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Teach an agent a workflow once. Have it remember after every rebuild. This tutorial shows how to deploy Nous Research Hermes Agent with NVIDIA NemoClaw and OpenShell, connect it to Slack, Outlook, GitHub, and NVIDIA developer forums, then turn a chat correction into a reusable skill. Private data stays... show more

NVIDIA AI

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70,146 views • 2 months ago •via X (Twitter)

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Turn a multi-step process into a single workflow, powered by NVIDIA RTX Spark. See how an agent like Nous Research Hermes Agent helps transform a concept sketch into a photoreal render, connecting Rhino, ComfyUI, and Blender 🔶 into one seamless workflow so designers can stay focused on their vision.

Turn a multi-step process into a single workflow, powered by NVIDIA RTX Spark. See how an agent like Nous Research Hermes Agent helps transform a concept sketch into a photoreal render, connecting Rhino, ComfyUI, and Blender 🔶 into one seamless workflow so designers can stay focused on their vision.

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Agents need more than a model. Jensen Huang breaks down the enterprise agent stack: models, orchestration, tools with skills, and a secure runtime to hold it all together. This is the NVIDIA toolkit for agents:

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NVIDIA

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The Hermes Agent Accelerated Business Hackathon presented by NVIDIA AI × Stripe × Nous Research starts now, for builders making agents that can earn, spend, and run real operations at any scale. Our NVIDIA integrations let your team run agents safely through NemoClaw, quickly on Nemotron 3 Ultra, and intelligently with access to their extensive agent skills. The new Stripe Skills for Hermes let your agent buy what it needs, provision its own SaaS, and pay for the services it uses. We want to see what kind of business tooling you can build on top of this foundation, whether it’s a fully automated company or a framework to accelerate enterprise functions. Prizes: 1st — $10,000 cash + NVIDIA DGX Spark + $5,000 Stripe Credits 2nd — $5,000 cash + NVIDIA DGX Spark + $3,000 Stripe Credits 3rd — $2,500 cash + NVIDIA DGX Spark + $1,000 Stripe Credits To enter: 1) Tweet a 1-3 minute demo video tagging Nous Research with a short writeup 2) Drop the link in the submissions channel: 3) Fill out the submission form: Judged by Nous Research, NVIDIA, and Stripe on usefulness, viability, and presentation. Submissions due EOD Tuesday, June 30.

The Hermes Agent Accelerated Business Hackathon presented by NVIDIA AI × Stripe × Nous Research starts now, for builders making agents that can earn, spend, and run real operations at any scale. Our NVIDIA integrations let your team run agents safely through NemoClaw, quickly on Nemotron 3 Ultra, and intelligently with access to their extensive agent skills. The new Stripe Skills for Hermes let your agent buy what it needs, provision its own SaaS, and pay for the services it uses. We want to see what kind of business tooling you can build on top of this foundation, whether it’s a fully automated company or a framework to accelerate enterprise functions. Prizes: 1st — $10,000 cash + NVIDIA DGX Spark + $5,000 Stripe Credits 2nd — $5,000 cash + NVIDIA DGX Spark + $3,000 Stripe Credits 3rd — $2,500 cash + NVIDIA DGX Spark + $1,000 Stripe Credits To enter: 1) Tweet a 1-3 minute demo video tagging Nous Research with a short writeup 2) Drop the link in the submissions channel: 3) Fill out the submission form: Judged by Nous Research, NVIDIA, and Stripe on usefulness, viability, and presentation. Submissions due EOD Tuesday, June 30.

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48,867 views • 1 month ago

Internally at NVIDIA, we use cuOpt based agentic workflows with agent skills to optimize our supply chains. Since it’s open source, you can too. With optimizations ready in minutes instead of weeks, the workflow uses multi-agent LangChain Deep agent orchestration and GPU-accelerated solvers to turn natural language into optimized decisions. Spin it up instantly with a Brev Launchable (preconfigured GPU environment) and grab free developer credits while they last.

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Addicted to Nous Research Hermes Agent's /learn. You can feed it any design and turn it into a reusable skill that makes Hermes design exactly how you want. /learn [URL or image] - extracts design tokens, layout structure, interaction states, component patterns, and a 10-point checklist Next time you want that exact style, just ask Hermes to use the skill it created. Example below 👇

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Mia

76,153 views • 1 month ago

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬

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Dami-Defi

11,668 views • 2 months ago

LFG! Nous Research's Hermes Agent is live on the Umbrel App Store! It's an AI agent that gets smarter the longer it runs, remembers stuff with persistent memory across sessions, learns your workflow, and autonomously creates its own reusable skills from experience. And it's sandboxed like every Umbrel app so it can't touch anything it's not supposed to. 1-click install. Login with ChatGPT, use an OpenRouter/OpenAI/Anthropic API key, or point it to a local Ollama model running on the same Umbrel.

