Status basis: npm_downloads_7d momentum fell to 0 per 3d from 21 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 21 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -1 per 3d from 17 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -1 per 3d from 17 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -176 per 3d from 18 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -176 per 3d from 14 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -139 per 3d from 15 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -1 per 3d from 54 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 14 per 3d from 157 (<= 40% of the prior window)
- Reactivated npm_downloads_7d did not move for 7d, then gained 14 in the last 3d
- Reactivated npm_downloads_7d did not move for 7d, then gained 14 in the last 3d
- Cooling npm_downloads_7d momentum fell to 14 per 3d from 157 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 15 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Anomaly daily change -158.0 is -7.0 standard deviations from this entity's own prior mean (7.8±23.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -158.0 is -7.0 standard deviations from this entity's own prior mean (7.8±23.7, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to -14 per 3d from 168 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -37 per 3d from 239 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -12 per 3d from 214 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 168 in the last 3d vs 48 in the 3d before (ratio 3.5x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to -12 per 3d from 214 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 239 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 214 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Anomaly daily change 83.0 is 4.0 standard deviations from this entity's own prior mean (6.8±19.0, baseline excludes this point) — unusual, not necessarily adoption
- Accelerating npm_downloads_7d gained 48 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Anomaly daily change 83.0 is 4.0 standard deviations from this entity's own prior mean (6.8±19.0, baseline excludes this point) — unusual, not necessarily adoption
| Machine-callable | — | unknown | — |
| Open source | 1 | inferred | 1 quote(s) |
| Self-hostable | 1 | inferred | 1 quote(s) |
| Bring your own key | — | unknown | — |
| Autonomy level | 2 | inferred | 3 quote(s) |
| Pricing model | — | unknown | — |
| Integrations | — | unknown | — |
“Unknown” means we have not verified it — it is not a “no”. Hard filters never treat unknown as false.
It addresses the core limitation of most AI coding tools—lack of memory—by enabling real, continuous learning across sessions.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“the nightly dream registers in Task Scheduler for your own user (no admin rights)”— readme
“a nightly "dreaming" process that learns from your conversations”— readme
“nightly dreaming all work — the nightly dream registers in Task Scheduler”— readme
“Give your AI a memory, habits, and a daily routine — with nothing but files.”— readme
“memory and self-improving skills for Claude Code/Codex”— topics
FAQ
What is Wienerdog?
Wienerdog enhances Claude Code and Codex by giving them persistent memory and the ability to learn from past interactions, improving coding suggestions over time.
What does Wienerdog do?
Remembering a developer's coding patterns and past context to deliver increasingly accurate and personalized code recommendations.
Why does Wienerdog matter?
It addresses the core limitation of most AI coding tools—lack of memory—by enabling real, continuous learning across sessions.
How much does Wienerdog cost?
Free + $20/mo
Is Wienerdog free?
Yes — Wienerdog has a free tier. Pricing as stated on its own page: Free + $20/mo
Is Wienerdog open source?
Yes — Wienerdog is open source.
Can I self-host Wienerdog?
Yes — Wienerdog can be self-hosted.
How popular is Wienerdog?
As tracked by KanonAgent: 8 upvotes (first indexed 2026-08-01).
What are the best Wienerdog alternatives?
Similar AI agents tracked by KanonAgent: Stealth Venture, taste-skill, archify, open-design, deer-flow, ruflo.