HexStrike AI 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
Lets AI agents drive about 150 security testing tools through an MCP server, for authorized pentesting.
Open source · MIT
Self-hosted AI agent system that runs penetration testing tasks in an isolated Docker sandbox.
The short answer
Choose HexStrike AI if you need…
- Security professionals who want to script or speed up recon with an AI assistant
- Students practicing on CTF challenges or lab machines they own
- Researchers experimenting with how LLM agents use security tooling
- Developers who already run Kali-style tool setups and want an MCP bridge
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
| HexStrike AI | PentAGI | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 13.3k | 33k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Clone and build from source | See the README |
| Main language | Python | Go |
| Last code update | 3 months ago | 14 days ago |
What each one is
HexStrike AI
HexStrike AI is an MCP server that connects AI assistants such as Claude, GPT, and Copilot to a large set of existing cybersecurity tools, including scanners, subdomain finders, and password crackers. The README describes it as a way for an AI agent to run these tools and chain results together for penetration testing, vulnerability discovery, and security research. The project is owned by OTT Cybersecurity LLC and released under the MIT license.
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
HexStrike AI
- Security professionals who want to script or speed up recon with an AI assistant
- Students practicing on CTF challenges or lab machines they own
- Researchers experimenting with how LLM agents use security tooling
- Developers who already run Kali-style tool setups and want an MCP bridge
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
HexStrike AI
- Connects several mainstream AI clients to a broad set of established security tools via MCP
- Open-source under MIT, so the code can be read and modified
- Active project with a large star count and recent pushes
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
HexStrike AI
- Anyone testing systems they do not own or have written permission to test, since the tools are offensive and the README does not address legal scope
- Beginners without Linux and security tool experience, because the setup assumes you can install and troubleshoup many external tools
- Teams needing a polished, supported product, since no release is tagged and the README is largely promotional
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
HexStrike AI
- Requires installing many external security tools separately, and the README does not fully explain each one
- Offensive tooling carries misuse risk and needs careful authorization and scoping by the user
- No tagged release, and some README claims such as tool counts and agent counts are not independently verified here
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
HexStrike AI
Clone the repository
git clone https://github.com/0x4m4/hexstrike-ai.gitCreate and activate a virtual environment
python3 -m venv hexstrike-env && source hexstrike-env/bin/activatePentAGI
Clone the repository
git clone https://github.com/vxcontrol/pentagiFrequently asked questions
What is the difference between HexStrike AI and PentAGI?
HexStrike AI: Lets AI agents drive about 150 security testing tools through an MCP server, for authorized pentesting. PentAGI: Self-hosted AI agent system that runs penetration testing tasks in an isolated Docker sandbox. HexStrike AI suits security professionals who want to script or speed up recon with an ai assistant, while PentAGI suits security engineers and penetration testers who want an autonomous assistant on their own servers.
Are HexStrike AI and PentAGI free?
HexStrike AI: Free and open source. PentAGI: Free and open source.
Is HexStrike AI or PentAGI more popular?
PentAGI has more GitHub stars (33k) than HexStrike AI (13.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to HexStrike AI and PentAGI?
Other options people compare them with include PentestGPT, Nuclei, MCP for Blender, SuperAGI, 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.