Memory that stays correct across time,
workflows, and systems
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Define exactly what AI remembers and how - with types, constraints,
relationships, and deduplication. Inherit the schema from your system of record,
or generate it with an agent for your domain.
Persistent or temporary.Prebuilt or on the fly.
90% accuracy is not enough for 90% of workflows
Normalise conversations into durable facts and events with dedupe, lineage, and control
Shared, durable execution state
for coordinating long-running agents.
Extract structured signals from documents for analytics and deeper processing.
Validate, stage, and audit AI writes to core systems without breaking data integrity.
Validate and fill API calls against schemas, constraints, and policies.
Having structure in AI memory allows building UI on top - for analytics, chat UX, and apps.
Here are a few of the many requests xmemory helps with.
Dear , please read the integration documentation and integrate xmemory into my project.
I want to use xmemory whenever they need to store information related to their context, execution steps, or tool usage. They should create memory schemas dynamically when needed for a task, or use schemas that I will explicitly define.
The core idea behind xmemory and why text-only memory misses many complex memory request types.
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