01 · record
Lossless transcript
Cleaned conversation rows land in an append-only Tier 1 store. Rows are never updated or deleted. It's the source of truth every other layer replays from.
tier 1 · messages · append-only
One endpoint. Lossless transcripts in, durable facts out. Every recall traced to its source.
Diagram: transcript messages are ingested losslessly, flow into light, REM, and deep extraction phases, and become durable facts. Each promoted fact links back to its exact source messages, including corrections that superseded earlier statements.
A vector store bolted onto an agent is just a cache. It never expires stale entries, ignores who each memory belongs to, and can't say where a recalled fact came from.
Context windows fill up and sessions restart, and everything the agent learned is gone. Long-running work needs memory that outlives a single conversation.
Naive memory keeps everything forever, so outdated facts surface next to fresh ones and the agent can't tell which to trust.
When an agent asserts something from memory, most systems can't tell you where it came from.
Three tiers: the Tier 1 transcript is canonical, Tier 2 stages candidate memories, and only reviewed facts reach the Tier 3 long-term store.
01 · record
Cleaned conversation rows land in an append-only Tier 1 store. Rows are never updated or deleted. It's the source of truth every other layer replays from.
tier 1 · messages · append-only
02 · stage
Light extraction stages candidate memories in Tier 2. A model-free REM pass reinforces what repeats, scoring candidates by embedding centrality with zero LLM calls. Deep review then promotes or supersedes them, and a memory only becomes durable after it passes review.
tier 2 · light → rem → deep
03 · promote
Every promoted Tier 3 fact carries provenance, confidence, and category, and points back to the exact transcript rows it came from. You can audit any recall, test it, and roll it back.
tier 3 · long_term_memory · durable
Memory behavior you can test, migrate, and debug, because every layer above the transcript is rebuildable.
Each durable memory carries source message ids, confidence, and category. Trace any recall back to the exact transcript rows it came from.
Unlike vector-store memory that ingests everything it sees, every extracted fact starts as a Tier 2 candidate and must earn promotion through reinforcement and review. When recall falls back to a candidate, it is labeled an unverified note, never passed off as durable memory.
Every surfaced fact logs a retrieval_events row and increments its retrieved counter in the same transaction, and used counts record which facts informed an answer. Audit what your agents recall, not only what they store.
FTS and vector indexes are rebuildable projections over canonical rows. Blow them away and rebuild. The transcript stays the source of truth.
Explicit MemoryScope binds every request to tenant, project, session, and agent. A null agent scope means shared, not a wildcard match.
A persistence and egress secret guard applies forbidden-value redaction before anything is stored or leaves the boundary.
Expose transcript and long-term search to agents over hosted HTTP MCP. Write, export, and admin tools are deliberately left out.
Ingestion, extraction passes, and index rebuilds run as a service, rolling out now through the early-access waitlist. Your agents call one endpoint, and Vexic handles durability, migrations, and reindexing.
{
"fact_id": 184,
"fact_text": "Prefers uv over pip.",
"subject": "tooling",
"category": "preference",
"importance": 7,
"confidence": 0.92,
"source_message_ids": [412, 418, 573],
"retrieved_count": 12,
"used_count": 4,
"editable": true,
"created_at": "2026-06-14T21:08:11Z"
}source_message_ids points at the exact Tier 1 transcript rows this fact came from. Every recall is traceable to its receipts.
One hosted endpoint, spoken over the protocols your agents already use.
Mint a single-use setup token in the console, then run one install-and-activate command on the machine. It installs the transcript recorder hook and session-start memory priming and exchanges the token for an Agent API key. The raw key is never shown in the browser.
Mint a setup token and run the install-and-activate command on the machine. It drops a TOML block into your Codex MCP config, pointed at your Vexic endpoint, for scoped read-only search from any Codex session.
Same setup-token flow, generic MCP client. Standard Model Context Protocol over streamable HTTP. Any MCP-capable agent gets search_transcript and search_long_term, and nothing else.
Mint a single-use setup token in the console, install the vexic CLI, then activate. The token exchanges for an Agent API key on the machine. It never touches the browser.
$ uv tool install vexic$ vexic setup claude-code --token <setup-token> --base-url https://api.vexic.dev✓ vexic connectedHosted Vexic is rolling out through the waitlist: managed ingestion, extraction, and recall behind one endpoint.
Yes. The memory engine, storage and retrieval, is open source on GitHub.
Vexic itself is the self-hosted memory engine: Apache 2.0, running on your own database and hardware. The hosted platform is the easy-setup layer on top: one endpoint, a console for projects and API keys, and the ingestion, extraction, and index-rebuild pipeline running as a service. It's the fast path to production-ready agent memory.
You can export your data or delete it. Deleted data is removed from Vexic's servers permanently. Export is available on request today, with self-serve export on the roadmap.
At launch, per-tenant data is encrypted using customer-managed keys. Vexic engineers don't have standing access to read customer data; any emergency access is logged and audited. Every request is also scoped to your tenant, project, session, and agent. There's no cross-tenant access, and a redaction guard strips forbidden values before anything is stored or leaves the boundary.
Pricing details will become available closer to launch.