Hermes
An embeddable assistant capable of streaming answers, rendering UI, and acting within applications.
Key Highlights
- Hermes is positioned as both a personal AI agent and an embeddable in-app assistant that can stream answers, render UI, and take actions.
- Recent use cases span desktop chief-of-staff automation, local coding-agent orchestration, cloud VM provisioning, and embedded product integrations.
- Hermes is especially relevant to AI PMs designing agentic UX, memory-driven personalization, and multi-tool execution workflows.
- Its ecosystem connections include OpenClaw, GBrain, xAI, Grok 4.5, Composio, Tailscale, Telegram, and Google Workspace.
Hermes
Overview
Hermes is an AI agent and embeddable assistant platform described across recent mentions as both a personal operator and an in-app agent runtime. It can stream answers and reasoning live, render UI directly inside applications, and take actions on the current page or connected systems. In practice, Hermes appears in several modes: as a desktop AI agent, as part of personal-agent stacks, as a local or cloud-executed coding/operator agent, and as an embeddable assistant developers can integrate into their own products.For AI Product Managers, Hermes matters because it represents a shift from chat interfaces to actionable, embedded agent experiences. Rather than just returning text, Hermes is repeatedly framed as software that can orchestrate tools, personalize through memory and skills, operate continuously, and execute work across environments like local machines, cloud VMs, messaging apps, and productivity suites. That makes it a useful reference point for PMs designing agentic UX, multi-tool workflows, personalization systems, and embedded copilots inside existing applications.
Key Developments
- 2026-05-02: Garry Tan used an OpenClaw/Hermes platform with 17 years of Foursquare check-in data to generate personalized travel guides, showing Hermes in a memory-rich personal knowledge workflow.
- 2026-05-06: Peter Yang benchmarked Hermes against OpenClaw, Claude Code, Codex, and Gemini, with no clear winner, positioning Hermes as part of the emerging personal agent category.
- 2026-05-17: xAI integrated X Premium subscriptions into Hermes Agent and added native search across X posts, expanding Hermes with platform-native discovery and subscription-linked capabilities.
- 2026-06-01: Garry Tan open-sourced GBrain and paired it with OpenClaw/Hermes agents to automate most tasks, highlighting Hermes as an execution layer over a large personal knowledge base.
- 2026-06-06: Multica connected its Kanban board to local AI coding agents including Hermes via a local “Multica Demon,” enabling direct task assignment from workflow tools to agents.
- 2026-06-25: A detailed setup guide showed Hermes as a 24/7 desktop AI chief of staff with GPT-5.5 configuration, Telegram and Google Workspace integrations, voice replies, personalization, and cron-based automations.
- 2026-07-11: Greg Isenberg used Grok 4.5 inside a Hermes agent on Orgo with connectors like Composio, Agent Mail, Agent Phone, and X MCP to provision cloud VMs, build a landing page, and run multi-step startup tasks in one session.
- 2026-07-14: Hermes agents were shown auto-detecting hardware over Tailscale, installing compatible local models, and maintaining multiple agent instances with failover roles in a home AI compute fleet.
- 2026-08-04: Peter Yang recapped Karan Malhotra’s use of Hermes as a personal agent, emphasizing personalization via memory and skills, response-style switching, evaluator-agent patterns, local use, model flexibility, and creative workflows.
- 2026-08-11: Santiago shared a Hermes integration that lets developers embed Hermes into any application, with live answer streaming, reasoning display, in-app UI rendering, and page-level actions.
Relevance to AI PMs
1. A blueprint for embedded agent UX: Hermes is a strong example of how assistants can move beyond chat into in-product experiences. PMs can study its combination of streaming responses, visible reasoning, rendered UI, and page-level actions when designing copilots that feel native instead of bolted on.2. A model for operational agent stacks: Hermes shows up in desktop, local, and cloud workflows with integrations across Telegram, Google Workspace, Kanban systems, X, and cloud provisioning tools. PMs building agent products can use these examples to think through connector strategy, orchestration patterns, and where agents should act autonomously versus ask for approval.
3. A practical reference for personalization and memory: Several mentions frame Hermes as a personal agent that improves through memory, reusable skills, and style controls. For PMs, this is directly relevant to decisions around long-term context, user-specific behavior, skill libraries, evaluation loops, and trust mechanisms for recurring workflows.
