How Hermes Agent Creates Reports and Optimizes SEO

Learning more makes you better, a principle long assumed to apply only to humans, turns out to hold true for Hermes Agent too, an open-source AI agent from Nous Research. Every time you work with it, Hermes Agent doesn't forget, it remembers, understands you more deeply, and gets better with each session, thanks to a memory system that can recall everything about you even after the machine has been off for a week. What Is Hermes Agent? Hermes Agent is an open-source AI agent developed and released under the MIT license by Nous Research, the lab behind the Hermes, Nomos, and Psyche model lines. Unlike Antigravity or Codex, which depend on an IDE environment, or ordinary chatbots that ultimately remain a thin wrapper calling a single API, Hermes Agent is built to run continuously on a user's own infrastructure, from a cheap VPS to a GPU cluster or serverless infrastructure, and it operates in a way fairly similar to OpenClaw. The core difference in Hermes Agent lies in how it manages long-term memory and converts experience into real skills. Instead of merely storing raw information or passively remembering preferences the way AI like Gemini or Claude do, Hermes runs a closed "learning loop," meaning that after every work session, it actively distills the process into new tools it can use the next time. This system is run by a background "Curator" agent that automatically scores, prunes, and merges accumulated knowledge, combined with FTS5 search technology that retrieves old memories roughly 4,500 times faster without spending any tokens. As a result, Hermes doesn't just respond and forget, it genuinely becomes a collaborator that grows more knowledgeable and capable over time. Four Features That Set Hermes Agent Apart Nous Research doesn't call Hermes Agent a chatbot or a copilot, it positions it as an agent with a built-in learning loop. The four feature groups below explain why that label isn't just marketing. Memory That Persists Across Sessions The biggest weakness of most AI today is that memory only stores raw chat text rather than how work actually gets done. Hermes Agent addresses this through three combined mechanisms: Fast retrieval: Uses FTS5 full-text search to pull up old memories roughly 4,500 times faster than conventional search, without spending extra tokens the way Gemini or Cowork do. User understanding: Integrates Honcho's dialectical user-modeling approach, helping the agent understand preferences, habits, and personal context in depth across thousands of sessions. Continuity: The agent picks up work exactly where you left off, even if that was a project from weeks earlier. Self-Generating and Self-Improving Skills This is the feature that makes Hermes Agent behave like a collaborator that accumulates experience, rather than just a tool that answers on request: Learning from real use: After completing complex tasks, Hermes Agent distills the process into new skills and stores them in a library to be reused automatically next time. Open agentskills.io standard: These skills follow an open standard, so they can be packaged, shared, and reused across different AI systems without being rewritten from scratch. The Curator mechanism: A background administrative agent periodically scores, prunes, and merges duplicate skills, which keeps the skill library from bloating and becoming disorganized over time. Present on More Than 23 Messaging Platforms Hermes Agent isn't confined to a computer, it integrates directly into the messaging channels people already use on their phones every day: Multiple channels, one brain: You can command Hermes Agent through Telegram, Discord, Slack, WhatsApp, Signal, email, or SMS. Context retained: Whether you message via Telegram in the morning or switch to Discord at night, the agent keeps a single thread of memory, never fragmented by channel. Multimodal interaction: Supports sending voice messages, images, and video, along with the ability to analyze multimodal content. Flexible Runtime Infrastructure Hermes Agent supports six backend types for executing commands: local machine, Docker, SSH, Daytona, Singularity, and Modal. With Daytona and Modal, the environment can hibernate when idle and cost almost nothing while waiting, waking up only when there's work to process. This is why Nous Research describes Hermes Agent as an always-on agent that doesn't require users to keep a server running 24/7 at high cost year-round. Hermes Agent can be installed with a single curl command, supporting Linux, macOS, and Windows via WSL2, or, as of June 5, 2026 with version v0.16.0 "The Surface Release," through an official Native Desktop app for Windows, macOS, and Linux with a fully polished GUI, making it accessible to everyday users without needing a terminal. Built-In Toolset and Limitations to Know 40-Plus Built-In Tools, From Web Search to Schedule Automation Hermes Agent ships with more than 40 built-in tools, including web search, browser actions, file handling, and Python script execution via RPC to run sub-tasks without consuming the main agent's context window. A natural-language