Let agents read and write in natural language while xmemory turns facts, relationships, and workflow state into validated, queryable memory.
Three places where agent memory stops being a prompt trick and starts becoming product infrastructure.
Build long-lasting agent relationships with compact user profiles that capture specific facts over time. xmemory keeps recall precise and fast.
See demo
Give agents reliable state for execution plans, task progress, long research findings, and accumulated tool knowledge across multi-agent and long-running workflows.
Bring observability and governance to regulated environments with schema-backed RBAC, lineage, debugging, and testing for every memory read and write.
We evaluate whether systems can store, update, deduplicate, and retrieve facts and relationships reliably, not just whether they can recall similar text.*
* Read more about measurement methodology and open benchmarks in our white paper.
Lower token use than text-based memory by optimising reads.
2x+ fewer tokens
Assuming 10 reads per write,
10 write tokens per 5 read tokens for xmemory,
and 5 write tokens per 12 read tokens for typical text-based storage architecture.
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.
Schema is the programming language for memory - easy to create, and kept perfectly fit by a schema evolution engine.
Why benchmark leadership can be misleading for AI memory, and what reliability metrics should measure instead.
As business logic moves into prompts, the boundary between agent reasoning and system-owned semantics needs to become much cleaner.
The core idea behind xmemory and why text-only memory misses many complex memory request types.
xmemory is not just a database exposed through MCP. It is a schema-based memory layer that lets agents read and write in natural language while xmemory owns the state-of-the-art harness that is otherwise fragile and spread across prompts, wrappers, and workflow code:
MCP can expose tools. xmemory is meant to make the memory behavior itself agent-native and reliable. For the deeper architectural argument, seeShould Agents Adapt to Systems - or Should Systems Adapt to Agents?.
You do not need to hand-design a perfect schema first. Schema is a control dial, not a gate:
SeeHow xmemory worksfor the full flow.
Text and vector memory are useful for recall, but reliable AI systems need short, explicit feedback loops. Agents need a programming language that tells them whether an update is correct, which data is present, and how it is represented without relying on adjacent context. That language is schema. It enforces correctness, reduces knowledge inference at read time, and prevents bloating, drift, and corruption, especially in long-running workflows or personalisation over longer periods. For the fuller argument, readSchema as the Core of Reliability in AI Memory.
xmemory is external storage that agents use like a database or file store to save and retrieve context. Agents can create memory instances themselves, define schemas for a task on the fly, or use fixed instances for longer-lived context. It can reliably replace an agentic system of record, or run as a sidecar validation engine with periodic sync to the system of record, so people can review updates before they merge. The simple rule is: whenever agents need to save and retrieve text information, that layer can be xmemory. You can jump straight to theintegration guidesinHow xmemory works.
Tell us about your workflow and we’ll help you choose the right integration path.
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