Imported from 24601/surreal-skills (SKILL.md). Install upstream with npx skills add 24601/surreal-skills. Copyright stays with the author (MIT).

SurrealDB 3 Skill

Expert-level SurrealDB 3 architecture, development, and operations. Covers SurrealQL, multi-model data modeling, graph traversal, vector search, security, deployment, performance tuning, SDK integration, and the wider SurrealDB ecosystem, including SurrealKit, SurrealMCP, n8n, CodeMirror, and agent-skill surfaces.

For AI Agents

Get a full capabilities manifest, decision trees, and output contracts:

uv run {baseDir}/scripts/onboard.py --agent

See AGENTS.md for the complete structured briefing.

Prerequisites

• surreal CLI -- brew install surrealdb/tap/surreal (macOS) or see install docs
• Python 3.10+ -- Required for skill scripts
• uv -- brew install uv (macOS) or pip install uv or see uv docs

Optional:

• Docker -- For containerized SurrealDB instances (docker run surrealdb/surrealdb:v3)
• SDK of choice -- JavaScript, Python, Go, Rust, Java, Kotlin, .NET, C, PHP, Swift, or Ruby

Security note: This skill documents package-manager and container installs only. Prefer auditable installs through Homebrew, apt/dnf, Cargo, npm, or Docker rather than remote one-line shell installers.

Quick Start

Credential warning: Examples below use root/root for local development only. Never use default credentials against production or shared instances. Create scoped, least-privilege users for non-local environments.

# Start SurrealDB in-memory for LOCAL DEVELOPMENT ONLY
surreal start memory --user root --pass root --bind 127.0.0.1:8000

# Start with persistent RocksDB storage (local dev)
surreal start rocksdb://data/mydb.db --user root --pass root

# Start with SurrealKV (time-travel queries supported, local dev)
surreal start surrealkv://data/mydb --user root --pass root

# Connect via CLI REPL (local dev)
surreal sql --endpoint http://localhost:8000 --user root --pass root --ns test --db test

# Import a SurrealQL file
surreal import --endpoint http://localhost:8000 --user root --pass root --ns test --db test schema.surql

# Export the database
surreal export --endpoint http://localhost:8000 --user root --pass root --ns test --db test backup.surql

# Check version
surreal version

# Run the skill health check
uv run {baseDir}/scripts/doctor.py

Environment Variables

These map directly to the surreal sql CLI flags (--endpoint, --user, --pass, --ns, --db) and are recognized by official SurrealDB SDKs.

Core Capabilities

SurrealQL Mastery

Full coverage of the SurrealQL query language: CREATE, SELECT, UPDATE, UPSERT, DELETE, RELATE, INSERT, LIVE SELECT, DEFINE, REMOVE, INFO, subqueries, transactions, futures, and all built-in functions (array, crypto, duration, geo, math, meta, object, parse, rand, string, time, type, vector).

See: rules/surrealql.md

Multi-Model Data Modeling

Design schemas that leverage SurrealDB's multi-model capabilities -- document collections, graph edges, relational references, vector embeddings, time-series data, and geospatial coordinates -- all in a single database with a single query language.

See: rules/data-modeling.md

Graph Queries

First-class graph traversal without JOINs. RELATE creates typed edges between records. Traverse with -> (outgoing), <- (incoming), and <-> (bidirectional) operators. Filter, aggregate, and recurse at any depth.

See: rules/graph-queries.md

Vector Search

Built-in vector similarity search using HNSW indexes (the v3 vector index type; brute-force is available as a fallback during index build/rebuild). Define vector fields, create indexes with configurable distance metrics (cosine, euclidean, manhattan, minkowski), and query with vector::similarity::* functions. Build RAG pipelines and semantic search directly in SurrealQL.

See: rules/vector-search.md

Security and Permissions

Row-level security via DEFINE TABLE ... PERMISSIONS, namespace/database/record-level access control, DEFINE ACCESS for JWT/token-based auth, DEFINE USER for system users, and $auth/$session runtime variables for permission predicates.

See: rules/security.md

Deployment and Operations

Single-binary deployment, Docker, Kubernetes (Helm charts), storage engine selection (memory, RocksDB, SurrealKV, TiKV for distributed), backup/restore, monitoring, and production hardening.

See: rules/deployment.md

Performance Tuning

Index strategies (unique, search, vector HNSW), query optimization with EXPLAIN, connection pooling, storage engine trade-offs, batch operations, and resource limits.

See: rules/performance.md

SDK Integration

Official SDKs for JavaScript/TypeScript (Node.js, Deno, Bun, browser), Python, Go, Rust, Java, Kotlin, .NET, C, PHP, Swift (iOS/macOS/visionOS), and Ruby. Includes the source-only C binding, Python v3 unreleased API boundary, .NET v0.10.2 beta surface, connection protocols, authentication flows, live query subscriptions, and typed record handling.

See: rules/sdks.md

Surrealism WASM Extensions

New in SurrealDB 3: extend the database with custom functions, analyzers, and logic written in Rust and compiled to WASM. Define, deploy, and manage Surrealism modules.

See: rules/surrealism.md

SurrealML In-Database Inference (preview / unstable)

surrealml has a GitHub v0.1.2 release, but PyPI still exposes surrealml 0.0.4 as the latest package as of this snapshot. The .surml artifact format and [sklearn], [torch], [tensorflow] extras are the stable documented boundary. The v1.4.0 documentation for DEFINE MODEL, INFO FOR MODEL, REMOVE MODEL, ml::name<version>(...), surreal ml import, db.upload_ml(...), and the SurMlFile.from_<framework>(...) factory methods was retracted in v1.4.1. The current Python setup path downloads native libraries from GitHub Releases unless LOCAL_BUILD=TRUE, so pin and audit before production use.

See: rules/surrealml.md

SurrealMCP -- Model Context Protocol Server

The official MCP server (surrealdb/surrealmcp) lets MCP-compatible AI hosts (Claude Desktop, Cursor, GitHub Copilot in VS Code, Zed, n8n) read and write SurrealDB through one configuration entry. Install from source (cargo install --path .) or Docker; not on crates.io or npm. Binary requires start subcommand. Tool wire-names are snake_case: query, select, insert, create, upsert, update, delete, relate, connect_endpoint, use_namespace, use_database, list_namespaces, list_databases, disconnect_endpoint, plus cloud tools.

See: rules/surrealmcp.md, skills/surrealmcp/SKILL.md

Editor Tooling

surrealql-language-server v0.1.3 is the first-party LSP baseline; surql-lsp v0.1.1 remains a separate community crate. The rule tracks tree-sitter, first-party editor extensions, and @surrealdb/codemirror / @surrealdb/lezer v1.0.5 for custom web editors. Per-extension command palettes and settings still need each extension README as source of truth.

See: rules/editor-tooling.md

LangChain Integration (Python only)

langchain-surrealdb 0.2.1 (Python; langchain-core ~= 1.1.0, surrealdb ~= 1.0.8 v1 SDK) exposes SurrealDB as a vector store via constructor SurrealDBVectorStore(embeddings, conn). JS package, async class, chat history, and hybrid retriever from v1.4.0 were retracted in v1.4.1.

See: rules/langchain.md

Ecosystem Integrations

Official-docs and first-party-adjacent surfaces that are not full dedicated rules yet: @surrealdb/n8n-nodes-surrealdb v0.6.0 for self-hosted n8n, the official AI framework docs index, Spectron / Agent Memory Context as roadmap-only, CodeMirror packages, and the official surrealdb/agent-skills repo. Use this rule to avoid presenting roadmap or pointer pages as production APIs.

See: rules/ecosystem-integrations.md

Ecosystem Tools

• Surrealist -- Official IDE and GUI for SurrealDB (schema designer, query editor, graph visualizer)
• Surreal-Sync -- Change Data Capture (CDC) for migrations from other databases
• SurrealFS -- AI agent filesystem built on SurrealDB
• SurrealKit -- Desired-state schema sync, rollout-based migrations, seeding, and declarative tests
• SurrealML -- Preview-stage .surml artifact tooling; server invocation surface still unstable
• SurrealMCP -- Model Context Protocol server for AI agents
• n8n -- Official scoped community package for self-hosted n8n workflows
• CodeMirror -- Official SurrealQL syntax packages for custom editors
• setup-surreal -- Official GitHub Action (surrealdb/setup-surreal@v2) for running SurrealDB in CI workflows. Not a CLI bootstrap (the v1.4.0 documentation that described it as one was retracted in v1.4.2)

See: rules/surrealist.md, rules/surreal-sync.md, rules/surrealfs.md, rules/surrealkit.md, rules/surrealml.md, rules/surrealmcp.md, rules/ecosystem-integrations.md, rules/deployment.md (setup-surreal section)

Doctor / Health Check

# Full diagnostic (Rich output on stderr, JSON on stdout)
uv run {baseDir}/scripts/doctor.py

# Quick check (exit code 0 = healthy, 1 = issues found)
uv run {baseDir}/scripts/doctor.py --check

# Check a specific endpoint
uv run {baseDir}/scripts/doctor.py --endpoint http://my-server:8000

The doctor script verifies: surreal CLI installed and on PATH, server reachable, authentication succeeds, namespace and database exist, version compatibility, and storage engine status.

Schema Introspection

# Full schema dump (all tables, fields, indexes, events, accesses)
uv run {baseDir}/scripts/schema.py introspect

# List tables with summary
uv run {baseDir}/scripts/schema.py tables

# Inspect a specific table
uv run {baseDir}/scripts/schema.py table <table_name>

# Export schema as SurrealQL (reproducible DEFINE statements)
uv run {baseDir}/scripts/schema.py export --format surql

# Export schema as JSON
uv run {baseDir}/scripts/schema.py export --format json

Introspection uses INFO FOR DB, INFO FOR TABLE, and INFO FOR NS to reconstruct the full schema.

Rules Reference

Workflow Examples

All workflow examples use root/root for local development only. For production, use DEFINE USER with scoped, least-privilege credentials.

New Project Setup

# 1. Verify environment
uv run {baseDir}/scripts/doctor.py

# 2. Start SurrealDB
surreal start rocksdb://data/myproject.db --user root --pass root

# 3. Design schema (use rules/data-modeling.md for guidance)
# 4. Import initial schema
surreal import --endpoint http://localhost:8000 --user root --pass root \
  --ns myapp --db production schema.surql

# 5. Introspect to verify
uv run {baseDir}/scripts/schema.py introspect

Migration from SurrealDB v2

# 1. Export v2 data
surreal export --endpoint http://old-server:8000 --user root --pass root \
  --ns myapp --db production v2-backup.surql

# 2. Review breaking changes (see rules/surrealql.md v2->v3 migration section)
# Key changes: range syntax 1..4 is now exclusive of end, new WASM extension system

# 3. Import into v3
surreal import --endpoint http://localhost:8000 --user root --pass root \
  --ns myapp --db production v2-backup.surql

# 4. Verify schema
uv run {baseDir}/scripts/schema.py introspect

Data Modeling for a New Domain

# 1. Read rules/data-modeling.md for schema design patterns
# 2. Read rules/graph-queries.md if your domain has relationships
# 3. Read rules/vector-search.md if you need semantic search
# 4. Draft schema.surql with DEFINE TABLE, DEFINE FIELD, DEFINE INDEX
# 5. Import and test
surreal import --endpoint http://localhost:8000 --user root --pass root \
  --ns dev --db test schema.surql
uv run {baseDir}/scripts/schema.py introspect

Deploying to Production

# 1. Read rules/deployment.md for storage engine selection and hardening
# 2. Read rules/security.md for access control setup
# 3. Read rules/performance.md for index strategy
# 4. Run doctor against production endpoint
uv run {baseDir}/scripts/doctor.py --endpoint https://prod-surreal:8000
# 5. Verify schema matches expectations
uv run {baseDir}/scripts/schema.py introspect --endpoint https://prod-surreal:8000

Upstream Source Check

# Check if upstream SurrealDB repos have changed since this skill was built
uv run {baseDir}/scripts/check_upstream.py

# JSON-only output for agents
uv run {baseDir}/scripts/check_upstream.py --json

# Only show repos that have new commits
uv run {baseDir}/scripts/check_upstream.py --stale

Compares current HEAD SHAs and release tags of all tracked repos against the baselines in SOURCES.json. Use this to plan incremental skill updates.

Source Provenance

This skill was refreshed on 2026-06-17 from these upstream sources:

Documentation: surrealdb.com/docs snapshot 2026-06-17.

Machine-readable provenance: SOURCES.json.

Output Convention

All Python scripts in this skill follow a dual-output pattern:

• stderr: Rich-formatted human-readable output (tables, panels, status indicators)
• stdout: Machine-readable JSON for programmatic consumption by AI agents

This means 2>/dev/null hides the human output, and piping stdout gives clean JSON for downstream processing.