Written by: Zhang Feng
In 2026, the global AI Agent race officially moved beyond the conceptual bubble and entered the deep waters of the industry, characterized by framework competition and arduous implementation. After two years of explosive growth, the industry has generally revealed common pain points: most general-purpose AI agents suffer from six core dilemmas: cyber amnesia, lack of autonomous growth, reliance on manual pre-setting, lack of data sovereignty, fragility in complex task execution, and unclear commercialization prospects. Many frameworks can only complete single dialogues and simple command execution, and cannot meet the real needs of long-term digital work, enterprise process automation, and personalized intelligent collaboration.
Against this backdrop, Nous Research's Hermes Agent (commonly known in the industry as "Hermes Agent"), released in February 2026, ignited the global developer community within just two months, breaking the previous open-source monopoly of OpenClaw and creating a new situation of two giants competing in the industry.
I. The phenomenal rise of the open-source sector has triggered a wave of agent migration across the entire domain.
Hermes Agent's rise to fame is not a random hype, but a hardcore technology event that has become popular due to the convergence of technological breakthroughs and industry pain points. As of April 20, 2026, its popularity data and industry landmark events can be summarized into four major nodes.
Secondly, domestic large-scale model manufacturers have been intensively integrating natively, opening up the domestic application ecosystem. Xiaomi MiMo-V2, MiniMax, Tencent Cloud, and Baidu AI Cloud have successively announced native adaptation to the Hermes Agent architecture, achieving full-link compatibility of the model gateway. Domestic enterprise services and developer communities have quickly followed up with private deployment solutions, enabling this overseas open-source framework to rapidly achieve localization and break the industry curse that overseas intelligent agent frameworks are difficult to adapt to Chinese scenarios. At the same time, Lark, WeChat Work, and DingTalk have officially opened up interface adaptation, achieving native access to office IM terminals. The speed of implementation in personal and enterprise office scenarios far exceeds that of similar projects at the same time.
Third, the mutual support from Web3 and productivity communities is expanding the boundaries of applications. On the one hand, quantitative developers are exploring the general-purpose framework's attributes, leveraging its secure sandbox, long-term memory, and API self-calling capabilities to create a foundation for secondary development of highly secure trading robots in the crypto asset field. On the other hand, developers in personal productivity, operations automation, and enterprise process fields are building dedicated digital employees on a large scale, covering diverse scenarios such as content creation, data processing, system maintenance, and workflow orchestration, enabling cross-domain reuse of the general-purpose foundation and moving beyond the limitations of a single tool.
Fourth, industry consensus is being reconstructed, defining a new generation of general-purpose intelligent agent standards. Hermes Agent's official slogan is "The agent that grows with you," and its release directly rewrites the industry's definition of a general-purpose agent: from a one-time command execution tool to a long-term digital collaborative entity that is 24/7 resident, has persistent memory, autonomous review, self-evolution, and data sovereignty, driving the entire industry's technology roadmap from "tool invocation" to "autonomous intelligence."
II. A universal self-evolving intelligent agent foundation for building a closed-loop collaborative system across all scenarios.
From a fundamental perspective, Hermes Agent is not a vertically specialized tool, a non-native trading robot, or a closed dialogue model. Instead, it is a full-stack, general-purpose autonomous intelligent agent framework developed by Nous Research using Python and released under the MIT open-source license. Its business model is built around a three-layer structure of "open foundation + native capabilities + scenario extension," which completely solves the problems of narrow business boundaries, rigid capabilities, fragmented deployment, and poor scenario adaptability of traditional agents.
(I) Core Underlying Business: Native Operating Engine for Intelligent Agents
As the foundational operating kernel, Hermes Agent features a complete autonomous operating loop: task reception—autonomous planning and decomposition—multi-toolchain invocation—task execution and implementation—result feedback and review—capability accumulation and iteration, forming a closed-loop business process that requires no human intervention throughout the entire process. Unlike traditional agents that rely on humans to write all execution scripts, pre-set steps, and fine-tune instructions, this framework only requires users to set core objectives. It can autonomously complete the entire process scheduling of complex and long tasks, covering all common capabilities such as web page retrieval, API calls, file processing, system commands, data computation, and cross-platform interaction. It serves as the technical foundation for all upper-layer applications.
