vs Guardrails AI
TapPass vs Guardrails AI
Guardrails AI is a Python library for validating LLM outputs. TapPass is a governance control plane that sits between your agents and the LLM with no code changes required.
Feature comparison
TapPass vs Guardrails AI
| Capability | TapPass | Guardrails AI |
|---|---|---|
| Output validation | ✓ Output scanning + DLP | ✓ Validators (type, format, content) |
| Structured output enforcement | ◐ Via output constraints | ✓ RAIL spec, Pydantic models |
| Input scanning | ✓ PII, injection, secrets, classification | ◐ Custom validators (manual) |
| Prompt injection defence | ✓ Multi-layer + indirect + tool | ✗ Not available |
| Data classification | ✓ Multi-level with LLM-assisted classification | ✗ Not available |
| Tamper-evident audit trail | ✓ Cryptographically chained | ✗ Not available |
| Tool / function call governance | ✓ Permissions, scanning, zones | ✗ Not available |
| Human approval gates | ✓ Real-time workflows | ✗ Not available |
| Behavioural drift detection | ✓ Statistical anomaly detection | ✗ Not available |
| Cost tracking | ✓ Per-agent enforcement | ✗ Not available |
| EU AI Act compliance | ✓ Art. 9, 12, 13, 14 | ✗ Not available |
| Architecture | ✓ External control plane | ◐ In-process library |
| Open source | ◐ SDK is Apache 2.0 | ✓ Open source library |
The difference
Library vs. control plane
Guardrails AI: in-code library
A Python library inside your app. Great for structured outputs. But no external audit trail, no centralised policy, no cross-agent governance.
TapPass: external control plane
An API gateway between agents and LLMs. Centralised policy, centralised audit, centralised compliance. One governance layer for all agents.
Many agents, one pipeline
With Guardrails AI, each app manages its own validation. With TapPass, one pipeline governs all agents with one audit trail, one dashboard.
Private beta
Output validation is step one. Governance is the full journey.
Centralised control plane. One audit trail for all agents.