Introduction
Last updated April 2025. Our stack is continuously expanding - follow us on Twitter or join our community on Discord to keep up with the latest.
What is LYS Labs?
LYS Labs is a blockchain data infrastructure focused on both real-time structured data streams served under 14ms and contextualized, AI-ready data served in under 30ms. This allows clients - from high-frequency traders to AI model trainers - to react before the rest of the market even knows what happened.
Context Matters
In Web3, data is power. But raw data from the blockchain is fragmented, noisy, and hard to use in real time. Raw blockchain data is just noise without context.
LYS transforms every transaction, event, and state change into contextualized, correlated, and enriched metrics that tell the full story - who is acting, why they might be acting, and how it connects to market dynamics.
We provide:
Real-time structured insights – every decoded trade, token launch, or wallet movement is available instantly.
Custom ontologies & schemas – define the exact data models you need for your strategies.
AI-ready pipelines – ontology-grounded graphs powering retrieval-augmented generation (RAG) for intelligent agents and trading models.
With <30ms end-to-end latency from chain to your API or WebSocket stream, we don’t just deliver fast data - we deliver fast answers.
Your journey through these docs
This documentation is structured to mirror your learning curve:
Who we serve
LYS isn’t just another analytics dashboard - it’s an intelligence platform. Here's how different power users benefit:
For High Frequency Traders:
Access structured wallet flows, PnL trails, and anomaly graphs in <14ms.
Execute faster with contextual alpha; skip 90% of data munging.
Use cases: Real PnL mapping, token flow velocity, backtestable signal construction.
For AI Agents and other automations:
Plug agents directly into contextualized data graphs with no pre-processing.
Deploy and monetize autonomous agents in hours.
Use cases: Auto-traders, copy- trading bots, NLP research agents.
For Trading Bots:
NLP + streaming insights → bots that react to smart wallet flows and token events.
Build smarter bots without touching RPC.
Use cases: Pump.fun snipers, whale-watch bots, token rotation alerts.
For MEV Searchers and Quants:
Direct access to block sequencing and mempool
Transaction ordering for sandwich/backrun detection
Arbitrage routing across DEXs at millisecond precision
For DeFi protocols and LPs:
Direct access to block sequencing and mempool
Transaction ordering for sandwich/backrun detection
Arbitrage routing across DEXs at millisecond precision
For Security and Compliance:
AML wallet clustering and transaction tracing
Multi-hop anomaly detection for exploits and market manipulation
Fully auditable query layer for compliance reporting
For Researchers and Aggregators:
Real-time Solana data at subgraph-level latency
Cross-chain analytics and historical snapshots
Build your own dashboards or run custom ML pipelines
Key capabilities
1. Real-time pipelines
Solana decoded in 14ms
Live trade and liquidity streaming
Support for EVM chains (block-by-block) (coming soon)
2. Custom ontologies
Define your own schema over blockchain activity
Baseline templates provided for fast start
3. Knowledge graphs
Multi-hop wallet, token, protocol relationships
Graph-native queries for advanced insights
4. AI-Optimized retrieval
Ontology-Grounded RAG (OG-RAG) integration
Built for LLMs and autonomous agents
How it works: The LYS data journey
1. Raw data ingestion
Direct from full nodes, mempool, or Geyser (for Solana)
Avoids third-party APIs = lower latency, more control
2. Parsing & decoding
Protocol-specific decoders identify real events (e.g. swaps, votes, liquidity adds)
Outputs are normalized to shared schemas
3. Indexing & aggregation
Events are indexed in real time and written to memory + database
Aggregators summarize trends (OHLCV, buy/sell counts, rug flags)
4. Contextualization
Link events across wallets and time
Discover hidden correlations (e.g., airdrop farming, vote manipulation)
5. Delivery
APIs and WSS streams deliver data to bots, dashboards, or AI agents
Sandbox enables querying in natural language, Cypher, or via API
Example:
Token Launch Detection
Here’s what happens when a token launches on Pump.fun:
Decoder detects token creation, wallet funding, and initial liquidity
Aggregator tracks early buys/sells, bundle flags, price rise
Sandbox shows key metrics like bonding % change or suspicious wallets
AI agents receive this context and decide whether to trade, alert, or ignore
Getting started with LYS
You can:
Use prebuilt aggregations (volume spikes, rug detection, bundle counts)
Subscribe to live streams of decoded Solana events
Train models on historical, structured token activity
Up next
In the next section, we’ll explore the LYS Platform in detail - how it all connects, what we’ve built, and how every layer serves data.
Ready? Let’s go.
Last updated