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JavaScript/TypeScript SDK for RedDB - unified multi-model database

Package Exports

  • reddb-cli

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RedDB

The AI-first multi-model database.

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

Release License npm


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.

Provider Keyword API Key Required
OpenAI openai Yes
Anthropic anthropic Yes
Groq groq Yes
OpenRouter openrouter Yes
Together together Yes
Venice venice Yes
DeepSeek deepseek Yes
HuggingFace huggingface Yes
Ollama ollama No (local)
Local local No
Custom URL https://... Configurable
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.

Language Example
SQL SELECT * FROM hosts WHERE os = 'linux'
Cypher MATCH (a:User)-[:FOLLOWS]->(b) RETURN b.name
Gremlin g.V().hasLabel('person').out('FOLLOWS').values('name')
SPARQL SELECT ?name WHERE { ?p :name ?name }
Natural Language show me all critical hosts
ASK (RAG) ASK 'what changed in the last 24 hours?'

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.


3 Deployment Modes

Mode Think of it as... Access via
Embedded SQLite Rust API -- RedDB::open("data.rdb")
Server Postgres HTTP + gRPC -- dual-stack
Agent MCP tool red mcp -- AI agent integration

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


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


MIT License -- Built by Filipe Forattini