Introduction

🌐 English · 中文版

Dr Strange is an AI-native embedded graph database, written in Rust.

It is a graph database designed from the outset for AI workloads: embeddings are a first-class value type, similarity search operates alongside graph traversal, and the engine exposes primitives suited to agents — natural-language queries, a live change feed, and time-travel — rather than adding AI features to a conventional graph database after the fact.

Like SQLite, it is embedded: a library linked into an application, backed by a single on-disk database, with no server to operate. Unlike SQLite, it can also serve — drsg serve exposes a JSON-RPC 2.0 API, a browser dashboard, and a WebSocket change feed, with client SDKs in six languages.

Intended audience

• Application developers constructing a knowledge graph, a GraphRAG pipeline, or an agent’s long-term memory, requiring graph and vector storage in a single system.
• AI and agent engineers who expect the database to perform more of the work: compiling a question into a query, pushing changes as they commit, and reconstructing historical state on demand.
• Readers interested in the construction of an AI-native store.

Organization

The early chapters establish the fundamentals: what Dr Strange is, how to build it, and what its AI-native design entails. The middle chapters are a reference for the query language and each access surface — the web UI, the language SDKs, the command line, and the MCP server for LLM agents. Two chapters then cover the code-intelligence half: the parser plugin system, and what it gives a coding agent. The final chapter documents the architecture.

Status. This book began alongside the v1.0 release and is kept current as features land.