Spectron is agent memory built on one ACID substrate. Graph, vectors, documents, and structured rows commit in one transaction. Every fact carries provenance. Corrections supersede, never overwrite. Hybrid retrieval fuses vectors, graph, BM25, and keywords. Traces feed back into ranking. Tri-temporal facts, multi-tenant scopes, and MCP support. No stitched stores. No sync pipelines.
I am Tobie, co-founder of SurrealDB. We are launching Spectron - the memory layer for AI agents, built on SurrealDB (open source; graph, vector, document, and structured records in one ACID transaction).
Why we built it
Agents kept looking great in demos and failing in week three: colliding embeddings with the same label, corrections losing to the next high-scoring vector, cross-tenant bleed, confident answers with no lineage. Those are data-layer problems, not “retrieval only.” Spectron is the data-layer answer on the engine we already had.
What it is
One substrate holding two kinds of memory, told apart by provenance rather than by separate stores:
Authoritative knowledge - org documents, policies, and product data, with ingestion for PDFs, code, images, audio, and video.
Conversational memory - the transcript itself, plus identity, knowledge, context, instructions, and unknowns the agent flags but cannot answer yet.
How it behaves
Provenance on every fact, down to the byte span in the originating turn or document chunk.
Corrections kept across three clocks tracked separately: when the database wrote a fact, when we first believed it, and when it was true in the world - plus where it was captured, when location matters.
Multi-tenancy and territory scoped in the engine, not patched on at the API.
When sources disagree, the more authoritative one is favoured and the conflict is recorded as an explicit uncertainty, never silently overwritten.
Memory evolves between interactions: background passes link previously-unrelated facts and crystallise beliefs from what the substrate has accumulated.
What early access includes
REST API, SDKs for Python, TypeScript, Kotlin, and Swift, MCP server (remember, recall, context, reflect, forget, upload, inspect).
We are opening access in waves. We will email you the moment your invite is ready, with everything you need to start building.
For this community: what is the worst memory failure you have seen ship to production, and what did the team do about it? The answers I trust are usually the unglamorous ones.
About Spectron on Product Hunt
“Agent memory you can trust”
Spectron launched on Product Hunt on June 3rd, 2026 and earned 180 upvotes and 45 comments, placing #7 on the daily leaderboard. Spectron is agent memory built on one ACID substrate. Graph, vectors, documents, and structured rows commit in one transaction. Every fact carries provenance. Corrections supersede, never overwrite. Hybrid retrieval fuses vectors, graph, BM25, and keywords. Traces feed back into ranking. Tri-temporal facts, multi-tenant scopes, and MCP support. No stitched stores. No sync pipelines.
On the analytics side, Spectron competes within Developer Tools, Artificial Intelligence and Database — topics that collectively have 987.1k followers on Product Hunt. The dashboard above tracks how Spectron performed against the three products that launched closest to it on the same day.
Who hunted Spectron?
Spectron was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Ben Lang
Hi Product Hunt 👋
I am Tobie, co-founder of SurrealDB. We are launching Spectron - the memory layer for AI agents, built on SurrealDB (open source; graph, vector, document, and structured records in one ACID transaction).
Why we built it
Agents kept looking great in demos and failing in week three: colliding embeddings with the same label, corrections losing to the next high-scoring vector, cross-tenant bleed, confident answers with no lineage. Those are data-layer problems, not “retrieval only.” Spectron is the data-layer answer on the engine we already had.
What it is
One substrate holding two kinds of memory, told apart by provenance rather than by separate stores:
Authoritative knowledge - org documents, policies, and product data, with ingestion for PDFs, code, images, audio, and video.
Conversational memory - the transcript itself, plus identity, knowledge, context, instructions, and unknowns the agent flags but cannot answer yet.
How it behaves
Provenance on every fact, down to the byte span in the originating turn or document chunk.
Corrections kept across three clocks tracked separately: when the database wrote a fact, when we first believed it, and when it was true in the world - plus where it was captured, when location matters.
Multi-tenancy and territory scoped in the engine, not patched on at the API.
When sources disagree, the more authoritative one is favoured and the conflict is recorded as an explicit uncertainty, never silently overwritten.
Memory evolves between interactions: background passes link previously-unrelated facts and crystallise beliefs from what the substrate has accumulated.
What early access includes
REST API, SDKs for Python, TypeScript, Kotlin, and Swift, MCP server (remember, recall, context, reflect, forget, upload, inspect).
We are opening access in waves. We will email you the moment your invite is ready, with everything you need to start building.
For this community: what is the worst memory failure you have seen ship to production, and what did the team do about it? The answers I trust are usually the unglamorous ones.