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Operational Database for AI Agents. Open source. Self-host for free or use our managed cloud. ZizkaDB gives AI agents persistent operational memory with semantic search, causal lineage, session replay, and SHA-256 checksums for every event—built with enterprise-grade security and EU compliance in mind. Deploy privately for enterprise environments, reduce token costs, optimize agent performance, and cut debugging time for development teams.
Hi Product Hunt! 👋 I'm Mir, founder of ZizkaDB.
As AI agents become more capable, they're also becoming harder to debug. When an agent fails, the biggest question is often: Why?
That's why we built ZizkaDB, an open-source operational database for AI agents.
It records every event, making it possible to:
* 🔍 Search agent history with semantic search
* 🌳 Trace execution with causal lineage
* ⏪ Replay sessions to understand failures
* 🔒 Verify every event with SHA-256 checksums
* ☁️ Self-host for free or use our managed cloud
Whether you're building with LangGraph, CrewAI, OpenAI Agents, or your own framework, ZizkaDB provides an operational layer to observe and understand agent behavior.
We're launching today to get feedback from developers and AI builders. We'd love to hear:
* What challenges do you face when debugging AI agents?
* What features would make an operational database most valuable for your workflow?
Thanks for checking out ZizkaDB, we're excited to hear your thoughts and answer any questions throughout the day!
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About ZizkaDB on Product Hunt
“Operational Database for AI Agents”
ZizkaDB was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #33 on the daily leaderboard. Operational Database for AI Agents. Open source. Self-host for free or use our managed cloud. ZizkaDB gives AI agents persistent operational memory with semantic search, causal lineage, session replay, and SHA-256 checksums for every event—built with enterprise-grade security and EU compliance in mind. Deploy privately for enterprise environments, reduce token costs, optimize agent performance, and cut debugging time for development teams.
ZizkaDB was featured in Artificial Intelligence (477.3k followers), GitHub (41.4k followers), Data & Analytics (5.8k followers) and Database (2.2k followers) on Product Hunt. Together, these topics include over 150.2k products, making this a competitive space to launch in.
Who hunted ZizkaDB?
ZizkaDB was hunted by Mir Arshad Ali Talpur. 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.
Want to see how ZizkaDB stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Mir Arshad Ali Talpur