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Agent memory management with decision models

Deciding what an agent should store, retrieve or forget, and which memories are relevant to the current step.

46 public projects · page 2 of 2

  • Codex port of fast-jev-compaction that re-injects Jev-selected verbatim history after compaction; the author advises against using it.

    PluginprototypeTypeScript
  • TypeScript framework for role-playing NPCs: Jev picks actions and communicative intents, the host composes dialogue.

    ProjectprototypeTypeScript
  • OMP extension keeping one endless chat as a summary tree, with five Jev judge gates for rules, prefetch, compression, tools and recall.

    PluginprototypeTypeScript
  • Research runtime where every agent step is a bounded typed decision, with a paged virtual option space over tools and memory.

    ServiceresearchPython
  • Research runtime that lets a bounded decision model like Jev operate over large tool, memory and context spaces via a virtual option space.

    ProjectresearchPython
  • Agent memory system where Jev or local Laya controls memory organisation, query routing, retrieval budgets and stopping.

    ProjectresearchPython
  • Zilliz's cross-agent Markdown memory with an optional Jev reranker that asks yes/no relevance questions about retrieved chunks.

    LibraryproductionPython
  • Production control plane for OpenClaw agents: Jev routes each Slack message to its task, with Temporal workflows, an evaluator and memory.

    LibraryproductionPython
  • Android AI chat client and SillyTavern-compatible endpoint where Jev screens memories for relevance and serves as a judge tool.

    LibraryproductionKotlin
  • Federated knowledge retrieval engine for agents that uses Jev to route queries to backends and re-rank evidence, with keyword fallback.

    LibraryprototypePython
  • Ten runnable JavaScript lessons where Jev judges agent situations like memory conflicts and handoffs while code enforces explicit policy.

    DemoprototypeJavaScript
  • AI town simulation forked from a16z ai-town where Jev picks each agent's next legal move and an LLM handles dialogue and memory.

    AppprototypeTypeScript
  • Agent context compactor where Jev scores transcript segments to keep verbatim or move behind a recoverable pointer.

    ServiceprototypePython
  • Dry-run Solana paper trader where Jev picks one action per cycle from the venues actually reachable; code owns sizing and risk.

    ModelprototypePython
  • Independent experiments on Jev for search stopping, memory reranking and multi-hop relation selection, with stated limits.

    Articleresearch
  • X walkthrough of the Jev API followed by three demos: voice-controlled browsing, AI memory and YouTube preprocessing.

    Discussiondiscussion