scheduling system lets you set recurring tasks like daily reports or data backups, then leaves the agent to run them without being reminded. For tasks that need full isolation, Hermes Agent also supports sub-agents with their own conversation, terminal, and scripts, allowing multiple jobs to run in parallel without diluting the main memory. Challenges and Security Considerations Despite rapid updates, Hermes Agent still has a few points users should keep in mind before deploying it: Stability of the self-learning mechanism: The ability to self-improve skills boosts success rates, with a Tencent Cloud report recording gains of up to 52% along with token savings of up to 61%. However, since this is a self-evolving mechanism, real-world effectiveness still depends on the underlying model chosen and still requires human oversight rather than full trust. Risk from high-level permissions, with security responsibility falling on the user: Hermes Agent can intervene deeply in a system (excessive agency), so connecting it directly to multiple messaging platforms requires users to manage their own API keys and set up guardrails. Unlike closed AI services, Hermes Agent hands full control over to the user, which means the user also bears greater responsibility for configuring access permissions to avoid information leaks. Why Is Hermes Agent Growing So Fast? Hermes Agent's growth could be attributed to Nous Research's marketing, but in our view it comes down to three main factors. A Frictionless Migration Path From OpenClaw Recognizing OpenClaw's large user base, Nous Research built a migration tool that lets users carry over their persona, API keys, the entire skill set, and memory to Hermes Agent with a single command, without losing old data and, of course, without having to reconfigure anything from scratch. If you're currently using OpenClaw and want to try Hermes Agent without losing your old data, look for the hermes claw migrate migration tool built into Hermes Agent before considering a fresh install. Betting on a Closed Learning Loop Instead of a Feature Race While many other agents compete on the number of tools they offer, Hermes Agent positions itself as a self-evolving entity, one that distills experience into new skills and retains long-term memory to understand users more deeply over time. This approach creates lasting value, and the community has already put it to use for projects such as automating large-scale content production with high consistency across many sessions. A Role as a Training Data Engine Beyond serving as a personal assistant, Hermes Agent also functions as a capable research tool. It can generate thousands of parallel tool-calling trajectories and compress them into training data for other AI models. By turning the agent's real-world experience into training data, Hermes becomes a platform that developers building the next generation of autonomous AI can't easily do without. How Is Hermes Agent Different From an Agent Harness? People new to the space often confuse Hermes Agent with the concept of an agent harness, which is the framework that decides how a model calls tools, handles the reasoning loop, and coordinates execution steps internally. If a harness is the engine and chassis that determine how a car drives, then Hermes Agent is like a car that already has that engine installed, plus seats, a navigation system, and the driver's own trip memory. In other words, a harness is the technical architecture layer underneath, while Hermes Agent is a complete end-user product that already packages memory, a skill system, communication channels, and a choice of runtime infrastructure. A developer can build their own harness to control every small detail, but most users don't need to go that deep, they just need an agent that runs right away and gets smarter through use. For a closer look at this underlying architecture layer, read more at What Is Agent Harness? The Framework That Makes AI Work Efficiently, which explains in detail how this type of framework operates. Is Hermes Agent Worth Trying Right Now? Being fully open source, collecting no user data, and supporting complete self-hosting, Hermes Agent is one of the few agents today that lets users keep full control over their own data while still getting a continuous assistant experience with real memory, not the simulated memory that only exists within a single chat. After v0.16.0, the biggest technical barrier for users unfamiliar with terminals has largely been removed, as the native desktop app for Windows, macOS, and Linux has fully replaced the pure CLI approach used before. What's left to judge about Hermes Agent isn't whether it runs, but what it learns after a few real weeks of use. The fastest way to find out is to install the desktop app or run the CLI on a cheap VPS, connect it to a familiar messaging channel like Telegram, then watch what skills the agent forms on its own from how you use it every day. That's also the groundwork for comparing Hermes Agent with other options on the market, from Agent Harness to OpenClaw and Claude Cowork, in the next part of this series.

