whylogs / WhyLabs
Open-source data logging library (whylogs) paired with managed monitoring platform (WhyLabs). whylogs computes statistical profiles of datasets and ML model inputs/outputs without storing raw data — only statistical summaries (histograms, counts, quantiles). WhyLabs platform uses these profiles for drift detection, data quality monitoring, and LLM content safety monitoring. Privacy-preserving: raw data never leaves your system.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Privacy-first design: raw data never leaves your environment — only statistical profiles uploaded. SOC2 Type II certified. Apache 2.0 open source. HTTPS enforced for profile uploads. Profile data is anonymous statistical summaries.
⚡ Reliability
Best When
You need privacy-preserving ML/LLM monitoring where raw data can't leave your environment — only statistical profiles are uploaded to WhyLabs for drift and quality alerting.
Avoid When
You need full request/response traces for debugging — whylogs doesn't store raw data. Use Langfuse or Arize for full observability with traces.
Use Cases
- • Monitor LLM agent input/output distributions for prompt injection patterns, toxic content, and response drift without sending raw text to third parties
- • Detect data drift in ML model features before it causes silent accuracy degradation — profile production data and compare to training baseline
- • Monitor agent tool call patterns and response quality metrics over time to detect behavioral drift or degradation
- • Validate data quality in agent pipelines — catch null rates, schema violations, and outliers before they propagate through the system
- • Profile and monitor RAG retrieval quality — track embedding similarity scores, chunk lengths, and retrieval patterns over time
Not For
- • Real-time alerting with sub-minute latency — whylogs profiles are typically computed in batch or micro-batch windows
- • Full request/response logging and replay — whylogs only stores statistical profiles, not raw data; use Langfuse or LangSmith for full trace logging
- • Teams not running Python — whylogs has Python and Java SDKs; limited support for other runtimes
Interface
Authentication
WhyLabs API key for uploading profiles from whylogs. Key scoped to organization and model/dataset. Environment variable WHYLABS_API_KEY and WHYLABS_DEFAULT_ORG_ID required. Open source whylogs runs without auth for local profiling.
Pricing
whylogs open source library is free (Apache 2.0). WhyLabs platform has free tier for small teams. Production monitoring with multiple models and long retention requires paid plan.
Agent Metadata
Known Gotchas
- ⚠ whylogs profiles statistical distributions, not raw values — agents debugging specific anomalies need to log raw data separately; whylogs only shows that drift exists, not which specific records caused it
- ⚠ WHYLABS_DEFAULT_ORG_ID and WHYLABS_API_KEY must both be set — missing either causes silent failures or unhelpful errors
- ⚠ Profile time windows must be configured correctly — data logged without timestamps goes to the current hour; wrong time window causes data to appear in wrong monitoring period
- ⚠ LLM content monitoring requires the whylogs LLM extras package (whylogs[llm]) — not included in base install
- ⚠ Schema inference works for pandas DataFrames but custom data types require manual column schema definition
- ⚠ WhyLabs free tier has 30-day data retention — historical drift analysis beyond 30 days requires paid plan
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for whylogs / WhyLabs.
Scores are editorial opinions as of 2026-03-06.