OT AIops

Industrial-AIOps

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Ask an AI agent why the line stopped — and get an answer that cites its evidence.

A vendor-neutral, read-first data tap for the factory floor. It speaks 15 field protocols, correlates what it reads *across* them, and hands your agent an evidence-cited verdict instead of a guess. Every call is audited, and no reading ever phones home.

pip install "iaiops[opcua]"      # pick your protocol — or [all]

iaiops init # write ~/.iaiops/config.yaml iaiops doctor # check the setup before you trust it

Prefer a container? The published image is cosign-signed and runs non-root. It speaks MCP over stdio, so keep stdin open and mount a volume for the audit store:

cosign verify --key deploy/margo/cosign.pub ghcr.io/industrial-aiops/iaiops:0.32.0-factory

docker run -i --rm -v iaiops-state:/home/iaiops/.iaiops \ ghcr.io/industrial-aiops/iaiops:0.32.0-factory

For a hardened or air-gapped deployment (read-only rootfs, cap_drop: ALL, no-new-privileges, optional on-box LLM) use deploy/margo/compose.yaml and deploy/airgap/. The analysis engine needs no GPU and no model API — it is deterministic; an LLM is optional and only *phrases* the verdict.

What you get

Eleven editions ship in this package — ten per industry (fab · factory · process · building · water · warehouse · clinical · pharma · renewables · plcnext), each adding its own read-only advisory checks, and one cross-protocol: sensor (connect, decode and read a sensor; IAIOPS_MCP=sensor). Substation / utility telecontrol (IEC-104 · DNP3 · IEC-61850) ships separately as iaiops-energy.

Your first five minutes

Four commands. Only one of them touches a device: doctor probes each configured endpoint (iaiops doctor --skip-probe contacts nothing).

pip install "iaiops[modbus]"     # pick the protocol you actually have, or [all]

iaiops onboard status # ← run this first. Contacts NOTHING. iaiops doctor # config, secrets, reachability — and the version iaiops readiness # every scenario, and what each gap needs

onboard status answers the smaller questions you have first: which journey is this site on — reading its devices directly, or subscribing to a UNS broker its data already flows through — which step of it are you on, and what is the one command that advances it? It is derived from your store and config.yaml every time. The one thing it keeps is a namespace audit verdict, and that counts only for the broker and topic filter it was taken on and always shows its age — so edit config.yaml by hand and the answer stays true.

readiness reads your config and local store and answers one question: which scenarios can this site run today, and what does each gap need? Every gap comes with the command that closes it, ranked by how much it unlocks. No agent, no cloud, no account, and nothing on the wire.

Then the path, in the order that matters — survey what is there, take a bounded sample, and only then explain it:

See the whole thing run against a real device in about two minutes, including a genuine mid-run outage, with ./demo/oee-line/run_demo.sh — no hardware, no configuration, nothing written outside a temporary directory. demo/oee-line/ explains what each step is for and what the numbers do and do not claim.

Why read-first

OT is exactly where you want an agent on a tight leash. The read paths are the product; the few write paths are OT-dangerous, off by default, and gated by MOC discipline — dry-run, one-shot approval, undo capture, hash-chained audit.

Proving the analysis needs no model

The analysis layers cannot reach a language model. That is a guard, not a slogan: tests/test_brain_is_llm_free.py scans eight packages — brain, discovery, runtime, readiness, collect, knowledge, retain, connectors — for any import that could reach one, and an empty result is the guarantee. A model is used in exactly two places, and neither is load-bearing: rca_narrate rephrases a verdict that was already computed and already cited, and an agent front-end decides *which* tool to call. Remove both and the numbers are the same numbers.

That guard is static — it proves nothing *can* call a model. For a validation team the sentence they are asked to accept is the executed one, so it is executed:

iaiops verify determinism --out determinism-record.json

A pinned in-repo dataset goes through availability, production counts, the Six Big Losses, ISA-18.2 alarm load, control charts, the conservative baseline and the RCA copilot. Each result is canonically encoded and digested; the suite runs twice in this process and once in each of two fresh interpreters started at different `PYTHONHASHSEED` values — the arm that catches a set or dict iteration order reaching a result, which a single run never can. The socket API raises throughout, so a computation that reached for a device or a hostname fails here instead of quietly working on a machine that happened to be online. Afterwards the run is asked what it pulled in: a model library that was *already* loaded (an MCP server holds iaiops.core.llm for the opt-in narration tool) is recorded, not judged — only what the suite itself imported can condemn it.

The record separates result (identical every run — the part to sign) from context (when and where this run happened). Two good runs are not byte-identical records, and someone will diff them, so the halves are named rather than mixed.

This is the form the claim has to take to be usable: not "our model is accurate", which is not evidence in a GxP context, but a test case someone can write into an IQ/OQ protocol — *remove the model, block the network, re-run the standard dataset, compare the hash* — execute, and sign. verify_determinism is the same check from the MCP side; iaiops verify suite lists what it covers without running it.

How far it's actually been verified

Short version: verified against real protocol libraries, containers and in-process servers — not yet against real plant gear. We grade evidence rather than saying "tested", because a real container round-trip and a synthetic fixture are not the same claim.

Each row's detail — and, per protocol, *what is still not covered* — is in docs/VERIFICATION-RECORD.md. That file is the record; this table is its summary, and a test fails if the two disagree.

**Per-protocol evidence — including what each test does *not* cover — is in docs/VERIFICATION-RECORD.md**, one row per protocol, naming the test behind each claim. Every 待核实 is hardware-gated, not forgotten — each one names the equipment that would settle it.

🧪 测试与共创 / Beta testing & co-creation

我们在找现场测试伙伴。 软件里能验证的我们都验证了（真实 in-process 服务器、真实协议库、Docker 容器 loopback）——剩下的 待核实 清单只有真设备能回答：物理 Modbus-RTU（RS-485）、EtherCAT 从站、HART 网关、在线 BACnet 楼宇设备、在线 Metasys/Niagara BAS 控制器、在线 Ignition 网关、国产 PLC（汇川/信捷）、真机 PLCnext、真实变电站 RTU/IED、欧姆龙 FINS 真机、IO-Link 主站。如果你是 OT 工程师、系统集成商或工厂团队，手上有任何这类设备：装上 iaiops，对你的设备跑一遍 iaiops doctor，把结果告诉我们。经你验证的设备会署名写进支持矩阵；现场反馈的问题我们优先分诊；功能可以通过 GitHub Issues/Discussions 直接共创。

We're looking for field-testing partners. Everything software-verifiable has been verified; what's left on the honest 待核实 list only real equipment can answer — physical Modbus-RTU (RS-485), EtherCAT slaves, HART gateways, live BACnet HVAC, live Metasys/Niagara BAS controllers, live Ignition gateway, domestic PLCs (Inovance/Xinje), live PLCnext, substation RTUs/IEDs, live Omron FINS PLCs, IO-Link masters. If you're an OT engineer, integrator, or factory team with access to any of these: install iaiops, run iaiops doctor against your gear, and tell us what happened. Verified-equipment reports get credited in the support matrix, field-reported issues get fast triage, and features are co-designed in the open via GitHub Issues/Discussions.

👉 参与入口 | Start here: [open an issue](https://github.com/industrial-aiops/industrial-aiops/issues/new) with the protocol and device model in the title, or email . Either reaches a person, and a report gets answered against the current release.

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Consolidated capability matrix

| OPC-UA | opcua_subscribe_sample | bounded sa