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Plexara
Product

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"

Recall

Four Retrieval Strategies

Different questions require different recall methods. The platform picks the right strategy automatically or lets you choose.

Entity Lookup

Direct retrieval by dataset URN 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

Vector similarity search across memory content. Finds conceptually related memories even when the exact entity is not referenced.

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

Graph Traversal

Follows DataHub 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.

Auto (Combined)

Runs all three strategies and merges results with deduplication. The platform selects the most relevant memories across all retrieval methods.

Best for: Default mode. Covers all recall paths without manual strategy selection.

Lifecycle

Remember, Recall, Manage

Memory operations are explicit and auditable. The platform remembers what you tell it, recalls it when relevant, and provides tools to update, forget, and review stale entries.

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

RememberStore a new memory with dimensions and entity tags
RecallRetrieve relevant memories using any strategy
UpdateModify an existing memory with new information
ForgetRemove a specific memory permanently
ListBrowse all stored memories with filters
Review StaleFind memories that may be outdated

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. Plexara composes entity lookup (exact match on people, tables, projects), semantic search (meaning-based via embeddings), and lineage graph traversal (related concepts). The platform picks the right strategy automatically per query; the agent can also call memory_recall directly when it knows what it needs.

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. The memory_manage tool exposes commands to remember, update, and forget. 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

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Knowledge Capture

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

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