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ViBo
Memory for AI agents — never costs more than no memory
Your AI agent finally remembers. ViBo stores facts between sessions and recalls them by meaning, not keywords. It compresses web search results before the LLM sees them (96.2% fewer tokens), keeps long threads as a compact summary (−72%), and gives agent teams shared memory — one compact context block. Secrets sit in encrypted L1/L2/L3 tiers, API keys never reach the model. One skill. $5/month.
Hey Product Hunt 👋 I'm Viacheslav.
I built ViBo out of pure frustration. Every agent I ran forgot everything between sessions, and every "fix" — bigger context, longer prompts, re-sending the same search results — just made the bill worse. I wanted memory that actually worked and never punished me for having it.
So ViBo does four things from one skill: remembers facts across sessions (encrypted L1/L2/L3 tiers, so secrets never reach the model), compresses web search results (96.2% fewer tokens), keeps thread history in a file with only a summary to the model (−72% tokens), and gives agent teams a shared memory — orchestrators read one compact context block, up to 96% fewer tokens per handoff.
The rule I refuse to break: ViBo never costs more than no-ViBo. Small memory → it stays silent and costs nothing.
What's next: more integrations, tighter compression, richer retrieval. I'd love to hear what your agent forgets first — comment below or email [email protected]. Two-day free trial, no card.
— Viacheslav Bochkarev
About ViBo on Product Hunt
“Memory for AI agents — never costs more than no memory”
ViBo was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #102 on the daily leaderboard. Your AI agent finally remembers. ViBo stores facts between sessions and recalls them by meaning, not keywords. It compresses web search results before the LLM sees them (96.2% fewer tokens), keeps long threads as a compact summary (−72%), and gives agent teams shared memory — one compact context block. Secrets sit in encrypted L1/L2/L3 tiers, API keys never reach the model. One skill. $5/month.
On the analytics side, ViBo competes within Productivity, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.7M followers on Product Hunt. The dashboard above tracks how ViBo performed against the three products that launched closest to it on the same day.
Who hunted ViBo?
ViBo was hunted by Viacheslav Bochkarev. 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 ViBo including community comment highlights and product details, visit the product overview.
Viacheslav Bochkarev