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Jev-Mem

rgreen4374/Jev-Mem

System-One-controlled agentic memory for long-running AI agents.

Agent Decisions0.91Runner-up: Scoring & RankingFunnel
Automated README summary

Automatically summarized from the README on 19 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

Jev-Mem is a memory system for long-running AI agents. It stores observations with provenance, links them across semantic, temporal, causal, and entity graphs, and retrieves evidence for a System-Two language model to answer questions.

How it uses Jev

Jev acts as the System-One controller. It judges inferred memory relations, routes queries across graph views, allocates traversal budgets, scores candidates, and assesses whether evidence is sufficient. Decisions are exposed as Noul binary propositions and Choice categorical decisions; ordinary code validates outputs and enforces graph and retrieval limits.

Primitives:choicenoul

Technique worth stealing

Separate frequent memory-management decisions into a typed System-One controller, leaving answer synthesis to a System-Two language model.

Try it

Run `python -m jev_mem.demo` for an offline demo with mock decisions and no API keys.

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-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

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 excerpt0.37
  • Shows a System One patternnot found in the excerpt0.35
  • Describes handling uncertaintynot found in the excerpt0.06
  • Reports measured numbersnot found in the excerpt0.08
  • Is a runnable toolnot found in the excerpt0.34
  • Top-10 learning shortlist signalnot found in the excerpt0.23
  • Is a replica of the modelFound in material0.68
  • Solves a common problem (0–2)score on a 0–2 scale1.58 / 2
  • About JevFound in material0.90
Signals
Jev is core to the project0.37Shows a System One pattern0.35Describes handling uncertainty0.06Reports measured numbers0.08Is a runnable tool0.34Top-10 learning shortlist signal0.23Is a replica of the model0.6800.51Solves a common problem (0–2)1.58 / 2012

no-judgment band (0.40–0.60)

Design patterns
Funnel0.85Check-up0.48Inspection0.46Ghostwriter0.2700.51

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 19 Oct 2026.