tanstack-ai-memory-in-memory

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.

Capacity

The adapter scans every record in a scope per recall. Fine up to ~100k records; beyond that, switch to Redis.