**LoopX** is a provider-neutral **state kernel and local-first control plane** for what its author calls *loop engineering* — keeping long-running agent work reviewable, restartable, and handoff-safe across many bounded turns. Its central claim is a separation of concerns: **LoopX governs durable state, while Codex, Claude Code, Cursor, or a custom runner does the actual work**. It explicitly does not replace the agent runtime and is not another orchestration framework. The problem it targets is the one that appears after the single-session demo ends. Objectives drift, owner decisions pile up, evidence goes stale, agents hand work to peers, and a scheduler happily keeps burning quota after no useful transition remains. LoopX folds the durable layer into one compact record — **objective, gates, todos, scope, evidence, and quota** — and drives a tick that asks whether human judgment is needed, whether a safe fallback exists, and whether the loop may continue at all. The mental model the docs offer is an **agent-native Kanban for long-running work**: cards carry identity, authority, evidence, and continuation, and moves are validated operators such as claim, gate, monitor, and writeback. Multi-agent coordination is treated as a peer problem rather than a hierarchy. Registered agents hold **claims, leases, task boundaries, and capabilities**, and typed continuation decides who acts next — there is no durable leader identity. The host matrix is broad: Codex App and Codex CLI, Claude Code via an opt-in adapter that gates the native `/loop`, OpenCode, Pi, and Cursor or any shell runner. The core tick is deliberately small enough to embed by hand — `quota should-run`, `todo claim`, `todo update`, `refresh-state`, `quota spend-slot`. Installation is genuinely lightweight. **Python 3.11+ with no runtime dependencies outside the standard library**, installable via a curl script without cloning, after which `loopx connect` and `loopx doctor` wire up a project. State lives in `.loopx/` and is meant to be gitignored rather than committed. The evidence presentation deserves credit for restraint. The headline showcases — a **200+ hour** public OpenViking contribution arc and a redacted owner-run AutoML experiment of similar span — are labeled as **elapsed wall-clock lifetime, not continuous model execution**, and the project states plainly that these are demo results and user reports rather than reproduced production outcomes. Independent-user cases (a four-day unattended run, seven merged PRs) are marked as attribution that remains unverified. The caveats follow from the same honesty. LoopX states it is **not an autonomous production controller** — dangerous permissions, publishing, production writes, and final ownership stay with a human — and the status badge still reads *loop agents early*. Documentation is bilingual but Chinese-first in places, with the user manual hosted on Feishu, which is friction for some readers. **MIT licensed.**