IOSG: The Journey of a Web3 Team Behind Hermes' Rise to the Top

Author: Jacob Zhao @ IOSG

The phenomenal growth of Hermes is not due to an exclusive technology that cannot be replicated based on the OpenClaw principle, but rather because it has precisely closed a "Challenger Growth System" during the critical window of personal Agent category formation: leveraging the user pool already educated by OpenClaw to establish "Delegation Trust," a more authentic experiential difference than the narrative of "self-evolution." As professional execution Agents become increasingly powerful, users still need a long-term, trustworthy steward.

Opening the public application leaderboard of OpenRouter, Hermes Agent ranks first across the platform with a staggering 30.5 trillion Token usage, while also leading in the categories of Productivity, Coding Agents, Personal Agents, and CLI Agents, far surpassing well-known Agents like OpenClaw and Claude Code.

▲ Figure 1 · Historical data snapshot of Hermes Agent on OpenRouter (captured on August 4, 2026; dynamic page data will change over time)

Although the statistical criteria of OpenRouter cannot cover the full industry Token consumption of directly connected official APIs (such as native subscriptions of Claude or Codex), its leaderboard serves as a strong "barometer" given its status as the largest AI large model routing and aggregation platform globally. While many users' core business workflows—complex code generation, architectural design, high-value data analysis—still flow towards Claude Code and ChatGPT at the high-end professional task level, Hermes maintains an advantage in scenarios such as backend automation, message entry response, long-term online monitoring, and lightweight task scheduling. As a product developed by a Web3 team, Hermes has achieved far beyond expected success in terms of dissemination, community engagement, and usage intensity, prompting us to consider:

• How did Hermes manage to surpass in OpenRouter inference calls?
• What are the real distinctions between it and OpenClaw?
• In its relationship with Claude Code and Codex, how does Hermes maintain "differentiated coexistence" rather than "direct competition"?

From Development Framework to Personal AI System—The Path of OpenClaw

Why Early Agent Frameworks Did Not Produce Consumer Products

Before the emergence of OpenClaw, although the Agent field had a mature infrastructure, it faced fundamental limitations: its unit of adoption was "developer project workflows," not "individual users." Early frameworks shared a common characteristic of being developer-oriented, outputting code or configurations—they built the infrastructure for Agents but did not deliver the Agents themselves. The high engineering threshold kept them stuck in the "developer tool" phase, lacking a productization closed loop that would transform technology into "personal exclusive assets," leaving the "personal Agent product layer" aimed directly at end users almost blank.

▲ Figure 1 · Six-layer structure of Agent technology stack (Model Layer → Protocol Layer → SDK Development Framework Layer → Orchestration Runtime Layer → Execution Infrastructure Layer → Deployment Governance Layer)

▲ Figure 1 · Historical data snapshot of Hermes Agent on OpenRouter (captured on August 4, 2026; dynamic page data will change over time)

What OpenClaw Truly Changed

OpenClaw did not reinvent the Agent Loop or task scheduling technology at the foundational level; its core contribution lies in the systematic encapsulation at the product level. LangChain addresses "how to build an Agent," while OpenClaw addresses "how to own an Agent." It bypassed the intermediate layers of the technology stack, integrating scattered framework capabilities into a complete product that individuals can directly configure and use long-term, achieving a fundamental shift in the unit of adoption from "development projects" to "individuals," specifically reflected in six dimensions of product innovation:

• Identity Personalization: Endowing Agents with continuous names and identities, breaking the tool-like feel of stateless API calls.
• Entry Normalization: Using high-frequency communication software like Telegram/WhatsApp as the interaction interface, replacing complex command lines or IDEs.
• State Persistence: Operating as a background process online for long periods, transitioning from passive "standby" to active "presence."
• Permission Materialization: Deeply incorporating users' file systems, browsers, terminals, and real-world capabilities into the operational boundaries of the Agent.
• Capability Expandability: Through Skills, Memory, and community plugins, sedimenting processes into reusable capabilities, expanding action boundaries.
• Mental Ownership: The most core transformation—users shift from "using an AI tool" to "owning a dedicated digital partner."

