a Node.js package (install with
npm, or run directly withnpx). The exact command is in the repository README. - Create an API key with the model provider and put it in an environment variable, never in a shared or versioned file.
- Give it a first playground with nothing at stake: a test folder, a secondary account or a throwaway project. Only open your real accounts once you have seen how it behaves.
- Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.
Security: risk level
A personal assistant often combines access to private data, reading outside content and sending messages: the combination that enables data leaks through prompt injection.
Points found for Kun:
- No licence: legally shaky for company use.
Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.
Project activity and releases
Created on May 21, 2026, the KunAgent/Kun repository has 6,315 stars, 589 forks and 8 open issues, with 3,858 commits in total. It received changes this month.
Over the last 52 weeks: 3,359 commits, an average of 64.6 per week.
103 releases in total; 25 in the last twelve months. Latest release: v0.4.1, on October 17, 2026.
Kun vs other personal assistants
GitHub stars of the best-rated open-source projects in the personal assistants family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.
Kun and other personal assistants at a glance
The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.
| Agent | Model | Score | Stars or entry price | Self-hostable | Human approval |
|---|---|---|---|---|---|
| Kun | open source | 62 | 6.3K ★ | yes | – |
| OpenClaw | open source | 95 | 392.9K ★ | yes | – |
| Hermes Agent | open source | 90 | 256.7K ★ | yes | – |
| nanobot | open source | 87 | 49.3K ★ | yes | – |
| CowAgent | open source | 87 | 47.6K ★ | yes | – |
| QwenPaw | open source | 86 | 36K ★ | yes | – |
| ZeroClaw | open source | 85 | 33K ★ | yes | – |
| OpenHuman | open source | 82 | 42.3K ★ | yes | – |
How we rated it
Score: 62/100, computed only from public, verifiable data, with no subjective testing or partnership:
- Adoption 22/40: 6,315 stars on a log scale (40 points from about 200,000).
- Activity 30/30: last commit less than a month ago, 25 releases in six months, 3,359 commits in a year.
- Framework 10/30: licence, default human approval, self-hosting, model choice and documentation site.
The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.
Verdict
Kun is a serious option; check the limitations below before granting it access.
Strengths
- Very active development
- Frequent releases (25 in a year)
- Can run on your side: data under control
Limitations
- No licence declared
- Tied to a single model provider (DeepSeek)
Alternatives
Learn more
Frequently asked questions
Is Kun free?
The software is public, with no declared licence. You still pay for the model calls, unless you use a local model.
Is Kun safe?
Our risk level: mid. Points found: no licence. Give it minimum rights and approve irreversible actions.
Which models does Kun use?
According to its documentation: DeepSeek.
Can Kun be installed on your own server?
Yes, from the KunAgent/Kun repository. Data then stays with you, but security and updates are your job.
What are the alternatives to Kun?
In the same family: OpenClaw, Hermes Agent, nanobot, and CowAgent.
Kun or OpenClaw: what is the difference?
Both belong to the personal assistants family. Kun has 6.3K stars against 392.9K for OpenClaw. The right choice mostly depends on the access you are willing to give it.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score