superagent is an MIT-licensed SDK that blocks prompt injections, redacts PII and secrets, and scans repositories for agent-targeted threats in authorized AI security workflows.
| Tool | superagent-ai/superagent — open-source SDK for AI agent safety with guard, redact, scan, and test primitives |
| Category | AI application security / LLM guardrails |
| Primary Use | Embedding runtime guard checks, PII redact pipelines, and repo scan analysis into TypeScript and Python AI applications |
| Safe Use | For developers and authorized security teams hardening their own AI applications, running defensive checks in labs and production systems they own |
| Telemetry Note | Defensive by design: the guard call returns classification and violation_types logs that can be recorded in SIEM pipelines to observe attempted injection against owned agents |
superagent from superagent-ai is a defensive SDK pitched with a blunt tagline — make your AI apps safe — and it backs that claim with four concrete primitives: guard, redact, scan, and a forthcoming test module for red team scenarios. With roughly 6.7k stars, an MIT license, a TypeScript primary language, and Y Combinator backing, it sits squarely in the current wave of LLM-safety infrastructure that treats prompt injection and data leakage as first-class runtime threats rather than afterthoughts. The repo's topic list — prompt-injection, guardrails, llm, anthropic, openai — tells you the intended audience: engineers shipping agentic applications who need a policy layer between users, tools, and the model.
Architecturally, the SDK is thin clients over a hosted service, distributed as two packages that share the name safety-agent — one on npm for TypeScript, one installable via uv add safety-agent for Python. Authentication is a single SUPERAGENT_API_KEY environment variable obtained by signing up at superagent.sh. That means the classification models, redaction logic, and repository analysis run server-side by default, which is worth noting for teams with data-residency constraints; the README partially addresses this with a claim that open-weight models can run Guard on your own infrastructure at 50-100ms latency, pointing to their HuggingFace model repository.
The guard API is the core runtime control. You pass input — typically a user message — and receive a structured result with a classification field that can come back as block, along with a violation_types array explaining what tripped. The README scopes this to three threat classes: prompt injections, malicious instructions, and unsafe tool calls. That last one matters for agentic architectures: as agents gain the ability to execute functions, browse, and write files, a guard that inspects the intent behind tool invocation is more useful than one that only screens raw text. The example checks result.classification === "block" before proceeding, which is the correct pattern — a deny-by-default gate ahead of the model call.
redact handles the outbound side of the data-leak problem. Feed it free text and it returns a redacted string with sensitive tokens replaced by typed placeholders like <EMAIL_REDACTED> and <SSN_REDACTED>. The example input covers both email and SSN, and the README says the target categories are PII, PHI, and secrets. Notably, the call accepts a model parameter — the example passes openai/gpt-4o-mini — suggesting the redaction pipeline is model-assisted rather than purely regex-based, which typically generalizes better to PHI variants and informal secret formats that pattern matching misses.
scan is the most novel primitive from a supply-chain perspective. It takes a repo URL and returns a security report analyzing the repository for AI agent-targeted attacks — specifically repo poisoning and malicious instructions embedded in code or documentation. This targets a real and growing attack surface: agents that read README files, ingest documentation, or execute repo code are increasingly susceptible to hostile instructions smuggled into content the agent trusts. The response includes a usage object with a cost figure, so each scan is metered, which hints at per-analysis LLM invocation under the hood.
The test module rounds out the picture but is explicitly marked coming soon. Its designed interface takes an endpoint — your deployed agent's chat URL — plus a scenarios array with values like prompt_injection and data_exfiltration, returning findings describing discovered vulnerabilities. Once shipped, this becomes an automated red team harness: point it at your own production agent, let it probe, review what it finds. It is worth repeating that this is framed for use against your own endpoints, in line with the SDK's overall defensive posture.
Integration options are broader than the two SDKs alone. Beyond sdk/typescript and sdk/python, the repo ships a cli for command-line testing and automation, and an mcp directory implementing an MCP server usable with Claude Code and Claude Desktop. The MCP integration is strategically smart — it means guard checks can be composed into agent toolchains through the Model Context Protocol rather than requiring custom middleware, meeting agentic frameworks where they already live.
The vendor's positioning emphasizes model-agnosticism — OpenAI, Anthropic, Google, Groq, and Bedrock are all named — which makes sense for a layer that sits outside the model provider. Latency is the recurring engineering constraint for runtime guardrails; a pre-inference check that adds hundreds of milliseconds to every turn will get disabled in practice, so the 50-100ms self-hosted claim is the number that determines whether guard is deployable on hot paths or only suitable for high-risk tool calls.
For defenders and authorized security teams, superagent is best evaluated as a control plane for AI applications you own. The guard response structure — classification plus violation_types — is naturally loggable, and piping those records into a SIEM gives you an observable trail of attempted injection attacks against your agent, turning a blocking control into a detection sensor. The scan primitive doubles as due diligence before wiring an agent to third-party repositories, which is exactly the workflow where indirect prompt injection is most likely to bite.
The dogfooding signal is unusually explicit: the README displays a live badge asserting the repo's own security posture is verified by superagent.sh, and the project points to a SECURITY.md for vulnerability reporting. A safety vendor running its product on its own repository is the kind of transparency you want to see, though the badge is generated by the same service it advertises, so treat it as posture signaling rather than independent attestation.
Caveats for adopters: the SDK depends on an external API for its hosted path, so review what data leaves your environment when guard or redact process user content — the irony of shipping PII to a redaction service is real unless self-hosted models are in play. Check the HuggingFace model pages for the open-weight option if data locality is a requirement, and consult SECURITY.md before deploying in regulated contexts. Within those constraints, superagent is a well-scoped, defensively-oriented addition to the emerging LLM security stack — not a scanner, not a framework, but a focused set of runtime primitives for keeping agents inside their lanes.
superagent-ai/superagent.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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