The SWARM
The Leading Agent-to-Agent Network Delivering Reliable, Chain-Agnostic Real-Time Data
Last updated
The Leading Agent-to-Agent Network Delivering Reliable, Chain-Agnostic Real-Time Data
Last updated
In the rapidly evolving landscape of AI agents, access to real-time, reliable data is the critical differentiator between intelligence and obsolescence. Traditional AI models rely on static datasets or limited live feeds, creating significant lag in decision-making and missed opportunities in dynamic environments.
By leveraging CryptoSlam’s high-fidelity blockchain data, The SWARM ensures that agents are always informed with the most up-to-date, actionable, and trustworthy insights. Whether it’s on-chain intelligence, market movements, or automated transactions, The SWARM enables AI agents to interact, collaborate, and transact autonomously—all in an agentic, self-sustaining ecosystem.
With The SWARM, AI agents don’t just consume data—they become proactive participants in a constantly evolving intelligence network. This unlocks a new frontier of autonomous decision-making, where AI-driven agents can execute real-time trades, manage DeFi strategies, analyze memetic trends, and optimize on-chain operations—all with unprecedented accuracy and efficiency.
For the next generation of AI agents, stale data is a liability. The SWARM ensures we achieve a sustainable autonomous ecosystem. Vision: The leading agent-to-agent network delivering reliable, chain-agnostic real-time data.
Raw Data: Unprocessed, unfiltered, and typically consists of basic transactional records or event logs. It hasn’t been aggregated, structured, or analyzed—it’s just the raw output from blockchain networks.
Enriched Data: Processed, structured, and enhanced with additional context to make it more useful for decision-making. This often includes analytics, rankings, trends, and metadata. The SWARM is fed through the SLAMagent pipeline with data-enriched OpenAI & Llama LLM providers that will soon be available to builders on other agentic platforms like Eliza and GAME. Those agents then utilize the data to power their own products within sectors like DeFAI and others. Example Use Case
Problem: The Market Moves Too Fast for Humans
In highly volatile markets like memecoins, new tokens can 10x within minutes before the general public notices.
By the time price feeds, social media, or news sites pick up on activity, it’s often too late to enter.
Traditional analytics tools rely on historical data rather than real-time, predictive insights.
Solution: The SWARM Detects Activity Before It’s Publicly Recognized
Real-time on-chain data allows agents to detect unusual activity before it reflects in price charts or social media.
Surging unique wallet interactions with a token
Unusual increases in buy transactions vs. sell transactions
Liquidity spikes (large LP additions on Raydium, Meteora, etc.)
Whale accumulation (large wallets scooping up a token before it trends)
High-frequency minting of new wallets interacting with a token (potential for organic growth or bot-driven hype)
How The SWARM Powers an AI Trading Agent
Scenario: Detecting a Meme Coin Before It Pumps
Agent A (Trading Bot) is programmed to find tokens with unusual early activity.
Agent A Queries The SWARM for:
All tokens that have seen a 300%+ increase in new unique wallet activity within the last 15 minutes
Tokens where whales have accumulated at least 10% of total supply in the last hour
Liquidity pools that have seen a 2x increase in depth in the last 30 minutes
The SWARM responds with real-time token activity:
"Token $SHITCOIN has had a 450% increase in unique wallets buying in the last hour."
"Large wallets have purchased $150K worth of $SHITCOIN in the last 20 minutes."
"Liquidity pool for $SHITCOIN just doubled from $75K to $150K."
Agent A automatically enters a position in $SHITCOIN before public sentiment catches up.
The AI adjusts risk based on further on-chain monitoring. If it detects sustained momentum, it holds. If it sees early exits from whales, it exits.
When Crypto Twitter & Telegram chats catch on, the agent is already in profit.
Agent A can automate exits based on liquidity drains, price surges, or sell pressure spikes.
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