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Mutant-AI
Expand eval coverage & use red agents to break AI systems
Hey Product Hunt! 👋
I built Mutant because I kept seeing AI evaluations become static pretty quickly ,you write a dataset, run it, and eventually stop discovering new failure modes.
Mutant has two core pieces:
• Behavioral mutations — take an existing evaluation dataset and generate targeted variations to expand evaluation coverage.
• Red-team agents — let autonomous agents attack LLMs, RAG systems, and AI agents to uncover vulnerabilities and unexpected failure modes.
The goal is simple: make it easier to continuously find the ways AI systems can fail before your users do.
Would love to hear what kinds of failure modes or attacks you’re currently struggling to evaluate. 🚀
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About Mutant-AI on Product Hunt
“Expand eval coverage & use red agents to break AI systems”
Mutant-AI was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #120 on the daily leaderboard. An open-source Python library for automated LLM red teaming, synthetic dataset augmentation, and behavioral evaluation of LLMs, RAGs, and AI Agents.
Mutant-AI was featured in A/B Testing (20.4k followers), Developer Tools (517.6k followers), Artificial Intelligence (476.1k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 221.6k products, making this a competitive space to launch in.
Who hunted Mutant-AI?
Mutant-AI was hunted by Ankit Mishra. 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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Ankit Mishra