Overview

Spectron 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. Spectron 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 operational sections (memory & knowledge, self-hosting, integrations, reference).

Architecture

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

• Principles and goals

• Eight pillars and six categories

• Coherence, retrieval, and cost tiers

• Traces and memory evolution

• Tri-temporal model

• Surface, models, and security

• Glossary

Welcome and quickstarts

• What is Spectron? – product positioning in one pass.

• Why agentic memory? – where naive context and pure-vector shortcuts fail.

• The accuracy promise – provenance, reconciliation, and auditability.

• How it works – end-to-end path from a turn to stored, retrievable state.

Quickstarts

• Surrealist dashboard quickstart – Spectron contexts on SurrealDB Cloud.

• Spectron on SurrealDB Cloud – Cloud API vs data plane and organisation roles.

• Hosted quickstart – Spectron Cloud, API key, first remember and recall.

• Self-hosted quickstart – Docker Compose and your first remember/recall calls.

• Embedded library – HTTP, MCP, and SDK integration surfaces.

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 Spectron without reading the full docs.

Mental model

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

• Unified substrate and authority – authoritative versus experiential streams in one graph.

• Contexts and scope

• Sessions and turns

• Memory categories

• Provenance and traceability

• Supersession, decay, and forget – how beliefs change, fade, and are removed

Product sections

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

• Integrations – SDKs, MCP, framework adapters.

• Self-hosting – deployment, security, operations, observability.

• Cookbooks – end-to-end patterns.

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