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Kage gives teams shared repo memory for AI coding agents. When an agent discovers a bug, workaround, test rule, or design decision, Kage saves it as a reviewable packet and links it to files, symbols, and tests. The next teammate or agent recalls that context before repeating the same investigation or breaking the same thing again.
Hey Product Hunt,
I built Kage because AI coding agents keep forgetting repo lore.
Every new session starts with the same painful loop: where is this logic, why is this workaround here, which tests matter, what broke last time, and why did another agent already make this decision?
Kage turns that knowledge into repo-local memory.
The important part: Kage does not just save notes. It connects memory to code.
Example: an agent fixes a flaky checkout retry test and discovers two retry paths look duplicated, but are intentionally different. One path retries external callbacks using idempotency keys. The other retries user checkout using session state.
Kage saves that as a memory packet and links it to the retry modules and tests.
Two weeks later, someone opens a fresh agent session and asks: "Clean up this duplicated retry logic."
A normal agent may blindly refactor it.
With Kage, the agent recalls: "This duplication is intentional. Here is why. These are the tests to run."
That is the gap Kage focuses on.
Agents do not just need more context. They need the right repo knowledge at the moment they touch the relevant code.
Kage is open-source, local-first, git-visible, CLI + MCP ready, and designed for teammates to share repo memory through the repo itself.
I would love feedback from people using coding agents seriously: would you commit this kind of repo memory with your code?
Website: https://kage-core.com
GitHub: https://github.com/kage-core/Kage
npm: https://www.npmjs.com/package/@k...
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About Kage on Product Hunt
“Shared repo memory for AI coding agents”
Kage was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #62 on the daily leaderboard. Kage gives teams shared repo memory for AI coding agents. When an agent discovers a bug, workaround, test rule, or design decision, Kage saves it as a reviewable packet and links it to files, symbols, and tests. The next teammate or agent recalls that context before repeating the same investigation or breaking the same thing again.
Kage was featured in Developer Tools (512.4k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 89.6k products, making this a competitive space to launch in.
Who hunted Kage?
Kage was hunted by Kushal Jain. 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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Kushal Jain