Business agents · AI agent
PentAGI: review, pricing, security and alternatives
PentAGI is an open-source AI agent published by VXControl, in the business agents family. PentAGI coordinates autonomous agents and security tools to carry out complex penetration testing tasks and investigations. Its repository has 32,270 stars and its last commit is from October 22, 2026.
What PentAGI does
PentAGI belongs to the business agents family: agents specialised in one job: customer support, sales, finance, legal, HR, security. On our grid it ranks #3 of the 52 projects of this family listed in the directory.
You use it from: web app. Usable models: Claude (Anthropic). It can run on your own machine or server.
Who is PentAGI for?
It targets a specific job (customer support, sales, finance, legal…) with workflows already set up. Typical tasks people give it:
- handle first-line customer requests
- prepare standard documents
- track cases and follow up
It is a good candidate if you prefer doing everything in a browser, with no install and your data must stay on your machines.
Access and human control
According to its documentation, PentAGI can:
- run commands in a terminal
- read and edit files
- call online services and APIs
- drive a web browser
By default it acts without asking for confirmation. Turn on an approval mode if it has one, or limit its rights to the bare minimum.
PentAGI pricing
The code is free, under the MIT licence.
Getting started with PentAGI
- Install it: the project ships as a single Go binary, downloadable from the repository releases. 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 business agent acts on the company's behalf: define what it may promise, refund or change, and keep a record of its decisions.
Points found for PentAGI:
- Sensitive access: it can run commands in a terminal, with no established default human approval.
- Outside content: it reads web pages or API responses, which may contain booby-trapped instructions (prompt injection).
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 January 6, 2025, the vxcontrol/pentagi repository has 32,270 stars, 5,003 forks and 23 open issues, with 953 commits in total. It received changes this month.
Over the last 52 weeks: 877 commits, an average of 16.9 per week.
10 releases in total; 7 in the last twelve months. Latest release: v2.2.0, on October 5, 2026.
PentAGI vs other business agents
GitHub stars of the best-rated open-source projects in the business agents family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.
PentAGI and other business agents 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 |
|---|---|---|---|---|---|
| PentAGI | open source | 77 | 32.3K ★ | yes | no |
| DeepTutor | open source | 82 | 42.5K ★ | yes | – |
| Vibe-Trading | open source | 81 | 36.5K ★ | yes | – |
| GenOffice | open source | 74 | 10.7K ★ | yes | – |
| HolmesGPT | open source | 74 | 3.7K ★ | yes | – |
| Text-to-CAD | open source | 73 | 22.9K ★ | – | – |
| YouTube Automation Agent | open source | 73 | 4.6K ★ | yes | yes |
| RedAmon | open source | 73 | 3.2K ★ | yes | yes |
How we rated it
Score: 77/100, computed only from public, verifiable data, with no subjective testing or partnership:
- Adoption 30/40: 32,270 stars on a log scale (40 points from about 200,000).
- Activity 27/30: last commit less than a month ago, 2 releases in six months, 877 commits in a year.
- Framework 20/30: 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
PentAGI is a serious option; check the limitations below before granting it access.
Strengths
- Widely followed: 32.3K stars
- Very active development
- Frequent releases (7 in a year)
- Can run on your side: data under control
- Permissive licence (MIT)
Limitations
- Broad access without default approval
- Tied to a single model provider (Claude (Anthropic))
Avoid if…
- You cannot isolate it (container, virtual machine)
Alternatives
- DeepTutor42.5K ★
- Vibe-Trading36.5K ★
- GenOffice10.7K ★
- HolmesGPT3.7K ★
- Text-to-CAD22.9K ★
- YouTube Automation Agent4.6K ★
Learn more
Frequently asked questions
Is PentAGI free?
The software is free (MIT licence). You still pay for the model calls, unless you use a local model.
Is PentAGI safe?
Our risk level: high. Points found: sensitive access and outside content. Give it minimum rights and approve irreversible actions.
Which models does PentAGI use?
According to its documentation: Claude (Anthropic).
Can PentAGI be installed on your own server?
Yes, from the vxcontrol/pentagi repository. Data then stays with you, but security and updates are your job.
What are the alternatives to PentAGI?
In the same family: DeepTutor, Vibe-Trading, GenOffice, and HolmesGPT.
PentAGI or DeepTutor: what is the difference?
Both belong to the business agents family. PentAGI has 32.3K stars against 42.5K for DeepTutor. 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