When your system spans dozens of repositories, a familiar problem appears: nobody, human or AI, can hold the whole thing in their head. An AI assistant asked "where is tenant resolution implemented?" will grep its way through repository after repository, burning time and context on every question. Graphify fixes this by turning the codebase into a queryable knowledge graph that is built once and reused.
From text search to structural query
Grep treats code as text. It finds a string, not a relationship. It cannot answer "what calls this method?" or "which types implement this interface across all repos?" without heroic effort. A knowledge graph treats code as what it actually is - a structure of types, members, and the relationships between them - extracted from the abstract syntax tree. Once that structure is captured, those questions become graph queries instead of text scans.
Build the graph from the AST
Graphify parses source with a language-aware extractor and emits a structured representation of the code into an output directory. You build it once per repository and commit the result so the whole team - and every agent - shares the same map:
graphify build .
# emits graphify-out/ with nodes (types, methods) and edges (calls, implements, references)
Because the output is committed, generating it is not on the hot path of every question. The expensive analysis happens at build time; queries against it are cheap.
Query and explain
With the graph in place, you ask structural questions directly:
graphify query "implementations of IOrderRepository"
graphify explain "OrderService.SubmitAsync"
The first returns every implementer across the indexed repositories; the second describes a symbol and its relationships. For an AI agent, this is transformative: instead of grepping and guessing, it issues a precise query and gets a precise answer, which means better responses using a fraction of the context.
Tip: Commit the generated graph and refresh it in CI. A stale graph is worse than none - it answers confidently with yesterday's structure, and that is exactly the kind of subtle wrongness that erodes trust.
Wiring it into the agent workflow
The payoff lands when the graph is part of how agents work, not a side tool. A short rule pointed at the agent tells it to prefer graphify query and graphify explain over raw search. Across a large ecosystem you roll the setup out repo by repo with a script, so every project exposes the same interface and an agent can move between them without relearning the terrain each time.
Why this matters at ecosystem scale
The value compounds with size. In one repository, grep is fine. Across ninety, the cost of re-deriving structure on every question becomes the dominant tax on both engineers and agents. A shared, committed graph pays that cost once and amortizes it across every query from every consumer - which is the only way code intelligence stays fast as the codebase grows.
Wrapping up
Graphify turns a sprawling .NET codebase from something you search into something you query. Extract structure from the AST, commit the graph, refresh it in CI, and point your agents at query and explain instead of grep. The result is faster, more accurate answers about your own system - and an AI workflow that scales with the number of repositories instead of slowing down with it.
Wrangling a large multi-repo codebase? Let's make it queryable.
