Comparison

Cognisafe vs LangSmith, Guardrails AI, Lakera Guard

An honest, feature-by-feature comparison. We've tried to be fair — if we got something wrong, email hello@cognisafe.uk and we'll update it.

Last verified: May 2026

Cognisafe

Agentic AI governance & runtime security

  • Runtime proxy + async scoring
  • Full OWASP LLM Top 10
  • Multi-agent & MCP coverage
  • SOC 2 evidence support

LangSmith

LLM observability & evals

  • Strong observability & tracing
  • LLM evaluation framework
  • No inline enforcement
  • No OWASP / security scoring

Guardrails AI

Input/output guardrails

  • SDK-based guardrails
  • Rich validator library
  • Always in the request path
  • No agent / MCP coverage

Lakera Guard

Prompt injection detection

  • Best-in-class prompt injection
  • Fast inference model
  • Limited to prompt injection
  • No compliance / governance
FeatureCognisafeLangSmithGuardrails AILakera Guard
Interception model
Runtime proxy interception
Logging only — no inline enforcement
SDK-only, not a true proxy
Block mode (reject harmful requests)
Observe mode (low-overhead logging)
Always in-path
Always in-path
Async safety scoring (off the hot path)
Agent coverage
Multi-agent pipeline support
Limited to single-agent flows
MCP tool call monitoring
Inter-agent message inspection
Agent-level attribution (per-agent scores)
Per-run, not per-agent
Safety scoring
OWASP LLM Top 10 coverage
All 10 categories
~5 categories
Jailbreak / prompt injection detection
PII detection
Custom scorer definitions (LLM-as-judge)
Regex & keyword scorers
Severity rating (1–5 Likert scale)
Red team & CI/CD
Automated red team campaigns
CI/CD integration (GitHub Actions)
LangSmith CI is evaluation-focused, not security
Observability
Cost & token tracking
Latency monitoring
Request history & replay
Compliance & audit
SOC 2 evidence support / PDF export
NIST AI RMF framework mapping
Tamper-evident audit trail (pgaudit)
Outbound webhooks & alerting
Deployment
Self-hosted / VPC deployment
Enterprise only, limited
Air-gapped deployment
Kubernetes Helm chart
Self-hosted LLM support (vLLM, Ollama, NIM)
Access control
SSO / SAML 2.0 / OIDC
Business+
RBAC with project isolation
Open source
Open source proxy / SDK
Supported
Partial / limited
Not supported

When to use each tool

Choose Cognisafe if…

  • You're running multi-agent pipelines with LangGraph, CrewAI, AutoGen, or Semantic Kernel
  • Your agents use MCP servers, tool calling, or access external systems
  • You need full OWASP LLM Top 10 coverage with compliance evidence
  • You operate in a regulated industry and need SOC 2 / NIST AI RMF mapping
  • You need self-hosted or air-gapped deployment with zero data egress
  • You want automated red team campaigns and CI/CD security gates

Choose LangSmith if…

  • Your primary need is LLM observability, tracing, and evaluation — not security
  • You're already invested in the LangChain / LangGraph ecosystem
  • You need rich dataset management and A/B evaluation of prompts
  • You don't have security or compliance requirements yet

Choose Guardrails AI if…

  • You want a code-first SDK with a large library of pre-built validators
  • Your architecture is a single-agent or single-LLM-call pattern
  • You need highly customisable, deterministic guardrails in Python
  • You can accept the latency of synchronous evaluation on every call

Choose Lakera Guard if…

  • Prompt injection detection is your single most important security requirement
  • You want a purpose-built, fast inference model for injection specifically
  • You don't need observability, cost tracking, compliance, or multi-agent coverage

See it for yourself

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