Why it matters
- AI coding agents are increasingly able to make autonomous code changes — Gryph provides the audit trail enterprises need for compliance and security review of AI-generated contributions.
- Supply chain security gap: most code security tools audit human-written code, but lack context for tracking which changes were AI-generated and whether those AI-introduced dependencies are safe.
- Traceability requirement: regulated industries (financial services, healthcare) need audit trails of all code changes regardless of who (or what) made them.
- Early tooling for a new security domain — as AI agents become standard in development workflows, audit trail tooling will become a compliance requirement.
Key capabilities
- Audit trail: Record and log all code changes made by AI coding agents.
- Dependency tracking: Monitor packages and dependencies introduced by AI agents.
- Security scanning: Flag risky dependencies using SafeDep's vulnerability and supply chain data.
- Agent attribution: Track which AI system made which code change.
- CI/CD integration: Run as part of pull request review pipelines.
- Open source: Self-hosted; Apache 2.0 license.
- SafeDep integration: Connects with SafeDep's vet tool for package security analysis.
Technical notes
- License: Apache License 2.0
- GitHub: github.com/safedep/gryph
- Stars: 53 (early-stage project)
- Made by: SafeDep
- Integration: CI/CD pipelines; GitHub Actions
- Focus: AI agent audit trail, supply chain security
Ideal for
- Security teams at organizations adopting AI coding agents who need auditability and compliance tracking for AI-generated code.
- DevSecOps pipelines where code provenance (human vs. AI-generated) needs to be tracked for compliance.
- Enterprise teams concerned about AI agents introducing vulnerable dependencies without security review.
Not ideal for
- Teams not yet using AI coding agents — Gryph's value is specifically in auditing agent-generated changes.
- General code security scanning (not agent-specific) — use Snyk, GitHub Advanced Security, or similar tools.
- High-volume observability needs — AgentOps provides broader AI agent monitoring with session replay and LLM call tracking.
See also
- AgentOps — AI agent observability platform; broader monitoring with session replay and cost tracking.
- Codeball — AI PR risk classification; flags risky pull requests for human review.
- Sourcegraph — Code search and intelligence; useful for auditing changes across large codebases.