Spectron

Spectron

Provenance-first, tri-temporal memory and knowledge layer for AI agents, built on SurrealDB.

Spectron is a stateless application tier providing persistent, queryable, autonomous memory for AI agents, built directly on SurrealDB. It unifies graph, vector, document, and structured records in one ACID transaction per write. Every fact carries provenance (source, trust, lexical span) and tri-temporal clocks (system time, known time, valid time). Supersession replaces overwrite. Eight pillars and six memory categories (episodic, identity, knowledge, context, instructions, uncertainty) deliver coherence across semantic, lexical, relational, temporal, and spatial dimensions. The MCP server, SDKs (Python, TS, Kotlin, Swift), and harness adapters (LangChain, Claude Code, OpenAI Agents, Vercel AI SDK) enable immediate integration with Cursor, Claude Desktop, Claude Code, and other MCP clients. Currently in invite-only early preview.

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How to use Spectron?

To use Spectron, start by joining the waitlist for early preview access. Once granted, download the single Rust binary which includes a built-in MCP server. Connect your MCP client (e.g., Cursor, Claude Desktop, Claude Code) with a single configuration entry. Alternatively, use one of the generated SDKs (Python, TypeScript, Kotlin, Swift) or a harness adapter (LangChain, OpenAI Agents, Vercel AI SDK, n8n, Zapier). Define a Context to scope memory. Ingest conversational turns or documents; extract entities, attributes, and relations automatically via configured LLM. Query using tiered retrieval (direct lookup, response reuse, hybrid retrieval, full-context fallback). Use autonomous mechanisms (reflection, elaboration, consolidation) to deepen understanding between interactions. Monitor the system via the trace graph and built-in commands (spectron entities show, spectron inspect trace).

Spectron 's Core Features

  • Provenance-first memory with source tracking on every fact
  • Tri-temporal versioning (system time, known time, valid time) for complete auditability
  • Supersession instead of overwrite, preserving all historical versions
  • Six typed memory categories: episodic, identity, knowledge, context, instructions, uncertainty
  • Eight pillars architecture including reconciliation, elaboration, reflection, consolidation, calibration, and collective memory
  • Four-tier cost-optimised query routing: direct lookup, response reuse, hybrid retrieval, full-context fallback
  • Autonomous background mechanisms: reflection, elaboration, and consolidation that deepen knowledge between interactions
  • Spectron 's Use Cases

  • Persistent memory for AI agents across sessions and conversations
  • Multi-agent coordination with shared, ACID-compliant knowledge graph
  • Document processing pipeline that turns PDFs, code, and media into structured knowledge
  • Audit-trail compliant AI workflows with full provenance and temporal history
  • Hybrid retrieval combining vector, BM25, graph traversal, keyword, geographic, and trace-derived signals
  • Multi-tenant agent systems with scope-level isolation and fine-grained permissions
  • Real-time agent responsiveness via live queries and events on data changes
  • Spectron 's FAQ

    Most impacted jobs

    AI Engineer
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