Cleanlab
Experience GenAI that doesn't hallucinate
Founded
2021
Starting Price
$0
Category
Developer Tools
Last updated March 4, 2026
About Cleanlab
Cleanlab is an AI safety and data quality platform that detects and remediates errors in AI agent outputs including hallucinations, retrieval failures, and policy violations in real time. Built on peer-reviewed research from MIT, its Confident Learning technology automatically identifies mislabeled data, outliers, and low-quality inputs across tabular, text, image, and audio datasets. It functions as an independent control layer that can be added to existing AI stacks without modifying underlying systems.
Pros & Cons
Pros
- Strong academic foundation with peer-reviewed research cited 4,000+ times
- Unique automatic data label quality scoring and correction at scale
- Flexible SaaS and VPC deployment for compliance-heavy industries
- Broad integration ecosystem with major cloud and LLM platforms
- Dual interface for non-technical teams (GUI) and developers (Python SDK)
Key Features
Real-Time AI Output Validation
Detects hallucinations, retrieval errors, and policy violations from AI agents as they occur
Trustworthy Language Model (TLM)
Wraps any LLM call and returns a calibrated trustworthiness score alongside the response
Automated Label Error Detection
Uses Confident Learning algorithms to find mislabeled data in classification and regression datasets
Outlier & Duplicate Detection
Flags near-duplicates, out-of-distribution samples, and low-quality data points automatically
No-Code & Python API
Supports both non-technical teams via GUI and developers via Python SDK and REST API
Human-in-the-Loop Remediation
Routes flagged AI responses to human reviewers with a structured workflow
Multi-Modal Support
Works on tabular, text, image, and audio data formats
Pricing
Open Source
$0
- cleanlab Python library
- MIT-licensed
- API only
- GitHub community support
TLM Free Trial
$0
Best For
AI Customer Support Agents
Detect hallucinated answers or policy violations in real time before they reach customers
ML Training Data Curation
Automatically find and fix mislabeled data before fine-tuning LLMs or training classifiers
Enterprise Data Quality Audits
Audit large datasets in Snowflake or Databricks for label errors, outliers, and duplicates
LLM Trustworthiness Scoring
Wrap any LLM API call with TLM to get calibrated confidence scores and catch unreliable responses
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