xelo 0.7.2

AI SBOM generator with portable schema

Xelo

Xelo is an open-source AI SBOM (Software Bill of Materials) generator for agentic and LLM-powered applications. It scans source code and configuration, produces an extensive, structured AI-BOM document and exports into various formats - markdown, CycloneDX, etc. It has plug-ins for security analysis. Most of these features work without using any LLM calls or requiring external network access. Truly deterministic and safe to run in any environment.

Inspiration

There is a genuine need for better visibility into the rapidly growing ecosystem of AI components in modern applications. Xelo is inspired by the success of software bills of materials (SBOMs) in improving supply chain security and transparency, and aims to bring similar benefits to the world of AI applications.

For official documentation, visit https://nuguardai.github.io/xelo.

What Xelo Does

Xelo analyses a repository and produces an AI SBOM — a machine-readable inventory of every AI component it can find:

• Agents — agentic orchestrators (LangGraph graphs, CrewAI crews, AutoGen agents, OpenAI Agents, …)
• Models — LLM and embedding model references, including provider and version
• Tools — function tools and MCP tools wired to agents
• Prompts — system instructions and prompt templates (full content preserved)
• Datastores — vector stores, databases, caches; with PII/PHI data-classification from SQL and Python models
• Guardrails — content filters and safety validators
• Auth — authentication nodes (OAuth2, API key, Bearer, JWT, MCP auth providers)
• Privileges — capability grants (db_write, filesystem_write, code_execution, …)
• Deployment — Docker image references, cloud targets, IaC context

Xelo runs a 3-phase pipeline: AST-aware adapters → regex fallbacks → optional LLM enrichment. The first two phases are fully deterministic and require no API key.

Supported Frameworks

Python: LangChain, LangGraph, OpenAI Agents SDK, CrewAI (code + YAML), AutoGen (code + YAML), Google ADK, LlamaIndex, Agno, AWS BedrockAgentCore, Azure AI Agent Service, Guardrails AI, MCP Server (FastMCP / low-level), Semantic Kernel

TypeScript / JavaScript: LangChain.js, LangGraph.js, OpenAI Agents (TS), Azure AI Agents (TS), Agno (TS), MCP Server (TS)

Installation

pip install xelo

Install for development (all extras):

pip install -e ".[dev]"

Quickstart

Scan a local repository:

xelo scan ./my-repo --output sbom.json

Scan a remote repository:

xelo scan https://github.com/org/repo --ref main --output sbom.json

Add LLM enrichment for richer output (recommended for production use):

export OPENAI_API_KEY=sk-...
xelo scan ./my-repo --llm --llm-model gpt-4o-mini --output sbom.json

Validate a produced SBOM against the bundled schema:

xelo validate sbom.json
# OK — document is valid

Print or save the JSON Schema:

xelo schema                            # print to stdout
xelo schema --output aibom.schema.json # write to file

CLI alias: ai-sbom. Run xelo --help for all flags.

Output Formats

Flag	Format
--format json (default)	Xelo-native AI SBOM (see schema docs)
--format cyclonedx	CycloneDX 1.6 JSON (AI components only)
--format unified	CycloneDX merged with standard dependency SBOM

Validate a produced document:

xelo validate sbom.json

Print the JSON schema:

xelo schema

Toolbox Plugins

Xelo ships with built-in analysis plugins invoked via xelo plugin run:

xelo plugin list                                          # list all plugins

xelo plugin run vulnerability sbom.json                   # offline VLA rules
xelo plugin run atlas sbom.json --output atlas.json       # MITRE ATLAS annotation
xelo plugin run sarif sbom.json --output results.sarif    # SARIF 2.1.0
xelo plugin run markdown sbom.json --output report.md     # Markdown report
xelo plugin run cyclonedx sbom.json --output bom.cdx.json # CycloneDX 1.6

Plugin	CLI name	Network	What it does
VulnerabilityScannerPlugin	vulnerability	No	Structural VLA rules — missing guardrails, unprotected models, over-privileged agents
AtlasAnnotatorPlugin	atlas	No	Maps every finding to MITRE ATLAS v2 techniques and mitigations
LicenseCheckerPlugin	license	No	Checks dependency licence compliance
DependencyAnalyzerPlugin	dependency	No	Scores dependency freshness and flags outdated AI packages
SarifExporterPlugin	sarif	No	Exports findings as SARIF 2.1.0 (GitHub Code Scanning / GHAS compatible)
CycloneDxExporter	cyclonedx	No	Exports nodes as CycloneDX 1.6
MarkdownExporterPlugin	markdown	No	Human-readable Markdown report
GhasUploaderPlugin	ghas	Yes	Uploads SARIF to GitHub Advanced Security
AwsSecurityHubPlugin	aws-security-hub	Yes	Pushes findings to AWS Security Hub (requires boto3)
XrayPlugin	xray	Yes	Pushes findings to JFrog Xray

Plugins are also importable as a Python library:

import json
from xelo import AiSbomExtractor, AiSbomConfig
from xelo.toolbox.plugins.vulnerability import VulnerabilityScannerPlugin
from xelo.toolbox.plugins.atlas_annotator import AtlasAnnotatorPlugin
from xelo.toolbox.plugins.sarif_exporter import SarifExporterPlugin
from xelo.toolbox.plugins.markdown_exporter import MarkdownExporterPlugin

doc = AiSbomExtractor().extract_from_path("./my-repo", config=AiSbomConfig())
sbom = doc.model_dump(mode="json")

vuln = VulnerabilityScannerPlugin().run(sbom, {})
print(vuln.status, vuln.message)

atlas = AtlasAnnotatorPlugin().run(sbom, {})
for finding in atlas.details["findings"]:
    print(finding["rule_id"], finding["severity"], finding["atlas"]["techniques"])

sarif = SarifExporterPlugin().run(sbom, {})
open("results.sarif", "w").write(json.dumps(sarif.details, indent=2))

md = MarkdownExporterPlugin().run(sbom, {})
open("report.md", "w").write(md.details["markdown"])

To add custom detection adapters (extending framework coverage), subclass xelo.plugins.PluginAdapter and register under the xelo.plugins entry-point group. Enable at scan time with AiSbomExtractor(load_plugins=True). See the Developer Guide for details.

Configuration

CLI flags take precedence over environment variables.

Variable	Purpose	Default
XELO_LLM	Enable LLM enrichment (true/1)	false
XELO_LLM_MODEL	LLM model passed to litellm	gpt-4o-mini
XELO_LLM_API_KEY	API key (or use provider-native env vars)	—
XELO_LLM_API_BASE	Base URL for self-hosted / proxy endpoints	—
XELO_LLM_BUDGET_TOKENS	Max tokens for enrichment	50000

Legacy AISBOM_* names are accepted as fallbacks.

Development

pip install -e ".[dev]"
ruff check src tests   # lint
mypy src               # type-check
pytest                 # all tests
pytest -m "not smoke"  # skip network-dependent tests

Run the benchmark evaluation suite against cached fixtures:

python -m tests.test_toolbox.evaluate --all --mode local --verbose

Documentation

• Getting Started
• AI SBOM Schema
• CLI Reference
• Developer Guide
• Troubleshooting
• Contributing
• Roadmap

License

Apache-2.0. See LICENSE.

Disclaimer

See XELO-Disclaimer.md.