Graphersal

An in-memory graph database and traversal engine in Rust, queried with GQL, the ISO standard graph query language, and with Gremlin, with vector search for GraphRAG.

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Why Graphersal

AI moves faster than the tools around it. We built Graphersal so you don’t have to wait for them: a lightweight, embeddable graph engine you can turn into whatever your AI solution needs — a library inside your application, a tool for your agents, or a full server with your own features on top.

It works out of the box: a browser UI, an MCP endpoint for AI agents, an AI chat where you simply ask about your graph in plain language and it writes and runs the queries for you, durable storage and transactions, and the safety to run queries you did not write — permissions, read-only access, resource limits and timeouts. You start with a working system, not with a list of components to wire together.

Vector search and GraphRAG

Store your embeddings next to the graph and search them in the same query that walks it. A vector is a value type of its own (f32, or quantized to f16, i8 or bit to save memory), declared in the schema, searched exactly with nearest() and similarity() (cosine, dot product, L2, Hamming). GraphRAG in one traversal: find the passages nearest to the question, then follow the edges to what the graph knows about them, and hand that context to your language model.

// "What should I cook for my cat?" ~ the question's embedding
g.v().has_label("doc").nearest("emb", question, 2)
  .project("title", "author", "topic")
    .by("title")
    .by(__.in("wrote").values("name"))
    .by(__.out("about").values("name"))