Cycling through all six. Tap any point to stop.

Measured on six things

Open-source platform that lets every team run AI agents — with isolation, spend caps, RBAC, and audit logs built in.

Growth

191 GitHub stars on platform repo, 27 org followers. Launched ~May/June 2026 with arXiv paper. Minimal community traction.

Screenshots

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Agyn

About this app

Agyn is the open-source management layer for AI agents — it takes agents off employee laptops and runs them safely across the whole organization. Every agent runs in an isolated sandbox. Secrets stay hidden from the model. Each team gets its own spend cap, role-based access, and a complete audit trail. Works with any agent (Claude Code, Codex, custom builds) and any model. Self-hosted or cloud. Built for the moment AI stops being a side project and starts touching production data — when security, finance, and IT need real controls before they say yes. Non-technical teams get agents they can actually use. Engineering keeps oversight. Finance sees every dollar spent on tokens. IT stops worrying about shadow AI. If your company is moving from agent experiments to agents in production, Agyn is the layer that makes it possible.

Key features

1Isolated sandboxes with secrets vault and zero-trust networking
2Per-team spend caps, usage alerts, and full audit trails
3Kubernetes-native, Terraform-managed, works with any agent/model

Target customer profile

Drawn from the product itself, not from a survey.

Demographic

Enterprise platform engineering teams

Pain points

Need to deploy AI agents internally but struggle with security, network access, and governance.

Primary needs

Secure deployment into private networks, least-privilege policies, and audit logging.

Demographic

AI/ML operations teams

Pain points

Lack budget controls and cost attribution for agent usage across teams.

Primary needs

Per-agent budgeting, usage alerts, and cost tracking.

Demographic

Security and compliance officers

Pain points

Concerned about prompt injection, data leaks, and unauthorized actions.

Primary needs

Policy gates, secrets vault, and rollback capabilities.

Scout Review

Written by AI from measured evidence, scored out of 100.

Agyn targets the exact pain point of moving AI agents from experiments to governed production use with isolation, budgets, and controls. Strengths in security architecture and open-source model are clear, but the product is too nascent for broad recommendation. Platform teams with K8s expertise may find value; others should wait for maturity.

Measure by measure

Usability

Self-hosted via Git clone + apply.sh; works with Claude Code/Codex out of box. RBAC and per-team caps simplify governance.

Design

Clean dashboard with real-time budget tracking, spend alerts, and team overviews. Kubernetes-native with Terraform config shown in demos.

Speed

Serverless Kubernetes runtime with signal-driven spawning. No public benchmarks beyond their SWE-bench claim of 72.2%.

Security

Isolated sandboxes, secrets vault (never to LLM), zero-trust networking, least-privilege, audit trails. AGPL-3.0 core.

Accessibility

No WCAG mentions or screen-reader testing. Web dashboard exists but enterprise focus means mobile/responsive is afterthought.

Growth

191 GitHub stars on platform repo, 27 org followers. Launched ~May/June 2026 with arXiv paper. Minimal community traction.

Improvement areas

Lack of independent benchmarks and security audits
Steep Kubernetes/Terraform learning curve for setup
Minimal community traction and third-party validation

Design & Usability

Agyn's interface centers on practical enterprise controls: live budget dashboards, team spend caps, and request metrics. The Terraform-driven config and Git-based deployment feel developer-friendly for platform teams. However, the UI remains basic and the self-hosted path demands significant DevOps knowledge, limiting broader adoption beyond engineering orgs.

Speed & Security

The serverless Kubernetes runtime promises efficient on-demand execution with isolation, but lacks independent benchmarks. Security stands out with credential isolation, sandboxes, and audit logging—addressing real shadow AI concerns. Without audits or performance data, these remain promising but unverified claims for production workloads.

Accessibility & Growth

Accessibility documentation is absent, typical for early infrastructure tools. Growth is severely limited: under 200 stars, recent launch, and almost no community signals. This fits the 'early-stage' rubric perfectly—traction is minimal, making high scores inappropriate despite solid technical direction.

Conclusion

Solid foundation for enterprises serious about agent governance, but expect rough edges and a steep setup curve right now.

Suggestions for improvement

Add public performance benchmarks and case studies
Publish security audit or bug bounty program
Improve onboarding docs for non-K8s teams
Expand integrations beyond Claude/Codex

How we score

Alternatives comparison

Named competitors, point by point. Nobody paid to appear here or to be left out.

	Agyn	LangChain	CrewAI	Microsoft Copilot Studio
Enterprise Security & Isolation	Zero-trust networking, sandbox isolation, secrets vault	Basic agent building; no built-in enterprise isolation	Multi-agent orchestration; limited security controls	Strong MS ecosystem governance; less open
Cost Controls & Budgeting	Per-team spend caps, alerts, usage tracking	No native budgeting or spend attribution	Minimal cost management features	Enterprise licensing with usage monitoring
Open-Source & Self-Hosted	Fully open-source K8s runtime, AGPL core	Open-source framework; self-host possible	Open-source; self-host friendly	Proprietary, cloud-tied to Microsoft