Xmemory
Schema-grounded memory for AI agents — agents read and write in natural language while Xmemory validates, deduplicates, and stores facts reliably.
| What is it | Schema-grounded memory for AI agents — agents read and write in natural language while Xmemory validates, deduplicates, and stores facts reliably. |
|---|---|
| Pricing | Unknown |
| Platform | Web Application |
| API | Yes |
| Works with | n8n |
| Best for | Building long-term personalized AI agents that remember user preferences, Reliable working memory for multi-step agent workflows and research tasks |
| Domain registered | 2025 |
Data updated Aug. 1, 2026
What does Xmemory do?
Xmemory is a schema-grounded memory engine built specifically for AI agents. Instead of storing raw text or relying on vector search to find relevant context, it lets agents read and write information in natural language while the system automatically extracts facts, relationships, and workflow state into validated, queryable schemas. This means agents get reliable memory that doesn't degrade over time — duplicates are caught, conflicts are flagged, and every piece of information can be retrieved with precision.
Here's how it works: an agent sends a natural language statement like 'The user prefers dark mode and lives in Berlin' to Xmemory. The system parses it, maps it to a schema (which can be pre-defined or inferred), validates types and constraints, and stores it as a structured record. When the agent later asks 'What are the user's preferences?', Xmemory returns the exact facts — no fuzzy vector matches or irrelevant context. The engine also handles schema evolution, so you can start with loose schemas and tighten them over time. Xmemory includes deduplication, conflict detection, and async write queues to keep latency low during long-running agentic workflows. It integrates with frameworks like LangChain, Pydantic AI, Mastra AI, and n8n, and provides visibility into every memory operation for debugging and governance.
The tool is most useful for developers building agents that need persistent, trustworthy memory. Common use cases include personalizing user experiences by storing preferences over time, giving agents a reliable working memory for multi-step tasks like research or report generation, and managing governed agentic workflows in regulated industries where every memory read and write must be auditable. If you've tried text-based or vector-based memory and found it unreliable or bloated, Xmemory offers a cleaner, more predictable approach.
Key features
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