Why the Lobster Craze Did Not Form a Second Mentality

The popularity of OpenClaw spawned numerous imitations. These products addressed real user issues: cumbersome installation processes, difficult environment configurations, missing channels like WeChat and Feishu, compatibility with domestic models, rapid deployment of cloud hosts, enterprise permission management, automatic updates, and security isolation, among others. They all have their user bases and reasonable business logic. However, almost none formed an independent brand mentality—the reason being that they answered the question of "how to use OpenClaw more easily," rather than "where should personal Agents evolve after OpenClaw?" The narrative challenger position is extremely scarce in the entire personal Agent market.

Why Hermes Ultimately Surpassed

The Model, Community, and Crypto-native Background of Nous

Nous Research originated from the Discord open-source AI research community in 2022 and officially completed its corporate operations in 2023. The core founding team includes Jeffrey Quesnelle, Karan Malhotra, Teknium, and Shivani Mitra, with business covering:

• Hermes Model Series: Nous's most representative open-source model brand, focusing long-term on model post-training, instruction fine-tuning, and Agent capabilities, establishing a large developer adoption base on Hugging Face.
• DisTrO (Distributed Training Over-the-Internet): Significantly reducing cross-node communication overhead in distributed training, making it a more feasible engineering path for cross-regional, heterogeneous hardware to participate in collaborative training under internet bandwidth conditions.
• Psyche Decentralized Training Network: Further networking DisTrO, coordinating global distributed computing nodes through Solana, enabling GPUs from different networks and hardware environments to jointly participate in large model training.
• Hermes Agent: A personal Agent product launched by Nous for end users, integrating Hermes models, tool calls, Memory, Skills, message channels, and long-term operational capabilities into a resident Agent.

In April 2025, Nous Research completed a $50 million Series A financing led by Paradigm, with a post-investment token valuation of $1 billion. Prior to this round of financing, the company had cumulatively completed about $20 million in early financing, with investors including Distributed Global, North Island Ventures, and Delphi Digital.

Nous has built a technological closed loop of "Hermes (model capabilities), DisTrO (distributed training), Psyche (decentralized computing network), and Hermes Agent (personal terminal product)." The release of Hermes Agent is not a temporary Fork chasing heat but a strategic extension initiated by Nous towards the demand side (real users, tasks, workflows) after long-term sedimentation on the supply side (data, models, training, open weights)—this provides a deeper starting point for establishing differentiation compared to ordinary imitations.

The Operational Pain Points of OpenClaw Become the Growth Engine of Hermes

Hermes and OpenClaw do not show significant differences in foundational encapsulation (model + tools + Memory + scheduling). Its phenomenal explosion does not rely on technological generational differences but rather precisely closes a systematic growth causal chain: seamlessly migrating tools to directly inherit users educated by OpenClaw who are already suffering from operational pain points, forming the core growth engine in the early stages.

Product Power Leap: Establishing "Delegation Trust"

The core product hypothesis of Hermes is to solve "the transfer of operational responsibility," promising "to absorb and repair errors internally within the system":

• Reliability Trust: Ensuring continuous task progression and failure recovery (persistent Kanban, /goal mode, tool self-healing).
• Security Trust: Preventing overreach, accidental deletions, or data leaks (Approvals approval flow, sandbox, strict permission boundaries).
• Verifiable Trust: Proving that tasks are genuinely completed (Completion Contract and Grounded Citations).

Conceptual Distinction: "Self-evolution" (Narrative Advantage) vs "Autonomous Recovery" (Experiential Difference)

In Hermes's product narrative, there is a significant difference in product value between "self-evolution" and "autonomous recovery":

• Self-evolution: Essentially based on Memory and Skills for process adaptation. Given that competitors have similar infrastructure, its differentiation lies more in being the first to integrate into a default system with lifecycle management, occupying a narrative advantage of "growth" rather than a proven, insurmountable technological barrier.

