Quickstart

Install

AIROM is a single static binary. Install it with the Go toolchain (Go 1.25+):

Install

go install github.com/airomhq/airom/cmd/airom@latest

The binary lands in $(go env GOPATH)/bin. If that directory is not on your PATH, the next step fails with command not found. See Installation for the fix and for building from source.

Verify

airom version

airom dev
  commit: none
  built:  unknown
  go:     go1.26.5 (darwin/arm64)

dev / none / unknown is expected here. The version, commit, and date are stamped in at build time via ldflags; a plain go install does not set them. Building from source produces a stamped binary.

This is the same ToolInfo that gets embedded in every AIBOM AIROM writes, so a document can always be traced back to the build that produced it.

Scan

Point it at your project. scan auto-detects what the target is: an existing local path, then a git URL, then an image reference.

Scan the current directory

airom scan .

The default output is a table on stdout:

AI Bill of Materials — /tmp/my-ai-app

┌─ Scan Summary ───────────────┐
│ Target        /tmp/my-ai-app │
│ Components    6              │
│ Relationships 1              │
│ Files         2 scanned      │
│                              │
│ By Type                      │
│   ai-config          2       │
│   framework          1       │
│   hosted-llm         1       │
│   library            1       │
│   vector-db          1       │
└──────────────────────────────┘

┌────────────┬─────────────┬─────────┬───────────┬───────┬────────────────────┬──────────┐
│ KIND       │ NAME        │ VERSION │ PROVIDER  │ CONF  │ LOCATION           │ EVIDENCE │
├────────────┼─────────────┼─────────┼───────────┼───────┼────────────────────┼──────────┤
│ ai-config  │ max_tokens  │ -       │ -         │ 0.5   │ src/rag.py:10      │ 1 occ    │
│ ai-config  │ temperature │ -       │ -         │ 0.5   │ src/rag.py:9       │ 1 occ    │
│ framework  │ langchain   │ 0.2.16  │ langchain │ 0.95  │ requirements.txt:1 │ 1 occ    │
│ hosted-llm │ gpt-4.1     │ -       │ openai    │ 0.85  │ src/rag.py:8       │ 1 occ    │
│ library    │ openai      │ 1.51.0  │ openai    │ 0.985 │ requirements.txt:2 │ 2 occ    │
│ vector-db  │ chroma      │ 0.5.5   │ chroma    │ 0.985 │ requirements.txt:3 │ 2 occ    │
└────────────┴─────────────┴─────────┴───────────┴───────┴────────────────────┴──────────┘

The scan exited 0. Findings are not failures — AIROM inventories, it does not judge. To make CI fail, you opt in with --fail-on.

Cut the noise

Extension-only dataset detection and keyword-only ai-config matching emit low-confidence rows (the 0.5 ai-config entries above). Filter them at the presentation layer:

Only high-confidence components

airom scan . --min-confidence 0.8

0.8 is the practical threshold on a general-purpose directory. See Confidence for how the scores are calibrated.

Emit real documents

The table is for humans. -o takes fmt[=path] and is repeatable, so one scan can produce every artifact you need:

CycloneDX + SARIF in one pass

airom scan . \
  -o cyclonedx=aibom.json \
  -o sarif=airom.sarif \
  -o table

That writes a CycloneDX 1.6 ML-BOM with evidence.occurrences[] intact, a SARIF file for code-review annotations, and still prints the table.

Need CycloneDX 1.7? --cdx-version 1.7. The default is 1.6.

Where the evidence lives

The table’s LOCATION column points at the primary sighting and EVIDENCE counts how many back a component. The full file:line provenance for every occurrence — plus the matched snippet, the enclosing symbol, the detector that fired, and a per-occurrence confidence — is carried in the structured outputs (and --wide expands them under each row).

Inspect the occurrences

airom scan . -o json=aibom.json

The gpt-4.1 component, as emitted

{
  "id": "airom:cfc27e587216febe",
  "kind": "hosted-llm",
  "name": "gpt-4.1",
  "provider": "openai",
  "confidence": 0.85,
  "evidence": {
    "occurrences": [
      {
        "location": {
          "path": "src/rag.py",
          "line": 8,
          "endLine": 8,
          "column": 9,
          "endColumn": 24
        },
        "detectorId": "rules/openai/model-literal",
        "method": "source-code-analysis",
        "confidence": 0.85,
        "snippet": "model=\"gpt-4.1\",",
        "fields": { "model": "gpt-4.1" }
      }
    ]
  },
  "props": [
    { "name": "airom:model.provider", "value": "openai" },
    { "name": "airom:model.id", "value": "gpt-4.1" }
  ]
}

That is the whole point: the gpt-4.1 row in the table is not an assertion, it is src/rag.py:8, column 9–24, matched by rules/openai/model-literal via source-code analysis, with the literal text that triggered it.

Wire it into CI

Use a --fail-on policy expression. & (AND) binds tighter than | (OR), and all terms in a clause must hold for a single component.

Fail the build on high-confidence hosted models

airom scan . \
  --fail-on "hosted-llm&confidence>=0.9" \
  -o sarif=airom.sarif

Exit code	Meaning
`0`	Scan completed. Findings are not failures.
`1`	The `--fail-on` policy matched. Override the value with `--exit-code`.
`2`	Fatal error — bad flags/config, or source acquisition failed.

Want to report matches without failing? --exit-code 0 alongside your --fail-on policy.

Installation

PATH setup, building from source, and the pre-release caveats.

CLI reference

Every command, flag, and exit code.

Evidence model

Occurrences, detection methods, and identity claims.

Scan a container image

Tarballs and OCI layouts, offline.

Get started

Concepts

Scanning

Output

CI/CD

Rules

Reference

Where the evidence lives

Wire it into CI

Next

Installation

CLI reference

Evidence model

Scan a container image

​Where the evidence lives

​Wire it into CI

​Next

Installation

CLI reference

Evidence model

Scan a container image

Where the evidence lives

Wire it into CI

Next