At the Google I/O 2026 event, the search giant stunned the entire developer community by officially announcing Antigravity 2.0. No longer a conventional AI-integrated IDE, Antigravity has now transformed into a standalone desktop application powered by Gemini 3.5 Flash, accompanied by an AI Ultra subscription package priced at $100/month. However, the complete removal of the integrated source code editor in favor of a minimalist Codex-like interface is generating intense controversy. How Antigravity 2.0 Has Transformed The decision to completely separate the source code editor from Antigravity 2.0 marks a bold move by Google in reshaping the future of software development. Instead of attempting to integrate AI features into a traditional IDE, this new version functions as a dedicated AI agent orchestration hub. This means users will focus entirely on setting up tasks and monitoring workflows rather than directly editing individual lines of code. This change is most clearly demonstrated by the launch of the AI Ultra service package, priced at $100 per month. This premium subscription offers 5 times the usage limit compared to the current AI Pro package, targeting businesses and professional developers who need to operate a large number of autonomous agents simultaneously to solve complex problems. Power from Gemini 3.5 Flash and Asynchronous Execution Workflow At the heart of Antigravity 2.0 is the Gemini 3.5 Flash large language model, specially optimized for high-speed agentic tasks. Thanks to its superior processing capabilities, the new system supports highly complex multi-agent workflows, allowing multiple subagents to collaborate on a large project. More specifically, these subagents will run entirely asynchronously in the background. This mechanism ensures that the application's main interface never freezes or is interrupted during processing, helping developers maintain a smooth workflow. This is a significant improvement over its predecessor, which often experienced delays when processing large codebases. New Tool Duo: Antigravity CLI and SDK Antigravity CLI, written in Go, completely replaces the old Gemini CLI, delivering high performance and extremely fast response times in the terminal. Gemini CLI and Gemini Code Assist IDE extensions will cease service from June 18, 2026. Google AI Pro and Ultra users need to switch to Antigravity CLI before this deadline. Antigravity SDK, written in Python, allows developers to build, customize configurations, and deeply integrate autonomous agents into their projects. Minimalist Codex-like Interface and Community Controversy Despite boasting numerous powerful technological upgrades, Antigravity 2.0 is facing a wave of criticism from the user community due to radical interface changes. The new interface is now merely a minimalist console focused on a chat window for issuing commands to agents, completely eliminating the familiar IDE workspace. Many opinions suggest that this design looks exactly like a replica of the Codex or Claude Desktop application. This excessive minimalism has left many developers feeling disappointed and empty, as they no longer have the ability to quickly view and modify files directly as before. Having to switch back and forth between Antigravity and an external editor significantly reduces their actual work efficiency. How to Restore the Traditional IDE Experience for Users To appease the negative reactions from the community, Google has offered some temporary solutions for those not yet ready to adapt to the new interface. Users can visit the official Antigravity homepage to download a separate IDE version. This version will help restore the familiar integrated workspace with traditional source code editing features. However, Google also issued a warning that this is only a temporary solution. In future updates, the agent management interface will be completely removed from the IDE as the company focuses all development resources on the standalone 2.0 application. Therefore, familiarizing oneself with the new working model is inevitable for developers in the long term. The Rapid Evolution of Tools like Antigravity and Codex The separation between traditional code editors and agent control interfaces is clear evidence that AI is shifting from a supportive tool to an autonomous partner. Developers need to proactively familiarize themselves with new control tools like CLI and SDK to gradually transition their role from direct code writers to managers and orchestrators of intelligent agent ecosystems.

Boris Cherny — the engineer behind Claude Code — doesn't like to explain the tool he created with slides; instead, he shares 13 practical tips from how the Anthropic team itself uses it daily. Below are 7 core principles I've extracted from that, and the common thread is that he doesn't use Claude Code as a code-writing chatbot but as an intelligent workflow. He shared this many months ago, so some things might be old, but there's still a lot to learn from these talented individuals. Clone Yourself by Running Multiple Parallel Sessions Claude Code is certainly not designed to be used in just a single tab. Boris runs 5 terminal tabs simultaneously, combining 5–10 sessions on claude.ai/code and the mobile app, with each session handling exactly one independent task. The practical reason: if too many tasks are crammed into one context, Claude starts to have priority conflicts, and output quality significantly decreases. Instead: Use the hand-off feature (&) to transfer tasks between terminal and web. "Teleport" context from computer to phone when needing to continue work on the go. This is a feature that has been available for a long time. One session, one task is a vital principle when using Claude Code to avoid context overflow. Choose the Strongest Model and Always Start with Plan Mode Boris uses Claude Opus 4.5 (as Opus 4.6 had not been released by January 2026) combined with Thinking for most serious work. Before letting Claude run code autonomously, he almost always starts with Plan Mode — pressing Shift + Tab twice. Plan