Four keys to building with agentic AI — GitDB for multi-agent code, Memory for long-term recall, Vector for full-fidelity search, and Embedded Robotics for on-device intelligence. One platform behind agents that actually deliver.
Here's what an agentic data layer looks like — agent swarms query in parallel, engineers stream what they need, and your data stays put in one secure home. This is GitDB at work; the same architecture powers Memory, Vector, and Embedded across the rest of keyes.ai.
Import from your existing Git host in a single pass, or start fresh with a new repo. After import, GitDB is the canonical home for your source — and the last place it lives.
Your AI agents connect with per-seat API keys and pull only the lines they need. Engineers open repos as `gitdb://…` workspaces in the VS Code extension, or review changes in the web reviewer — code is streamed file-by-file, never written to a laptop disk.
Every read, write, and merge is recorded with a clear identity. Scoped access for every human and every agent. Bulk-access bursts trigger alerts in real time — and the whole picture is one query away when you need it.
Every product runs on the same core engine. Start with one, add the rest when you're ready — same API key, same dashboard, same bill.
Your codebase, ready for AI. Agents read the exact lines they need — not the whole file — so they think faster and cost less.
Long-term memory for your agents. Nothing summarized, nothing dropped — recall what was said, when, and why.
High-fidelity semantic search at any scale. A drop-in replacement for legacy vector databases — without the accuracy trade-offs.
Persistent memory and on-device intelligence in a tiny binary. The robot finally remembers what it saw yesterday.
Most data platforms are AI-flavored databases bolted onto last decade's engines. We rebuilt the stack from the ground up — for memory, vector, and code at agent speed.
Engineered for modern hardware from the ground up. No waiting, no contention — just steady, predictable performance under any load.
We didn't take the off-the-shelf option. Our storage engine is tuned for AI workloads — fast writes, fast reads, no surprise pauses.
Both vector engines live on disk — no mmap, no brute force, no shortcuts. Sub-millisecond search at small scale, billions of vectors at large scale, full-fidelity recall throughout.
Mix semantic search, structured queries, and agentic reasoning in a single call. No glue code between five different services.
Run AI agent swarms that ship features in parallel — while your source code never lands on a laptop. The same platform that makes your team faster keeps your IP from walking out the door.
No clones, no ZIPs, no full-repo downloads — ever. Engineers review code through the VS Code extension or the web reviewer, streamed file-by-file. The source stays in GitDB; the laptop stays clean.
Architect, coder, reviewer, tester — a swarm of specialist agents shipping features in parallel. Each one has its own identity, scoped access, and a clear line in the trail. The swarm ships; you keep the receipts.
Nobody opens 500 files in a minute by accident. Because agents do the heavy work now, those bursts have nowhere to hide — GitDB throttles the session, alerts your team, and hands you the precise file list.
Every human and every agent gets its own identity and its own scoped access. Onboard in seconds, close every active session in one click when the work is done.
Join the private beta. Get early access to GitDB, Memory, Vector, and Embedded Robotics services.