Agent Skills › TanStack/ai › tanstack-ai-memory-in-memory

tanstack-ai-memory-in-memory

GitHub

提供TanStack AI内存适配器配置指南，适用于本地开发、测试及单进程演示。说明setup流程、参数选项（如embedder、extract）及使用限制，明确不适用于多进程或需持久化的生产环境。

packages/ai-memory/skills/tanstack-ai-memory-in-memory/SKILL.md TanStack/ai

触发场景

需要为AI应用配置临时内存存储进行本地开发或单元测试时的记忆功能设置询问为何不使用持久化存储

安装

npx skills add TanStack/ai --skill tanstack-ai-memory-in-memory -g -y

更多选项

非标准路径

npx skills add https://github.com/TanStack/ai/tree/main/packages/ai-memory/skills/tanstack-ai-memory-in-memory -g -y

不安装直接使用

npx skills use TanStack/ai@tanstack-ai-memory-in-memory

指定 Agent (Claude Code)

npx skills add TanStack/ai --skill tanstack-ai-memory-in-memory -a claude-code -g -y

安装 repo 全部 skill

npx skills add TanStack/ai --all -g -y

预览 repo 内 skill

npx skills add TanStack/ai --list

SKILL.md

Frontmatter

{
    "name": "tanstack-ai-memory-in-memory",
    "description": "Use when wiring inMemory() from @tanstack\/ai-memory\/in-memory — explains setup, options (embedder, extract, topK\/minScore), when to pick it (dev\/tests\/single-process demos), and what NOT to use it for (multi-process or persistent)."
}

In-Memory Memory Adapter

Zero-dependency recall/save adapter backed by a Map. Records vanish on process restart.

When to use it

Local development.
Vitest / Playwright tests.
Single-process demos where users don't need persistence.

When NOT to use it

Production multi-process deployments — every worker has its own Map; users get inconsistent memory.
Anything that needs survival across restarts.

For production, use redis() (see the tanstack-ai-memory-redis skill).

Setup

import { memoryMiddleware } from '@tanstack/ai-memory'
import { inMemory } from '@tanstack/ai-memory/in-memory'

const memory = inMemory()

memoryMiddleware({ adapter: memory, scope })

Options

inMemory(options?) accepts:

topK (default 6), minScore (default 0.15), kinds — recall tuning.
embedder: { embed(text): Promise<number[]> } — enable semantic scoring (both recall and save embed through it).
extract(turn, scope) — return derived facts to persist alongside the raw turn (e.g. call an LLM to pull out preferences). Without it, save stores the raw user/assistant messages and recall scores them lexically + by recency.
render(hits) — replace the built-in prompt renderer.