Mode forces Claude to plan in writing before execution, and this is when problems are detected earliest with the lowest correction cost. According to Boris: "A good plan is really important." for Claude Code to have a planning step, it's extremely crucial, just like a builder laying a foundation without a blueprint. Build CLAUDE.md as a Shared Team Memory CLAUDE.md is the single file the entire team checks into git, containing all accumulated conventions, context, and lessons. Operating principles: Every time Claude makes a mistake, such as a convention error, incorrect processing flow, or wrong directory structure, Claude is made to not only fix that instance but immediately add it to CLAUDE.md so it doesn't repeat next time. During code review, tag @.claude to add context directly from the PR to this file. Boris calls this "compounding engineering": the system doesn't stand still; it improves daily with every recorded error. After 3 months of serious use, a team's CLAUDE.md truly becomes an engineering asset — not just a config file. Automate All Repetitive Tasks with Slash Commands and Subagents Anything you type more than 3 times a day should become a Slash command. Place them in .claude/commands/. For example: /commit-push-pr — combines the entire commit, push, and PR creation flow into a single command. For standard steps in the PR process, Boris uses Subagents like code-simplifier or verify-app to handle them automatically without manual intervention. The result is a significantly shortened time from "code finished" to "PR ready for review" and fewer errors due to missed steps. Use Hooks and Permissions for Self-Operating Systems Three important mechanisms Boris configures for Claude Code: PostToolUse hook — automatically formats code every time Claude uses a tool. You no longer need to remember to run the linter manually. /permissions — pre-declares safe bash commands so Claude doesn't ask for confirmation every time. Boris does not recommend using --dangerously-skip-permissions because it bypasses control without a truly good reason. Agent Stop hook or ralph-wiggum plugin — for long-running background tasks, this hook allows Claude to self-verify results upon completion without you having to wait. Instead of monitoring a 20-minute session, you let it report when it's done. Connect Claude with All Team Tools Claude Code is not a standalone tool. Boris configures Claude to have access to Slack (via MCP), BigQuery, Sentry, terminal, and any other tools the team is using. Of course, all MCP configurations and shared permissions are set up so everyone on the team has the same environment. In practice, when Claude can directly query Sentry for stacktraces and simultaneously view BigQuery to understand data patterns, the debugging quality is vastly different from manually pasting data into chat. Creating a Testing Loop is the Most Important Step Boris emphasizes this as "perhaps the most important thing" among all 13 tips. Core idea: instead of you checking Claude's output, give Claude a way to check itself. The testing loop can be: A web browser extension that automatically takes screenshots and compares UI. A test suite that runs after every change. A bash script that checks endpoints. A simulator that recreates user flows. When the feedback loop works well, Claude immediately knows whether its output is correct or incorrect without you acting as an arbiter. From this, output quality can increase 2–3 times, not because the model is better, but because Claude has the information to self-adjust. Lessons from How Boris Uses Claude Code Looking back at the 7 principles, the common thread isn't "use this feature, enable that feature" but rather the mindset of using Claude Code as an agent with context, tools, a way to self-check results, and accumulated memory over time. If you're using a single tab, typing prompts, and waiting — you're only utilizing about 10% of this tool's capabilities. A practical step today: create a CLAUDE.md file in your current project, and write down a convention or an error Claude just made. That's the starting point of an accumulating system, and it begins with the first line. If you want to refer to Boris's 13 tips, you can find the actual article here https://x.com/bcherny/status/2007179832300581177

Receiving a task and then staring at the screen for an hour not knowing where to start is something that happens to Antigravity users no less than regular workers. The problem isn't that you're incompetent or lazy, but that your brain doesn't fear difficult tasks; it fears unclear ones. And when you give AI a vague request, the results Antigravity produces will be equally vague. Why does delegating tasks to Antigravity still yield poor results? Antigravity is a true agent because it can plan, write code, execute commands, and self-verify results. But this is precisely why many people are disappointed on their first use: they immediately assign Antigravity a huge and vague task, and the agent runs for 30 minutes in the wrong direction, exhausting the quota with unusable results. Cognitive scientists call the state of freezing before a large task "cognitive overload." The brain doesn't know where to start processing, so it chooses the safest option: doing nothing, and the familiar loop looks like this: Brain fears making mistakes → freezes Cannot start → deadline approaches Becomes more fearful → freezes again With Antigravity, user cognitive overload directly leads to poor prompts, and poor prompts cause the agent to run in the wrong direction. This loop, of course, consumes more tokens and time than any technical error. There are three approaches to break that loop, depending on how well you understand the requirements and how much you've established the process. Three Effective Approaches to Working with Antigravity Method 1: Download Source Code from Experienced Users This is the fastest way to get started without spending time setting up from scratch, especially suitable when you don't yet know what your process should look like. Antigravity works best when it