Cleanlab
FreeDetect and remediate hallucinations in any LLM application.
FreeFree tier
Inputs: textOutputs: text
About Cleanlab
Cleanlab's Trustworthy Language Model (TLM) is a real-time API that scores the trustworthiness of responses from any large language model (LLM) and can also be used as a drop-in replacement to produce higher accuracy outputs. TLM works out of the box without requiring training on user data, is compatible with any LLM including reasoning models and agentic frameworks, and offers flexible latency/cost configurations for enterprise applications. Benchmarks show TLM reduces hallucination rates of GPT-4o by 27%, o1 by 20%, and Claude 3.5 Sonnet by 20%, and detects incorrect answers in RAG applications with 3x greater precision than other detectors.
Key Features
Trustworthiness scoring for any LLM response (including human-written text)
Can be used as a drop-in LLM to produce higher-accuracy responses with trust scores
Works with any LLM, reasoning model, or agentic framework
No training on user data required – works out of the box
Scalable real-time API with flexible latency/cost configurations
Private deployment options available
Future-proof – improves automatically when base models are updated
Pros & Cons
Pros
- State-of-the-art precision in detecting hallucinations across multiple frontier models
- Reduces incorrect responses of GPT-4o by 27%, o1 by 20%, Claude 3.5 Sonnet by 20%
- 3x greater precision than other hallucination detectors in RAG applications
- Works instantly without any dataset preparation or labeling
- Compatible with any current and future LLM without retraining
Cons
- May incur additional latency/cost depending on configuration
- Relies on an underlying base LLM, so cost and performance are partially tied to the chosen model
Best For
RAG (Retrieval-Augmented Generation)SummarizationData ExtractionStructured OutputsClassificationData Labeling