What do you want to do?
3 use cases
-
Run an authorized pentest with a team of AI agents
Describe a target you are allowed to test, and let PentAGI plan, run tools in a sandbox and write the report.
View prompt
Help me install vxcontrol/pentagi with Docker Compose on an isolated lab server, configure <provider>, and run a flow against my own test VM at <lab address> only. Confirm the scope before starting.
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Run PentAGI on local models so findings never leave your network
Point PentAGI at Ollama or vLLM so prompts and results stay on your hardware.
View prompt
Configure vxcontrol/pentagi to use my local <Ollama or vLLM> endpoint with <model>, enable execution monitoring, and run a short flow against my lab VM. Report which steps the local model struggled with.
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Start and collect pentest flows from your own tooling via API
Use the REST or GraphQL API with a Bearer token to trigger flows and pull results into your pipeline.
View prompt
Write a script that uses vxcontrol/pentagi's REST API with a token from an environment variable to start a flow on <authorized lab target>, wait for it, and save the Markdown report.
Quick start with PentAGI
- Check you have what it needs: Docker and Docker Compose (an interactive installer sets it up on Linux, macOS or Windows); An LLM provider key or a local model via Ollama or vLLM; Written authorization to test every target.
- Open vxcontrol/pentagi on GitHub and read the README and license.
- Install with the interactive installer (Docker Compose)
- Configure an LLM provider
- Log in to the web UI and start a flow with the target and goal in plain language
- Watch agents run tools such as nmap and sqlmap in the Docker sandbox
These steps come from our top use case, Run an authorized pentest with a team of AI agents, which also has a copy-paste agent prompt.
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About this repo
- Who it is forSecurity engineers, pentesters and red teams who are authorized to test the target systems and want an autonomous assistant on their own servers.
- Why it is hot32k stars, talked about in 2 AI sources we track
- LanguageGo
- Sourcegithub.com/vxcontrol/pentagi · Back to Hot repos
Requirements
- Docker and Docker Compose (an interactive installer sets it up on Linux, macOS or Windows)
- An LLM provider key or a local model via Ollama or vLLM
- Written authorization to test every target
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Questions about PentAGI
What is PentAGI?
PentAGI is an open-source, self-hosted multi-agent system for autonomous penetration testing, for authorized testing only. You describe a target and goal in plain language; specialized agents plan the work, run 20+ tools such as nmap, Metasploit and sqlmap inside an isolated Docker sandbox, keep what they learn in PostgreSQL with pgvector, and produce a report. It works with 10+ cloud LLM providers or local models through Ollama or vLLM, and has REST and GraphQL APIs. It is an open-source project on GitHub: https://github.com/vxcontrol/pentagi.
How do I use PentAGI?
Install with the interactive installer (Docker Compose); then configure an LLM provider; log in to the web UI and start a flow with the target and goal in plain language. The use cases on this page give the full steps and a copy-paste agent prompt for each job.
Who is PentAGI for?
Security engineers, pentesters and red teams who are authorized to test the target systems and want an autonomous assistant on their own servers.
What can I do with PentAGI?
3 use cases so far, for example: Run an authorized pentest with a team of AI agents; Run PentAGI on local models so findings never leave your network; Start and collect pentest flows from your own tooling via API. Each has steps and a copy-paste agent prompt.
What do I need to run PentAGI?
Docker and Docker Compose (an interactive installer sets it up on Linux, macOS or Windows); An LLM provider key or a local model via Ollama or vLLM; Written authorization to test every target.
What are alternatives to PentAGI?
Other hot AI repos in Security and science: Security Audit Skill, Agentbaseline, RiftX, Hacker, Codex Privacy Hud.
Is PentAGI safe to run?
Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.