(II) Mid-level competency business: Layered memory and autonomous skills system
This is its core differentiating module. The framework natively builds a four-layered persistent memory architecture, using a local SQLite database as the storage layer. It is divided into four modules: immediate conversation memory, long-term historical memory, user preference model, and procedural skill memory. Combined with the FTS5 full-text search engine, it can accurately retrieve information across months, conversations, and terminals, fundamentally solving the industry-wide pain point of "cyber amnesia"—memory clearing after conversation ends, context truncation, and information forgetting across scenarios. At the same time, it has a built-in GEPA self-evolving business loop. After completing a task, it automatically reviews the process, extracts experience, generates structured reusable skill files, and stores them in the local skill library. Subsequent similar tasks can directly call upon these files and continuously optimize the process, realizing execution as accumulation and use as growth, transforming passive execution into proactive capability accumulation.
(III) Upper-layer extended services: full model compatibility and multi-terminal scenario access
In terms of business compatibility, Hermes Agent integrates with over 200 mainstream large model interfaces globally, including overseas GPT-4o and Claude 3 series, domestic Kimi, MiniMax, Tongyi Qianwen, and local open-source Ollam models. It supports seamless one-click switching of underlying models, without being tied to a single model vendor, giving users model selection and technical autonomy. For terminal deployment, it supports local Windows, macOS, and Linux systems, while also adapting to various deployment environments such as Docker, cloud servers, and low-configuration VPS, enabling 24/7 background operation. It integrates with mainstream social and office platforms such as Lark, WeChat Work, Telegram, and Discord, ensuring seamless interoperability of skill data across all platforms. This allows for one-time deployment and reuse across all scenarios, catering to the diverse needs of individuals, developers, and SMEs.
(iv) Defining Business Boundaries
It is necessary to clarify the boundaries of business attributes: the official native framework does not have encrypted trading, financial quantitative trading, or vertical industry-specific functional modules. All financial and industry-specific capabilities come from secondary skill development by the community. The framework only provides a general intelligent execution base. All scenario-based capabilities are based on the native kernel extension. It is a general infrastructure without scenario binding, rather than a vertical application product.
III. Open source customer acquisition ecosystem and tiered commercialization of value-added services
Currently, the AI intelligent agent sector generally faces commercialization dilemmas such as open source projects lacking profitability, closed source projects struggling to gain widespread adoption, weak user retention, and unclear monetization paths. Many projects are either completely free with no revenue or closed-source projects with a very small user base, making it difficult to form a virtuous cycle of business. Hermes Agent, relying on the complete business layout of its development entity Nous Research, adopts a mature open source technology business model that combines core open source with free access and upper-layer value-added monetization, balancing community ecosystem expansion with long-term commercial profitability, resulting in a clear and sustainable path.
(I) Foundation Layer: The framework is completely open source, providing a free platform for building the user base.
Hermes Agent's underlying core operating framework is entirely open-source under the MIT license. The source code, basic deployment tools, and basic memory modules are completely free and open-source, with no copyright restrictions, allowing for free secondary development, private modification, and commercial use without barriers. The core objective of this strategy is not direct profit, but rather to rapidly capture the developer market, accumulate a global user base, and build an open-source community barrier. By offering a free platform, it attracts a massive number of developers, forming a large user base and secondary development community, thus providing a traffic foundation for monetization of upper-layer businesses.
(ii) Middle layer: Official cloud service subscription monetization, core essential value-added revenue
Nous Research has officially launched the Nous Portal cloud service platform as a value-added service accompanying its framework. It provides users with one-stop, zero-configuration access to 400+ large models, cloud-hosted operation, high-concurrency computing power support, official technical maintenance, enterprise-level security hardening, and cloud backup and hosting services, using a monthly/annual subscription payment model. Lightweight subscriptions are available for individual users, while customized packages are offered for enterprise users. This caters to users who cannot deploy locally or privately and require stable cloud operation, and is currently the core and most stable source of revenue.
