Hallucination Python Packages

uqlm

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

openfactverification-kongzii

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

wauldo

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

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)

dingo-python

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

qwed

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

jingu-trust-gate

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

hdm2

HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification

ega

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

hallx

Lightweight hallucination risk scoring for LLM outputs

ai-trust-validator

🛡️ Validate AI-generated code for security, hallucinations & logic errors. Open-source trust layer for AI-assisted development.

llm-testlab

Comprehensive Testing Tool for Large Language Models

phase-slip-sampler

Phase-Slip is a stochastic intervention architecture that operates on the Key-Value Cache of the model. Phase-Slip gently rotates the semantic vectors of the context window, asking the model: "How would you finish this sentence if you looked at it from a slightly different perspective?"

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.

cgd-validator

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

maven-ai

Multi-Agent Verification Engine - Production-ready hallucination detection for high-stakes AI applications

eval-suite

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

sot-validator

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

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.

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.

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.

clarity-gate

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.

memory-trail

Decision memory and session logging for AI-assisted development - track architectural decisions across sessions

sot-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.