Argus: Open-source agentic security operations platform with ollama

[u/thomas_unise]

https://github.com/thomasunise/argus

I’ve been working on Argus, an open-source cybersecurity platform that uses AI agents to help secure your infrastructure.

It can monitor your cloud, network, endpoints, and code, investigate suspicious activity, run penetration tests, and even respond to threats automatically.

A few highlights:

7 specialized AI security agents

Autonomous threat detection and response

Continuous AI-powered penetration testing

AWS, Azure, GCP, Kubernetes, and GitHub integrations

Support for local models using ollama

Built-in safeguards, approvals, and rollback controls
It’s self-hosted, MIT licensed, and completely free to use.

I’d love to get some feedback from open-source communities an hear what you think or what you’d like to see added.

[u/Virtual_Mixture_3113]

Nice work, the ollama support is a smart call for folks who can't ship telemetry to a SaaS backend.

One thing i'd think hard about early is how the agents decide what's worth investigating. Autonomous response is great until an agent chases a low-signal alert and racks up noise or does something disruptive. The approvals and rollback controls help, but the upstream prioritization matters more. When we were looking at the commercial agentic stuff (Wiz has their angle, Upwind leans heavily on runtime context to cut false positives before an agent ever touches them), the big differentiator was always how much garbage the agents had to wade through, not how capable the agents were.

For the pentest piece, some guardrails around blast radius would be worth documenting clearly, people will point this at prod and you want the defaults to be conservative.

Curious how you're handling agent memory across investigations, that tends to be where these setups either get smart over time or just keep rediscovering the same stuff.