OnGarde – Runtime content security proxy for self-hosted AI agents

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Built this because I had heard some horror stories about companies leaking PII from high compliance environments to ChatGPT. I wanted something that would auto-filter any dangerous traffic between my AI agent and the LLM API without requiring code changes in the agent itself. The filtering list has expanded a bit to include PII, secret keys and I've started a prompt injection library thats being filtered on as well. The problem: self-hosted agent platforms (OpenClaw, Agent Zero, CrewAI) have no runtime content layer. If your agent leaks an API key, gets prompt injected, or decides to forward someone's SSN to GPT-4, nothing stops it. The platforms don't try to stop it either. OnGarde is a proxy. You change one line in your config (swap baseUrl) and every request gets scanned before it leaves. Catches credentials, PII, prompt injection, dangerous shell commands. If the scanner fails, it blocks it; never silently passes through. npx @ongarde/openclaw init handles the OpenClaw setup automatically. Also on PyPI if you're doing something custom. Dashboard is localhost-only with a SQLite audit log. Nothing phones home. v1 just shipped: https://github.com/AntimatterEnterprises/ongarde/releases/ta... I am looking for feedback on this project. Let me hear your thoughts.

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, openclaw · Missing: mac, macos, cursor

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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