ENDEVSOLS/LongTracer
Detect hallucinations in LLM responses. Verify every claim against source documents using hybrid STS + NLI. Works with LangChain, LlamaIndex, or any RAG pipeline. pip install longtracer
This tool helps developers and AI engineers ensure the reliability of responses generated by Large Language Models (LLMs) in applications like chatbots or knowledge retrieval systems. It takes an LLM's response and a set of source documents, then verifies each claim made in the response against those sources. The output is a clear verdict on whether the response contains factual errors or 'hallucinations', along with a trust score and details on conflicting statements.
Available on PyPI.
Use this if you are building an LLM application and need to automatically detect and prevent incorrect or fabricated information in the model's output by cross-referencing it with provided source materials.
Not ideal if you need to evaluate the LLM's creativity, coherence, or style, as this tool focuses strictly on factual accuracy against provided sources.
Stars
12
Forks
1
Language
Python
License
MIT
Category
Last pushed
Apr 03, 2026
Commits (30d)
0
Dependencies
5
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