Graph
Knowledge Graph
Understand how Origin links people, projects, tools, observations, and relations so recall can recover context through more than text similarity.
At a glance
01
The graph turns repeated names, projects, tools, and observations into retrievable context instead of leaving every memory isolated.
02
Graph context supplements hybrid search; it does not replace source-backed memories, pages, or human review.
01
Why the graph exists
Text search can find matching words, and vector search can find similar meaning. The graph helps with relationships: who worked on what, which project uses which tool, and which observations belong together.
Origin uses graph context as part of the local memory layer so agents can recover surrounding facts without replaying a full chat history.
02
Entities, relations, observations
An entity is a named thing such as a person, project, tool, repo, or concept. A relation connects two entities. An observation is a grounded statement attached to an entity.
The goal is practical retrieval. The graph should help the next session find the right surrounding context, not become a hand-maintained ontology.
- Entity: Origin, Claude Code, a client project, a person, a tool, or a repo.
- Relation: works_on, uses, depends_on, belongs_to, supersedes, or another useful link.
- Observation: a sourced note about an entity that should help future recall.
03
How graph context is created
Post-ingest enrichment can link entities, deduplicate overlapping captures, enrich titles, grow matching pages, and update effective confidence based on memory type, access, and age. Optional local models or API keys can make extraction and graph linking richer.
Claude Code skills can also help because the agent already has language judgment during capture and distillation.
04
Wikilinks and imports
Imported Markdown vaults can carry useful links. Origin can preserve those relationships as graph signal while still treating imported content as context that should prove itself useful in recall.
Wikilinks are helpful, but they are not the same as source-backed page provenance. A page claim is stronger when it traces to specific source memory IDs.
05
Graph in retrieval
Hybrid retrieval can combine vectors, full-text search, pages, and graph context. The graph adds nearby related facts when text similarity alone would miss why a memory matters.
Treat graph context as support, not authority. The source memory, page provenance, and local git history remain the inspectable record.
06
Review and cleanup
Graph data can be wrong when a name is ambiguous, a project changes direction, or an imported link was noisy. Use review, corrections, spaces, and forget when the memory layer needs cleanup.
If graph context feels like it is mixing unrelated worlds, check the active space before changing the data model. Many apparent graph problems are actually context-boundary problems.
Next
Source-Backed Pages
Understand how Origin turns atomic captures into readable pages with source memory IDs, revision state, and refresh paths.
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