InsAIts
Open-source monitoring for AI-to-AI, detect hallucinations
What it does
Agents contradict facts, fabricate citations/URLs/DOIs, lose confidence, and spread errors silently, one agent's hallucination becomes another's "truth." InsAIts: 5 hallucination subsystems (cross-agent contradictions, phantom citations, document grounding, confidence decay, self-consistency) + 6 anomalies. Features: Open-source core Privacy-first: all local 3-line setup, any LLM/Ollama Integrations: LangChain, CrewAI, LangGraph Slack/Notion exports, forensic tracing
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- OSOpen-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
- OAOpenJuris – AI legal research with citations from primary sourcesFeb 2026 · openjuris.org · ▲18
We built tooling that connects LLMs directly to case law databases with citation verification to address hallucination in legal AI. Think of it as giving the model access to actual legal sources instead of relying on training data.
- HCHalgorithem – Catching AI Hallucinations Using Trees, No AI in PipelineMay 2026 · github.com · ▲5
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