Status basis: npm_downloads_7d momentum fell to -284 per 3d from 57 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -284 per 3d from 57 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -283 per 3d from 84 (<= 40% of the prior window)
- Anomaly daily change -118.0 is -3.4 standard deviations from this entity's own prior mean (3.7±36.1, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to -165 per 3d from 84 (<= 40% of the prior window)
- Anomaly daily change -118.0 is -3.4 standard deviations from this entity's own prior mean (3.7±36.1, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to 57 per 3d from 162 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 57 per 3d from 162 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 84 per 3d from 289 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 84 per 3d from 289 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 206 in the last 3d vs 116 in the 3d before (ratio 1.8x, threshold 1.5x)
- Anomaly daily change 135.0 is 5.4 standard deviations from this entity's own prior mean (2.7±24.7, baseline excludes this point) — unusual, not necessarily adoption
- Accelerating npm_downloads_7d gained 289 in the last 3d vs 10 in the 3d before (ratio 28.9x, threshold 1.5x)
- Anomaly daily change 135.0 is 5.4 standard deviations from this entity's own prior mean (2.7±24.7, baseline excludes this point) — unusual, not necessarily adoption
- Accelerating npm_downloads_7d gained 175 in the last 3d vs 1 in the 3d before (ratio 175.0x, threshold 1.5x)
- Anomaly daily change 71.0 is 3.3 standard deviations from this entity's own prior mean (0.5±21.7, baseline excludes this point) — unusual, not necessarily adoption
- Reactivated npm_downloads_7d did not move for 7d, then gained 175 in the last 3d
- Anomaly daily change 83.0 is 5.5 standard deviations from this entity's own prior mean (-2.4±15.6, baseline excludes this point) — unusual, not necessarily adoption
- Accelerating npm_downloads_7d gained 175 in the last 3d vs 1 in the 3d before (ratio 175.0x, threshold 1.5x)
- Anomaly daily change 83.0 is 5.5 standard deviations from this entity's own prior mean (-2.4±15.6, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to -11 per 3d from 11 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 11 in the last 3d vs 1 in the 3d before (ratio 11.0x, threshold 1.5x)
- Accelerating npm_downloads_7d gained 11 in the last 3d vs 1 in the 3d before (ratio 11.0x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to -1 per 3d from 29 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -1 per 3d from 29 (<= 40% of the prior window)
| 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 | 1 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.
By combining AI code generation with visual workflow design, it creates a collaborative front-end layer that makes AI agents more accessible and usable in real development environments.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“npx @airshiplabs/cli --target 3000”— readme
“watch Claude Code, Codex or OpenCode update the source”— readme
FAQ
What is Airship?
Airship offers a Figma-style visual editor for AI coding agents, letting developers drag, arrange, and collaborate on AI-generated code logic in a intuitive interface.
What does Airship do?
To visually compose, debug, and collaborate on complex code workflows generated by AI assistants.
Why does Airship matter?
By combining AI code generation with visual workflow design, it creates a collaborative front-end layer that makes AI agents more accessible and usable in real development environments.
Is Airship open source?
Yes — Airship is open source.
Can I self-host Airship?
Yes — Airship can be self-hosted.
How popular is Airship?
As tracked by KanonAgent: 1 upvotes (first indexed 2026-08-11).
What are the best Airship alternatives?
Similar AI agents tracked by KanonAgent: Stealth Venture, open-design, deer-flow, ruflo, career-ops, cherry-studio.