WhyLabs

AI observability and LLM security platform with a REST API and open-source whylogs library that monitors data quality, detects drift, and provides real-time LLM guardrails — blocking or flagging harmful, off-topic, or policy-violating LLM inputs and outputs.

Evaluated Mar 07, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ AI & Machine Learning whylabs whylogs ml-monitoring data-quality llm-security drift-detection guardrails open-source
⚙ Agent Friendliness
55
/ 100
Can an agent use this?
🔒 Security
81
/ 100
Is it safe for agents?
⚡ Reliability
78
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
78
Error Messages
76
Auth Simplicity
72
Rate Limits
68

🔒 Security

TLS Enforcement
100
Auth Strength
78
Scope Granularity
65
Dep. Hygiene
82
Secret Handling
80

HTTPS enforced. Dual-credential auth (API key + Org ID). whylogs profiles contain statistical sketches, not raw data, which limits PII exposure risk. SOC 2 Type II certified. US data residency only.

⚡ Reliability

Uptime/SLA
80
Version Stability
78
Breaking Changes
78
Error Recovery
78
AF Security Reliability

Best When

You are running LLM agents in production and need real-time guardrails against prompt injection or data leakage, combined with statistical data drift monitoring for the underlying ML data pipeline.

Avoid When

You primarily need LLM trace visualization and debugging — WhyLabs is monitoring and guardrails focused, not a trace explorer.

Use Cases

  • Logging statistical data profiles (via whylogs) of production feature data to detect drift between data pipeline runs
  • Implementing real-time LLM input/output guardrails that block prompt injection, toxic content, or sensitive data leakage
  • Monitoring LLM application health metrics (latency, token usage, rejection rates) via the WhyLabs observability API
  • Setting threshold-based alerts that fire when data quality metrics (nulls, outliers, distribution) exceed defined bounds
  • Auditing LLM interactions for compliance by logging and analyzing all agent inputs and outputs through WhyLabs

Not For

  • Teams that need full LLM trace replay or detailed span-level debugging — WhyLabs focuses on statistical profiling and guardrails, not trace visualization
  • Computer vision or audio ML monitoring — whylogs statistical profiling is optimized for tabular data and text
  • Small-scale LLM apps where the overhead of guardrail integration exceeds the risk of the attacks being guarded against

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
Yes

Authentication

Methods: api_key
OAuth: No Scopes: No

API key plus Org ID required for all requests. Keys are organization-scoped. Set via WHYLABS_API_KEY and WHYLABS_DEFAULT_ORG_ID environment variables. No operation-level scope granularity.

Pricing

Model: freemium
Free tier: Yes
Requires CC: No

whylogs (the open-source profiling library) is Apache 2.0 licensed and free. WhyLabs cloud is the managed monitoring platform. LLM guardrails feature is available on paid tiers.

Agent Metadata

Pagination
offset
Idempotent
Partial
Retry Guidance
Not documented

Known Gotchas

  • Both WHYLABS_API_KEY and WHYLABS_DEFAULT_ORG_ID are required — missing Org ID causes authentication failures that look like API key errors
  • whylogs profiles must be uploaded per dataset timestamp — using incorrect timestamps causes data to appear in wrong time buckets in the monitoring UI
  • LLM guardrail policies must be configured in WhyLabs UI before the API will enforce them — agents cannot define policies via API at runtime
  • Profile upload is async — uploaded profiles may take minutes to appear in dashboards and trigger alerts
  • whylogs statistical profiles are sketch-based (approximate) — not suitable for exact count or compliance auditing that requires precise record-level logging

Alternatives

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Scores are editorial opinions as of 2026-03-07.

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