A lightweight NoSQL database with vector search, TOON format, and enterprise security built-in
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TOON (Token-Oriented Object Notation) reduces token usage for GPT-4, Claude, and other LLM APIs
[
{
"_id": "abc123",
"name": "Alice Johnson",
"email": "alice@example.com",
"age": 28,
"city": "San Francisco",
"role": "engineer"
},
{
"_id": "def456",
"name": "Bob Smith",
"email": "bob@example.com",
"age": 34,
"city": "New York",
"role": "manager"
}
]collection: users
documents[2]{_id,name,email,age,city,role}:
abc123,Alice Johnson,alice@example.com,28,San Francisco,engineer
def456,Bob Smith,bob@example.com,34,New York,manager
count: 2Reduce LLM API costs by 40-50%. For 1M API calls, save $400-500 on GPT-4 or Claude.
Less data means faster LLM responses. Get your results quicker with smaller payloads.
Fit more data in token limits. Perfect for RAG systems and long-context applications.
MySQL-like interactive CLI built in Rust. Zero dependencies, lightning fast, works everywhere.
$ nexa -u root -p
Password: ********
Connected to NexaDB v3.0.4
Binary Protocol: localhost:6970
Multi-Database Architecture ✓
nexa(default)> databases
✓ Found 3 database(s):
[1] default
[2] analytics
[3] production
nexa(default)> use_db analytics
✓ Switched to database 'analytics'
nexa(analytics)> collections
✓ Found 3 collection(s):
[1] events (1,000,000 docs)
[2] users (50,000 docs)
[3] metrics (250,000 docs)
nexa(analytics)> use events
✓ Switched to collection 'events'
nexa(analytics:events)> query {"type": "purchase"}
✓ Found 125,000 documents
nexa(analytics:events)> create_db staging
✓ Database 'staging' created
nexa(analytics:events)> help
Database: databases, use_db, create_db, drop_db
Collection: collections, use, create, query, update,
delete, count, vector_search, help, exitSearch naturally. No exact keywords needed. This is what vector search does.
Calculate your LLM cost savings with TOON format
TOON (Token-Oriented Object Notation) removes redundant JSON formatting, reduces field name repetition, and uses compact syntax. Your data becomes 40-50% smaller, which means 40-50% fewer tokens sent to LLM APIs. You can use TOON with jsontooncraft (any database) or get built-in export in NexaDB.
Pricing data from vellum.ai/best-llm-for-coding (Jan 2025)
LLM optimization, vector search, admin panel - all included. No extra tools needed.
HNSW algorithm for semantic search. 200x faster than linear scan. No need for separate Pinecone/Weaviate. Perfect for RAG and AI apps.
Custom binary protocol on port 6970 is 10x faster than JSON REST APIs. Most databases only have slow HTTP/JSON. We have both.
brew install nexadb → nexadb start → Done! No configuration files, no setup wizards, no Docker required. Pure Python, works everywhere.
Advanced indexing (B-Tree, Hash, Full-text) delivers 100-200x speedup. <1ms lookups, 20K reads/sec. Fast enough for real apps.
Built-in TOON export for 40-50% LLM cost savings. Just convenience - you can use jsontooncraft or any TOON library with your JSON data.
Gorgeous UI out of the box. Query editor, TOON export, real-time monitoring. Dark/light themes. No extra tools needed.
Built-in encryption, RBAC, API keys, and audit logging. Secure enough for production without complex setup. MongoDB-inspired security model.
Not trying to beat PostgreSQL. Just fast enough for MVPs and production apps with thousands of users.
Enterprise-grade architecture designed for performance, reliability, and scale
Beautiful, modern admin interface with TOON export included. Access at http://localhost:9999



From zero to semantic search in 5 minutes. No ML expertise required.
One command. No Docker. No config files. Done.
brew install nexadbBuilt for speed, simplicity, and AI apps. Not trying to replace MongoDB - just better for rapid development.
Ship in hours, not days. Zero config, admin panel included, fast enough for production. Perfect for hackathons and proving concepts quickly.
Vector search + TOON format = perfect for ChatGPT wrappers, semantic search, and AI chatbots. Save 40-50% on LLM costs instantly.
Build fast, iterate faster. When you need to ship features daily and MongoDB feels like overkill. Production-ready but not over-engineered.
Simple API with TOON format support - Python and JavaScript clients available, Java coming soon
const { NexaClient } = require('nexaclient');
const client = new NexaClient({
host: 'localhost',
port: 6970,
username: 'root',
password: 'nexadb123'
});
await client.connect();
// Export in TOON format (40-50% fewer tokens)
const { toonData, stats } =
await client.exportToon('users');
console.log('Token Reduction:',
stats.reduction_percent + '%');from nexaclient import NexaClient
client = NexaClient(
host='localhost',
port=6970,
username='root',
password='nexadb123'
)
client.connect()
# Export in TOON format
toon_data, stats = client.export_toon('users')
print(f"Token Reduction: {stats['reduction_percent']}%")// Spring Boot auto-configuration
@Service
public class UserService {
private final NexaClient client;
public UserService(NexaClient client) {
this.client = client;
}
public String createUser(String name) {
Map<String, Object> user =
Map.of("name", name);
Map<String, Object> result =
client.create("users", user);
return (String) result.get("document_id");
}
}Join developers building AI applications with NexaDB and TOON format