(III) Ecosystem Layer: Skills Market and Ecosystem Licensing Revenue Sharing
Agentskills.io, an official skills ecosystem platform, has been established, bringing together industry-specific skills, workflow templates, and scenario-based plugins developed by developers worldwide. High-quality paid skills on the platform adopt a revenue-sharing model between the platform and developers. Simultaneously, it provides commercial framework licensing, customized kernel adaptation, and image service deployment licensing for cloud vendors, enterprise software service providers, and cloud infrastructure vendors. Domestic companies such as Wangsu Science & Technology have already launched their dedicated cloud image services, realizing B-end commercial revenue through technology licensing.
(iv) Extension Layer: Model API and Enterprise Customized Solutions
Leveraging its accumulated Hermes series of open-source large model technologies and combining the advantages of the agent framework, the company provides commercial APIs for large models and integrated customized services combining agents and models. For large enterprises, it offers customized project services such as private deployment, deep kernel customization, internal business process adaptation, dedicated multi-agent cluster setup, and security system transformation, unlocking high-value business potential and covering monetization across all customer segments, from individuals and SMEs to large corporations.
The overall profit logic is as follows: open source framework attracts new users and builds an ecosystem → cloud subscription services provide stable cash flow → ecosystem skills market expands revenue → enterprise customization unlocks high value, perfectly avoiding the industry dilemma of pure open source having no revenue and pure closed source having no ecosystem.
IV. Addressing the industry's fundamental pain points and building four irreplaceable barriers to entry.
In light of the common shortcomings in the current general-purpose intelligent agent industry, Hermes Agent's core competitiveness is built entirely around the industry's original pain points. It is not a simple addition of functions, but rather an innovation at the underlying architecture level, forming four core barriers, which is also the fundamental reason why it has been able to break through quickly.
First, its native four-layer persistent memory system eradicates the common problem of "cyber amnesia" in general-purpose agents. Traditional intelligent agents are almost all stateless designs, relying only on short-term dialogue context. All information is lost when the conversation ends, with no cross-conversation memory or long-term user accumulation. Repetitive tasks require repeated instructions. Hermes builds a hierarchical memory using a local database, coupled with full-text search, to long-term store historical tasks, user habits, and execution experience. The longer it is used, the deeper its understanding of user needs becomes, completely solving the industry pain points of memory loss and context fragmentation.
Secondly, GEPA's closed-loop self-evolution capability enables the agent to grow autonomously. This is its core technological barrier. Most existing agents rely on pre-defined rules and mechanically execute instructions; their capabilities are entirely human-defined, lacking the ability to autonomously optimize or accumulate experience, resulting in stagnant efficiency in repetitive tasks. Hermes, however, incorporates a complete review and evolution cycle, autonomously summarizing tasks, generating skills, iterating processes, and patching itself. Each task execution translates into an increase in its own capabilities, making it stronger with use. This marks the first time in the open-source field that the self-evolution of an agent has been engineered and implemented.
Third, it offers full model and platform compatibility, along with strong data sovereignty and privatization. Most industry frameworks are deeply tied to a single underlying large model, locking users into the vendor's technology and posing a high risk of privacy breaches due to data being uploaded to third-party clouds. Hermes eliminates model-binding barriers, seamlessly compatible with various large models globally; all memory, task, and skill data is stored locally, without uploading to third-party clouds, ensuring complete data sovereignty for the user. Combined with a five-layer security sandbox, approval for dangerous operations, and container isolation protection, it addresses pain points related to model binding, data security, and privacy breaches.
Fourth, it is lightweight, stable, and can run continuously, with a low deployment threshold and adaptability to diverse scenarios. Compared to the complex deployment and high computing costs of heavy enterprise agents, and the problems of lightweight frameworks being prone to crashing and unable to run in the background for complex tasks, Hermes can achieve stable 24/7 background operation on low-configuration VPS, and can be quickly deployed with a single command, balancing deployment convenience with the stability of long-term task execution. At the same time, the on-demand activation mechanism of tool invocation significantly reduces model illusions and balances execution flexibility with operational reliability.
V. Open source and decentralized ecosystem, driven by both open platform and community co-creation.
Hermes Agent adopts a decentralized open-source ecosystem model led by the official kernel, co-created by the global community, and widely compatible with upstream and downstream. It is different from the centralized plugin platform ecosystem of OpenClaw and the closed private ecosystem of large companies. It builds an open and win-win ecosystem and solves the problems of closed intelligent agent ecosystem, homogenized skills, and fragmentation of upstream and downstream.
