With 32,800 GitHub stars, 180 contributors, and version 3.2 shipping in July 2026, SurrealDB eliminates the database zoo by unifying document, graph, relational, time-series, geospatial, and key-value data models into a single Rust binary queried through SurrealQL — an intuitive SQL-like language that handles graph traversals, record links, subqueries, and computed fields without switching between multiple database engines. Purpose-built for AI applications, it integrates vector indexing, full-text search, and hybrid retrieval that blends semantic similarity with graph and relational intelligence for context-aware RAG pipelines and recommendation engines. Real-time subscriptions and event-driven triggers push live data changes to connected clients without requiring external message brokers like Kafka. Multi-row, multi-table ACID transactions guarantee consistency while incrementally computed views deliver pre-calculated analytics without batch processing. Role-based access control with record-level permissions, JWT authentication, and multi-tenant isolation enables backend-as-a-service usage where client applications connect directly with fine-grained security. SDKs for JavaScript, Python, Go, Rust, .NET, and Java connect via WebSocket or HTTP APIs. Storage and compute separation allows deployment as an embedded library, a single-node server, or a highly-scalable distributed cluster with TiKV or FoundationDB backends. Deploy via Docker with persistent volumes on any Linux host. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Source-available licensed.

FalkorDB is the first queryable property graph database to leverage sparse adjacency matrices and linear algebra for graph traversal, replacing traditional pointer-chasing with GraphBLAS-accelerated computation. Originally the RedisGraph engine, it was relaunched as FalkorDB in 2023 and rewritten from C to Rust in 2026 for improved memory safety and performance. The database supports the OpenCypher query language with proprietary extensions, translating queries into linear algebra expressions that exploit AVX hardware acceleration. Indexing options include full-text search, vector similarity for embedding-based retrieval, and range indexing, while connectivity supports both the RESP protocol for Redis clients and the Bolt protocol for Neo4j-compatible tooling. The GraphRAG SDK enables ingestion of documents in text, PDF, and Markdown formats into knowledge graphs, with schema-guided entity extraction, hybrid retrieval combining vector and graph traversal, relationship expansion, and cited answers for LLM applications. Official client libraries cover Python, Node.js, Java, Rust, Go, PHP, and C#. Multi-tenant support handles over 10,000 concurrent graphs with zero overhead and full isolation. Docker deployment runs the falkordb/falkordb image on ports 6379 for the database server and 3000 for the built-in browser UI, with persistent volume storage and optional authentication. A production falkordb-server image excludes the browser for lighter deployments. On RepoCloud, deploy FalkorDB on a dedicated VPS with root SSH access, persistent storage for your graph data, and complete control over authentication, thread count, and memory configuration, all under the SSPLv1 license.