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Hallucination Python Packages

Python packages with the GitHub topic hallucination. Sorted by relevance, with stars and monthly downloads.
cvs-health
uqlm

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

5K 1K 123
Libr-AI
openfactverification-kongzii

Loki: Open-source solution designed to automate the process of verifying factuality

4K 1K 63
wauldoai
wauldo

Official Python SDK for Wauldo — verified AI answers with zero hallucinations. pip install wauldo

4K 1 2
MigoXLab
dingo-python

Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool

1K 700 72
pulkitj
groundguard

Verify LLM output against your source documents. Catch hallucinations in RAG pipelines and agentic workflows before they reach users.

1K 0 0
FastBuilderAI
fastmemory

FastMemory is a topological representation of text data using concepts as the primary input. It helps in improving the RAG(by replacing embedding and vectors entirely), AI memory and LLM queries by upto 100% as in the huggingface benchmarks(22+ SOTA)

935 37 5
QWED-AI
qwed

The Deterministic Verification Protocol for AI - 11 verification engines for math, logic, code, SQL, facts, images, and more. Now with Agentic Security Guards.

831 55 8
ylu999
jingu-trust-gate

jingu-trust-gate – deterministic admission layer that blocks LLM hallucinations from becoming system state

595 1 0
bh3r1th
ega

Runtime enforcement layer for LLM outputs. Verifies claims against source evidence before emit. Not an eval tool.

456 1 0
Saivineeth147
llm-testlab

Comprehensive Testing Tool for Large Language Models

379 6 0
rudra496
ai-trust-validator

AI Code Trust Validator - Free open-source security scanner for AI-generated code. Detect vulnerabilities, backdoors, hallucinations & logic errors in Python, JavaScript, TypeScript. pip install ai-trust-validator

313 4 1
aimonlabs
hdm2

HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification

302 12 0
dhanushk-offl
hallx

Lightweight, model-agnostic hallucination risk engine for LLM outputs

299 3 0
frmoretto
cgd-validator

Validator for Clarity-Gated Document (.cgd) files - verified documents for safe LLM ingestion

180 25 3
Mmorgan-ML
phase-slip-sampler

An entropic perturbation sampler for LLMs using orthonormal vector rotation and logit anchoring.

177 6 0
frmoretto
cgd-verify

Stop LLMs from hallucinating your guesses as facts. Clarity Gate is a verification protocol for your documents that are going to be provided to LLMs or RAG systems. Place automatically the missing uncertainty markers to avoid confident hallucinations. HITL for non-directly verifiable claims.

173 28 3
rwondo
maven-ai

Multi-Agent Verification & Evaluation Network - Reduce AI hallucinations through multi-model consensus

169 1 0
IAAR-Shanghai
eval-suite

[ACL 2024] User-friendly evaluation framework: Eval Suite & Benchmarks: UHGEval, HaluEval, HalluQA, etc.

160 180 13
zjunlp
deco-mllm

[ICLR 2025] MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

130 144 13
frmoretto
cgd-generator

Stop LLMs from hallucinating your guesses as facts. Clarity Gate is a verification protocol for your documents that are going to be provided to LLMs or RAG systems. Place automatically the missing uncertainty markers to avoid confident hallucinations. HITL for non-directly verifiable claims.

126 28 3
frmoretto
sot-validator

Validator for Source of Truth (.sot) files - epistemic quality verification for AI-safe documentation

120 24 2
frmoretto
cgd-creator

Stop LLMs from hallucinating your guesses as facts. Clarity Gate is a verification protocol for your documents that are going to be provided to LLMs or RAG systems. Place automatically the missing uncertainty markers to avoid confident hallucinations. HITL for non-directly verifiable claims.

120 28 3
frmoretto
clarity-gate

Pre-ingestion verification for epistemic quality in RAG systems

114 28 3
frmoretto
sot-verify

Stop LLMs from hallucinating your guesses as facts. Clarity Gate is a verification protocol for your documents that are going to be provided to LLMs or RAG systems. Place automatically the missing uncertainty markers to avoid confident hallucinations. HITL for non-directly verifiable claims.

109 28 3
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