• Autonomous Recovery: This is currently the most significant experiential difference worth validating. Thanks to structured error returns and automatic fallback from the provider, Hermes can internalize failures within the system. This system-level stability, characterized by "infrequent user disruptions," represents a more direct and perceptible product strength difference.

Architectural Benefits: Delegation and Supervision Capabilities for Professional Agents

The core value of Hermes lies not in personally executing all professional tasks, but in serving as an orchestrator that undertakes demand fulfillment, task decomposition, routing monitoring, and final acceptance. By delegating specific tasks to external CLIs like Claude Code/Codex through built-in skills, the community has established a practical paradigm of "Hermes orchestration + external CLI as Workers" (such as the /goal mechanism and the oh-my-hermes collaborative tool), demonstrating its architectural advantage in elevating task complexity limits through the scheduling of professional agents.

From Crypto-native to Crypto-invisible: The Web3 Backend Operating System of Hermes

Attributing the success of Hermes solely to its "Web3 background" is an oversimplification. Web3 provides Nous with an "organizational operating system" that is difficult for other AI startup teams to simultaneously acquire, allowing it to penetrate the mainstream market with a seamless experience typical of standard AI products:

• Patience of Venture Capital: Crypto-native capital supports long-term, high-uncertainty, and multi-path investments, enabling Nous to simultaneously develop models, training, runtime, and cloud without prematurely converging on a single revenue validation.

• Ready User Market: It provides a familiar user base of Crypto AI users who are accustomed to Telegram, servers, APIs, and self-hosting, significantly reducing cold-start educational costs and fostering high-intensity usage, tutorial dissemination, and skills contribution.

• User Sovereignty Values: Adhering to self-hosting, openness, portability, and anti-platform lock-in orientations, this is directly implemented in the underlying architecture through MIT License, multi-provider support, BYOK, and transferable memory/skills.

• Community R&D and Verticalization: Leveraging global remote collaboration and open-source culture, users spontaneously become contributors, skill authors, and product designers for vertical scenarios.

Hermes almost entirely conceals Crypto from the user interface. Using its agents, memory, skills, and automation capabilities does not require connecting wallets, purchasing tokens, or understanding Solana. Meanwhile, Paradigm Capital, Psyche, distributed training, and the Crypto AI community still exist in the product's backend. This creates a product form that can be summarized as "Crypto-native in organization, crypto-invisible in product"—retaining the most valuable aspects of Crypto at the organizational level (capital, global community, user sovereignty, and coordination capabilities) while removing the elements that most hinder mainstream adoption at the product level (wallets, tokens, speculative narratives, and on-chain operational friction).

Why OpenClaw Rejects Crypto and Hermes Hides It

The apparent opposition between OpenClaw and Hermes on the Crypto issue is not an ideological struggle of "rejection" versus "embrace"; from the product outcomes, both reflect a tendency towards open-source, user control, and reducing platform lock-in. The difference lies in Nous further utilizing cryptoeconomic mechanisms for distributed training coordination, while OpenClaw primarily achieves user sovereignty through a local-first architecture:

• OpenClaw (Local-first Sovereignty): Resisting financial speculation and defending "local-first" sovereignty. Due to early encounters with counterfeit scams, it adopts a "zero tolerance" approach to Crypto. By implementing pure open-source and local operation, it defends user sovereignty in a non-blockchain manner, firmly rejecting financialization at the product level.

• Hermes/Nous (Cryptoeconomic Sovereignty): Engineering-oriented, with Crypto serving merely as a foundational coordination tool. Introducing blockchain is a pragmatic choice to address engineering challenges (e.g., Psyche network utilizing Solana to coordinate heterogeneous computing power), rather than constructing a financial narrative aimed at end users.

The Advanced Model of Hermes—From Personal Agents to Task General Managers

This section aims to address a more fundamental question: What is the justification for Hermes as an independent product when Claude Code and Codex can already complete most professional execution tasks with high quality?

