Why We Built Our Own Graph Database

Why a graph database at all

EdgeX11's hermetic organizational memory isn't a pile of documents. It's a living graph: code connected to the decisions behind it, deployments connected to what broke and how it was fixed, people connected to the systems they know. The value is in the relationships, and relationships are exactly what relational tables make expensive to traverse.

So a graph database was never optional for us. When an AI employee reasons about a change, it walks that graph — hop by hop, the way a tenured engineer would — instead of running a dozen joins. The database isn't infrastructure sitting beneath the product. For us, it is the product.

Why not just use Neo4j

To be clear: Neo4j is a superb database, and we ran on it happily for a long time. This isn't a teardown. It's a story about fit.

As the memory layer became the core of everything, a few things started to rub. Neo4j runs on the JVM, which means a heap to tune and a memory footprint that felt heavy for what we were doing. It wants to be operated — another moving part to run, watch, and pay for. And the licensing model kept forcing decisions we didn't want to keep making as a small team shipping a multi-tenant product.

What we actually wanted was almost embarrassingly simple: a graph database we could ship as a single binary, embed when we needed to, run per-tenant without ceremony, and otherwise forget about. Something that did graph queries and vector search in one place, because our workload is retrieval-augmented reasoning over a graph.

We didn't want a database to operate. We wanted a database to forget about.

What Axon is

Axon is a property-graph database written in pure Go. The whole thing is one static binary of about 19 MB — the same size as a Docker image you can docker run and be querying in under a minute. No JVM, no cluster to stand up.

• Cypher. It speaks the query language you already know — MATCH, MERGE, CREATE, aggregation, WITH pipelines, parameters, and CALL procedures.
• Vector search, built in. HNSW indexes (cosine and euclidean) over node properties, live-synced on write. One query can traverse relationships and rank by embedding similarity — which is the whole game for GraphRAG, without gluing a separate vector database alongside.
• 17+ graph algorithms. PageRank, community detection (Louvain, label propagation), centrality, connected components, shortest path, and more, as callable procedures.
• Ontology & inference. Classes, cycle-checked subclass hierarchies, and label-subsumption inference at query time.
• Multi-tenant by design. Per-database isolated storage engines — the same tenant isolation the EdgeX11 security model depends on.
• Built to run anywhere. Static binary, small Docker image, an embeddable Go library, an HTTP JSON API, and an interactive REPL. BadgerDB (an LSM engine) with MVCC transactions underneath.

The benchmarks — including where we lose

A benchmark you can only win is marketing, not information. Here's the full picture from our harness, so you can decide whether Axon fits your workload rather than ours.

Setup: Apple M2, 8 GB RAM · 10,000-node dataset · p50 latency over 30–200 requests · Axon vs Neo4j 2026.06.0. Small dataset on a laptop — treat it as directional, not gospel.

Scroll the table sideways on a narrow screen.

The shape is clear and we're not going to spin it. Axon is fast where we tuned first: writes, and reads that go through an index. Neo4j is still ahead on aggregation, unindexed scans, and variable-length traversal — the things a mature, cost-based query planner does well after years of work.

That's not a mystery; it's a roadmap. Those exact workloads are what our current query-engine release targets. Which is a good moment to be honest about what Axon is and isn't yet.

Where Axon is on the road

Axon is young and we'd rather say so than imply otherwise. The single-node core is shipped and hardened; the work now is making the query engine as smart as the storage engine is fast.

• Shipped — the core: graph storage, a Cypher lexer/parser/executor, MVCC transactions, the algorithm library, HNSW vector search, ontology inference, multi-tenancy, the HTTP API and shell, cross-platform binaries and CI.
• In progress — query-engine maturity: range indexes and constraints, a cardinality-driven planner, EXPLAIN/PROFILE, cost-based join ordering, and parallel scans are in. Composite indexes, parallel aggregation, streaming results and full-text search are next — which is precisely where the benchmark gaps close.
• Next — ecosystem and AI: a Bolt protocol server, official Go/Python/JavaScript clients, a GraphQL API, and an MCP server so LLM agents can query the graph directly.
• Later — a deeper semantic layer, enterprise operations (RBAC, SSO, audit, encryption at rest), and distributed clustering.

The roadmap moves faster than a blog post can, so treat the list above as a snapshot rather than a commitment.

Why the engine stays in-house

When we first wrote about Axon we published it under an open-source licence, and we said the database would stay that way. Four days later we changed our minds. That is a short half-life for a commitment, so it deserves an explanation rather than a quiet edit.

What changed is what we're building on top of it. The next thing Axon needs to do is enforce access control at the level of individual nodes and relationships — so a fact created by one team is not merely filtered out of another team's results, but never matched in the first place — and to make deletion provable: remove a source, and every fact, edge, summary and embedding derived from it goes with it, with a certificate to show for it.

Both of those are only possible inside the engine. Anyone building a memory layer on a database they didn't write can filter after the query returns, and can delete the rows they know about. They cannot enforce during the match, and they cannot guarantee that nothing survives in a vector index they don't control. That capability is the product, not a feature of it.

So Axon is proprietary from v0.8 onward. The preview releases we published under the old licence stay under it — we're not pretending otherwise or trying to claw them back. If this reads as a reversal, it is one, and we would rather say so plainly than quietly rewrite the earlier post and hope nobody kept a copy.