Tanstack AI Memory In Memory

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()

// A static scope is fine for dev/tests; derive it from the session in real apps.
memoryMiddleware({
  adapter: memory,
  scope: { threadId: 'demo-thread', userId: 'alice' },
})

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.