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Umbrel ☂️

29,882 views • 3 months ago

HERMES AGENT BUILT A SKILL THAT AUTOMATED AN 8-HOUR WORKFLOW INTO 80 MINUTES. THE AGENT BUILT IT FROM ITS OWN COMPLETED SESSIONS. one user ran their customer pipeline through Hermes repeatedly: meetings → recordings → product requirements → technical specs → development → delivery. after enough runs, Hermes identified the pattern and created a skill that handles the full pipeline: → takes meeting recordings → extracts requirements automatically → runs research on the extracted data → generates product requirement documents → generates technical specifications → delegates the build to a coding agent → sends a link to the finished product what Hermes skills contain: → when to use (trigger conditions) → step-by-step procedure → known pitfalls to avoid → verification steps (how to confirm it worked) stored as markdown in ~/.hermes/skills/. readable, editable, permanent. agents with 20+ self-created skills finish similar future tasks ~40% faster. month 1 you explain everything. month 3 the agent runs your core workflows from the skills it built along the way. full breakdown of Hermes as a Personal AI OS in the article 👇

HERMES AGENT BUILT A SKILL THAT AUTOMATED AN 8-HOUR WORKFLOW INTO 80 MINUTES. THE AGENT BUILT IT FROM ITS OWN COMPLETED SESSIONS. one user ran their customer pipeline through Hermes repeatedly: meetings → recordings → product requirements → technical specs → development → delivery. after enough runs, Hermes identified the pattern and created a skill that handles the full pipeline: → takes meeting recordings → extracts requirements automatically → runs research on the extracted data → generates product requirement documents → generates technical specifications → delegates the build to a coding agent → sends a link to the finished product what Hermes skills contain: → when to use (trigger conditions) → step-by-step procedure → known pitfalls to avoid → verification steps (how to confirm it worked) stored as markdown in ~/.hermes/skills/. readable, editable, permanent. agents with 20+ self-created skills finish similar future tasks ~40% faster. month 1 you explain everything. month 3 the agent runs your core workflows from the skills it built along the way. full breakdown of Hermes as a Personal AI OS in the article 👇

YanXbt

29,819 views • 2 months ago

Someone just open-sourced the world's largest integration database for AI agents. - 47,000 agentic actions across 250+ apps - Connect to Slack, GitHub, Gmail, Stripe, Discord, Google Sheets and more - Every action is verified your agent never hallucinates API knowledge again - Deploy with a single CLI, connect once, use everywhere This is the integration layer agents have been missing. Built by Moe

Someone just open-sourced the world's largest integration database for AI agents. - 47,000 agentic actions across 250+ apps - Connect to Slack, GitHub, Gmail, Stripe, Discord, Google Sheets and more - Every action is verified your agent never hallucinates API knowledge again - Deploy with a single CLI, connect once, use everywhere This is the integration layer agents have been missing. Built by Moe

0xMarioNawfal

122,244 views • 4 months ago

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇

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186,667 views • 2 months ago

audit and fix your entire developer docs site in under a minute We built an AI agent with Browserbase and Cerebras that audits your entire docs site > point it at any documentation site > agent goes down the link tree > agent crawls and verifies every page, checks every link, reads every code snippet > agent also compares content to your GitHub repo > returns a full report with suggested fixes and source references The full code and tutorial for how to build this yourself is live now.

audit and fix your entire developer docs site in under a minute We built an AI agent with Browserbase and Cerebras that audits your entire docs site > point it at any documentation site > agent goes down the link tree > agent crawls and verifies every page, checks every link, reads every code snippet > agent also compares content to your GitHub repo > returns a full report with suggested fixes and source references The full code and tutorial for how to build this yourself is live now.

