OKF Agent Memory – Git-native persistent memory for AI coding agents

Re:OKF Agent Memory – Git-native persistent memory for AI co... by esafak on Sunday September 06, 2026 @12:51PM (#49588361)

Has anyone else benchmarked all these tools for precision/recall? I too want to know if agent memory is something I should add. I only do session memory for now and that is quite useful.

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Re:OKF Agent Memory – Git-native persistent memory for AI co... by glub on Monday September 07, 2026 @07:29AM (#49597034)

Yes. See LongMemEval, LoCoMo. Tons of research here.

But precision/recall is relatively "solved". What nobody has gotten close to solving is maintenance and provenance - what goes into memory, what qualifies as truth, how stale memory gets invalidated/superseded.

We're now in the phase of re-discovering 30+ years of pain of knowledgebases.

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Re:OKF Agent Memory – Git-native persistent memory for AI co... by olenzma on Tuesday September 08, 2026 @01:45AM (#49605930)

Invalidation is not an algorithm, but a judgment made in context

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[dead]

Re:OKF Agent Memory – Git-native persistent memory for AI co... by opwizardx on Monday September 07, 2026 @05:36PM (#49603210)

I did similar tests on my own corpus when was considering whether to keep semantic search in default path for pond.

On 3 months of my own sessions I’ve seen that BM25 search was finding the correct answer in ~61%, where semantic had shown only ~37% of success.

After that it was easy for me to make the decision.

Got all info on how I did evals in here, if interested: https://github.com/tenequm/pond/tree/main/docs/researches/26...

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