Cleanlab
FreeAn API for detecting and scoring hallucinations in LLM outputs.
FreeFree tier
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About Cleanlab
Cleanlab TLM (Trustworthy Language Model) is an API that detects and scores hallucinations, wrong context, and knowledge gaps in AI-generated responses. It provides real-time guardrails for LLMs, RAG systems, and AI agents, delivering a single trustworthiness metric derived from multiple operations. According to benchmarks, Cleanlab achieves 34% better precision/recall than other methods for LLM reliability, 3× better for RAG reliability, and high accuracy across five agent architectures. The API integrates with just a few lines of code and supports pre-optimized models with latency as low as 300ms, making it suitable for production environments.
Key Features
Detects hallucinations, wrong context, and knowledge gaps in AI responses
Provides a single trustworthiness score with clear explanation
Real-time guardrails optimized for accuracy in production
Supports 15+ evaluation models with 5 quality settings
Latency as low as 300 ms with pre-optimized models
Easy integration with just a few lines of code
Proven best on benchmarks: 34% better precision/recall for LLMs, 3× better for RAG
Pros & Cons
Pros
- Achieves highest accuracy across latency and cost profiles according to benchmarks
- Provides clear, actionable trustworthiness scores with explanations
- Low latency (300ms) suitable for real-time applications
- Easy to integrate with a few lines of code
- Covers multiple error types: hallucination, wrong context, knowledge gaps
Best For
Detecting hallucinations in LLM outputsMonitoring RAG pipelines for incorrect context retrievalEscalating untrustworthy AI responses to humans or fallback flowsEnsuring reliability of AI agents with tool-using workflowsAdding production guardrails to AI applications
FAQ
What types of AI errors does Cleanlab detect?
Cleanlab detects three types of issues: hallucinations (AI makes up an answer regardless of context), wrong context (AI cannot find the correct context in the knowledge base), and knowledge gaps (AI returns 'I don't know' when context is missing).
How accurate is Cleanlab compared to other methods?
Cleanlab is proven best at detecting AI hallucinations. On RAG benchmarks including CovidQA, DROP, FinanceBench, and PubmedQA, Cleanlab achieves 34% better precision/recall than other methods for LLM reliability, and 3× better for RAG reliability.
How easy is it to integrate Cleanlab?
Integration requires just a few lines of code. Cleanlab provides pre-optimized models with 15+ evaluation models and 5 quality settings, allowing engineers to choose between lower latency or higher accuracy.