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DebugBundle
Production debugging for AI coding agents
DebugBundle captures errors and surrounding context at the moment of failure and packages them into one structured artifact: the debug bundle.
Hi everyone! Owen here 👋 Really happy to finally share DebugBundle with you.
It started with something simple: I wanted my coding agent to get the context it needs to investigate a production issue in one structured bundle, without having to navigate a complex monitoring product.
That’s still the main idea. Keep the debugging evidence easy to access and understand, so the agent spends less time gathering context and more time investigating the actual problem. 🪲
I know this is a crowded space, but that simplicity is what made me keep pursuing it. As agents take on more of our development work, I think they need tools built around how they work too.
I’ve spent the past eight months building, testing, and refining DebugBundle, starting with my own projects. It’s now running on eight production sites across my own work, friends, and existing clients. Today I’m excited to share it beyond that circle and hear what others think.
The core and SDKs are open source under Apache-2.0. You can run it locally or use the cloud service, and access the bundles through CLI, API, MCP, or the dashboard.
I’d genuinely love your feedback. Does this approach make sense for how you work? What do you still gather manually before your coding agent can help?
Thanks for taking a look, and happy to answer questions! 🙂
About DebugBundle on Product Hunt
“Production debugging for AI coding agents”
DebugBundle was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #107 on the daily leaderboard. DebugBundle captures errors and surrounding context at the moment of failure and packages them into one structured artifact: the debug bundle.
On the analytics side, DebugBundle competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how DebugBundle performed against the three products that launched closest to it on the same day.
Who hunted DebugBundle?
DebugBundle was hunted by Owen Far. 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 DebugBundle including community comment highlights and product details, visit the product overview.
Owen Far