AI-BOM Tool Comparison

March 15, 2026 ยท View on GitHub

This document compares ai-bom with other AI Bill of Materials tools currently available in the ecosystem.

The goal is to help users understand feature differences and choose the right tool for their workflow.


Feature Comparison

Featureai-bomCisco AIBOMSnyk AIBOM
LicenseApache 2.0Apache 2.0Proprietary
Open SourceYesYesNo
Scanners13+ (code, cloud, Docker, GitHub Actions, Jupyter, MCP, n8n, etc.)1 (Python-focused)Unknown
Output Formats9 (Table, JSON, SARIF, SPDX, CycloneDX, CSV, HTML, Markdown, JUnit)JSON, CSVUnknown
CI/CD IntegrationGitHub Action, GitLab CINoYes
LLM EnrichmentYesYesEarly access / limited preview
n8n ScanningYesNoNo
MCP / A2A DetectionYesNoNo
Agent Framework DetectionLangChain, CrewAI, AutoGen, LlamaIndex, Semantic KernelLimitedUnknown
Binary Model DetectionYes (.onnx, .pt, .safetensors, etc.)NoUnknown
Policy EnforcementCedar policy gateNoYes
Best ForMulti-framework projects needing multiple formats and optional LLM enrichmentPython projects needing LLM enrichmentExisting Snyk customers

Notes

ai-bom

  • Open-source AI Bill of Materials scanner focused on discovering AI/LLM usage across codebases and infrastructure.
  • Supports multiple scanners, formats, and compliance mappings (OWASP Agentic Top 10, EU AI Act).
  • LLM enrichment (--llm-enrich) uses litellm to extract specific model names from code, supporting OpenAI, Anthropic, Ollama, and 100+ providers.
  • Designed for developer workflows with CLI, CI/CD, and dashboard support.

Cisco AIBOM

  • Open-source tool focused primarily on Python projects.
  • Uses LLM-based enrichment to extract model usage.
  • Limited scanner coverage and output formats compared to ai-bom.

Snyk AIBOM

  • Proprietary feature integrated into the Snyk platform.
  • Currently in early access / limited preview.
  • Provides CI/CD integration.
  • Public documentation on supported scanners and formats is limited.

Last updated: 2026