What is Dr Strange?

Dr Strange is an AI-native embedded graph database, written in Rust. This sentence makes three claims, examined in turn below: it stores a graph, it is embedded, and it is AI-native — designed for AI workloads from the outset rather than retrofitted.

The data model: a property graph, in planes

Dr Strange stores a property graph:

• Nodes are the entities — a person, a document, a product. Every node has a set of labels (its types, e.g. Person, Doc) and a free-form map of properties.
• Edges are directed, typed relationships between nodes — (alice) -[:KNOWS]-> (bob). Edges carry properties too.
• Properties are typed values: strings, numbers, booleans, byte blobs, lists, nested maps — and, crucially, vectors (embeddings), which are a first-class value type, not an afterthought.

Nodes can carry a caller-supplied external key (a stable, human-meaningful id, unique within its plane) alongside the engine’s internal numeric id.

Data is organized into planes — independent, named graph namespaces that live in one database file. A plane is like a schema or a namespace: you might keep one graph per project, per tenant, or per experiment. Ids are globally unique across the whole database, but every query runs in the context of one plane, so planes remain cleanly separated. A new database always contains a startup plane.

The AI-native thesis

Most databases that add AI capabilities pair a conventional engine with a vector column and a call to an external model. Dr Strange inverts this arrangement: the capabilities an AI application requires are core engine features:

• Embeddings are first-class. A vector is a property value like any other, indexed natively with HNSW, so similarity search is fast and lives inside the query engine.
• Retrieval fuses signals. Vector similarity, BM25 keyword relevance, and graph proximity combine into a single ranked result — the core of the GraphRAG pattern.
• The engine talks to agents. Ask a question in plain language and get a query plan back, run it, and receive a connected subgraph. Subscribe to a plane and receive mutations as they commit. Read the graph as of any past point.

A concrete example: GraphRAG

Suppose you have ingested a corpus of documents as a graph — Doc nodes with an embedding property, linked by CITES and MENTIONS edges to Concept nodes. A GraphRAG lookup is a single query:

SEARCH (d:Doc) ON embedding NEAR "how does time-travel work" TOPK 5 RETURN d

This embeds the question, retrieves the five most similar documents, and — because the results are graph nodes — traversal continues directly from them, into the concepts they mention and the documents that cite them, assembling grounded context beyond the reach of a standalone vector store.

Where Dr Strange fits

• Like SQLite, it is embedded: a library and a single database on disk, with no server to run. You can also start a server (drsg serve) when you want a dashboard, an HTTP/WebSocket API, and multi-client access.
• Unlike a classic graph database (e.g. Neo4j), vectors and hybrid retrieval are built into the core, and the surface is designed around agents — natural language, change feeds, time-travel.
• Unlike a pure vector database, the unit of storage is a graph: you get relationships, traversal, and graph algorithms, not just nearest neighbors.

For applications built around a knowledge graph, a GraphRAG pipeline, or an agent’s long-term memory, Dr Strange is intended to serve as the single store for all of it.

Feature tour

What v1.0 is

v1.0 is a complete, embedded, AI-native graph database with all of the above, one storage engine (a hand-rolled LSM, with a legacy alternative), and a stable JSON-RPC wire protocol that the SDKs are generated from. The chapters that follow show you how to use each part.

What v2.0 provides

v2.0 adds a repository’s own source tree as a second kind of graph. Sandboxed wasm parser plugins turn source files into a resolved call graph — symbols, callers with call sites, and every other structural edge — with no model in the loop, covering eight languages out of the box. serve watch keeps the resulting plane synced to the repository’s own commit history, folding each commit’s changes in as they land. Twelve agent verbs — context, search, describe, grep, trace, impact, fathom, snippet, history, deployed, recall, wire — turn that graph into single-round-trip answers for a coding agent, each one stating the bounds of what it found: candidates instead of a guessed match, an UnresolvedRef ledger entry instead of a silently wrong edge. drsg init bootstraps all of it — digest, watch, and MCP configuration for whichever host tool is in use — in one command. Plugins and Coding Agent cover this ground in depth.

Reading in another language? 中文版见 Chinese edition。