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Overview

Principles, architecture, quickstarts, and mental model for SurrealDB Agent Memory - memory and knowledge for AI agents on SurrealDB.

SurrealDB Agent Memory is a memory and knowledge layer for AI agents - a horizontally scalable application tier in front of SurrealDB, which holds every durable record (graph, vector, document, relational, geospatial) with ACID writes, first-class provenance and trust, graph-resident traces, and tri-temporal belief history. It aims for memory that associates related ideas and keeps straight what was said, what is true now, and what used to be true - much like people do, but queryable and auditable in software.

Use this hub to go from principles to running code, then dive into the product sections (memory & knowledge, integrations, cookbooks, reference).

Note

SurrealDB Agent Memory was developed under the project name Spectron, and that name is retained throughout the shipped interface: the spectron and spectrond binaries, the SPECTRON_* environment variables, the spectron_* MCP tool names, and SDK packages such as @surrealdb/spectron. The rule of thumb is that prose uses the product name and anything you type or configure uses spectron. These names will be renamed in a future release.

What SurrealDB Agent Memory is built to do, what it is not, and how retrieval, traces, and time work:

Quickstarts

Building with AI coding tools? Start with Agent guide (AGENTS.md) - copy it into Cursor rules or a project skill so your agent can learn SurrealDB Agent Memory without reading the full docs.

How isolation, sessions, categories, and provenance fit together:

  • Memory & knowledge - authoritative and experiential ingest, unified retrieval, reasoning, operations, tuning.

  • Integrations - SDKs, MCP, framework adapters.

  • Cookbooks - end-to-end patterns.

  • Reference - REST, management API, CLI, configuration, errors.

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