Fraim – A framework for using LLMs in security workflows

Hacker News

We built Fraim to help security teams harness the power of LLMs without having to worry about all the complex glue and infrastructure. Fraim is an open-source project that provides a modular, extensible framework for easily integrating LLM-driven workflows into common security processes such as vulnerability triage, misconfiguration detection, and automated remediation suggestions. Security teams often want the productivity boost of LLMs, but find themselves bogged down by handling API integrations, structured data management, error handling, workflow chaining, and tool integrations. Fraim abstracts all these challenges away, enabling teams to quickly create powerful, custom workflows that output standardized SARIF reports. Currently, Fraim supports code repositories as input, but we're actively working on additional integrations such as GitHub PRs, VSCode integration, and more. It's modular by design, so extending it with new workflows or integrations is straightforward. We're excited to share our initial release and would welcome your feedback, bug reports, and contributions. Check out Fraim on GitHub: https://github.com/fraim-dev/fraim

Visit product View on Hacker NewsJun 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos

91%91% 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: supports · Missing: reddit linkedin, podcasting, created

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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