Related
- OpenClaw: Frequently paired with Hermes in personal-agent and local-compute setups; often appears as a complementary agent framework.
- GBrain / gbrain-repo: A personal knowledge and memory system used alongside Hermes to make agents more context-aware and useful over time.
- Claude, Claude Code, Codex, OpenAI Codex, Gemini: Competing or adjacent agent/coding assistant systems used as benchmarks or alternatives to Hermes.
- xAI / Grok 4.5: Hermes has been used with xAI capabilities, including X-native search and Grok 4.5 as an underlying model in agent workflows.
- Orgo: Cloud environment where Hermes agents were provisioned to run autonomous startup and execution tasks.
- Composio: Connector layer used with Hermes to access external tools and workflows.
- Tailscale: Networking layer used in local fleet orchestration scenarios involving Hermes agents.
- Telegram and Google Workspace: Productivity integrations that position Hermes as an always-on chief-of-staff style assistant.
- Twilio and WebRTC: Relevant adjacent infrastructure for voice, communication, or real-time interaction patterns that may support embedded or interactive agent use cases.
- Garry Tan, Peter Yang, Karan Malhotra, Alex Finn, Santiago: Notable operators and commentators who demonstrated, benchmarked, or explained Hermes use cases.
Newsletter Mentions (12)
“Santiago shared a Hermes integration that will let developers embed Hermes into any application.”
Santiago shared a Hermes integration that will let developers embed Hermes into any application. Hermes could stream answers and reasoning live, render UI within the app, and act on each page.
“in Peter Yang recapped six takeaways from Nous Research co-founder Karan Malhotra on using Hermes as a personal agent, including building personalization through memory and skills, switching response styles, and using one agent to work and a fresh agent to evaluate.”
#15 in Peter Yang recapped six takeaways from Nous Research co-founder Karan Malhotra on using Hermes as a personal agent, including building personalization through memory and skills, switching response styles, and using one agent to work and a fresh agent to evaluate. Malhotra also highlighted skill cleanup, local and model-flexible use, preserving open source, and using Hermes for creative projects. Also covered by: @Peter Yang
“OpenClaw and Hermes agents auto-detect hardware over Tailscale, install compatible models (GLM 5.2 Opus 48-level, Qwen 3.6–35B, Ornith 1.0–35B), and maintain five agent instances with failover roles.”
#18 ▶️ Local AI models explained: How to run a fleet of Mac Studios and GPUs at home How I AI Podcast Alex Finn demonstrates how he orchestrates a home AI compute fleet of three Apple Mac Studio 512 GB machines, an Nvidia DGX Spark, and a custom RTX 5090 build using Tailscale, OpenClaw & Hermes agents, and Claude Code loops to run 24/7 local inference tasks like security scanning, code review, and social monitoring.
“Greg Isenberg Uses Grok 4.5 inside a Hermes agent on Orgo—with connectors like Agent Mail, Agent Phone, Agent Card, Composio, Idea Browser MCP, X MCP, and vidIQ—to autonomously provision cloud VMs, craft a startup landing page in ~40 seconds, and generate startup ideas, video thumbnails, market insights, and a cold-email sequence in one session.”
#21 ▶️ Grok 4.5 is a bigger deal than Fable 5 Greg Isenberg Uses Grok 4.5 inside a Hermes agent on Orgo—with connectors like Agent Mail, Agent Phone, Agent Card, Composio, Idea Browser MCP, X MCP, and vidIQ—to autonomously provision cloud VMs, craft a startup landing page in ~40 seconds, and generate startup ideas, video thumbnails, market insights, and a cold-email sequence in one session. Grok 4.5 delivers Opus 4.8-level intelligence at ~1/10th the cost and 10–15× the execution speed of Fable. A text command (“spin up a new computer with Hermes installed and Grok 4.5”) launched a fresh Orgo cloud VM with Hermes agent, injected API key, and pinned the model in seconds.
“Step-by-step setup of the Hermes desktop AI agent as a 24/7 AI chief of staff, including GPT 5.5 model configuration, Telegram and Google Workspace integrations, voice replies, personalization, and cron job automations.”
Hermes is described in a long hands-on setup walkthrough that includes a VPS, Mac mini, dedicated accounts, and automation details. It serves as a concrete example of a personal AI operator stack.