(I) Kernel Layer Ecosystem: The Official Team Guards the Boundaries of the Underlying Foundation
Nous Research focuses solely on iterating the underlying engine, optimizing the memory architecture, upgrading the security system, and adapting the model gateway to the underlying layer. It does not monopolize upper-layer applications, restrict skill development, or force users to use their own models. It continuously maintains the stability of the underlying framework and updates its basic capabilities, keeping the kernel open source and providing a stable technical foundation for the entire ecosystem.
(II) Developer-level ecosystem: Global community for secondary development and co-creation
Based on the open-source license, individual developers and technical teams worldwide can freely develop scenario skills, workflow templates, industry plugins, and terminal adaptation solutions based on the kernel. All unofficial skills are contributed and accumulated by the community, forming a massive and diverse application resource. Developers in fields such as personal productivity, operations and maintenance development, office automation, Web3 quantification, and data analysis continue to enrich the ecosystem. The official team only conducts security audits and does not restrict development directions, achieving a unified foundation and a flourishing application ecosystem.
(III) Upstream and downstream compatible ecosystem: cross-framework collaboration and vendor access
The ecosystem is highly open and can work collaboratively with other intelligent agent frameworks. An industry-wide model has emerged where Hermes is used for top-level task planning, and OpenClaw for long channel tool execution. These two leading frameworks complement each other rather than compete in a zero-sum game, breaking down industry barriers to technological collaboration. Meanwhile, cloud vendors, IM office platforms, large model vendors, and computing power service providers have all integrated and adapted, creating a complete upstream and downstream industry ecosystem that connects the entire chain from model supply, computing power support, terminal access, and application deployment.
(iv) Ecological value closed loop
The underlying framework is open source → developers co-create skills and applications → users use it in diverse scenarios → user feedback feeds back into kernel iterations → more vendors connect to improve the infrastructure → attracting more developers to join, forming a positive cycle ecosystem, breaking free from dependence on a single project, and achieving self-growth of the ecosystem.
VI. The industry's two leading players engage in a differentiated battle, with clear divergences in their strategies.
Currently, the global open-source agent market is dominated by Hermes Agent and OpenClaw, while also competing with closed intelligent agent products such as Claude Code and OpenAI Codex. The underlying approaches, capabilities, and applicable scenarios of each are significantly different. A comprehensive horizontal comparison can clearly show their respective positioning and value boundaries.
(a) Core Competitors: Hermes Agent VS OpenClaw (Lobster)
These two technologies represent two completely different technological paths in the industry. They are not about comprehensive replacement or competition, but rather about complementary capabilities. A detailed comparison is as follows:
Underlying Positioning: Hermes is a self-evolving intelligent engine, focusing on agent self-growth, deep execution, and experience accumulation; OpenClaw is a multi-channel gateway scheduling platform, focusing on multi-terminal access, task distribution, and tool link management. In industry parlance: OpenClaw manages the entry channels, Hermes manages the intelligent brain.
Memory system: Hermes uses a four-layer local persistent database memory for cross-month information retrieval and long-term user modeling; OpenClaw relies only on file-based short-term memory, has no native long-term storage, suffers from severe cross-session forgetting, and has no positive correlation between usage duration and capabilities.
Skill Mechanism: Hermes autonomously generates and automatically iterates private skills, accumulating abilities from tasks; OpenClaw relies on manually uploaded preset plugins, with all skills coming from the community marketplace, and there are a large number of malicious plugin security risks.
Models and Deployment: Hermes offers seamless compatibility across all models, strong local deployment with high data security; OpenClaw boasts a rich ecosystem of plugins and numerous access channels, but relies heavily on cloud operation, posing a high risk of data leakage and making it prone to crashing during complex tasks.
Applicable scenarios: Hermes is suitable for long-term personal digital partners, enterprise private processes, permanent automation of operations and maintenance, and complex tasks that require continuous growth; OpenClaw is suitable for one-time lightweight tasks, multi-platform message scheduling, rapid prototyping development, and calling lightweight programming tools.