• Model A: Direct Collaboration (Limited Gains): Users are accustomed to manually generating prompts in LLM and handing them over for execution, manually transporting results and reviewing them. While the quality of single outputs is high, users must bear all project management and multi-agent coordination work. For such hands-on users, Hermes's automation is seen as an "intermediate layer that increases opacity," failing to effectively reduce their burden.

• Model B: Delegated Management (Significant Gains): Users treat Hermes as a permanent orchestrator, issuing only final goals. Hermes is responsible for task decomposition, delegating sub-tasks, tracking GitHub/CI statuses, and automatically triggering rework. Community practices (such as oh-my-hermes) show that the core value of Hermes lies in replacing cumbersome cross-agent coordination and project management tasks.

Within this framework, Hermes and Claude Code/Codex are not in a substitutive relationship but rather a layered one: the latter provides third-level execution quality, while the former offers second-level continuity, cross-session state, and cross-agent coordination. The value of Hermes is not evenly distributed among all users but may be highly concentrated among advanced user groups engaged in cross-agent, cross-system, and long-term asynchronous tasks. This judgment is more precise than the vague assertion that "the personal agent's second mind has already formed" and is better suited to guide commercialization and product priorities.

▲ Figure 2 · Overview of Hermes Agent Technical Architecture (User Entry → Gateway → Control Core → Provider Layer → Execution Layer → Orchestration Layer → Status Layer → Governance Layer)

Based on official documentation and community research, the panoramic framework of Hermes Agent's technical architecture covers the entire link from user interaction to learning governance:

• System-level support for autonomous recovery: The "control core" clearly includes context compression, provider fallback, and interruption state preservation, providing the technical foundation for fault recovery and system self-healing capabilities in case of task failure.

• "Delegation rather than substitution" execution logic: The "tools and professional execution layer" places external CLIs like Claude Code, Codex, and Hermes's native tools (Terminal, Browser, etc.) on an equal footing, confirming its positioning as a scheduling hub.

• "Self-evolving" governance attributes: The "learning, maintenance, and governance layer" includes nodes like Curator and Skill/Command Approval, indicating that its experience accumulation possesses a governance process with human intervention mechanisms rather than being a fully automated black box.

Business Model—Who Pays for "Hermes"?

If we only compare its token costs with direct subscriptions to Claude Code/Codex, we would arrive at misleading conclusions. This algorithm ignores the core value of Hermes: replacing users' hands-on project management, context transportation, and cross-agent coordination work.

User Value Formula: Hermes User Value = Saved Manual Coordination Time + Asynchronous and Unattended Value + Cross-System Automation Gains − Token and Tool Costs − Manual Intervention Costs − Failure and Security Risks

Therefore, the economics of Hermes is not absolute but highly dependent on the user's "delegation depth":

• High Delegation Depth (Economics Valid): If Hermes can transform tasks that originally required hours of manual monitoring into truly unattended execution, even if the token cost is slightly higher, its overall time cost and efficiency gains remain positive.

• Low Delegation Depth (Economics Collapse): If users still need to frequently intervene to correct errors and troubleshoot, Hermes becomes merely a token consumer and a fault magnifier.

This mechanism precisely explains why different user groups have starkly contrasting evaluations of Hermes's economics and suggests that the key to validating its business logic lies in quantifying the "unattended completion rate" and "number of manual interventions per task," rather than simply comparing the unit price of model APIs.

Commercialization Foundation: Nous Portal and Hermes Cloud

Hermes Agent is open-sourced under the MIT license, positioned as an ecological growth engine. The real commercialization loop focuses on Nous Portal, whose core value proposition is "one subscription, integrating multiple types of API keys," covering three major modules:

• Model Routing: Aggregating 252 models (providing inference through OpenRouter and direct connection to providers).

• Tool Gateway: Built-in high-frequency tools such as Firecrawl (web search), FAL (image generation), Browser Use (cloud browser), Modal (sandbox execution), and OpenAI Audio (TTS).

