Let agents read and write memory in natural language while xmemory stores validated, queryable state underneath.
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Natural language on top. Structured state underneath.
Stores notes, summaries, and recalled context.
Stores schema-based state behind a natural-language interface.
User likes red flowers.
Amy is now Kevin’s boss.
Vacation from March 11 to 15.
Preference: flower_color = red
Relationship: Amy manages Kevin
Plan: vacation_start = March 11, vacation_end = March 15
Result: agents can write and query memory naturally, without relying on text reconstruction for correctness.
Use an existing system of record, describe what should be remembered, or let xmemory generate the schema.
Agents store and update memory in plain language while xmemory maps it to structured state.
Query memory naturally and get consistent, deduplicated state across tasks, workflows, and systems.
90% accuracy is not enough for 90% of workflows
Shared, durable execution state for coordinating long-running agents across tasks and workflows.
Keep user facts, preferences, and workflow context as durable memory across chats and systems.
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.
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 handles structured extraction, updates, relations, and deduplication underneath. MCP can expose tools. xmemory is meant to make the memory behavior itself agent-native and reliable. For the deeper architectural argument, see Should Agents Adapt to Systems - or Should Systems Adapt to Agents? .
You do not need to hand-design a schema first. You can start from an existing system, describe the memory you need in plain language, or let xmemory generate a schema for the workflow. The goal is to make structured memory available even when you know what should be remembered but do not want to model everything from scratch. See How xmemory works for the full flow.
Text and vector memory are useful for recall. xmemory is built for reliable state. If memory is stored mainly as text, every read becomes another act of interpretation. xmemory’s approach is to keep memory structured underneath, so facts can be updated explicitly, relationships stay clear, and answers do not depend on reconstructing meaning from narrative history every time. For the fuller argument, read Schema as the Core of Reliability in AI Memory .
Yes. xmemory is designed to fit into existing stacks. You can bring structure from an existing system of record, then integrate through MCP, API, Python, or TypeScript depending on how your agents and workflows already run. The docs also include examples for frameworks like LangChain, Pydantic AI, Mastra, Google ADK, and n8n. You can jump straight to the integration guides in How xmemory works .
Tell us about your workflow and we’ll help you choose the right integration path.
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