Surrealist

Product Introduction

1. Surrealist is a visual integrated development environment (IDE) designed specifically for interacting with SurrealDB databases through graphical interfaces and query management tools. It enables users to visualize database queries as interactive knowledge graphs while combining graph, vector, and document data models in a unified workspace.
2. The core value lies in its ability to simplify complex database operations by providing real-time relationship mapping, schema design tools, and AI-powered insights without requiring infrastructure management. It accelerates development cycles by offering embedded machine learning capabilities and multi-platform accessibility for cloud or local databases.

Main Features

1. The Query View supports simultaneous execution of multiple SurrealQL queries through tabbed interfaces with live data streaming, automatic variable inference, and result visualization options. Users can save frequently used queries as reusable templates and format complex syntax directly within the editor.
2. The Schema Designer provides graphical tools to define table relationships, indexes, and events through drag-and-drop interfaces while generating real-time visualizations of data connections. It automatically translates graphical configurations into SurrealQL schema definitions for direct database deployment.
3. The Table Explorer enables no-code browsing of database records with clickable relationship traversal, in-place data editing, and contextual links to connected entities. It displays records in human-readable formats while maintaining direct access to raw JSON outputs for technical validation.

Problems Solved

1. Surrealist eliminates the complexity of manually writing and debugging SurrealQL queries for relationship-heavy operations in graph databases. It addresses visibility gaps in traditional database IDEs by mapping data connections visually and providing schema change simulations.
2. The tool targets developers building AI-driven applications requiring hybrid vector/graph search capabilities and data engineers managing multi-model databases. It also serves analysts needing real-time exploration of interconnected datasets without deep SQL/NoSQL expertise.
3. Typical use cases include optimizing GraphRAG implementations for AI context enrichment, troubleshooting permission configurations through visual scope mapping, and accelerating fraud detection workflows via real-time relationship pattern analysis.

Unique Advantages

1. Unlike generic database clients, Surrealist natively integrates SurrealML for direct execution of machine learning models as database functions with version control. This eliminates separate ML pipeline tooling while maintaining audit trails for AI inference processes.
2. The platform uniquely combines live query result streaming with historical version comparison, allowing users to track data evolution during iterative testing. Desktop editions include one-click local database instances with integrated log monitoring and performance profiling.
3. Competitive differentiation stems from SurrealDB's native multi-model architecture, which Surrealist leverages to offer simultaneous interaction with graph nodes, vector embeddings, and document stores through a single authentication session.

Frequently Asked Questions (FAQ)

1. How does Surrealist handle database version control? Surrealist automatically versions schema changes and stored procedures through Git integration in desktop editions, enabling rollbacks and collaborative development workflows with conflict resolution tools.
2. Can Surrealist connect to existing SurrealDB cloud instances? Yes, it supports TLS-encrypted connections to Surreal Cloud or self-hosted databases using WebSocket or HTTP protocols with granular permission scopes for team-based access management.
3. What machine learning frameworks work with SurrealML integration? Surrealist supports ONNX runtime for model execution and provides Python toolkits for converting PyTorch/TensorFlow models into SurrealDB-compatible formats with automated quantization and dependency packaging.