inno-agent is a education AI agent for Automatically plan learning paths, remember content, and proactively remind me to review to avoid forgetting and inefficient learning. Pricing: Free. As of 2026-10-27, KanonAgent records 1.3k upvotes. First indexed by KanonAgent on 2026-07-25.
An open-source personal learning agent with multi-layer memory, proactive scheduling, and a workspace-scoped lab for self-directed knowledge acquisition.
1.3k upvotes
Tracked by Kanon since Jul 25, 2026
Status basis: npm_downloads_7d gained 159597 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
📈 Timelinewhat changed, and when
- Accelerating npm_downloads_7d gained 159597 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 159597 in the last 3d vs 103558 in the 3d before (ratio 1.5x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 159597 in the last 3d vs 103558 in the 3d before (ratio 1.5x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to -834870 per 3d from 147556 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -812523 per 3d from 435951 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 664417 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 147556 per 3d from 737842 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 147556 per 3d from 737842 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 664417 in the last 3d vs 359057 in the 3d before (ratio 1.9x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 737842 in the last 3d vs 309260 in the 3d before (ratio 2.4x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 587523 in the last 3d vs 257652 in the 3d before (ratio 2.3x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 587523 in the last 3d vs 257652 in the 3d before (ratio 2.3x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to 309260 per 3d from 2170674 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 257652 per 3d from 2084206 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 257652 per 3d from 2084206 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 2170674 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 2084206 in the last 3d vs 76824 in the 3d before (ratio 27.1x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 972862 in the last 3d vs 115219 in the 3d before (ratio 8.4x, threshold 1.5x)
- Anomaly daily change 959393.0 is 6.1 standard deviations from this entity's own prior mean (5787.9±157198.6, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 34715 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 972862 in the last 3d vs 115219 in the 3d before (ratio 8.4x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 34715 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -535913 per 3d from 295646 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -574308 per 3d from 295646 (<= 40% of the prior window)
🔎 Known / Unknown
14 of 23 fields unknown
| Machine-callable | — | unknown | — |
| Open source | 1 | verified | 1 quote(s) |
| Self-hostable | — | unknown | — |
| Bring your own key | — | unknown | — |
| Autonomy level | 2 | inferred | 2 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.
🤖 Agent teardown · education
Job to be doneAutomatically plan learning paths, remember content, and proactively remind me to review to avoid forgetting and inefficient learning.
AutonomyL2 · tool-calling(evidence: "personal IM channels (Feishu / WeChat)…")
Who it is forTo C (consumers)
Prerequisitesopen source
PricingFree
Traction · why it is rising402 Product Hunt votes, active GitHub repo with regular commits
Why it matters
The combination of three-tier memory and proactive scheduling creates a long-term, evolving personal learning agent with strong reusability and open-source potential.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“personal IM channels (Feishu / WeChat)”— readme
“proactive scheduler, personal IM channels”— description
Signal source: GitHub
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FAQ
What is inno-agent?
An open-source personal learning agent with multi-layer memory, proactive scheduling, and a workspace-scoped lab for self-directed knowledge acquisition.
What does inno-agent do?
Automatically plan learning paths, remember content, and proactively remind me to review to avoid forgetting and inefficient learning.
Why does inno-agent matter?
The combination of three-tier memory and proactive scheduling creates a long-term, evolving personal learning agent with strong reusability and open-source potential.
How much does inno-agent cost?
Free
Is inno-agent free?
Yes — inno-agent has a free tier. Pricing as stated on its own page: Free
Is inno-agent open source?
Yes — inno-agent is open source.
How popular is inno-agent?
As tracked by KanonAgent: 1.3k upvotes (first indexed 2026-07-25).
What are the best inno-agent alternatives?
Similar AI agents tracked by KanonAgent: Confidential Startup, Codédex, Stealth Company, Dataford Inc., Atlas, Verbi.