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jarviscore-framework

Prescott-Data/jarviscore-framework · Homepage

Peer-to-peer agent runtime with typed Jev decisions and zero-trust credentials.

Classification & Routing0.38Runner-up: Agent DecisionsFunnel
Automated README summary

Automatically summarized from the README on 23 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

JarvisCore is a Python runtime where AI agents act as equal peers on a mesh, discovering each other by capability, sharing a Redis ledger, and reaching external services only through a zero-trust broker. It targets teams building multi-agent systems.

How it uses Jev

From 1.12 agents ask typed Choice, Score and Noul questions through a decision client separate from the text model. The Kernel uses Choice to pick a specialist subagent, and each retrieved RAG passage is withheld from the generating model when its prompt-injection Noul exceeds 0.70.

Primitives:choicescorenoul

Technique worth stealing

Gate retrieved RAG passages on a calibrated prompt-injection Noul score before they reach the generating model.

Try it

pip install jarviscore-framework, then jarviscore init and jarviscore check --validate-llm.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.14.0
Project signals
jev-1.14.0
Category
jev-1.14.0
Pattern tags
jev-1.14.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectnot found in the excerpt0.25
  • Shows a System One patternFound in material0.88
  • Describes handling uncertaintynot found in the excerpt0.24
  • Reports measured numbersnot found in the excerpt0.02
  • Is a runnable toolFound in material0.99
  • Top-10 learning shortlist signalnot found in the excerpt0.32
  • Is a replica of the modelnot found in the excerpt0.34
  • Solves a common problem (0–2)score on a 0–2 scale1.74 / 2
  • About JevFound in material0.71
Signals
Jev is core to the project0.25Shows a System One pattern0.88Describes handling uncertainty0.24Reports measured numbers0.02Is a runnable tool0.99Top-10 learning shortlist signal0.32Is a replica of the model0.3400.51Solves a common problem (0–2)1.74 / 2012

no-judgment band (0.40–0.60)

Design patterns
Funnel0.82Ghostwriter0.67Blind review0.27Inspection0.2300.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 23 Oct 2026.