Hoop — Secures AI agents by masking sensitive data and blocking dangerous commands at runtime.
Analyzed by Sai Pavan Gopularam · AI · Security · View on GitHub
- Stars: 831
- Forks: 66
- Commits last 30 days: 100
- Health: Active (100 commits this month)
- Language: Go
- License: MIT
What It Is
Hoop is like a security guard for your AI agent. It sits between your agent and its data sources (like databases or APIs), inspecting every command and response. It's a 'sidecar' – a small, independent program that runs alongside your main application, intercepting traffic without needing changes to your agent's code.
This matters because AI agents, while powerful, can be unpredictable. Hoop prevents them from accidentally (or maliciously) accessing sensitive data, deleting critical records, or executing harmful commands. It makes deploying AI agents in production environments much safer and more compliant, killing the problem of uncontrolled agent actions.
License Verdict
MIT License — Build and Sell Freely — Commercial Use Approved • No Copyleft Restrictions
The MIT license is highly permissive. You can use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the software. This includes using it in proprietary software and commercial products without requiring you to open-source your own code.
How to Use It
Set up Hoop by installing it via Homebrew and configuring a YAML file to define data masking and guardrail rules. Then, point your AI agent to Hoop's listening port instead of the original resource.
Prerequisites:
- Homebrew
- Go (for local dev/build)
- YAML (for config)
Estimated setup time: 10 minutes.
brew tap hoophq/brew https://github.com/hoophq/brew.git
brew install hoop
# Create a config.yaml file (example from README)
hoop start sidecar --config config.yamlWhat I'd Build With This
AI Agent Security Proxy for Developers (micro-saas)
Offer a hosted Hoop instance as a managed proxy service. Developers connect their local AI agents to your endpoint, and you handle the Hoop configuration, data masking, and guardrails. This targets indie hackers and small teams who want to secure their agents without managing infrastructure. Market through developer communities and AI forums.
Effort: 2 Weeks Build Time · Target: Indie Hackers, Small AI Dev Teams · Pricing: $29/mo for basic, $99/mo for advanced rules
Managed AI Agent Guardrails Platform (saas)
Build a full SaaS platform around Hoop's sidecar and control plane. Provide a user-friendly UI for defining complex data masking, guardrails, and session analysis rules across multiple AI agents. Offer centralized logging, auditing, and incident response for agent actions. Target mid-market companies deploying AI, selling based on compliance and risk reduction.
Effort: 6 Months Build Time · Target: Mid-Market Enterprises · Pricing: $500/mo to $5,000/mo depending on usage
AI Data Governance & Compliance Suite (enterprise)
Develop an enterprise-grade solution that integrates Hoop with existing corporate data governance, IAM, and SIEM systems. Offer on-premise deployment options, custom protocol support, and advanced review workflows for high-stakes agent operations. Focus on large corporations in regulated industries (finance, healthcare) with strong data privacy and compliance requirements, selling through direct sales and partnerships.
Effort: 1 Year+ Build Time · Target: Large Enterprises in Regulated Industries · Pricing: $50,000+/year, custom pricing
Sai Pavan Gopularam's Take
This project solves a critical problem for anyone trying to put AI agents into production: safety. The idea of a sidecar that just intercepts traffic is brilliant because it means no SDKs or prompt engineering. I'd bet a managed version of this, with a slick UI for rule creation, could easily fetch $1,000/month from a single mid-sized company.
Watch Out For
- Control Plane UI Incomplete: The Control Plane, while having an API, currently lacks a complete UI for authoring configurations and managing the review queue, meaning some administrative tasks require API calls.
- Private Module Dependency: Building the `sidecar/` module requires access to a private Go module (`github.com/hoophq/libhoop`), which means contributions to the core inspection logic might be hindered without proper credentials.
- Gateway vs. Sidecar Focus: The repository contains both the new sidecar/control plane and the older 'gateway' product for human access. While the gateway is supported, the project's future focus is on the sidecar, which might lead to some confusion for new users.
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