PentAGI vs PentestGPT
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Self-hosted AI agent system that runs penetration testing tasks in an isolated Docker sandbox.
Open source · MIT
AI agent that runs penetration tests and CTF challenges in stages, with human-in-the-loop legacy mode.
The short answer
Choose PentAGI if you need…
- Security engineers and penetration testers who want an autonomous assistant on their own servers
- Red teams testing systems they are authorized to assess
- Researchers who want to use local models through Ollama or vLLM
- Teams that want REST or GraphQL API access to automate testing
Choose PentestGPT if you need…
- Security students working through CTF challenges
- Security professionals who want an AI-assisted pentest workflow on authorized targets
- Researchers studying LLM-driven penetration testing
- Users who already have Claude Code or Codex CLI set up
Side-by-side
| PentAGI | PentestGPT | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 33k | 16.2k |
| Setup difficulty | Advanced | Advanced |
| How you get it | See the README | Clone and build from source |
| Main language | Go | Python |
| Last code update | 14 days ago | 4 months ago |
What each one is
PentAGI
PentAGI is an open-source, self-hosted system where you describe a target and a goal in plain language, and a team of AI agents plans the work and runs security tools such as nmap, Metasploit and sqlmap inside an isolated Docker sandbox. It keeps results in PostgreSQL with pgvector, and it produces a report at the end. It is meant for authorized security testing on your own infrastructure.
PentestGPT
PentestGPT is a Python tool that uses large language models to help with security testing. In its main mode it runs an autonomous agent, driving Claude Code or Codex, through staged phases such as recon, exploit and walkthrough for CTF challenges, or asset discovery, vulnerability identification and report for penetration tests. An older interactive mode, pentestgpt-legacy, keeps a human in the loop and supports several LLM providers. It is published alongside a USENIX Security 2024 paper.
Best for
PentAGI
- Security engineers and penetration testers who want an autonomous assistant on their own servers
- Red teams testing systems they are authorized to assess
- Researchers who want to use local models through Ollama or vLLM
- Teams that want REST or GraphQL API access to automate testing
PentestGPT
- Security students working through CTF challenges
- Security professionals who want an AI-assisted pentest workflow on authorized targets
- Researchers studying LLM-driven penetration testing
- Users who already have Claude Code or Codex CLI set up
Strengths
PentAGI
- Runs on your own infrastructure with the LLM provider of your choice, including local models
- Tools run inside an isolated Docker sandbox
- Offers REST and GraphQL APIs with Bearer token authentication for automation
PentestGPT
- Multi-stage pipeline passes findings from one stage to the next
- Supports several LLM providers in legacy mode, including local Ollama models
- Sessions can be saved and resumed, and the project is MIT licensed
Not ideal for
PentAGI
- Anyone testing systems without explicit authorization, since the project states it is for authorized testing only
- Beginners with no Docker or security background, because setup and safe use require operational knowledge
- Users who want a fully hosted service with no server to run, since it is self-hosted
PentestGPT
- Anyone testing systems they do not have explicit permission to test, since it is built to attack targets
- Beginners with no security or command-line background, because the setup and results need expert review
- Teams needing a guaranteed, audited scanner, because agent output depends on the underlying model and may be wrong
Trade-offs
PentAGI
- Requires running Docker and configuring an LLM provider, which takes operational effort
- Autonomous tool use against targets carries real risk and needs careful authorization and scope control
- The README is truncated, so some details of limits and stability could not be verified
PentestGPT
- Autonomous mode depends on Claude Code or Codex CLI being installed and authenticated
- Requires an API key or paid account for the chosen model provider; costs are not described in the README
- Telemetry sends anonymous usage data to Langfuse by default, and users must opt out explicitly
Getting started
PentAGI
Clone the repository
git clone https://github.com/vxcontrol/pentagiPentestGPT
Clone the repository
git clone https://github.com/GreyDGL/PentestGPT.gitEnter the project folder
cd PentestGPTFrequently asked questions
What is the difference between PentAGI and PentestGPT?
PentAGI: Self-hosted AI agent system that runs penetration testing tasks in an isolated Docker sandbox. PentestGPT: AI agent that runs penetration tests and CTF challenges in stages, with human-in-the-loop legacy mode. PentAGI suits security engineers and penetration testers who want an autonomous assistant on their own servers, while PentestGPT suits security students working through ctf challenges.
Are PentAGI and PentestGPT free?
PentAGI: Free and open source. PentestGPT: Free and open source.
Is PentAGI or PentestGPT more popular?
PentAGI has more GitHub stars (33k) than PentestGPT (16.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to PentAGI and PentestGPT?
Other options people compare them with include Nebula, HexStrike AI, invisible_dots, Edict, Hermes Agent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.