Graph geeks
If you've worked with graph databases, knowledge graphs, or ontologies before, here's how Atlas maps to concepts you already know.
It's a labeled property graph
Atlas is a pre-typed labeled property graph with a defined ontology. Every Point is a node with a type label. Every Path is a directed edge with a type and optional properties. Both nodes and edges can carry arbitrary key-value properties.
| You say in Atlas | Graph people say |
|---|---|
| Point | Node / Vertex |
| Path | Edge / Relationship |
| View | Subgraph / Projection |
| Atlas | Graph |
| Type | Label / Class |
| Properties | Properties / Attributes |
Pre-typed, not schema-free
This is the key difference between Atlas and a raw graph database. You can't just create any node with any label. Atlas enforces an ontology: a defined set of valid Point types and Path types, with constraints on which types can connect to which.
Person → has_role → Position is valid. System → has_role → Metric is not.
This is deliberate. Schema-free graphs get messy fast. A typed, constrained graph is queryable, validatable, and AI-readable. The ontology is what makes this a knowledge graph, not a whiteboard.
Ontology categories
Point types are organized into semantic categories:
| Category | Types | Represents |
|---|---|---|
| Actor | Person, Position, Group, Organization, Vendor, Agent | Who |
| Action | Process, Step, Decision, Approval, Review, Handoff | What happens |
| Resource | System, Database, Data Table, Artifact, Document, Equipment, Policy, Skill | What's used |
| Business Model | Product, Customer | What's sold, and to whom |
| Outcome | Outcome, Metric, Risk | What's produced |
| Accounting | Account | The chart of accounts |
| Treasury | Bank Account | Money at the bank |
| Location | Physical Site | Where |
| Time | Date, TimeRange, Trigger | When |
Path types are constrained too. reports_to runs from a Position or Agent to a Position. has_step runs from a Process to a Step, Decision, Approval, Review, Handoff or another Process. The full ontology is in the Type glossary.
Graph algorithms
Atlas supports standard graph analytics:
- PageRank: find the most influential Points
- Betweenness centrality: find bridge Points between communities
- Community detection: groups of points more connected to each other than to the rest
- Connected components: find disconnected subgraphs
- Node similarity: Jaccard similarity between Points
- Shortest path: BFS between any two Points
- Degree centrality: most connected Points (in/out/both)
Available through the MCP server. See the API reference.
SQL queries (AtlasQL)
Run read-only SQL against your Atlas with the query_atlas MCP tool. Points and paths are tables: join paths.source_id and paths.target_id to points.id, and read the relationship from paths.type. Call describe_schema first for the tables, columns and the types in use. See the API reference for details and examples.