Nebula vs PentAGI
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
Open-source tool that links computers across the internet into one private, encrypted network.
Open source · MIT
Self-hosted AI agent system that runs penetration testing tasks in an isolated Docker sandbox.
The short answer
Choose Nebula if you need…
- Teams connecting servers across several clouds or data centers
- People who want a private network between their own machines without a fixed address plan
- Operators who want group-based firewall rules similar to cloud security groups
- Users comfortable managing certificates and a small cloud host
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
Side-by-side
| Nebula | PentAGI | |
|---|---|---|
| Type | App | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 18.5k | 33k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Install with a package manager | See the README |
| Main language | Go | Go |
| Last code update | 7 days ago | 14 days ago |
What each one is
Nebula
Nebula is a networking tool that connects computers wherever they are into a single private network, so they can reach each other as if on the same local network. It uses certificates and encryption to authenticate each machine, and lets you write firewall rules based on named groups. The README says it runs on Linux, macOS, Windows, iOS, and Android, and can connect from a few computers up to tens of thousands.
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.
Best for
Nebula
- Teams connecting servers across several clouds or data centers
- People who want a private network between their own machines without a fixed address plan
- Operators who want group-based firewall rules similar to cloud security groups
- Users comfortable managing certificates and a small cloud host
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
Strengths
Nebula
- Built on established cryptography: the README names the Noise Protocol Framework, ECDH, and AES-256-GCM by default
- Runs on many platforms, including mobile, and is available through several package managers and Docker
- MIT licensed and actively maintained, with an official example configuration and documentation site
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
Not ideal for
Nebula
- People who want a plug-and-play VPN with no setup: the README describes creating a certificate authority, signing certs, and editing config files
- Teams that do not want to run their own certificate authority and lighthouse; the README points to a paid managed service from Defined Networking for that
- Anyone needing a consumer 'hide my browsing' VPN, since the README frames Nebula as a network overlay for connecting hosts
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
Trade-offs
Nebula
- Setup involves several steps: binaries, a certificate authority, host certificates, config files, and a lighthouse with a routable IP
- The README notes that certificate authorities expire after one year by default and need rotation
- Building with FIPS or BoringCrypto options involves extra build targets, and the README says BoringCrypto support is deprecated
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
Getting started
Nebula
Install on macOS with Homebrew
brew install nebulaInstall on Arch Linux
sudo pacman -S nebulaPentAGI
Clone the repository
git clone https://github.com/vxcontrol/pentagiFrequently asked questions
What is the difference between Nebula and PentAGI?
Nebula: Open-source tool that links computers across the internet into one private, encrypted network. PentAGI: Self-hosted AI agent system that runs penetration testing tasks in an isolated Docker sandbox. Nebula suits teams connecting servers across several clouds or data centers, while PentAGI suits security engineers and penetration testers who want an autonomous assistant on their own servers.
Are Nebula and PentAGI free?
Nebula: Free and open source. PentAGI: Free and open source.
Is Nebula or PentAGI more popular?
PentAGI has more GitHub stars (33k) than Nebula (18.5k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Nebula and PentAGI?
Other options people compare them with include PentestGPT, 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.