Web3 is overflowing with data, but understanding it requires more than just access—it demands intelligence. Swarms of AI agents work together to track, analyze, and interpret everything from onchain trends to social sentiment. In this deep dive we look at the different types of swarms that can be found within Hivemind.
Hivemind is powered by the endless efforts from dozens, hundreds or even thousands of specialized AI agents. These agents monitor, analyze and synthesize data from across the Web3 landscape. We’re not building generic AI agents, but true specialists with deep expertise and understanding of specific topics, projects, product categories and blockchain ecosystems.
Users interested in a certain topic, will have their question delegated to the corresponding agent. This agent can then delegate the question to other AI agents, in order to provide the alpha the user is looking for. One agent may delve information from X, while another monitors Discord. Of course there’s also an agent looking into onchain data, powered by DappRadar. All these agents work together in a swarm.
AI Agent Swarm variants
Built using ElizaOS, the agentic operating system, AI Agent Swarms consist of multiple AI agents, each of them an expert in their very own niche topic. Each swarm and agent keeps its own context, but can signal others. This enables delegation, consensus and balancing of workloads between agents.
This also means that AI Agent Swarms come in different variants, each specializing in a certain project or ecosystem. There are all kinds of swarms possible, but for now we will focus on three examples:
- Project Swarms
- Category Swarms
- Chain Swarms
Within an AI Agent Swarm, different AI agents operate. How many and with what type of functionality depends on the type of project or ecosystem. You can imagine that a gaming dapp requires different agents than a DeFi protocol or a Layer-2 network.
Let’s take a closer look at the example swarm variants.
Project Swarms
A Project Swarm can track decentralized applications, but also NFT collections or perhaps a crypto wallet. Within the swarm every AI agent has its own task. We listed a few examples of the possible tasks for these agents:
- Onchain activity – Some check NFT trading activity, dapp usage metrics and token prices, powered by DappRadar’s comprehensive data sets.
- Technical documentation – Another type of agent crawls whitepapers, governance proposals and development updates.
- Social intelligence – This agent looks at community sentiment, discussions on X, Telegram or Discord, and of course official announcements from the team.
- Market context – Analyzing trading patterns, user adoption trends and the wider competitive landscape.
Chain Swarms
Chain Swarms can be seen as a variant of Project Swarms, but these instead track blockchain activity as a whole. The agents track onchain activity, but also follow the official website, social media, developer activity, and so on. In addition, a Chain Swarm can identify activity between different chains, or reveal what types of products thrive in their ecosystem.
- Onchain activity – Track dapp volume, number of transactions, trading volumes, and more onchain data, powered by DappRadar’s data sets.
- Technical documentation – Gathering information from the whitepaper, official website, developer activity etc.
- Social intelligence – This agent tracks community sentiment, social media discussions across various platforms.
- Chain updates – Spot opportunities and find interesting developments on any or specific blockchain ecosystems.
Category Swarms
An AI Agent Swarm for a product category, specializes in Web3 verticals. It’s capable of synthesizing insights from across multiple projects, tapping into information from Project Swarms and Chain Swarms and applying that to market data within the product category. Examples of possible Category Swarms:
- Gaming Swarm – Understands Web3 gaming, analyzes its performance, recognizes trends and spots opportunities. Connects various Project Swarms from gaming projects with each other.
- DeFi Swarm – Analyzes decentralized finance protocols, tracks their yield opportunities, and identifies market movements. Combines information from various Project Swarms and Chain Swarms.
- Token Swarm – Capable of tracking token collections, such as RWA, Memecoins and NFTs, in real-time, analyzing market activity and Web3 cultural trends. Also here, information can be gathered from various Project and Chain Swarms in the Hivemind.
- Infrastructure Swarm – Capable of comparing L1 vs L2 performance, tracking developer activity, and analyzing growth in the ecosystem. Various Chain Swarms can be combined to reveal a trend, or signal a change in market movement.
How would this work
First of all, the information shared in the example below just highlights the potential functioning of Hivemind. None of this should be considered final or conclusive information about the functionality, features and possibilities Hivemind offers.
The end user always interacts with a Master Agent. This agent then delegates the question or request for information to various underlying AI agents. Users may ask Hivemind about the recent success of a dapp.
DAPP seems to be popular. Why is that happening?
The Master Agent recognizes that the question is about “DAPP”, and it also quickly learns that “DAPP” is in a certain product category. Let’s say “Gaming”.
That means that the Master Agent can delegate the question to the Project Swarm connected to “DAPP”, and the Gaming Swarm.
These two swarms then start analyzing onchain data, searching through the internet and analyzing social sentiment to come up with an answer. The Master Agent then combines their information, and formulates an informative and conclusive answer for the end user.
“On July 1st 2025, DAPP launched a major update, introducing social features and multiplayer gameplay. This now allows players to form guilds and invite their friends. Since the update, the number of Unique Active Wallets went up by 750%, attracting 888,000 wallets in the past 7 days. DAPP is among the top 10% most talked about projects in Web3 gaming right now. Its popularity also shines on the chain, as DAPP now sits at 3rd place in the Games Rankings on DappRadar.”
The end user received their requested insight within 20 to 30 seconds. Waiting time is shorter or longer depending on complexity and readily available information in the Hivemind memory. By asking questions to Hivemind, the user saved themselves at least 30 minutes of scrolling on X, reading articles and analyzing data.
Moreover, the answer from Hivemind is made up from factional information, community sentiment and verifiable onchain data. As a cherry on the pie, Hivemind provides the sources for the information it provides. Not only did the user save themselves a lot of time, but they also ensured that they received the most complete, accurate and verifiable information possible.
Closing words
AI Agent Swarms allow a group of highly specialized AI agents to work together to provide the end user with a correct and insightful set of information. Within Hivemind these AI Agent Swarms can also share information and data with each other, allowing Hivemind to provide users with the most complete answer possible. Users will engage with Hivemind as if it’s a single entity, but in the background an army of agents analyzes data, crawls information, and measures community sentiment to give the end user the clearest answer possible. That’s how Hivemind will help you spot opportunities.
