Hosted memory for AI agents — mutations are recorded as traces, retrievals come with inspectable summaries. Scoped, portable, and inspectable end-to-end by design.
Low-latency
retrieval
Traced
mutations
OSS
self-host core
import { MemoryClient } from '@atomicmemory/sdk'
const memory = new MemoryClient({
providers: {
atomicmemory: {
apiUrl: 'https://api.atomicstrata.ai',
apiKey: process.env.ATOMICMEMORY_API_KEY,
},
},
})
// Ingest a memory
await memory.ingest({
messages: [{ role: 'user', content: 'I prefer aisle seats.' }],
scope: { user: 'user_001' },
})
// Search memory
const results = await memory.search({
query: 'seat preference',
scope: { user: 'user_001' },
})// Capabilities
Most AI memory tools optimize for recall. AtomicMemory Cloud optimizes for controlled memory state — visible, queryable, and traceable at every layer.
See what the memory layer stores and why. Claims are visible and queryable through the dashboard and API.
Core reconciles new facts against existing memory; Cloud records mutation traces — full AUDN decision rendering in progress.
Inspect retrieval summaries and returned memory IDs for each query — ranking and exclusion views in progress.
Pack memories into model-ready context with token budgets. See exactly what was included and what was excluded.
Isolate memory by scope. Project-scoped isolation with user and agent scoping on API paths.
Start with the hosted endpoint. Migrate to self-hosted via the open-source Core — same SDK contract; self-host with Postgres/pgvector.
// Deploy your way
Start in minutes. Managed endpoint, zero infrastructure.
Same SDK contract; self-host the open-source Core with Postgres/pgvector.
Ship agents with memory you can actually reason about — then take the core with you when you're ready.