The knowledge graph (Cortex)
How Cortex maps your documents and knowledge into a graph you can search and steer.
Cortex is Command Center's knowledge layer: a private graph of your documents and the concepts inside them, stored locally on your machine. It is what turns a folder of Markdown files into something agents can navigate — find the right document by topic, walk from a document to the entities it mentions, and answer "how do these things connect" without reading everything.
The headline is the cost model: by default, the graph is built with zero external AI calls. The base layers cost nothing to build and nothing to maintain. Paid semantic search is an opt-in upgrade, not a dependency.
One graph, many workspaces
There is one knowledge graph. A workspace is a lens over it — a named subgraph you build for a purpose: a research corpus, a project's documentation, the knowledge attached to a context artifact. The same file can belong to several workspaces at once without being duplicated; the graph stores one node per file and records its memberships.
One workspace is special: the root workspace, which mirrors your entire documents/ tree automatically. A background sync watches the filesystem and updates the graph as files land — you write a report, and moments later it exists as a node in the graph. You never maintain the mirror by hand.
The three depths
Every workspace is built to one of three depths, and the depth you choose is a cost decision:
| Depth | What it adds | What it costs |
|---|---|---|
| Structure | Folders and documents, mirrored mechanically from disk | Nothing — no AI involved at all |
| Entities | Concepts extracted from the documents, plus the relationships between them | No external API — your Claude agents do the extraction as part of their work |
| Full | Vector embeddings that enable semantic search | Uses an optional Google Gemini key you bring |
Depth is not a label you have to manage — the platform derives it from what the workspace actually contains. A workspace with extracted entities reads as entities-deep; one with vectors reads as full. What you see always reflects reality.
What "ingest" means
Ingesting a document means registering it in the graph — creating its node, linking it into its folder, recording its description. At structure depth that is the whole story, and it is instant and free.
Entity enrichment is a separate, deliberate step. When you choose to enrich a document (or a whole workspace), an agent reads the material and extracts the concepts and relationships in its own reasoning — no external extraction service, no per-token bill. You decide what is worth enriching; the platform never burns budget on it silently.
Upgrading a workspace to full depth runs the embedding pipeline over it, which is when the Gemini key comes into play.
When to use which depth
- Stay at structure for most of your workspace. Agents locate documents by name, folder, description, and summary remarkably well — the root workspace runs at structure depth by design.
- Add entities where the connections matter: a research corpus you will ask conceptual questions of, a domain you want mapped, a context artifact's attached graph.
- Go full only for a corpus where phrasing varies enough that meaning-based search genuinely beats graph search — dense technical references, jargon-heavy research. Full is the exception, not the default.
Searching respects the same split: graph search works at every depth, and semantic search works at full depth. Ask a semantic question of a structure-depth workspace and you get a ranked document search instead of an error.
The Cortex view
The Cortex view is where you see and steer all of this: an interactive graph canvas for browsing workspaces, folders, documents, and entities; workspace creation and ingest actions; an entities toggle to reveal a workspace's concept layer; a lint surface that flags structural issues (orphans, stale nodes) for cleanup; and an activity log of what changed in the graph and when. Everything you can do there, your agents can do through their own tools — same graph, two doors.
One practical note: the graph runs on a local database engine that the platform provisions and manages for you. If the Doctor view ever reports it unreachable, the graph surfaces pause until it is back — your documents themselves are never affected.
Where to go next
- Memory layers — where the knowledge graph sits in the wider memory system
- Context artifacts & grounding — attaching a purpose-built graph to an agent session
- Knowledge graph infrastructure — the deeper technical reference