Ross ROSS = Recommend OSS · open-source software intelligence for agents

superagent-ai/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. observed · 2026-08-28

github.com/superagent-ai/superagent · homepage · TypeScript · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

78/100

  • Activity 99
  • Release rhythm 47
  • Longevity 86
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0.0
  • age_days: 1211
  • days_rel: 353
  • days_push: 8
  • n_releases_24m: 19

Full methodology

Adoption not part of the score

6719 stars · 964 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Superagent is an open-source SDK and platform for securing AI applications and agents, offering runtime guardrails that block prompt injections, PII/secret redaction, repository threat scanning, and red-team testing. It integrates via TypeScript and Python SDKs, GitHub Apps for PR security checks, and MCP tools for agent-facing context scoring.

Use cases

  • block prompt injection attacks in my llm app
  • redact pii and secrets from user input before sending to an llm
  • scan a github repo for malicious instructions targeting ai agents
  • run red team tests against my ai agent
  • add guardrails to what my coding agent can do at runtime
  • score packages and urls before my agent consumes them
  • add security checks to pull requests on github

When to choose

  • you are building LLM or agent applications and need runtime protection against prompt injection and data leaks
  • you want automated security scanning and red teaming for AI-native codebases via GitHub
  • you need compliance-friendly PII/PHI redaction in AI pipelines
  • you want to gate what agents consume (files, URLs, packages, MCP servers) with trust scores

When to avoid

  • you need general-purpose application security testing unrelated to AI/LLM workloads
  • you require fully offline, self-contained guardrails with no cloud API dependency
  • your stack has no TypeScript or Python SDK support and you cannot call a REST API

Facets

library · maturity active

security llm-inference agent-framework mcp vulnerability-scanning sdk middleware security artificial-intelligence large-language-models developer-tools privacy python cloud self-hosted guardrails prompt-injection pii-redaction red-teaming supply-chain-security ai-safety llm-security runtime-protection ai-agents nodejs typescript web-server

6 sources

Member repositories

RepositoryRoleHealth v2
superagent-ai/superagentmain78

For agents

markdown · JSON · MCP: product_card(name="superagent-ai/superagent")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem