RedDB

The AI-first multi-model database.

Tables. Documents. Graphs. Vectors. KV. One engine. Ask it anything.

The Killer Feature: ASK

ASK 'who owns CPF 000.000.000-00 and what services do they use?'

One command. RedDB searches across tables, graphs, vectors, documents, and key-value stores -- builds context -- calls an LLM -- returns a natural-language answer. No pipelines. No glue code. No other database does this.

7 Data Models, 1 Engine

Stop running Postgres + Neo4j + Pinecone + Redis + Mongo + InfluxDB + RabbitMQ. RedDB unifies them.

-- Relational rows
INSERT INTO users (name, email) VALUES ('Alice', 'alice@co.com')

-- JSON documents
INSERT INTO logs DOCUMENT (body) VALUES ('{"level":"info","msg":"login"}')

-- Graph edges
INSERT INTO network EDGE (label, from, to) VALUES ('CONNECTS', 1, 2)

-- Vector similarity search
SEARCH SIMILAR TEXT 'anomaly detected' COLLECTION events

-- Key-value
PUT config.theme = 'dark'

-- Time-series metrics (with retention & downsampling)
CREATE TIMESERIES cpu_metrics RETENTION 90 d
INSERT INTO cpu_metrics (metric, value, tags) VALUES ('cpu.idle', 95.2, '{"host":"srv1"}')

-- Message queues (FIFO, priority, consumer groups)
CREATE QUEUE tasks MAX_SIZE 10000
QUEUE PUSH tasks '{"job":"process","id":123}'
QUEUE POP tasks

Same file. Same engine. Same query language.

AI-Native From Day One

-- Semantic search without managing vectors yourself
SEARCH SIMILAR TEXT 'suspicious login' COLLECTION logs USING groq

-- Auto-embed on insert -- vectors are created for you
INSERT INTO articles (title, body) VALUES ('AI Safety', 'Alignment research...')
  WITH AUTO EMBED (body) USING openai

-- Context search: find everything related to an entity across all models
SEARCH CONTEXT '192.168.1.1' FIELD ip DEPTH 2

-- Ask questions in plain English
ASK 'what vulnerabilities affect host 10.0.0.1?' USING anthropic

RedDB retrieves context from every data model, feeds it to the LLM, and gives you a grounded answer. RAG built into the database layer.

11 AI Providers

Swap providers with a keyword. No code changes.

ASK 'summarize alerts' USING groq MODEL 'llama-3.3-70b-versatile'
ASK 'summarize alerts' USING ollama MODEL 'llama3'
ASK 'summarize alerts' USING anthropic

Set a default provider so you can drop USING from every query:

# Set default provider -- no more USING on every query
curl -X POST http://127.0.0.1:8080/ai/credentials \
  -d '{"provider":"groq","api_key":"gsk_xxx","default":true}'

-- Now ASK uses groq by default
ASK 'what happened?'

# Export/import all config as JSON
curl http://127.0.0.1:8080/config

Probabilistic Data Structures

Built-in approximate data structures for real-time analytics at scale.

-- HyperLogLog: count unique visitors (~0.8% error, ~16KB memory)
CREATE HLL visitors
HLL ADD visitors 'user1' 'user2' 'user3'
HLL COUNT visitors

-- Count-Min Sketch: frequency estimation
CREATE SKETCH click_counter WIDTH 2000 DEPTH 7
SKETCH ADD click_counter 'button_a' 5
SKETCH COUNT click_counter 'button_a'

-- Cuckoo Filter: membership testing with deletion (unlike Bloom filters)
CREATE FILTER active_sessions CAPACITY 500000
FILTER ADD active_sessions 'session_abc'
FILTER CHECK active_sessions 'session_abc'
FILTER DELETE active_sessions 'session_abc'

Advanced Indexes

Beyond B-tree. Create the right index for your workload.

-- Hash index: O(1) exact-match lookups
CREATE INDEX idx_email ON users (email) USING HASH

-- Bitmap index: fast analytical queries on low-cardinality columns
CREATE INDEX idx_status ON orders (status) USING BITMAP

-- R-Tree: spatial queries on geo data
CREATE INDEX idx_loc ON sites (location) USING RTREE
SEARCH SPATIAL RADIUS 48.8566 2.3522 10.0 COLLECTION sites COLUMN location LIMIT 50
SEARCH SPATIAL NEAREST 48.8566 2.3522 K 5 COLLECTION sites COLUMN location

SQL Extensions

RedDB extends SQL with WITH clauses for operational semantics:

-- TTL: auto-expire records
INSERT INTO sessions (token) VALUES ('abc') WITH TTL 1 h

-- Context indexes for cross-model search
CREATE TABLE customers (cpf TEXT) WITH CONTEXT INDEX ON (cpf)

-- Graph expansion inline with SELECT
SELECT * FROM users WITH EXPAND GRAPH DEPTH 2

-- Metadata on write
INSERT INTO logs (msg) VALUES ('deploy') WITH METADATA (source = 'ci')

-- Absolute expiration
INSERT INTO events (name) VALUES ('launch') WITH EXPIRES AT 1735689600000

6 Query Languages

Write in whatever you think in. The engine auto-detects the language.

All six hit the same engine, same data, same indexes.

48 Built-in Types

Not just TEXT and INTEGER. RedDB understands your domain.

Network: IpAddr, Ipv4, Ipv6, MacAddr, Cidr, Subnet, Port Geo: Latitude, Longitude, GeoPoint Locale: Country2, Country3, Lang2, Lang5, Currency Identity: Uuid, Email, Url, Phone, Semver Visual: Color, ColorAlpha Cross-model refs: NodeRef, EdgeRef, VectorRef, RowRef, KeyRef, DocRef, TableRef, PageRef Primitives: Integer, UnsignedInteger, Float, Decimal, BigInt, Text, Blob, Boolean, Json, Array, Enum Temporal: Timestamp, TimestampMs, Date, Time, Duration

Validation on write. No parsing in your app.

Backup & Recovery

Built-in backup scheduler, WAL archiving, Change Data Capture (CDC), and Point-in-Time Recovery framework:

# Poll real-time changes
curl 'localhost:8080/changes?since_lsn=0'

# Trigger manual backup
curl -X POST localhost:8080/backup/trigger

# Check backup status
curl localhost:8080/backup/status

Remote backends: S3, R2, DigitalOcean Spaces, GCS, Turso, Cloudflare D1, local filesystem.

KV REST API

Every collection doubles as a key-value store with dedicated REST endpoints:

# Write a key
curl -X PUT http://127.0.0.1:8080/collections/settings/kvs/theme \
  -H 'content-type: application/json' -d '{"value": "dark"}'

# Read a key
curl http://127.0.0.1:8080/collections/settings/kvs/theme

# Delete a key
curl -X DELETE http://127.0.0.1:8080/collections/settings/kvs/theme

Config keys work the same way -- read, write, or delete any red_config setting at runtime:

# Set a config key
curl -X PUT http://127.0.0.1:8080/config/red.ai.default.provider \
  -d '{"value": "groq"}'

# Read a config key
curl http://127.0.0.1:8080/config/red.ai.default.provider

# Or manage config from SQL
SET CONFIG red.ai.default.provider = 'groq'
SHOW CONFIG red.ai

3 Deployment Modes

Same storage format across all three. Start embedded, scale to server, expose to agents -- no migration.

Performance

RedDB uses multiple optimization techniques for fast queries at scale:

• Result Cache -- identical SELECT queries return in <1ms; auto-invalidated on INSERT/UPDATE/DELETE (30s TTL, max 1000 entries)
• Hot Entity Cache -- get_any(id) lookups served from an LRU cache (10K entries), O(1) instead of scanning all collections
• Binary Bulk Insert -- gRPC BulkInsertBinary with zero JSON overhead, protobuf native types -- 241K ops/sec
• Concurrent HTTP -- thread-per-connection model; each request handled in its own OS thread
• Parallel Segment Scanning -- sealed segments scanned in parallel via std:🧵:scope; auto-detects single-core and skips parallelism
• Hash Join -- O(n+m) joins instead of O(n*m), auto-selected for large datasets
• Lazy Graph Materialization -- only loads reachable nodes instead of full graph
• Pre-filtered Vector Search -- metadata filters applied before HNSW indexing
• Index-Assisted Scans -- bloom filter + hash index hints for WHERE clauses
• Column Projection Pushdown -- only materializes SELECT columns
• Query Plan Caching -- LRU cache with 1h TTL for repeated queries
• Batch Entity Lookup -- multi-entity fetches resolved in a single pass
• Background Maintenance Thread -- backup scheduling, retention, and checkpoint run off the hot path

Durability & Corruption Defense

RedDB uses 7 layers of protection to keep your data safe:

Every page has a CRC32 checksum (verified on read). Every WAL record has a CRC32 checksum. The binary store format (V3) includes a full-file CRC32 footer.

Eventual Consistency

RedDB supports per-field eventual consistency via an append-only transaction log with periodic consolidation. Inspired by CRDT principles (commutative, associative reducers), it enables high-throughput write patterns while guaranteeing convergence.

# Track clicks with async consolidation (returns instantly)
curl -X POST localhost:8080/ec/urls/clicks/add -d '{"id": 1, "value": 1}'

# Check consolidated + pending value
curl localhost:8080/ec/urls/clicks/status?id=1

See the Eventual Consistency Guide for the theory (CAP theorem, CRDTs, convergence) and full API reference.

Geographic Operations

Built-in geo functions with no external dependencies. Supports both spherical (Haversine) and ellipsoidal (Vincenty/WGS-84) models.

-- Distance from each store to a point (in km)
SELECT name, GEO_DISTANCE(location, POINT(-23.55, -46.63)) AS dist
FROM stores ORDER BY dist

-- Vincenty for sub-millimeter accuracy
SELECT name, GEO_DISTANCE_VINCENTY(location, POINT(40.71, -74.00)) AS dist
FROM airports

# HTTP API
curl -X POST localhost:8080/geo/distance -d '{
  "from": {"lat": -23.55, "lon": -46.63},
  "to": {"lat": -22.91, "lon": -43.17}
}'

Also available: destination point, bounding box, polygon area, spatial search (RADIUS, BBOX, NEAREST). See the Geo Operations Guide.

Vector Clustering

Standalone K-Means and DBSCAN clustering on vector collections, with SIMD-accelerated distance computation and automatic parallelization.

# K-Means: group products into 5 clusters
curl -X POST localhost:8080/vectors/cluster -d '{
  "collection": "products", "algorithm": "kmeans", "k": 5
}'

# DBSCAN: discover clusters automatically (no K needed)
curl -X POST localhost:8080/vectors/cluster -d '{
  "collection": "products", "algorithm": "dbscan", "eps": 0.5, "min_points": 3
}'

K-Means uses parallel assignment (multi-threaded for datasets > 1K vectors). DBSCAN labels unreachable points as noise (-1), useful for outlier detection. See the Vector Clustering Guide.

Quick Start

# Install
curl -fsSL https://raw.githubusercontent.com/forattini-dev/reddb/main/install.sh | bash

# Start the server
red server --http-bind 127.0.0.1:8080 --path ./data.rdb

# Insert data
curl -X POST http://127.0.0.1:8080/query \
  -H 'content-type: application/json' \
  -d '{"query":"INSERT INTO hosts (ip, os) VALUES ('\''10.0.0.1'\'', '\''linux'\'')"}'

# Query it
curl -X POST http://127.0.0.1:8080/query \
  -H 'content-type: application/json' \
  -d '{"query":"SELECT * FROM hosts"}'

Or via npm:

npx reddb-cli@latest server --http --bind 127.0.0.1:8080

Links

• Documentation
• GitHub
• npm package
• Releases

MIT License -- Built by Filipe Forattini