Khaos Brain is a local-first predictive memory system for AI agents. It turns task experience, preferences, workflow lessons, and skill-use evidence into visible Git-versioned cards. Agents retrieve relevant cards before work, write observations afterward, and Sleep/Dream/Architect maintenance keeps the library reviewable instead of becoming a black-box memory store.
Hey Product Hunt,
I built Khaos Brain because most AI memory features feel too shallow for real agent work.
Saving "remember this next time" is useful, but the more valuable unit is accumulated experience: what condition appeared, what action was taken, what result happened, which route failed, and which route later became reliable.
Khaos Brain is an open-source, local-first predictive memory system for AI agents. It stores experience as visible file-based cards instead of an opaque memory box.
The current release is Codex-first, but the idea is broader:
- before a task, the agent retrieves relevant experience
- after a task, it writes observations and lessons back
- maintenance workflows such as Sleep, Dream, and Architect keep the card library organized over time
- organization mode can share reviewed experience models through GitHub without mixing personal preferences into the shared pool
The cards are readable, searchable, reviewable, diffable, mergeable, and reversible with Git. The goal is not to replace RAG, vector databases, or ordinary notes. It is to make agent working experience visible enough that a human or team can inspect what the agent is about to reuse.
Latest release: v0.4.7
Repo: https://github.com/liuyingxuvka/...
Feedback discussion: https://github.com/liuyingxuvka/...
The easiest way to try it is to hand the repo URL to a capable coding agent and ask it to install and enable Khaos Brain, then run the health check.
The feedback I care about most:
After trying it on a real AI-agent workflow, does Khaos Brain help the agent start from reusable prior experience instead of a blank context, and do visible cards with source/status/confidence feel more useful than your current AI memory, notes, or vector-store workflow?
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About Khaos Brain on Product Hunt
“Local predictive memory for AI agents”
Khaos Brain launched on Product Hunt on May 12th, 2026 and earned 64 upvotes and 2 comments, placing #36 on the daily leaderboard. Khaos Brain is a local-first predictive memory system for AI agents. It turns task experience, preferences, workflow lessons, and skill-use evidence into visible Git-versioned cards. Agents retrieve relevant cards before work, write observations afterward, and Sleep/Dream/Architect maintenance keeps the library reviewable instead of becoming a black-box memory store.
Khaos Brain was featured in Developer Tools (512.4k followers), Artificial Intelligence (468.5k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 182.5k products, making this a competitive space to launch in.
Who hunted Khaos Brain?
Khaos Brain was hunted by Yingxu Liu. 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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Yingxu Liu