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LamantinAI/kaeru

LamantinAI/kaeru

Memory for LLM agents: a local graph wher
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add kaeru -- npx -y kaeru
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
40 ★ · LamantinAI/kaeru · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 40 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 LamantinAI/kaeru;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

蛙 kaeru

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

kaeru is a **cross-agent cognitive engine** for LLM agents — a typed graph that agents think in, plus a recollection layer for long-term ideas and outcomes. Local-first for each agent, with an optional shared cloud tier so a whole team of agents and people build on one another's memory.
Designed for **multi-session, multi-agent continuity**: when an agent opens a project, it has full context of what was being thought about, can follow provenance chains, can consolidate outcomes into stable long-term knowledge, and can pull in what the rest of the team has shared.
Inspired by the LLM-wiki pattern (Karpathy, gist 442a6bf555914893e9891c11519de94f), the bi-temporal knowledge graph approach of Graphiti / Zep, the curator-driven knowledge engine of Cognee, and the reasoning-based hierarchical-summary navigation pattern from PageIndex. Two-tier design grounded in the hippocampus / cortex split.
Name: 蛙 (*kaeru*, "frog"; homophonic with 帰る "to return" and 変える "to change") — the agent that returns, recalls, and reshapes.
A kaeru vault rendered by kaeru-viz — each project a constellation of stars around its core, sized by memory layer, with ochre cross-project bridges, reasoning-chain replay, and a time-lapse of how the knowledge grew. Hover a node to trace its neighbours; click to pin them.
Overview
kaeru is built around a typed property graph stored in CozoDB. Two tiers, biological analogy:
- **cognitive (operational / hippocampus)** — high-velocity working graph where the agent actively thinks: episodes, scratch, drafts, hypotheses, experiments, audit events.
- **recollection (archival / cortex)** — settled ideas, outcomes, summaries, references; mostly read.
Every node and edge is **bi-temporal** — the substrate stores assertion / retraction history natively, so time-travel queries are out of the box and conflict resolution is non-destructive (the old version is invalidated, not deleted).
Per-initiative subgraphs through a junction-relation pattern: one substrate, many initiatives, multi-membership. An agent working on project A asks "what was I doing here last time?" and gets an answer scoped to A. The same node can belong to several initiatives at once.
kaeru is a **facilitator, not an enforcer**. The curator API exposes ~70 primitives (awake, recall, drill, claim, synthesise, at, history, settle, …) as available tools. The agent and user choose when to invoke them; the daemon hints but doesn't block.
Features
- **Two-tier graph** — operational (cognitive / hippocampus) for active thinking; archival (recollection / cortex) for settled knowledge. settle promotes across the boundary, preserving provenance.
- **Bi-temporal** — native assertion / retraction history. at reads a node in full as it is now or as-of any past moment; conflicts are non-destructive (the old version is invalidated, not deleted).
- **Per-initiative scoping + layered re-entry** — one substrate, many projects. awake restores a project's working set by memory layer (Core → Hot → Warm) and surfaces the archival **cortex** (settled knowledge) alongside it, so durable facts re-enter every session; surface reaches the archived Cold / Frozen on demand.
- **Reasoning chains** — chain saves the load-bearing weighted path between two nodes as a recallable trail with an agent-authored summary; why reads a trail — give it a chain for its ordered steps, or any node to reach the chain it belongs to. Duplicates are folded at creation, and rechain refreshes a trail after the graph changes (re-links, re-weights).
- **Self-maintenance** — reflect computes a tidy-up work-list: orphan nodes to link, overdue tasks, chains gone stale, settled work to promote into cortex, and shared/cloud items whose rebalancing is escalated to the user. The agent calls it at the end of a piece of work; layer tidying runs on its own in the background (hygiene).
- **Role slots** — slot gives an initiative a role held by exactly one live node (handoff, entrypoint, queue). Filling it archives the previous holder to Cold and links supersedes from the new holder to it, so a project cannot drift into three "current" handoffs. Nothing is deleted; the predecessor stays reachable through at / surface.
来源
awesome · https://github.com/LamantinAI/kaeru
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