(ii) Comparison with other competing products
Claude Code: A closed-system proprietary intelligent agent with high execution efficiency but deeply bound to the Anthropic model. It has no model selection rights, no long-term memory or autonomous evolution capabilities, serves only its own ecosystem, and has extremely poor versatility.
OpenAI Codex focuses on specialized intelligent agents for the programming field, with strong system-level control capabilities, but is limited to vertical scenarios, lacks general productivity capabilities, is closed-source and has high barriers to commercialization.
Domestic native closed-source agents: Most rely on their own large models for closed development, resulting in closed ecosystems, poor compatibility, high customization costs, no open-source foundation, and difficulty in secondary expansion.
In summary, Hermes leads in all aspects, including long-term memory, autonomous evolution, data security, model compatibility, and private deployment; OpenClaw has advantages in plugin ecosystem, number of channels, ease of use, and lightweight execution speed; closed commercial intelligent agents are limited to their own ecosystem and their versatility is far inferior to the two open-source giants.
VII. The technology is not yet mature, and there are still many shortcomings in industrial application.
Despite Hermes Agent's breakthroughs in addressing many underlying pain points in the industry and its outstanding overall capabilities, as a new-generation framework that has only been online for two months, it still suffers from significant technical shortcomings, ecosystem deficiencies, and challenges in industrial implementation, resulting in substantial limitations in its industry development.
First, the project version is relatively new, and its overall technical maturity is insufficient. Currently, it has only been updated to version v0.8, and the kernel is still in a rapid iteration phase. The stability of some complex and long-chain tasks is insufficient, the planning logic is prone to deviation in extreme scenarios, and the collaborative capabilities of complex multi-agent clusters are not yet perfect. There is still room for optimization before it can reach a large-scale enterprise-level highly reliable production environment.
Secondly, the native plugin ecosystem lags far behind OpenClaw. OpenClaw has a well-developed plugin market with abundant ready-made tools and resources after long-term development; Hermes' ecosystem is mainly based on community-developed skills, with fewer general-purpose ready-made plugins, insufficient coverage of scenarios, a lack of resources for beginners to use out of the box, and a certain amount of secondary development cost required for initial use.
Third, the inference overhead is relatively high, and the execution speed is relatively slow. Affected by the multi-layer memory system, the autonomous review and evolution module, and the security sandbox verification mechanism, the computing power consumption of a single task is higher, and the execution speed of simple short tasks is slower than that of lightweight gateway-type frameworks, making it less efficient in lightweight scenarios.
Fourth, there is the issue of skill library redundancy and retrieval burden. With long-term use, the number of locally accumulated skills continues to increase, which can easily lead to skill redundancy, invocation conflicts, and decreased retrieval efficiency. The framework has not yet perfected the intelligent skill simplification and automatic cleanup mechanism for expired skills, resulting in increased long-term operation and maintenance costs.
Fifth, common challenges across the industry have not yet been fully resolved. The industry-wide pain points, such as the illusion of large-scale underlying models, the inexplicability of complex, long-chain decision-making processes, and limited adaptability to highly complex cross-industry businesses, remain unresolved. Furthermore, the overall global industrial-scale deployment rate of AI agents is low, making it difficult for companies to realize ROI, and the widespread adoption of the ecosystem is still constrained by the broader industry environment.
Sixth, the risk of confusion caused by projects with the same name and the problem of abuse of application boundaries. There are crypto trading bots and on-chain protocol projects with the same name on the entire network, which can easily cause confusion among users; at the same time, some users abuse the framework API capabilities to conduct virtual currency transactions, crossing regulatory red lines and posing application compliance risks.
8. From Personal Digital Partners to Distributed General-Purpose Intelligent Infrastructure
Combining technological iteration, ecosystem expansion, and industry trends, and leveraging its underlying architectural advantages, Hermes Agent has a clear future development path. It will evolve along four major directions: technological improvement, ecosystem expansion, scenario deepening, and industry popularization, continuously expanding the value boundaries of general-purpose intelligent agents.