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26,679 views • 4 months ago

HERMES AGENT + STRIPE PAYMENTS + NVIDIA NEMOTRON. YOUR AGENT CAN NOW RUN A BUSINESS. ACCEPT PAYMENTS. PAY FOR SERVICES. PROVISION ITS OWN INFRASTRUCTURE. ALL INSIDE A SECURITY SANDBOX. two years ago the question was: can an AI agent run a business autonomously? the answer shipped this week. Hermes already handles workflows: cron jobs, sub-agents, kanban orchestration, multi-profile pipelines, scheduled research. what it couldn't do: spend money and prove it's safe. Stripe solved the first problem. Nvidia solved the second. WHAT AUTONOMOUS BUSINESS OPERATIONS LOOK LIKE: → customer sends a request via email → agent reads, scopes the project, estimates cost → provisions the infrastructure it needs (pays via Stripe, you approve on your phone) → builds and deploys the deliverable → sends the result to the customer → creates a payment link via Stripe (Stripe API integration, separate from Link CLI) → tops off its own API credits when balance drops → reports daily costs and progress to your Telegram → all within security policies you set once you set the rules. the agent runs the operation. you review revenue reports. not tasks. this is already happening. Dark Factory: autonomous software factory. send an idea before bed. wake up to a deployed URL. live entry in the Hermes Accelerated Business Hackathon. HOW STRIPE MAKES THE AGENT FINANCIALLY AUTONOMOUS: Stripe Link CLI gives your agent a scoped wallet. not your credit card. one-time-use virtual cards. → agent finds a product or service it needs → creates a spend request via Stripe Link → you get a notification on your phone (Link app) → you review: merchant, amount, context → one tap to approve or reject → agent receives a one-time virtual card → completes the purchase → card expires after single use your real card details never enter agent context. never printed in chat. never exposed to the merchant. Hermes cannot self-approve. you confirm every spend. install: hermes install skills/optional/payments/stripe-link-cli link-cli auth login what the agent can pay for: → API credits (Nous Portal, OpenRouter) → SaaS subscriptions it needs for operations → domain names, hosting, cloud credits → products from any online store currently US only. HOW NVIDIA MAKES THE AGENT SAFE TO TRUST: an agent with spending authority and no security boundaries is a liability. NemoClaw solves this. three layers: 1. OPENSHELL (sandbox) kernel-level isolation. controls network, filesystem, syscalls. default deny. you whitelist what's allowed. agent tries to reach a blocked domain = rejected. agent has no idea it's sandboxed. 2. NEMOTRON (private models) open-weight models on your own hardware. Nemotron 3 Super 120B MoE (48GB+ VRAM). Nemotron 3 Nano 4B (8GB VRAM, edge). fully private. no data leaves your machine. without GPU: inference routes to cloud via Privacy Router. 3. PRIVACY ROUTER (automatic split) decides per query: local or cloud. private data → local Nemotron. general web research → Claude, GPT, Gemini. automatic. per query. no manual routing. install: export NEMOCLAW_AGENT=hermes curl -fsSL https:// www.nvidia. com/nemoclaw.sh | bash requires Docker. NemoClaw is alpha software. APIs may change. test in non-production first. THE FULL PICTURE: before this stack: → agent could work but couldn't pay for anything → agent could pay but couldn't be trusted → agent could be trusted but couldn't operate 24/7 now: → Hermes runs the business logic (workflows, memory, skills, cron, sub-agents) → Stripe runs the financial layer (Link CLI for spending, Stripe API for receiving) → NemoClaw runs the trust layer (sandbox, policies, private routing) → VPS keeps everything always on → Telegram keeps you in the loop TYPES OF BUSINESSES THIS ENABLES: → autonomous software factory (customer request → build → deploy → payment link) → content agency (brief → research → draft → deliver → bill) → lead generation service (scrape → qualify → outreach → book calls) → SaaS monitoring and maintenance (detect issues → fix → deploy → report) → e-commerce operations (inventory → pricing → fulfillment → support) each one: Hermes profiles handle the workflows. Stripe handles the payments (in and out). NemoClaw handles the security. you handle the strategy. THE HACKATHON: Hermes Agent Accelerated Business Hackathon with Nvidia and Stripe. cash prizes + Stripe credits + Nvidia DGX Spark. ends June 30. the goal: build agents that earn, spend, and run real operations autonomously. link in the Nous Research Discord. full Hermes architecture deep-dive in the article 👇

YanXbt

37,709 views • 1 month ago

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

YanXbt

22,720 views • 1 month ago

Hermes Agent. Zero to full autonomous operation. One complete course. Installation. Skills. Memory. MCP. Scheduler. Multi-agent. Works while you sleep. The people who build this system will never manually operate a content, research, or business workflow again. The compounding starts from the first automated skill that runs. Read this and bookmark it now.

Hermes Agent. Zero to full autonomous operation. One complete course. Installation. Skills. Memory. MCP. Scheduler. Multi-agent. Works while you sleep. The people who build this system will never manually operate a content, research, or business workflow again. The compounding starts from the first automated skill that runs. Read this and bookmark it now.

CyrilXBT

80,668 views • 2 months ago

Hermes Agent. Zero to full autonomous operation. One complete course. Installation. Skills. Memory. MCP. Scheduler. Multi-agent. Works while you sleep. The people who build this system will never manually operate a content, research, or business workflow again. The compounding starts from the first automated skill that runs. Read this and bookmark it now.

Hermes Agent. Zero to full autonomous operation. One complete course. Installation. Skills. Memory. MCP. Scheduler. Multi-agent. Works while you sleep. The people who build this system will never manually operate a content, research, or business workflow again. The compounding starts from the first automated skill that runs. Read this and bookmark it now.

CyrilXBT

176,900 views • 2 months ago

We created a staking contract in a recent video. Now it's time to deploy it. Initially the AI agent pushes the contract to an invalid address, where it fails. The agent suggests adding a valid address to fix the issue, then builds the UI and workflow. Once deployed, it confirms the contract address and transaction hash - and there you have it! You've successfully deployed a staking contract.

We created a staking contract in a recent video. Now it's time to deploy it. Initially the AI agent pushes the contract to an invalid address, where it fails. The agent suggests adding a valid address to fix the issue, then builds the UI and workflow. Once deployed, it confirms the contract address and transaction hash - and there you have it! You've successfully deployed a staking contract.

Wayfinder Foundation 🧭

49,953 views • 1 year ago