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Drop in security gateway for AI agents. One env variable, no code changes. Catches and blocks prompt injection, jailbreaks, and agent abuse before they cause damage. No GPU, fast, and fully open source.
I build AI agents, and what kept bothering me is how blind they run. You wire an agent to an LLM and some tools, and you have no real view into what's flowing through it. Is a prompt injection coming in through a tool response? Is a jailbreak getting through? What is the agent actually calling, and with what? I had no visibility into any of it.
I tried existing options, but most wanted an SDK, code changes, or routing my traffic through someone else's cloud.
I just wanted to see what was happening without rewriting anything. So I built Calus.
What it does:
🔌 Drop in with one env variable. No code changes, no SDK. Works with anything OpenAI-compatible.
🔎 Scans every call for prompt injection, jailbreaks, and agent abuse, and shows the verdict in a live dashboard and in response headers.
🧩 Traces every agent and the tools it actually calls, so you can see what your agents are doing.
⚡ Layered engine, not an LLM call. Layer 1 is pattern matching for known attack signatures. Layer 2 is a capability flow graph that catches abuse by consequence, even when no text pattern matches. Both layers can actively block in opt-in gateway mode. Layer 3, an open weight classifier model, is coming soon. Fast, no GPU, easy to audit.
🛡️ Flags by default, never touches your traffic out of the box. Blocking is opt-in, so you stay in control of when enforcement turns on.
It's open source under MIT, and the README publishes honest benchmarks, including where the engine only catches partially.
This is my first real release. I'd genuinely value blunt feedback, especially on the detection approach and the false-positive rate. Happy to answer anything here.
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About Calus on Product Hunt
“Drop in security gateway for AI agents ”
Calus was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #45 on the daily leaderboard. Drop in security gateway for AI agents. One env variable, no code changes. Catches and blocks prompt injection, jailbreaks, and agent abuse before they cause damage. No GPU, fast, and fully open source.
Calus was featured in Open Source (68.6k followers), Developer Tools (515.4k followers) and Security (2.7k followers) on Product Hunt. Together, these topics include over 93.4k products, making this a competitive space to launch in.
Who hunted Calus?
Calus was hunted by Mohith Karthikeya M. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Mohith Karthikeya M
Hi everyone, I'm Mohith, the maker. 👋
I build AI agents, and what kept bothering me is how blind they run. You wire an agent to an LLM and some tools, and you have no real view into what's flowing through it. Is a prompt injection coming in through a tool response? Is a jailbreak getting through? What is the agent actually calling, and with what? I had no visibility into any of it.
I tried existing options, but most wanted an SDK, code changes, or routing my traffic through someone else's cloud.
I just wanted to see what was happening without rewriting anything. So I built Calus.
What it does:
🔌 Drop in with one env variable. No code changes, no SDK. Works with anything OpenAI-compatible.
🔎 Scans every call for prompt injection, jailbreaks, and agent abuse, and shows the verdict in a live dashboard and in response headers.
🧩 Traces every agent and the tools it actually calls, so you can see what your agents are doing.
⚡ Layered engine, not an LLM call. Layer 1 is pattern matching for known attack signatures. Layer 2 is a capability flow graph that catches abuse by consequence, even when no text pattern matches. Both layers can actively block in opt-in gateway mode. Layer 3, an open weight classifier model, is coming soon. Fast, no GPU, easy to audit.
🛡️ Flags by default, never touches your traffic out of the box. Blocking is opt-in, so you stay in control of when enforcement turns on.
It's open source under MIT, and the README publishes honest benchmarks, including where the engine only catches partially.
I'd rather you see the gaps up front.
Website: https://usecalus.com
Repo: https://github.com/wholesphereai/calus
This is my first real release. I'd genuinely value blunt feedback, especially on the detection approach and the false-positive rate. Happy to answer anything here.