• Hosting Services: Out-of-the-box Hermes Cloud instances (charging daily operation fees, excluding inference and tool call fees).

Nous's actual revenue heavily relies on user usage paths, which currently show significant structural differentiation:

Open Source and Commercialization: Will Hermes Become the "Linux of the Agent World"?

Hermes's MIT open-source strategy drives explosive growth while also creating structural constraints for commercialization. The self-hosted free model requires its paid version to offer irreplaceable additional value, but a clear differentiated monetization path has yet to be established. A deeper risk lies in "value capture": if Hermes continues to be widely integrated as an optional runtime by cloud vendors, it may replay the classic dilemma faced by Linux or K8s, where the core commercial value is captured by cloud vendors providing computing power and hosting. The MIT license, while fostering ecological prosperity, also means relinquishing absolute control over distribution channels. As long as users can freely choose between "self-hosting + proprietary API" or "third-party cloud deployment," the massive usage cannot be forcibly converted into direct revenue, putting Nous in a severe test of "ecological position enhancement" versus "actual commercial returns mismatch."

Agent Ecological Position - The Triangular Pattern of Personal Butler, Professional Tools, and Major Company Claw

OpenClaw, Hermes, and Claude Code, Codex, along with major company hosting products, exhibit significant differences in target users and core propositions, belonging to different niche tracks. To clarify the current market landscape, the core competitive matrix of AI Agents is as follows:

Hermes does not pursue a mass market but precisely targets four types of high-density Power Users, forming the cornerstone of its phenomenal dissemination:

• Self-hosting and infrastructure players: Familiar with VPS/Docker/SSH, viewing Hermes as a natural control layer over existing infrastructure.
• Multi-model arbitrageurs: Rejecting vendor lock-in, accustomed to dynamically scheduling cutting-edge or local models based on tasks.
• Multi-Agent coordinators: Urgently needing to automate the orchestration of complex cross-platform, cross-tool workflows.
• Open-source and Crypto AI community: Highly resonating with user sovereignty and decentralization concepts, deeply aligning with Nous's organizational culture.

Although the base of these groups is small, they possess extremely high Token consumption, code contribution, and technical advocacy capabilities, serving as the core engine driving early word-of-mouth dissemination.

Claude Code/Codex: Both Supplier and Threat

Short-term Symbiosis: Raising Execution Limits

In practical workflows, Hermes acts as the control layer, invoking Codex (code implementation) and Claude Code (architecture and review) through a delegation mechanism. The stronger the underlying professional Agents, the higher the complexity of tasks Hermes can deliver, forming a symbiotic relationship where "Hermes is responsible for routing and acceptance, while professional Agents handle execution."

Long-term Potential to Ingest Hermes's Independent Value

Model vendors are accelerating their penetration into the control layer, posing a threat closer than expected. Anthropic's Claude Managed Agents now support multi-Agent parallel orchestration; OpenAI has explicitly positioned Codex App as the "command center for agents," supporting multi-Agent parallelism, automation, and long-cycle background operations. This means that Codex's multi-Agent control capabilities within the software engineering boundary have matured relatively, even surpassing Hermes in some areas, no longer merely a "bottom-level executor."

Hermes currently enjoys advantages in personal control planes across channels, models, and projects; however, Codex already possesses strong task ownership and multi-Agent management capabilities within the software engineering boundary, which may present a stronger competitive edge than Hermes. The core competitive question is: Can Hermes, ahead of model vendors, solidify users' project states, approval rules, Skills, Memory, and cross-Agent workflows at its layer, forming assets that users are reluctant to migrate? Or will it ultimately be absorbed as a standard feature by model-native products?

