Status basis: daily change -159.0 is -3.4 standard deviations from this entity's own prior mean (-7.5±44.4, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -159.0 is -3.4 standard deviations from this entity's own prior mean (-7.5±44.4, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to 5 per 3d from 46 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 5 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Anomaly daily change -159.0 is -3.4 standard deviations from this entity's own prior mean (-7.5±44.4, baseline excludes this point) — unusual, not necessarily adoption
- Cooling npm_downloads_7d momentum fell to -8 per 3d from 238 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 239 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 46 per 3d from 184 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 46 per 3d from 184 (<= 40% of the prior window)
- Anomaly daily change -174.0 is -4.2 standard deviations from this entity's own prior mean (-15.9±37.5, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -174.0 is -4.2 standard deviations from this entity's own prior mean (-15.9±37.5, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -174.0 is -4.2 standard deviations from this entity's own prior mean (-15.9±37.5, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -174.0 is -4.2 standard deviations from this entity's own prior mean (-15.9±37.5, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -174.0 is -4.2 standard deviations from this entity's own prior mean (-15.9±37.5, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- Anomaly daily change -153.0 is -5.5 standard deviations from this entity's own prior mean (-12.0±25.7, baseline excludes this point) — unusual, not necessarily adoption
- first observed Kanon started keeping permanent history for this agent
| Machine-callable | — | unknown | — |
| Open source | 1 | inferred | 1 quote(s) |
| Self-hostable | 1 | inferred | 1 quote(s) |
| Bring your own key | — | unknown | — |
| Autonomy level | 3 | inferred | 2 quote(s) |
| Pricing model | — | unknown | — |
| Integrations | mcp | inferred | 2 quote(s) |
“Unknown” means we have not verified it — it is not a “no”. Hard filters never treat unknown as false.
It provides a modular, reliable foundation for mathematical reasoning—critical for agents that must go beyond approximation and deliver exact results.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“For a guided user-local install:”— readme
“work with solver artifacts”— readme
“An agent decides which operations to use and in what order.”— readme
“It runs as an MCP server and is also available as a CLI and Python library.”— readme
“mcp,mcp-server,model-context-protocol”— topics
FAQ
What is Jacobian?
Jacobian is a specialized math library that gives AI agents the ability to perform precise symbolic and numerical computations, designed for developers building agents requiring rigorous mathematical reasoning.
What does Jacobian do?
To enable AI agents to reliably execute complex mathematical derivations and symbolic calculations.
Why does Jacobian matter?
It provides a modular, reliable foundation for mathematical reasoning—critical for agents that must go beyond approximation and deliver exact results.
How much does Jacobian cost?
Free
Is Jacobian free?
Yes — Jacobian has a free tier. Pricing as stated on its own page: Free
Is Jacobian open source?
Yes — Jacobian is open source.
Can I self-host Jacobian?
Yes — Jacobian can be self-hosted.
What does Jacobian integrate with?
mcp
How popular is Jacobian?
As tracked by KanonAgent: 1 upvotes (first indexed 2026-08-07).
What are the best Jacobian alternatives?
Similar AI agents tracked by KanonAgent: Stealth Venture, taste-skill, archify, open-design, deer-flow, ruflo.