has sufficient project context, meaning it can see the rules, workflows, skills, and memory directories that record old knowledge. Instead of building everything yourself, you copy the source code from someone who has fully set it up, download it, and let the agent read the entire existing configuration, provided, of course, that person has agreed or made it public. Note: Many people have exploited this to spread malware, so only install source code officially from Anthropic, Google, xAI, OpenAI,... or reputable individuals. When you copy the code repository from someone who has fully set it up, download it, and let the agent read the entire existing configuration, you gain two benefits simultaneously: The agent immediately understands the writing style for skills, workflows, technical foundations, and project rules from day one without you needing to re-explain. You learn how experienced individuals set up processes — from organizing memory directories to writing rules for the agent — without having to figure it out from scratch. However, if you don't understand the author's intentions, you won't be able to fully utilize the functions of this source code, much like wearing an oversized shirt. Method 2: Solve Small Steps Yourself Before Delegating Large Tasks This is the most quota-saving method and also a lesson I learned after many instances of waste due to delegating overly large tasks from the start. The 4C framework — Clarify, Chunk, Consult, Commit — originally used for human task management, is extremely effective when applied to Antigravity for a simple reason: the clearer you are before delegating, the less the agent has to guess. Clarify Step: Before typing anything into Antigravity, answer these 4 questions yourself: What does the final result look like? Who will use this? What is the actual deadline? What constitutes successful completion of this task? Five minutes spent answering will completely change the quality of your command. Instead of "build me a login system," you'll be able to write "build a login system using Google OAuth for a Next.js application, save the session to Firestore, redirect to the main page after successful login, run it locally, and take a screenshot for me to review." Chunk Step: Based on the Zeigarnik effect, once you start even a small step, your brain automatically wants to complete the subsequent steps. Ask the agent "break the task into the smallest steps to begin?" and go through each step. Allocate a specific amount of time to understand the structure and check if the agent correctly understands the requirements before letting it run a large task. But remember to only allocate a specific amount of time, because many problems only truly emerge during execution, and that's when we find solutions. In this step, we can immediately use Fast Mode for the agent to execute without needing to create a framework or deep thinking, or even if there's nothing special, Gemini Flash can perfectly handle this part, saving significant tokens for Gemini Pro and Claude Opus. Consult Step: Don't make it hard on yourself when others have gone before you. Similar to Method 1 of downloading others' source code, this step involves actively finding and reading how they approach problems, how they break down tasks, how they write commands, and how they set up processes, then distilling suitable methods to apply to your own work. You don't need to copy verbatim; just learn from their thought structure. This is especially valuable for tasks you've never delegated to an agent before, as those who have done it often discover common pitfalls you might not be aware of. Commit Step: Instead of trying to plan the entire task perfectly before starting, commit just the first 10 to 15 minutes to understanding it. Ask the agent a small question, see how it responds, and always add the prompt: “If the problem is unclear, you can always ask again; do not make arbitrary decisions.” There will certainly be shortcomings, but we will feel that we have come a long way with Antigravity and the task, instead of spending hours writing perfect prompts without accomplishing anything, which would surely be very boring. Method 3: Delegate Large Tasks Immediately When a Process is Already Established This method only works when you have gone through the previous two methods — having clear processes, contextual memory skills, and the agent being familiar with the rules and workflows. This can be considered the Commit step in the 4C framework: instead of worrying about the entire task, you need to guide the agent towards a specific outcome and let the agent handle the rest. At this point, Plan Mode is a better choice than Fast Mode because the agent must create a detailed execution plan before performing the task, allowing you to review that plan and leave notes for adjustments before letting the agent run. This method combines the agent's speed with your strategic vision because the process is already in place, so the clarification step should be integrated into the rules, workflows, and skills, eliminating the need for you to re-explain the context each time. This is especially a favorite method for Pros who use Claude for excellent planning and then feed it to GLM for task execution to save tokens. Which Method Should We Choose for Our Work? These three methods used with Antigravity are not mutually exclusive but are ordered from less to more context: Vague tasks, don't know where to start: Copy others' source code or use the 4C framework to clarify first. Understood but large and complex tasks: Go through small steps, use Flash for simple steps, and reserve Pro for steps requiring deep thought. Tasks with clear processes: Delegate directly with Plan Mode, letting the agent handle it while you work on other things. The common thread among all three methods is that you must do one thing before opening Antigravity: think. Not long thinking — just 5 to 10 minutes to clarify the requirements before delegating to the agent. That amount of time saves more quota than any other prompt optimization technique.