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Open-source eval framework for AI agents
aligned to the OWASP Agentic Security Initiative Top 10
I just published an open-source framework for red-teaming AI agents. Not LLM chatbots — agents. The kind built on LangChain, CrewAI, AutoGPT-style architectures that use tools, call APIs, and take multi-step actions in the world. GitHub: https://lnkd.in/eCSea5ak If you're building agents and you've hit unexpected failure modes — I'd like to hear about them.
Hey Product Hunt! 👋
I built safelabs-eval because I kept seeing the same pattern: teams moving fast with AI agents, shipping to production, and only discovering the failure modes after something went wrong.
The problem isn't that people don't care about safety — it's that there was no practical, framework-agnostic tool to systematically test AI agents against known attack vectors before deployment. OWASP published their LLM Top 10 but there was nothing open-source that actually operationalized it for agents specifically.
So I built it to work with the frameworks teams are already using — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, and Google ADK — so you don't have to change your stack to add safety evaluation.
What I'd love to hear from you:
Are you currently red-teaming your AI agents before shipping?
What attack vectors worry you most in production?
Happy to answer any questions about the framework, the OWASP alignment, or where we're taking this next. 🙏
About Open-source eval framework for AI agents on Product Hunt
“aligned to the OWASP Agentic Security Initiative Top 10”
Open-source eval framework for AI agents was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #144 on the daily leaderboard. I just published an open-source framework for red-teaming AI agents. Not LLM chatbots — agents. The kind built on LangChain, CrewAI, AutoGPT-style architectures that use tools, call APIs, and take multi-step actions in the world. GitHub: https://lnkd.in/eCSea5ak If you're building agents and you've hit unexpected failure modes — I'd like to hear about them.
On the analytics side, Open-source eval framework for AI agents competes within Open Source, Artificial Intelligence and GitHub — topics that collectively have 580.8k followers on Product Hunt. The dashboard above tracks how Open-source eval framework for AI agents performed against the three products that launched closest to it on the same day.
Who hunted Open-source eval framework for AI agents?
Open-source eval framework for AI agents was hunted by Waqar Javed. 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.
For a complete overview of Open-source eval framework for AI agents including community comment highlights and product details, visit the product overview.
Waqar Javed