Persistent memory for language-model tools

Give long-running assistants a user-controlled record of preferences, project history, and reusable knowledge.

First seen 2026-07-08 · Last updated 2026-08-28 · Recalculated daily

7.0

Evidence confidence, not a return forecast

Independent source items, deduplicated by post

Public source families

Payment evidence: current spend or explicit intent

ProblemMany language-model tools start each conversation with limited continuity, forcing users to restate context and rebuild project knowledge.

People affectedPeople using conversational tools for long projects, personalized assistance, and ongoing research

Named alternativesClaude Code, Codex 5.5 medium, Maya

TopicsPersistent memoryKnowledge managementLocal assistants

Weekly mentions · 12 weeks

06-0807-0608-0308-24

Trend factor ×0.5, capped at 2.0.

Representative evidence

2 public excerpts · 4 items in the full chain

Product complaint★★★★★First-hand pain

Post title: Maya hung up on me after I berated it for minutes because it can’t help with the most basic brainstorming

Maya is dumb as a rock all of a sudden. Literally …

The public layer keeps only a minimal excerpt. Open the source for full context.

Reddit2026-08-17View source ↗

Pain★★★☆☆First-hand pain

Post title: How to stop the AI from forgetting information?

the longer the chat goes, the more information it …

The public layer keeps only a minimal excerpt. Open the source for full context.

Reddit2026-08-17View source ↗

★★★★☆

2 more items — unlock free with your email

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What this evidence does not show yet

To reach Payment-backed we still need:

1 more independent post(s) — we have 4
a first-hand payment statement — either someone naming what they already pay, or 2 more statement(s) with a specific amount they would pay (we have 0)
renewed mentions — the recent trend is below the promotion bar

No first-hand payment statement is on record here, so this page carries no pricing suggestion. A price with no payment evidence behind it is a guess wearing a number.

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Scoring summary

Evidence confidence = strength × evidence volume × source diversity × payment × competition × trend × 10. Counts use independent content items; only first-hand pain, current spend, explicit willingness to pay and concrete feature requests affect the score. Repeated posts by one author are discounted. Scoring and status changes follow deterministic rules.

“Payment-backed” means first-hand payment evidence exists in the record. It does not mean the business is worth building. “Fading” is a recency tag shown alongside any evidence level, not a lower level. Read the full methodology.