ElizaOS vs Swarm AI: Who Will Define the Next Generation of Web3 Agent Standards?
Recently, the crypto industry has been quite turbulent, with the historic Bybit hacking incident followed by the theft of $50 million from the web3 "Yu'ebao" inita. Although the impact on both projects is not catastrophic, the expected selling pressure from the stolen funds and recent struggles have led to extremely poor market conditions, with the overall market in a state of stagnation. Conversely, this is a great time to seriously build, explore new projects, and accumulate positions at lower prices. The biggest narrative for 2024-2025 may very well be the AI Agent sector, where future new leaders are likely to emerge. Today, I will provide a simple comparative analysis of two leading AI projects from a technical route perspective, hoping to spark further discussion.
I. Technical Duel: The Underlying Logic of Two Disruptive Architectures
1. ElizaOS: Modular and Open Source Ecological "Lego-style" Expansion
ElizaOS is centered around a modular open-source architecture, allowing developers to freely combine functional plugins (such as TEE privacy computing and multi-chain interaction modules) to build lightweight or enterprise-level AI agents. Its design philosophy originates from the exploration of basic human-computer interaction by MIT's ELIZA program in 1966, but it has achieved three key breakthroughs through blockchain:
Pluggable Model Integration: Supports plug-and-play integration of large models like GPT-4 and Claude with Web3 protocols (such as DeFi contracts and NFT standards);
Decentralized Governance: Incentivizes developers to contribute code through the $ELIZA token, with agents in the ecosystem required to allocate 5% of their income to framework maintainers;
Eternality Assurance: Smart contracts ensure that agent logic runs permanently, allowing for iteration even if the founding team disappears.
Typical case: The AI trading fund AI16ZDAO in the Solana ecosystem is developed based on ElizaOS, with its agents achieving automated arbitrage with annualized returns exceeding 300% by integrating oracle on-chain data and TEE privacy strategies.
2. Swarm AI: The "Swarm Revolution" of Collective Intelligence and Collaborative Networks
Swarm AI was founded by 20-year-old prodigy Kye Gomez, and its multi-agent collaboration framework redefines the paradigm of complex task processing:
Swarm Nodes: A serverless infrastructure that allows 45 million agents to coordinate simultaneously, solving hardware dependency and cost issues;
Hybrid Flow Model: Combines SSM (State Space Model) and MoE (Mixture of Experts) to achieve superhuman accuracy in scenarios like medical diagnosis;
Cross-chain Memory Repository: A distributed database shared by agents, supporting long-term context tracking and cross-task knowledge reuse.
Market performance: Although the $SWARMS token plummeted 35% in the short term due to speculative bubbles, its enterprise clients (such as automated insurance claims for JPMorgan) have validated the technical feasibility.
II. Market Game: Market Value, Community, and Capital Undercover Wars
1. Market Value Divergence: Modular Light Assets vs Heavy Operation Enterprise Services
Revenue models:
— ElizaOS: Development profit sharing + protocol commission
— Swarm: B-end subscriptions + SNAI node leasing.
Key Differences:
ElizaOS relies on viral spread from the open-source community, attracting developers through an airdrop mechanism (e.g., holding $ELIZA gives priority access to new agent tokens);
Swarm AI focuses on enterprise payment scenarios, but its token economy has failed to effectively bind customer growth, leading to a disconnection between market value and business.
However, these two different routes have sparked a conflict:
a16z attempts to implant ElizaOS into over 50 projects it has invested in, raising concerns about centralization;
The Swarm community accuses Kye of overly relying on personal IP, with a lack of transparency in the technical route.
III. Technical Strengths and Fatal Flaws
1. ElizaOS: The Pitfalls of Lightweight and Compatibility
Advantages:
Deploy a DeFi arbitrage agent in 5 minutes (integrating Uniswap and dYdX interfaces);
Supports multi-chain environments like Ethereum, Solana, and Base, with migration costs approaching zero.
Weaknesses:
Low communication efficiency between modules, with complex task delays reaching up to minutes;
Over-reliance on external large models, with weak localization optimization capabilities.
2. Swarm AI: The Double-Edged Sword of Collaborative Networks
Advantages:
Hundreds of agents collaborate to handle insurance claims, with an error rate 90% lower than humans;
Self-developed SSM + MoE model training costs only 1/20 of GPT-4.
Weaknesses:
Task allocation imbalance during node network congestion, which previously caused collective failures of medical diagnosis agents;
Enterprise clients refuse on-chain integration due to data privacy concerns, conflicting with the Web3 vision.
IV. Ultimate Showdown: Five Key Battlefields in 2025
1. Developer Mindshare Battle
ElizaOS attracts Web2 transformers with "low-code + high returns," surpassing 7,000 stars on GitHub;
Swarm AI cultivates deep users through hackathons, but its Python SDK has a steep learning curve.
2. Privacy Computing Standard Setting
ElizaOS integrates TEE plugins but has been exposed for vulnerabilities that can extract smart contract keys;
Swarm AI develops ZKP verification nodes, sacrificing speed for security, leading to community division.
3. On-chain Agent Regulatory Game
The SEC has sued ElizaOS ecosystem project AI16ZDAO for "market manipulation," demanding disclosure of agent trading logic;
Swarm AI faces a class-action lawsuit due to misdiagnosis by medical agents, with its decentralized architecture serving as an excuse for liability.
4. Underlying Public Chain Alliance Competition
ElizaOS collaborates with Solana to build a dedicated sidechain for AI agents, boosting TPS to the 100,000 level;
Swarm AI chooses Avalanche subnets, customizing virtual machines to optimize swarm scheduling.
5. Life-and-Death Red Line: Energy Efficiency
ElizaOS consumes an average of 0.3 kWh per agent per day, facing resistance from environmental organizations;
Swarm AI reduces energy consumption by 90% through task compression technology, but sacrifices the ability to handle complex tasks.
V. Industry Predictions: Speculations on the 2026 Endgame
Scenario 1: ElizaOS Dominates the Landscape
The modular framework becomes the "default option" for Web3 agents, with a market value exceeding $50 billion;
Cost: Innovation stagnates, and developers become ecological tenants.
Scenario 2: Swarm AI Makes a Comeback
Swarm collaboration spawns super AI, with autonomous agents taking over 60% of on-chain transactions;
Risk: An uncontrolled agent network triggers a financial crisis.
Scenario 3: Dual Sovereignty and Long-tail Revolution
ElizaOS monopolizes the simple task market, while Swarm AI focuses on high-end enterprise services;
New forces (such as federated learning + DAO governance) disrupt the existing landscape.
Conclusion: Technological Utopia or Monopoly Tool?
The confrontation between ElizaOS and Swarm AI is essentially a battle between the spirit of Web3 and the centralization of AI. While ElizaOS lowers the development threshold through modularization, the capital behind it quietly weaves a control network; as Swarm AI raises the banner of collective intelligence, the black-boxing of technology may give rise to new hegemony. Perhaps the true answer lies not in the code, but in whether humanity can uphold the bottom line of "agent sovereignty."
“All technological revolutions start as a carnival of idealists and end as a chessboard of capital and power.” — Anonymous AI agent developer @0xKongKong_
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