pgvector

Retrieval is the part you already got working.
The problem starts after the agent acts on what it found.The file has hard limits, and a growing project hits all of them
.

No outcome signal

A row that caused an incident and a row that shipped clean keep the same score forever.

Similarity ranks on how the text reads. Whether acting on it worked never enters the number.

Nothing expires

The row you wrote in January about a service you have since rewritten still comes back at full weight.

Recency is a column you sort by. It says nothing about whether the row is still true.
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Erasing the evidence

When a fact changes you overwrite the row, and what the agent believed last month is gone.

After a bad run there is no way to reconstruct what it actually read at the time.

What changes with SenseLab

Outcome Weighting

Rows that led somewhere bad
stop coming back

Your agent reports how the task ended. Every row it read before a success gains confidence, and every row it read before a failure loses it.

Success increases confidence, failures decreases it.

A row that led to a failure stops coming back

Agents stop repeating mistakes the team already paid for

No scoring job or cron to keep the table honest

Rows nobody has validated fade on their own

You can see which knowledge is earning its place

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Versioned Rows

Writes supersede.
Nothing is overwritten.

Every write inserts a new version and marks the previous one superseded.

You can read the full history of a key and diff any two versions, so what the agent believed in January is still there in March.

Answer what the agent knew at the moment it acted

Restore a previous value without touching a backup

Find when a fact changed by diffing two versions

A correction never destroys what it replaced

Post-incident review reads out of the table itself

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What you maintain without SenseLab
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The table was an afternoon.
The rest is the project.

Everything past similarity search is code you own forever.

Re-embedding
You change model or dimension and the whole corpus needs re-embedding with the index kept in sync. It comes back every time the embedding stack moves.
More than one agent
The moment two agents write, you need attribution, conflict handling and scoped access. That is schema and migrations, not a query.
Decay and pruning
Without a policy the table grows and old rows keep ranking. You end up writing a scoring job and a cron to hold it back.

With SenseLab, you maintain nothing
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Continual Learning

Nothing to build.
Nothing to run.

Your agents call SenseLab and start building on what they learn from the first task. There is no memory service in your stack to design or keep alive.

Running the same day, with no schema to design

Each agent starts from what the others already learned

No embedding pipeline or index to keep alive

Ranking and decay get better without work on your side

Your engineers stay on the product

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Confidence Scoring

Your fleet knows what to trust

Every piece of knowledge carries a score that updates from real outcomes. When a finding leads to a good decision, it gets stronger. When it doesn't, it degrades. Agents don't treat a stale guess the same as a validated pattern.

Updates from real production outcomes

Per-entry confidence scores

Automatic degradation over time

Query by confidence threshold

No manual curation required

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Explore Features

Shared Knowledge

What one agent learns
‍‍every agent can build on

Findings, decisions, and patterns write to a shared store the moment they're created. Any agent in the fleet can read them, build on them, or challenge them.

Cross-agent reads with full lineage

Works across frameworks and models

Conflict detection built in

Millisecond read latency

Scoped access per agent or team

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