What Is Context Assembly? How Graph Engines Assemble Agent Context

Context assembly is the process of gathering, structuring, and serving the right data to an AI agent at query time. A graph engine supports it by storing relationships as first-class data, then combining vector similarity, full-text search, and graph traversal into a single ranked result. This approach fits teams running multiple agents over shared, changing data who need typed writes, versioned snapshots, and reviewable changes rather than ad hoc retrieval stitched together per prompt.

Why agents need structured context, not just retrieval

A retrieval pipeline that returns a flat list of text chunks gives an agent fragments without relationships. Context assembly asks a different question: which entities matter, how are they connected, and what is the current, consistent state of that information?

That distinction matters when agents write as well as read. If several agents mutate the same store concurrently, last-write-wins behavior can silently corrupt context. A graph engine designed for assembly treats the context layer like code: typed, versioned, reviewed, and merged.

How hybrid retrieval assembles a single answer

Omnigraph's documentation describes each "lens" on the graph as a typed, linted query file. A lens can fuse multiple retrieval signals in one pass. The example query find_experts ranks people by vector similarity, full-text match, and graph structure simultaneously, combining them with reciprocal rank fusion (rrf()):

query find_experts($topic: String) {
  match { $p: Person }
  return { $p.name, $p.role }
  order { rrf(nearest($p.embedding, $topic), bm25($p.bio, $topic)) }
  limit 5
}

Three properties make this useful for context assembly:

• One query, multiple signals. Vector, full-text, and traversal results are fused rather than concatenated, so ranking reflects all three.
• The lens is a file. It can be linted, versioned, and reviewed like any other code artifact.
• Typed schema enforcement. Writes are checked against the schema and rejected at the branch before they land.

Keeping concurrent agent writes safe

When many agents work on the same graph, isolation becomes the core problem. Omnigraph uses Git-style branching: each agent forks main, writes on its own branch, and merges only through review.

The documented workflow has four steps:

1. Branch — an agent creates a branch from main (omnigraph branch create).
2. Write — the agent adds nodes and edges on its isolated branch (omnigraph mutate); schema and policy are enforced at write time.
3. Review — a human or another agent inspects the diff (omnigraph diff) before it lands.
4. Merge — approved changes land on main in review order via three-way merge, attributed to the authoring agent (omnigraph branch merge).

No agent writes to main directly. The schema rejects malformed writes at the branch, policy decides who may merge, and the commit graph records every change and its author.

Practical properties for agent context

When this approach fits

Choose a versioned graph engine for context assembly when agents share mutable data, when auditability matters, or when retrieval quality depends on relationships rather than text similarity alone. A simpler vector store may suffice for read-only, single-agent retrieval over static documents.

Omnigraph Cloud is listed as a private preview opening in early October, with a waitlist; the project is also open source (GitHub, 1.6k stars) and documented for deployment on infrastructure you own.