AI agents remember things. Over weeks of conversations they accumulate facts:
you are 20, you love pizza, you live in Pasadena. But memory systems almost
never check whether those facts agree with each other. When a new memory
says "you are 21" and an old one says "you are 20", one of them is wrong —
stale entry, misheard sentence, upstream bug — and nobody notices.
I built contradiction-judge
(pip install contradiction-judge) to fix that. Feed it a JSON file of memory
entries; it returns every contradicting pair plus an arbitration suggestion
telling you which entry should win.
contradiction-judge check memory.json
{
"entries": 2,
"pairs_checked": 1,
"contradictions": [
{
"entry_a": "m1",
"entry_b": "m2",
"kind": "numeric",
"engine": "rules",
"explanation": "'I am 20 years old.' vs 'I am 21 years old.': person.age cannot be both 20.0 and 21.0.",
"arbitration": {
"strategy": "recency",
"recommendation": "keep m2, drop or correct m1",
"reason": "Both entries are timestamped; the newer record (m2) is more likely to be current."
}
}
]
}
Two layers
Rules (stdlib only, zero dependencies). Numeric conflicts ("20 years old"
vs "21 years old", unit-aware: "240 months old" == "20 years old"), date
conflicts ("birthday May 5, 2006" vs "2006-06-01"), factual conflicts (name,
location), and polarity conflicts ("I love pizza" vs "I hate pizza", "I am a
student" vs "I am not a student").
A pluggable LLM judge hook for the subtle stuff rules can't see:
from contradiction_judge import check_memories, set_judge
def gpt_judge(text_a, text_b):
# call your model; return {"contradiction": bool, "explanation": str}
...
set_judge(gpt_judge)
report = check_memories(entries) # semantic pairs also get judged
The hook only runs on pairs the rules did not flag, so you pay for LLM
calls only where they add value. (--hook module:func on the CLI.)
How it differs from memgovern
If you've seen my memgovern
project: that one is a governance framework — provenance tracking, source
trust levels, write policies, retention rules. It answers "should this
memory be trusted and kept?"
contradiction-judge is the auditor. It answers "do these memories
contradict each other, and which one should win?" — semantic
fact-contradiction detection with arbitration suggestions. Same roadmap
family (the LLM-judge idea came straight from memgovern's roadmap), but a
different job. Govern with memgovern, audit with contradiction-judge.
Design notes
- Recency wins, by default. When both entries are timestamped, the newer record is recommended — facts change, and location or preference conflicts may reflect a genuine move rather than an error. Without timestamps, the pair is flagged for manual review.
-
CI-friendly:
--failexits 1 when any contradiction is found. - 15 smoke tests, all green before release.
On the roadmap: embedding-based topic alignment ("the Bay Area" vs
"San Francisco"), confidence scores, and an auto-fix mode that rewrites the
stale entry instead of just flagging it.
Repo: https://github.com/hahahahahahahahah6/contradiction-judge
PyPI: https://pypi.org/project/contradiction-judge/
