Signetai

When you're juggling multiple coding agents — Claude Code for one project, Codex for another, OpenClaw for something else — memory that stays inside a single harness is a trap. Signet fixes that by keeping context and credentials outside any one tool. It's a genuine differentiator: switch harnesses, keep your memory, and your secrets stay encrypted. Pick this if you build multi-agent systems or run a swarm of tools. The dreaming loop automatically distills idle sessions into structured memory, extracting entities and claims with provenance. That's a massive time-saver for anyone tired of manually saving context. The graph-based retrieval with no LLM at search time is also fast and private — everything stays on-device. The flip side: this is not for the faint of heart. You need comfort with CLI setup, daemon management, and reading logs. There's no managed cloud offering, so you own the maintenance. If you prefer zero-config, fully managed memory, you'll want something like MemGPT or a built-in memory feature instead. Compared to those alternatives, Signet's main edge is portability and inspectability. Every memory carries a source — you can trace why the agent knows something. That's a level of transparency most cloud services don't offer, and it's valuable for debugging and trust. The 97.6% LongMemEval accuracy from MemoryBench is impressive, but remember it's under a strict rules profile — real-world results will vary. Watch out for the learning curve. The CLI wizard is simple enough — one command installs everything — but managing the daemon and understanding hooks/MCP takes time. Also, memory quality depends on your own embedding and model stack, so you'll want to configure that carefully. If you're okay with that, Signet is a powerful addition to your AI