How does Plexara structure memory across sessions?

Plexara structures memory into five dimensions: knowledge (facts, definitions, business rules), events (migrations, incidents, schema changes), entities (people, systems, teams), relationships (how those connect), and preferences (per-user formatting and workflow habits). Storing each kind separately means it is indexed and recalled the way that kind needs, rather than dumped into a single bucket.

How does multi-strategy recall pick the right memories?

Different questions need different recall methods, so Plexara composes entity lookup (exact match on people, tables, projects), semantic search (meaning-based via embeddings), and lineage graph traversal (related concepts). All three run inside one universal search tool that reaches memory alongside the catalog, knowledge pages, insights, saved assets, and prompts, and returns results grouped by source. The agent does not pick a strategy or a place to look; it asks once and sees the shape of the whole answer space.

How is memory different from the catalog?

Memory is personal and persists across sessions for a specific user or persona. The catalog is organization-wide structured documentation. Memory captures what an individual taught the agent during their work; once an admin reviews and promotes it, that observation can become catalog metadata everyone benefits from.

Can users update or remove their own memories?

Yes. Recording is one action, memory_capture, and the memory_manage tool exposes commands to update, forget, list, and review what has gone stale or duplicated. Stale memories surface in periodic review prompts so users can keep their context fresh. Nothing is locked in.

Is memory shared across users?

No. Memory is scoped per user and persona. Cross-user sharing happens through the insights pipeline: an observation captured in one user's memory can be promoted, with admin review, to catalog documentation that all future agents see. That is intentional, not a bypass.

Memory

Persistent knowledge across sessions. The platform remembers what you taught it, recalls relevant context automatically, and gets smarter with every conversation.

Memory Dimensions

Five Types of Memory

Memory is structured into five dimensions, ensuring that different types of knowledge are stored, indexed, and recalled appropriately.

Knowledge

Facts about data, business rules, and domain expertise.

"Column amt is gross transaction amount in cents, divide by 100 for display"

Events

Things that happened: migrations, incidents, schema changes.

"The revenue table was restructured in Q3 2025, old columns are deprecated"

Entities

People, systems, teams, and their roles in the data landscape.

"The finance team owns all revenue datasets and prefers net_amt over amt"

Relationships

Connections between entities, datasets, and business concepts.

"The orders table feeds the revenue pipeline through a nightly ETL job"

Preferences

User-specific settings, formatting choices, and workflow habits.

"This user prefers CSV exports with headers and ISO date formatting"

Classification

What Stays Yours, and What Gets Proposed

Every memory also carries a class, shown beside it in the portal, and the class is the mechanism behind promotion. Two classes are personal by definition and never leave your own records. The other three assert something about the business, so they are recorded as proposals and go to a person before they reach anyone else.

Personal

Live for you immediately

Nobody reviews these, because there is nothing for anyone else to agree with. They are yours, and they stay yours.

Preference: How you like to work: formats, defaults, habits.
Event: Something that happened, in your own working history.

Reviewed

Recorded as a proposal

These are claims about how the business works, so they enter the review queue as insights and become shared knowledge only once someone promotes them.

Business knowledge: A fact about the business that the rest of the team would benefit from.
Operational rule: A standing rule about how the work gets done here.
Schema/entity: A fact about one table, column, or catalog entry.

The Memory tab of the Knowledge page, with Total, Active, Stale, and Archived counters, a search box with status and class filters, and a list of active memories each showing its class, capture date, text, and the table it is linked to — Personal memory is a list you can read, filter, and search yourself.

The Insights tab of the Knowledge page with the Review queue selected, showing Pending Review, Total Insights, Top Category, and Applied counters above a table of insights with captured-by, category, confidence, and status columns — The three reviewed classes land here as insights, each with a visible status.

Promotion is covered on Knowledge Capture, including where each promoted fact lands and how a promotion is undone.

Recall

One Search, Four Ways In

There is no separate command for reading memory back. The one search that covers your catalog, knowledge pages, assets, prompts, and connections covers memory too, and returns it grouped alongside everything else. Underneath, it reaches memory four ways and merges what they find.

Entity Lookup

Direct retrieval by dataset or entity reference. Finds memories explicitly tagged to a specific table, column, or catalog entity.

Best for: When querying a known dataset and need its accumulated context.

Semantic Search

Meaning-based ranking across memory content, blended with an exact-term signal so an identifier, column name, or error code is not underweighted by similarity alone.

Best for: Exploratory questions where the relevant dataset is not yet identified.

Keyword Match

Full-text matching on the words themselves, with no interpretation in between. It is also what search falls back to rather than failing, and it says so when it does.

Best for: Hunting for a term you know appears verbatim.

Graph Traversal

Follows catalog lineage to find memories attached to upstream and downstream datasets. If you query a derived table, memories about its source tables surface automatically.

Best for: Lineage-dependent questions where context propagates across related data.

Lifecycle

Capture, Correct, Consolidate

Memory operations are explicit and auditable. Capture is the one way in, and it checks what you already have before writing: a restatement of something you said before supersedes it rather than sitting beside it, and a near-match is offered back so the agent can update instead of duplicating.

Memory is personal and persists across sessions. It is distinct from knowledge capture, which is organizational and feeds the catalog. Memory stores what a specific user or agent has learned. Knowledge capture stores what the organization has validated.

Memory commands

CaptureRecord a memory with its class and entity links

FindRead it back through the one search, alongside everything else

UpdateRevise the content, category, or tags of a record

ForgetArchive a memory so it stops surfacing

ListBrowse your own records with filters

Review staleSee what the lineage watcher flagged as outdated

Review duplicatesSee your closest near-duplicate pairs, highest first

ConsolidateKeep one of a pair and retire the other, chain intact

Common questions

Memory FAQ

: Plexara structures memory into five dimensions: knowledge (facts, definitions, business rules), events (migrations, incidents, schema changes), entities (people, systems, teams), relationships (how those connect), and preferences (per-user formatting and workflow habits). Storing each kind separately means it is indexed and recalled the way that kind needs, rather than dumped into a single bucket.
Learn more: Five kinds of memory, and how each comes back
: Different questions need different recall methods, so Plexara composes entity lookup (exact match on people, tables, projects), semantic search (meaning-based via embeddings), and lineage graph traversal (related concepts). All three run inside one universal search tool that reaches memory alongside the catalog, knowledge pages, insights, saved assets, and prompts, and returns results grouped by source. The agent does not pick a strategy or a place to look; it asks once and sees the shape of the whole answer space.
Learn more: Letting the agent find the right tool
: Memory is personal and persists across sessions for a specific user or persona. The catalog is organization-wide structured documentation. Memory captures what an individual taught the agent during their work; once an admin reviews and promotes it, that observation can become catalog metadata everyone benefits from.
Learn more: Knowledge: from memory to insights
: Yes. Recording is one action, memory_capture, and the memory_manage tool exposes commands to update, forget, list, and review what has gone stale or duplicated. Stale memories surface in periodic review prompts so users can keep their context fresh. Nothing is locked in.
Learn more: Knowledge: from memory to insights
: No. Memory is scoped per user and persona. Cross-user sharing happens through the insights pipeline: an observation captured in one user's memory can be promoted, with admin review, to catalog documentation that all future agents see. That is intentional, not a bypass.
Learn more: Knowledge: from memory to insights

Knowledge Capture

How every conversation improves your data catalog through governed knowledge capture.

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