Pairing Graphify with Headkey
Graphify gives you a queryable map of your codebase. Headkey turns it into a memory that evolves with your team — across renames, refactors, and the decisions that never made it into a comment.
If you've used Graphify, you already know the pitch: point it at a repo, and you get back a queryable knowledge graph. Functions, modules, "god nodes," design rationale pulled out of comments — the whole shape of the codebase, ready for an AI assistant to read instead of grepping its way around.
It's a great map. But maps are static.
A team's understanding of its own code isn't. People rename things. Architectures drift. Decisions made in a Slack thread end up contradicted by the next sprint's PR. The map you generated last Thursday is already a little wrong by Monday morning, and nothing in a graph.json file tells you which parts went stale, or why.
Headkey is the layer that closes that loop.
What Headkey adds
Headkey is a cognition system for AI agents. You feed it observations — events, decisions, conversations, structured data from your own tools — and it forms beliefs about the world. Those beliefs get reinforced when new evidence agrees, weakened when evidence conflicts, and qualified as context shifts. The system tracks who said what, when, and how confident it is now.
That's a different shape from a knowledge graph. A graph says what is true at the moment of extraction. A memory says what we've come to believe over time, and how that belief is moving.
Pair them, and Graphify gets a partner that:
- Carries identity across renames and refactors. When
AuthService.validateTokenbecomesTokenVerifier.verify, Headkey doesn't fork into two unrelated nodes. The old name and the new name live on the same entity, and questions about either return the full history. - Holds knowledge that doesn't live in code. The reason your team picked RS256 over HS256 may be in a Slack message, a security review, or someone's head — never in a comment. Headkey captures those alongside the structural facts Graphify extracts, and the chat agent doesn't have to know which source it came from.
- Spans repositories. Graphify produces a graph per repo. Headkey gives you beliefs per organization. An agent asking "who depends on the JWT verifier?" gets an answer drawn from every repo whose graph has been ingested, even when the surface-level names differ.
- Knows when its own knowledge is stale. Beliefs that haven't been reinforced fade. Beliefs that conflict with new evidence get challenged rather than silently overwritten. Six months in, you can ask the agent what it used to think, and why it changed its mind.
Why teams reach for this combination
Graphify is the right tool for getting structure out of a codebase — local-first, fast, deterministic, language-aware. Most teams don't want to rebuild that.
The reasons we hear for adding Headkey on top:
The chat agent needs more than the code. A useful engineering assistant fields questions about architecture, history, and intent — not just call graphs. "Why does our payment flow retry three times?" isn't answered by the AST; it's answered by the post-mortem from last quarter. Headkey holds both, behind a single ask.
A PRD or tech spec generator needs typed, validated data. When Graphify hands its output to a downstream document generator, the consumer wants guarantees: every Function has a name, every Endpoint has a method, the relationships use the vocabulary your team agreed on. Headkey enforces that with a per-tenant ontology — your declared types and constraints, validated at write time. Off-schema data is rejected at the boundary.
Multiple repos, one knowledge surface. Engineering orgs rarely live in a single repository. Without a shared memory, you end up with N independent graphs and N independent assistants, each blind to the others. Headkey gives you a tenant-scoped memory where every repo's graph contributes to a unified picture, and access stays governed by the visibility rules you set.
Evolving knowledge, not snapshots. When the same entity gets re-ingested across commits, Headkey reconciles. New attributes update; conflicting facts get scored against priors; renames get linked rather than duplicated. The graph in your CI artifact is overwritten on every run; the memory accumulates.
What the integration looks like
The connection is deliberately thin. Graphify produces typed entities and relationships; Headkey accepts them as a structured ingest. Your CI step calls a single endpoint with a batch — entities, relationships, optional renames, source reference — and Headkey takes care of validation, identity reconciliation, belief formation, and indexing.
You declare your ontology once. After that, every ingest is checked against it. Renames are first-class: when Graphify detects Foo became Bar, both names continue to resolve to the same memory, and queries about either surface the combined history.
Reads are natural-language. Your chat agent and your artifact generators both ask Headkey questions in plain English; Headkey answers from the merged picture across structural data, conversation history, and whatever else you've fed it.
That's the whole shape. No new infrastructure on the Graphify side, no schema migration, no rewriting your extraction pipeline.
What changes for the team
Day-to-day, the difference is subtle and then it isn't.
The first time someone renames a function and the assistant still finds it — without anyone re-indexing — that's the moment teams notice. The second moment is usually a question that mixes code and context: "Why is RetryPolicy configured the way it is?" The answer comes back with the call sites and the PR discussion that set the values, because both are in the same memory, anchored to the same entity.
After a few weeks, the assistant stops being something that searches your code and starts being something that remembers your code. That's the shift Graphify alone can't make on its own — not because anything is wrong with Graphify, but because a graph is, by design, a snapshot.
Getting started
If you're already running Graphify in CI, the integration is a small addition: declare your ontology, add a step that posts your typed extraction to Headkey, and point your chat agent at Headkey's read endpoint. Your existing graph.json workflows keep working — Headkey doesn't replace them, it gives them somewhere to live and grow.
A static map of your codebase is a good thing to have. A memory of your codebase is what your AI agents have actually been missing.
Want to try this with your own repos? Get in touch — we'll help you scope an on-prem or managed setup against your team's ontology and scale.
