MIT · Python 3.9+ · Model-agnostic

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Hallucination risk scoring
for production LLMs

hallx is a lightweight guardrail layer that scores LLM outputs across schema validity, response consistency, and context grounding — giving you a single confidence score before any downstream action.

$ pip install hallx

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Risk signals

LLM adapters

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Downloads / month

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GitHub stars

How it works

Three signals. One confidence score.

hallx evaluates every LLM response across three dimensions and aggregates them into a single actionable score.

01 · schema

Schema Validity

Validates output structure against a JSON schema. Catches null injections, missing required fields, and type mismatches before they reach downstream consumers.

02 · consistency

Response Consistency

Re-runs generation multiple times and checks stability. Flip-flopping outputs signal uncertainty — a reliable indicator of potential hallucination.

03 · grounding

Context Grounding

Measures claim-to-context alignment using fast fuzzy matching. Detects when responses drift from the provided source material.

Safety profiles

Pick your risk tolerance

Three built-in profiles tune scoring behavior to your deployment context.

Use Cases

Who uses hallx and where it helps

hallx is useful wherever LLM output can trigger business actions, user-facing answers, or structured data writes.

Medical

Clinical assistant safety checks

Screen patient-facing summaries, triage notes, and medication guidance before delivery. Route high-risk outputs to clinician review.

Legal

Contract and policy copilots

Validate groundedness and consistency for clause summaries, legal Q&A, and compliance responses before sharing with clients or teams.

Agentic

Tool-using autonomous agents

Gate action-taking agents before database writes, external API calls, or workflow triggers so risky generations do not execute automatically.

Customer Support

Support chat and ticket replies

Score assistant-generated responses for policy accuracy and consistency. Escalate uncertain answers before they reach customers.

Finance

Financial analysis and reporting

Apply stricter checks for earnings summaries, risk notes, and advisory drafts where unsupported claims can create compliance exposure.

Research

Literature and evidence assistants

Check citation-like claims against provided context and flag low-confidence outputs during paper summarization and evidence synthesis.

Search Intent

Where hallx fits among LLM guardrail alternatives

If you are searching for hallx alternatives, judge-model evaluators, or heavier AI guardrail stacks, hallx is the lean option: no extra judge model, no embedding service, and no complex infra just to score hallucination risk.

lightweight

Fewer moving parts

hallx focuses on fast schema, consistency, and grounding checks instead of requiring a second LLM or a retrieval pipeline to begin getting value.

python

Built for Python AI apps

Use hallx in APIs, assistants, RAG systems, autonomous agents, or internal tooling where you want a practical hallucination detection layer without heavyweight ops.

creator

Created by Dhanush Kandhan

Branded searches for Dhanush Kandhan, hallx, or the hallx Python package should all lead back to the same open-source project, docs, and community links.

Pipeline

hallx in your pipeline

hallx sits between your LLM call and any downstream action — API responses, database writes, automation triggers.

Collect inputs

Prompt, optional context documents, optional JSON schema.

Generate response

Via a provider adapter or your own async callable.

Run three checks

Schema, consistency, and grounding evaluated in parallel.

Get confidence score

Weighted scores aggregated into 0.0 – 1.0 with risk level.

Apply policy

Use recommendation.action to proceed or retry.

Record & calibrate

Persist reviewed outcomes and tune thresholds over time.

Hallucination risk scoring
for production LLMs

Three signals. One confidence score.

Schema Validity

Response Consistency

Context Grounding

Pick your risk tolerance

Fast

Balanced

Strict

Who uses hallx and where it helps

Clinical assistant safety checks

Contract and policy copilots

Tool-using autonomous agents

Support chat and ticket replies

Financial analysis and reporting

Literature and evidence assistants

Works with your stack

Where hallx fits among LLM guardrail alternatives

Fewer moving parts

Built for Python AI apps

Created by Dhanush Kandhan

hallx in your pipeline

Collect inputs

Generate response

Run three checks

Get confidence score

Apply policy

Record & calibrate

Ready to reduce hallucination risk?

Hallucination risk scoringfor production LLMs

Three signals. One confidence score.

Schema Validity

Response Consistency

Context Grounding

Pick your risk tolerance

Fast

Balanced

Strict

Who uses hallx and where it helps

Clinical assistant safety checks

Contract and policy copilots

Tool-using autonomous agents

Support chat and ticket replies

Financial analysis and reporting

Literature and evidence assistants

Works with your stack

Where hallx fits among LLM guardrail alternatives

Fewer moving parts

Built for Python AI apps

Created by Dhanush Kandhan

hallx in your pipeline

Collect inputs

Generate response

Run three checks

Get confidence score

Apply policy

Record & calibrate

Ready to reduce hallucination risk?

Hallucination risk scoring
for production LLMs