Major Internet Companies' Agent Route Choices

To discuss how major companies respond to the personal Agent wave, it is essential to clarify their product boundaries: the resident Agent hosting aimed at individuals (such as Tencent's QClaw, Byte's ArkClaw) and the general work Agents aimed at office/enterprise (such as WorkBuddy, Trae) have distinctly different positioning:

• Major Company Claw Route: Lowering barriers through one-click deployment, preset templates, and local ecological access. However, the deeper chasm lies in the untrustworthiness of platform incentives: regardless of how many external models are supported, users inherently believe that the ultimate goal is to funnel them into their own cloud and model systems.
• Hermes Runtime Integration: Byte's ArkClaw and Tencent Cloud have officially integrated Hermes Agent as an optional plugin or exclusive template into their cloud control consoles, establishing a clear multi-Runtime strategy: major companies retain their own cloud hosting, billing, security, and enterprise-level control bases while viewing Hermes as a pluggable advanced component, achieving complementary coexistence between the open-source ecosystem and commercial cloud platforms.
• General Office Agent Transformation: Currently, major companies are shifting core resources from Claw to general office Agent platforms with clear demands, easy acceptance, and direct monetization potential (such as WorkBuddy). Such tasks can be deeply bound to their own ecosystems like WeChat, DingTalk, and Feishu, converting into revenue.

Insights from Hermes for Crypto AI

Web3 has not directly made Hermes a smarter Agent, but it has provided Nous with a capital structure, organizational approach, seed users, and value sources distinct from traditional AI startups. Hermes at least proposes a more mature Crypto AI path: making Crypto the organization and infrastructure, rather than a product interface users must confront.

Hermes has completed the transition from a Crypto AI research brand to a global open-source Agent product, establishing large-scale attributable reasoning activities and a clear second mindset—yet this mindset is still concentrated within the OpenRouter ecosystem and the global developer circle, not yet translating into a comprehensive overtaking of OpenClaw in terms of GitHub Stars or overall community scale. It does not possess exclusive technologies that OpenClaw cannot replicate; rather, it has achieved a noteworthy challenger product iteration by precisely engaging high-intensity users and establishing "delegable" and "self-evolving" capabilities.

• Insight One: Crypto can serve as an "organizational operating system" rather than a product feature: The true value of Web3 can be reflected in capital structure, early high-intensity user pools, and foundational values, without needing to be forcibly exposed as wallet or Token interactions. Achieving "Crypto-native at the organizational level, Crypto-invisible at the product level" is an effective strategy balancing innovation drive and user experience.
• Insight Two: Decentralized infrastructure must anchor demand-side entry to form a closed loop: Pure supply-side distributed training networks (like DisTrO, Psyche) without real user entry and execution data support struggle to self-validate commercial value. Hermes Agent is the key validation for Nous's leap from underlying computing power infrastructure to real demand-side.
• Insight Three: Moats can be built on "delegated trust" rather than merely "model capability": The differentiation of personal Agents may not stem from stronger single-execution capabilities but from whether "users dare to entrust long-term responsibilities to it." This soft trust asset is a dimension often overlooked yet highly barrier-creating in Crypto AI projects.
• Insight Four: The relationship with cloud giants is not a zero-sum game but ecological complementarity: Major companies integrating Hermes as an optional Runtime proves that open-source Runtimes and major company control planes can coexist. For entrepreneurs, "being integrated" is a viable commercialization path, but caution is needed regarding the risk of core value being captured by cloud vendors' hosting layers.
• Insight Five: The ultimate competition will shift from "single-execution capability" to "task ownership and trust accumulation": The most valuable in the future may not be the strongest model at the execution layer but the "upper control system" that can receive final goals, maintain long-term context, intelligently schedule professional executors, and allow users to confidently entrust responsibilities.

OpenClaw has made "personal ownership of Agents" a clear product category; Hermes, through persistent state, task recovery, evidence acceptance, multi-model supply, and professional Agent delegation, has advanced "long-term entrusted Agents" into a more systematic product direction. The real test is: when Claude Code and Codex's control capabilities within the software engineering boundary continue to strengthen, and major cloud platforms make multi-Runtime integration smoother, will users still be willing to entrust their final goals and long-term trust to this open Runtime from a Web3 background—and continue to pay for it?

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