# leochlon/hallbayes

Repository: https://github.com/leochlon/hallbayes
Canonical: https://ross.abutalabs.com/products/hallbayes
Language: Python
License: MIT
License Family: permissive
Last push: 2026-06-17T03:54:59+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 26
- inputs: {"age_days": 366, "days_push": 77, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1692, forks 163 (observed 2026-08-28T04:05:22.763263+00:00)

## What it is
Berry is a local MCP server that detects hallucinations by measuring whether an LLM's claims are actually supported by the cited evidence, using an information-budget (logprob) test. It returns fail-closed pass/flag decisions per claim and records results in a tamper-evident ledger, integrating with coding assistants like Claude Code, Cursor, and Codex.

## Use cases
- detect hallucinated claims in LLM answers
- verify answers are grounded in cited evidence
- flag unsupported statements from AI coding agents
- audit LLM outputs with a tamper-evident decision ledger
- add grounding checks to Claude Code, Cursor, or Codex workflows
- score claim-level entailment in a single logprob pass

## When to choose
- you need claim-level grounding verification rather than fact-checking against world knowledge
- you want a local, fail-closed hallucination detector wired into MCP-based coding assistants
- you need an auditable, tamper-evident record of grounding decisions
- you want fast detection in a single logprob pass without extra model calls

## When to avoid
- you need to verify factual truth against external sources rather than grounding in provided evidence
- your stack has no MCP support and you cannot use CLI-based integration
- you need multilingual or non-logprob-based verification methods
- you require a hosted SaaS with dashboards rather than a local tool

## Facets
- artifact type: service
- maturity: active
- function: mcp, llm-inference, nlp, security
- domain: large-language-models, developer-tools
- platform: python, cli, self-hosted
- tags: hallucination-detection, grounding-check, mcp-server, logprob-analysis, tamper-evident-ledger, ai-safety, ai-agents, retrieval-augmented-generation

## Member repositories
- leochlon/hallbayes (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:22.763263+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:38:14.057298+00:00, confidence not recorded.
  - readme: https://github.com/leochlon/hallbayes (fetched 2026-08-28T04:05:22.763263+00:00, sha 45646eeab40f)
- Data as of 2026-08-30T08:39:29.467469+00:00.