**The local control plane for long-running AI agent work.**
Keep objectives, gates, todos, evidence, quota, and handoffs stable while Codex, Claude Code, Cursor, or your own runtime executes bounded turns.
[](LICENSE) [](https://github.com/huangruiteng/loopx/releases/latest) [](pyproject.toml) [](docs/public-private-boundary.md) [](docs/product/release-readiness.md)
[Try LoopX](#try-loopx) · [See real loops](#evidence) · [How it works](#why-loopx) · [Hosted frontstage](https://huangruiteng.github.io/loopx/frontstage/) · [User manual](https://my.feishu.cn/wiki/CaL5wMk9ui17ngkWzeUcMlAYnZg) · [简体中文](README.zh-CN.md)
**把会干活的 Agent,接成可管理、可复盘、可持续改进的数字员工。**
LoopX's creator uses this path as an
[OpenViking contributor](https://github.com/volcengine/OpenViking/pulls?q=is%3Apr+author%3Ahuangruiteng).
The represented public contribution sequence spans more than 200 elapsed hours
from its first PR creation to the latest represented review or update. The
[Issue-Fix capability](docs/capabilities/issue-fix/README.md) keeps rolling
repository context, revision-stamped fix knowledge, and reviewer-facing
preferences separate; linked PRs plus current checkout source and tests remain
authoritative.
### Auto ML Experiment
**200+ hour owner-run experiment arc: hypotheses, matched evidence, invalid
lineages, running replicates, and promote/stop gates remain visible in one
graph.**
The redacted public-safe graph preserves decision lineage across that 200+ hour
elapsed window. It is trajectory evidence, not a claim of continuous compute,
independent reproduction, or a production result.
### Auto Research
**Proposer, executor, and evaluator/promoter agents iterate in parallel while
todo, quota, evidence, and targeted wake remain visible.**
More inspectable surfaces:
- [Hosted frontstage](https://huangruiteng.github.io/loopx/frontstage/) and its
[public demo script](docs/outreach/frontstage-demo-script.md);
- the [showcase catalog](docs/showcases/README.md), including
[blocked-P0 safe rotation](docs/showcases/cases/0617-blocked-p0-safe-rotation.md),
[LoopX self-iteration](docs/showcases/cases/0619-loopx-self-iteration.md), and
[dynamic workflow orchestration](docs/showcases/cases/0619-dynamic-workflow-hardware-agent.html);
- the [cross-runtime implementation review demo](docs/product/use-cases/cross-runtime/cross-runtime-impl-review-demo.md);
- the public [user manual](https://my.feishu.cn/wiki/CaL5wMk9ui17ngkWzeUcMlAYnZg).
## Try LoopX
Requirements: Python 3.11+, `curl`, `tar`, and a macOS or Linux shell. Git is
only needed for contributor clone/canary workflows. The Python package has no
runtime dependencies outside the standard library.
Install without cloning:
```bash
curl -fsSL https://raw.githubusercontent.com/huangruiteng/loopx/main/scripts/install-from-github.sh | bash
export PATH="$HOME/.local/bin:$PATH"
loopx doctor
```
Then connect from your project root:
```bash
cd /path/to/your-project
loopx connect
loopx status
```
If the project has not been initialized and `connect` tells you state is
missing, use the guided path:
```bash
loopx start-goal --guided --project . --goal-text "Your long-running objective"
```
LoopX should reuse existing state rather than overwrite it. Keep `.loopx/`,
`.codex/goals/`, and `.local/` ignored.
### Start From Your Agent
| Host | Recommended start | Loop driver |
| --- | --- | --- |
| Codex App | Ask the agent to connect this project to LoopX, run `loopx doctor`, preserve existing state, and report the current gate and next todo. Then use `$loopx

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