superagent
Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers.
Star Growth
Overview
Superagent is an open-source SDK designed to protect AI applications from security vulnerabilities and compliance risks. As AI agents become more prevalent in production environments, they face increasing threats from prompt injections, data leaks, and malicious outputs that can compromise user data and system integrity. Superagent addresses these critical security gaps by providing runtime protection mechanisms that can be embedded directly into AI applications. The toolkit offers four core security functions: Guard for detecting and blocking prompt injections and unsafe tool calls in real-time, Redact for automatically removing personally identifiable information (PII), protected health information (PHI), and secrets from text, Scan for analyzing code repositories to identify AI agent-targeted attacks like repo poisoning, and Test for running red team scenarios against production agents. With over 6,400 GitHub stars and Y Combinator backing, Superagent has gained significant traction in the AI security space. The SDK supports both TypeScript and Python environments, making it accessible to a wide range of developers. By providing these security layers, Superagent enables organizations to deploy AI applications with greater confidence while meeting compliance requirements and protecting sensitive data from emerging AI-specific attack vectors.
Deep Analysis
YC-backed AI safety SDK that pivoted from general agent building to focused safety tooling — provides guard, redact, and scan capabilities with open-weight models for self-hosting, filling the gap between building agents and securing them
⚡ Capabilities
- • AI agent safety SDK — prompt injection detection and blocking
- • PII and secrets redaction from text
- • Repository scanning for AI agent-targeted attacks
- • Red team scenario testing against production agents
- • Open-weight guard models (0.6B to 4B parameters)
- • MCP server integration for Claude Code/Desktop
🔗 Integrations
✓ Best For
- ✓ Teams adding safety layers to production AI agents
- ✓ Enterprises requiring PII redaction and prompt injection protection
- ✓ Security-focused AI deployments with compliance requirements
✗ Not Ideal For
- ✗ Building AI agents from scratch (safety-only SDK)
- ✗ Simple chatbot applications without security concerns
Languages
Deployment
⚠ Known Limitations
- ⚠ Red team testing feature is still 'coming soon'
- ⚠ Guard accuracy varies by model size (0.6B vs 4B tradeoff)
- ⚠ Self-hosted deployment requires GPU for reasonable latency
- ⚠ Focused on safety/security — not a general agent framework
Pros
- + Comprehensive AI security coverage with multiple protection layers including prompt injection detection, PII redaction, and repository scanning
- + Production-ready SDK with dual language support (TypeScript and Python) and straightforward API integration
- + Open-source with strong community backing (6,500+ GitHub stars) and Y Combinator validation
Cons
- - Requires API key and external service dependency, potentially adding latency to AI application workflows
- - Red team testing feature is still in development (marked as 'coming soon')
- - May introduce additional complexity and cost considerations for high-volume AI applications
Use Cases
- • Protecting customer-facing chatbots from prompt injection attacks that could expose system prompts or cause harmful outputs
- • Sanitizing AI-processed documents and conversations to automatically redact sensitive information like SSNs, emails, and medical data for compliance
- • Securing AI development pipelines by scanning code repositories for malicious instructions or AI agent poisoning attempts