Status basis: npm_downloads_7d gained 6 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 6 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 6 in the last 3d vs 2 in the 3d before (ratio 3.0x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 6 in the last 3d vs 2 in the 3d before (ratio 3.0x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 6 in the last 3d vs 2 in the 3d before (ratio 3.0x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to -3 per 3d from 11 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -3 per 3d from 11 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -12 per 3d from 11 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -12 per 3d from 11 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -1217 per 3d from 217 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -568 per 3d from 205 (<= 40% of the prior window)
- Anomaly daily change -580.0 is -3.1 standard deviations from this entity's own prior mean (143.7±232.3, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to -391 per 3d from 28 (<= 40% of the prior window)
- Anomaly daily change -580.0 is -3.1 standard deviations from this entity's own prior mean (143.7±232.3, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to 217 per 3d from 645 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 217 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Anomaly daily change -580.0 is -3.1 standard deviations from this entity's own prior mean (143.7±232.3, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to 205 per 3d from 645 (<= 40% of the prior window)
- first observed Kanon started keeping permanent history for this agent
| Machine-callable | — | unknown | — |
| Open source | 1 | inferred | 1 quote(s) |
| Self-hostable | — | unknown | — |
| Bring your own key | 1 | inferred | 1 quote(s) |
| Autonomy level | 2 | inferred | 1 quote(s) |
| Pricing model | — | unknown | — |
| Integrations | github | inferred | 2 quote(s) |
“Unknown” means we have not verified it — it is not a “no”. Hard filters never treat unknown as false.
Provides a critical layer of reliability for AI agents by enabling dynamic, behavior-aware testing, addressing a core gap in agent development.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“It runs on [Jev by TypeSafe], with your own `TYPESAFE_API_KEY`.”— readme
“It asks for your TypeSafe API key (get one at <https://typesafe.ai>)”— readme
“It runs on [Jev by TypeSafe](https://typesafe.ai), with your own `TYPESAFE_API_KEY`.”— readme
“github.com/stardeckai/lgtm”— readme
“github/actions/workflow”— readme
FAQ
What is lgtm??
A validation system powered by Jev that checks AI agents’ code and behavior in real time to ensure correctness and safety.
What does lgtm? do?
Automatically verify the accuracy and safety of code and actions generated by AI agents during development.
Why does lgtm? matter?
Provides a critical layer of reliability for AI agents by enabling dynamic, behavior-aware testing, addressing a core gap in agent development.
Is lgtm? open source?
Yes — lgtm? is open source.
What does lgtm? integrate with?
github
How popular is lgtm??
As tracked by KanonAgent: 2 upvotes (first indexed 2026-09-20).
What are the best lgtm? alternatives?
Similar AI agents tracked by KanonAgent: Stealth Venture, ECC, orca, Agent-Reach, taste-skill, archify.