attune-verify

Generation fact-checker for the attune-* family. Verifies named entities in LLM-generated content actually exist — imports import, CLI flags are real, links resolve, counts match source — so hallucinations that pass unit tests are caught before they reach a reader.

Install

pip install attune-verify

With the optional LLM semantic layer (requires attune-rag):

pip install 'attune-verify[rag]'

Quick start

from attune_verify import verify, VerifyContext
from pathlib import Path

ctx = VerifyContext(
    project_root=Path("."),
    allowed_help_cmds=frozenset(["attune"]),
)
result = verify(generated_content, ctx)
if not result.ok:
    for f in result.findings:
        print(f"{f.kind}: {f.detail}")

Part of the attune family

  • attune-rag grounds generation in accurate retrieved sources (input-side)
  • attune-verify checks that named entities in the output actually exist (output-side)

Together they bracket generation: rag verifies "is this claim supported?"; verify checks "does this named thing exist?"

What each checker verifies

Checker Claim it settles Truth source you declare
imports every import in a Python code fence resolves, by full dotted path env_python
flags every --flag in an inline span or shell fence appears in that command's --help help_commands / allowed_help_cmds
links every local markdown link target exists under the project project_root
counts every numeric claim matches the number it names count_sources

Findings are error when a claim is refuted and warning when it cannot be checked — an unverifiable claim is never a silent pass. result.ok is False only on errors; raise_if_failed(result) turns it into a hard gate.

Status

Beta — the deterministic core (imports, flags, links, counts) is stable and guarded by a labeled precision/recall corpus (gated ≥ 0.95 each) and mutation testing (gated ≥ 0.75). The public API above (verify, VerifyContext, VerifyResult, Finding, FindingKind, raise_if_failed, and the Judge protocol) is what beta covers: it will not change shape without a deprecation in a minor release. The LLM semantic layer is optional via the [rag] extra and is the least settled part of the surface.

Links are read from prose only. Inline ([text](target)) and all three reference forms — full [text][label], collapsed [text][], and shortcut [text] — resolve against the document's [label]: target definitions. An explicit reference whose label is never defined is an error: it renders as literal text, so the link does not exist. An undefined shortcut is ordinary prose (the [3] case is not a broken link) and is skipped, as are GFM footnotes, which share the same syntax.

Known limitations

  • Short flags (-v) are not checked; only --long forms are.
  • A flag written as bare `--flag` in prose is attributed to the nearest preceding word, so it may degrade to a warning rather than resolve.
  • Counts are matched to a source by keyword overlap with the claim's surrounding text; a numeric claim whose context names no source is skipped rather than guessed at.

License

Apache 2.0

Project links

Data verified by PyPI on Aug 11, 2026
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Key dates

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  • Released:
Newer release available (0.5.0)

1 maintainer

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Avatar for patrick.roebuck2 from gravatar.com patrick.roebuck2

Credits

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Author: Patrick Roebuck

GitHub Statistics

Data verified by PyPI on Aug 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache License)

Requires

Python >=3.10

Provides Extra

rag dev

Tags

verification fact-checking hallucination llm attune grounding faithfulness

Classifiers

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