Atomic Radar Early Access

Your AI forgot everything it learned yesterday.

Atomic Memory gives AI systems persistent, governed, correctable context across sessions, agents, users, and teams.

Built for

Atomic Memory Cloud provides the hosted layer; Atomic Radar helps teams review memory quality.

npm install @atomicmemory/sdk
Loading Atomic Memory flow
What you getEarly access to Atomic Memory / Atomic Radar, routed by your ad source, role, and use case so follow-up stays relevant.
Why it mattersBuilt for developers and teams using Claude Code, Cursor, Codex, MCP tools, and memory-enabled AI products.
What this validatesWhether the broad memory-infrastructure message drives early-access demand.
Best fitDevelopers, AI teams, agent teams, and organizations treating memory as infrastructure.

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Benchmarks · V66

At the published ceiling.
At a fraction of the cost.

Matched methodology against published competitor results, lenient scoring. Every number below is quotable with its dataset, sample size, and cost per query.

LoCoMo10

gpt-4o-mini binary · n=1540
AtomicMemory
0.8396
Mem0 paper
0.6686
+0.171 vs Mem0 paper$0.066 / query

BEAM-1M

lenient · n=80
AtomicMemory
0.6625
Mem0 paper
0.6405
+0.022 vs Mem0 paper$0.083 / query

BEAM-100K

lenient · n=80
AtomicMemory
0.7375
Hindsight
0.7500
parity$1.26 / query

BEAM-10M

lenient · n=80
AtomicMemory
0.4875
Mem0-new
0.4860
parity$0.081 / query

Scores are benchmark results under matched methodology at the linked dataset, model, and date. Hindsight-scale temporal retrieval remains the known open frontier. Reproducibility artifacts and harness details ship with the benchmark materials.