First, the kernel technology will continue to iterate, addressing any shortcomings in maturity. Subsequent versions will focus on optimizing the stability of complex task planning, reducing inference computing power overhead, improving the intelligent skill management mechanism, strengthening multi-agent cluster collaboration capabilities, and improving the fault tolerance and rollback mechanism for long-running tasks, gradually reaching enterprise-level high-reliability production standards and bridging the industry gap from "usable" to "reliable." Simultaneously, the security system will be deepened, improving end-to-end auditing, access control, and risk interception, adapting to the compliance requirements of highly sensitive industries such as finance and government.
Second, the ecosystem continues to expand, achieving mutual improvement in both community and localization. On the one hand, it enriches the official and community skill libraries, supplements out-of-the-box application resources, and narrows the gap with leading framework ecosystems. On the other hand, it continues to deepen the adaptation to Chinese scenarios, deeply integrates with the domestic office ecosystem and enterprise digital systems, improves the full-link native compatibility of domestic large-scale models, and completes full localization. At the same time, it deepens the cross-framework collaborative ecosystem, forming a complementary and division-of-labor industry collaboration system with frameworks such as OpenClaw, jointly promoting the overall development of the open-source agent industry.
Third, the application scenarios are expanding from personal productivity to enterprise-wide implementation across all industries. Initially, it focuses on personal digital assistants, developer tools, and lightweight operation and maintenance automation; in the mid-term, it fully penetrates the process automation of SMEs, internal digital employees, and business data processing; in the long term, it enters the deployment of private intelligent agents in large enterprises, undertaking internal system scheduling, autonomous execution of business processes, and cross-system data collaboration, becoming the underlying intelligent infrastructure for enterprise digital transformation.
Fourth, deepen the business model and build a complete open-source business closed loop. Based on the existing subscription, licensing, and customization services, improve the skills market revenue sharing system, expand the new business model of distributed computing power network combined with the framework, and combine our own model technology to create an integrated full-stack service of "large model + intelligent agent + computing power" to form a sustainable and high-growth business system, verify the commercialization feasibility of general open-source intelligent agents, and provide a commercial model for the entire industry.
Fifth, moving towards a distributed general-purpose intelligent infrastructure. In the long run, as multi-agent collaboration technology matures, Hermes will evolve from a single-agent framework into a distributed personal and enterprise intelligent network core. With self-evolution and long memory capabilities as its core, it will connect various software, hardware, and business systems, becoming the general-purpose intelligent underlying foundation of the next-generation digital world, and undertaking the large-scale general collaboration needs in the early stages of AGI implementation.
In 2026, the competition in the AI intelligent agent field will no longer be a superficial contest of the number of tools and plugins, but a profound revolution in the field, encompassing underlying architecture, memory capabilities, autonomous intelligence, data security, and the business ecosystem. Looking at the industry as a whole, the vast majority of general-purpose intelligent agents are still trapped in the fundamental dilemma of "being able to converse but difficult to execute, lacking memory, not being able to grow, and being difficult to implement," merely completing a simple encapsulation of large model capabilities without touching the true core value of intelligent agents.
The rise of Hermes Agent is essentially a return to the value of architecture. Instead of blindly piling up tools and interfaces, it precisely addresses the five core pain points of the industry: memory loss, inability to evolve, data insecurity, model binding, and unclear commercialization. With its self-evolving kernel, layered persistent memory, full model compatibility, private security, and a healthy open-source business ecosystem, it redefines the product standard for general-purpose intelligent agents. It doesn't just solve the tool needs of a single scenario, but rather the systemic problems of general-purpose intelligent agents across the industry: "execution without accumulation, dialogue without intelligence, framework without ecosystem, and product without commercial viability."
Of course, the road to technological maturity is long and arduous, and the project still faces many unresolved challenges related to ecosystem, speed, and stability. However, from an industry development perspective, Hermes Agent has forged a new path for the development of general-purpose intelligent agents, distinct from the gateway scheduling route. With "growing together with users" as its core, it drives AI to transform from a one-off dialogue tool into a long-term companion, autonomously evolving, and deeply collaborative digital partner. This provides a new and feasible model for the large-scale deployment and widespread civilian application of AI intelligent agents globally, and lays a solid open-source foundation for the construction of the next generation of general artificial intelligence infrastructure.
