PentAGI: a self-hosted AI team that runs a full pentest

2 months ago

Point this at a system you're allowed to test, hand it a goal, and a whole team of AI agents runs the pentest for you. The recon, the exploit, and the write-up.

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  1. 0:00Slide 1
  2. 0:08What it is
  3. 0:28Automated, and built to run safely
  4. 0:45Not one agent. A team, with a boss
  5. 0:59A supervision layer keeps the loop honest
  6. 1:21Every run learns from the last
  7. 1:36You pick the tools' power and the model's cost
  8. 1:54Getting it is a Docker Compose up
  9. 2:10PentAGI
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Slide 1

GitHub Trending · Ep 06 · Jul 11, 2026
PentAGI: a self-hosted AI team that runs a full pentest
Runs the recon, the exploit, and the write-up
Go MIT self-hosted

Point this at a system you're allowed to test, hand it a goal, and a whole team of AI agents runs the pentest for you. The recon, the exploit, and the write-up.

What it is

An AI agent that automatically determines and executes penetration-testing steps.
open-source self-hosted autonomous
autonomous and assistant-guided, not a breach-and-attack-simulation product

PentAGI is open-source, and you run it yourself. Give it a target and an objective, and a team of specialist agents plans the test, runs it, and writes up what they find. It's built for security pros doing authorized work. And it's honest about the edges: this is an autonomous, assistant-guided platform, not a canned breach-simulation product.

Automated, and built to run safely

System context: a security engineer uses PentAGI, which reaches a target system, an LLM provider, search systems, and reports to Langfuse and Grafana dashboards
isolated Docker sandbox self-hosted bring your own model

Automating a pentest sounds risky, so here's why it's safe to run. Every operation happens inside isolated Docker containers. You self-host the whole thing, so your targets and your data never leave your own infrastructure. And you bring your own model, any provider, or a local one that never phones home.

Not one agent. A team, with a boss

Agent interaction sequence: an orchestrator directs researcher, developer, and executor agents across research, planning, and execution phases, each consulting a vector store and knowledge base
Orchestratorruns the show
Researcherstudies the target, hunts known weaknesses
Developerplans the attack
Executorruns it
all sharing one memory of what's worked before

Here's the clever part. It isn't one giant agent, it's a team. An orchestrator runs the show. A researcher studies the target and hunts known weaknesses. A developer plans the attack. And an executor runs it. Every one of them checks a shared memory of what's worked before.

A supervision layer keeps the loop honest

Mentor steps in on loops repeated ≥5 · runaway ≥10 tool calls
Plans 3 to 7 steps first breaks a job into concrete steps
Hard tool-call caps 100 general · 20 limited
The project reports about 2x result quality on smaller local models (its own testing, Qwen3.5-27B-FP8), at 2 to 3x the tokens and time. Both features beta.

So what stops a loop of agents from going off the rails? A supervision layer. A mentor spots repeated or runaway tool calls and steps in with a new plan. A planner breaks each job into three to seven concrete steps first. And every agent has a hard cap on its tool calls. The project reports that roughly doubles quality on smaller local models, at more tokens and time. Both pieces are still beta.

Every run learns from the last

Memory system: episodic, long-term, and working memory feed a vector store and knowledge base that guide the current goals and task state
long-term · working · episodic PostgreSQL + pgvector · optional Graphiti/Neo4j

And runs aren't amnesiac. PentAGI keeps three kinds of memory: long-term, for past findings and tool habits, working memory for the current goal, and episodic memory of what actually succeeded last time. So the next run starts from what already worked.

You pick the tools' power and the model's cost

Built-in tools nmap · metasploit · sqlmap 20+, auto-selected on a Kali Linux base
Bring your own model OpenAI · Anthropic · Gemini · Bedrock · Ollama · DeepSeek · GLM · Kimi · Qwen · custom 10+ providers · local / air-gapped

It comes with the toolbox built in. Over twenty professional security tools, things like nmap, metasploit, and sqlmap, picked automatically per task from a Kali Linux base. And it runs on ten-plus model providers. OpenAI, Anthropic, Gemini, or Ollama for something fully local. You pick the tools' power and the model's cost.

Getting it is a Docker Compose up

curl -O raw.githubusercontent.com/…/pentagi/docker-compose.yml
docker compose up -d
then open https://localhost:8443 (self-hosted web UI)
Go React self-hosted MIT v2.1.0
What you get back: a real report of the vulnerabilities it found, with exploitation guides, ready to export as Markdown or PDF.

Getting it is a Docker Compose up. Go backend, React front end, self-hosted, MIT licensed, latest release two point one oh. And what you get back is a real report: the vulnerabilities it found, with exploitation guides, ready to export as Markdown or PDF.

PentAGI

An authorized pentest, run by a team of agents you host yourself
github.com/vxcontrol/pentagi
New repo tomorrow.

So that's PentAGI. An authorized pentest, run by a supervised team of agents you host yourself. You just bring the model. If you work in security, it's worth a look. The link's on screen, and there's a new repo tomorrow.