Memori
Agent-native memory that cuts context tokens 95% while keeping accuracy
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Memori is a memory infrastructure layer designed specifically for AI agents. It captures conversations and execution traces and structures them into facts, preferences, rules, and summaries — the kinds of things agents need to recall in production.
The retrieval side uses semantic search with targeted recall instead of stuffing entire transcripts into the context window. Memori reports a 95% reduction in token usage versus full-context retrieval, with 81.95% accuracy on its benchmarks.
Memori Labs ships a freemium tier and integrates with CockroachDB for storage. The target is teams running agents in production who hit the wall of cost and latency from naive context management.
AI-assisted draft, human-reviewed before publishing — see how we choose & review tools.
Why we picked it
Memori earns its spot on a specific claim worth naming: structuring conversations into facts and preferences instead of stuffing raw transcripts into context, cutting tokens by a reported 95%. If your agent's context bill is the actual pain point, this is the pick that targets it directly.
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