“Multica uses a local “Multica Demon” script to bridge its Kanban board with local AI coding agents (Cloud Code, CodeX, Hermes), enabling direct assignment of tasks to agents and daily shipping by a four-person team.”
#11 ▶️ Your AI Agents Block on You - Here's the Fix 🧵 SyntaxGTM Multica uses a local “Multica Demon” script to bridge its Kanban board with local AI coding agents (Cloud Code, CodeX, Hermes), enabling direct assignment of tasks to agents and daily shipping by a four-person team.
“Garry Tan open-sourced GBrain (MIT-licensed) on GitHub and outlines a 30-minute setup using his 350k-page markdown LLM wiki plus an OpenClaw/Hermes agent that automates most tasks.”
#6 𝕏 Garry Tan open-sourced GBrain (MIT-licensed) on GitHub and outlines a 30-minute setup using his 350k-page markdown LLM wiki plus an OpenClaw/Hermes agent that automates most tasks.
“#2 𝕏 xAI integrates X Premium subscriptions into Hermes Agent and equips it with native search across X posts.”
Today's top 13 insights for PM Builders, ranked by relevance from X, Blogs, and LinkedIn. Why LLM features need end-to-end observability metrics #1 𝕏 Boris Cherny upgraded /usage to show personalized token usage by plugin, skill, and parallel agent, so you can pinpoint high-consumption drivers and maximize your doubled rate limits. #2 𝕏 xAI integrates X Premium subscriptions into Hermes Agent and equips it with native search across X posts. #3 📝 PromptLayer Blog A deep dive into LLM observability tools - Discusses the need for observability when shipping LLM-powered features, since models can return confidently wrong answers while logs show successful API responses. Argues observability must connect inputs, outputs, latency, cost, and quality to diagnose real production issues. #4 𝕏 Sebastian Raschka presents a visual overview of recent LLM architectures—from Gemma 4 to DeepSeek V4—showcasing long-context efficiency tweaks. He dives into innovations like KV sharing, per-layer embeddings, layer-wise attention budgets, compressed attention, and mHC. #5 𝕏 Garry Tan launched GBrain, an open-source knowledge system (not RAG in a box) with eight memory-enhancing layers that make agents like OpenClaw and Hermes feel clairvoyant about you, paving the way for personal AI.
“in Peter Yang benchmarks five personal AI agents—OpenClaw, Hermes, Claude Code, Codex, and Gemini—and finds no clear winner.”
#9 in Peter Yang benchmarks five personal AI agents—OpenClaw, Hermes, Claude Code, Codex, and Gemini—and finds no clear winner.
“Garry Tan imported 17 years of Foursquare check-in data (5,000+ entries) into his OpenClaw/Hermes platform to auto-generate personalized travel guides, starting with his top spots in San Francisco.”
Garry Tan imported 17 years of Foursquare check-in data (5,000+ entries) into his OpenClaw/Hermes platform to auto-generate personalized travel guides, starting with his top spots in San Francisco. Garry Tan released GBrain v0.25 to let contributors benchmark AI evaluations against their own real-world brain queries.
Related
Anthropic’s coding agent environment used for building workflows, sessions, and handoffs.
Anthropic’s AI assistant/model family used for coding and review workflows. The newsletter references Claude’s built-in /code-review feature as part of adversarial code review.
Product and AI commentator who recaps practical lessons from builders and teams.
OpenAI’s coding assistant platform used for agentic development workflows.
A plugin included with TencentDB Agent Memory. It appears to be part of the framework's integration layer for agent memory workflows.
Google’s AI assistant and app ecosystem. The newsletter cites its voice usage growth, regional dialect expansion, and monthly active user milestone.
Y Combinator leader and frequent AI product commentator. Here he highlighted YC’s QM and also commented on OpenAI’s platform strategy.
An unnamed AI practitioner/commentator cited for rejecting line-by-line review of AI-generated code and focusing on system-level verification.
An AI company associated with the Grok family of models and open-sourcing its build system. The newsletter mentions backlash over a privacy-related feature and the release of the Grok Build codebase.
GBrain is an agentic retrieval system or method described as state-of-the-art without LLM rewriting. It is highlighted for strong evaluation results.
OpenAI’s coding agent used for autonomous implementation, browser scraping, and prototype generation in this newsletter. It is relevant for agentic coding workflows and PM-led prototyping.
Cloud Code appears to be a coding agent or coding workflow used to generate launch videos from websites. The newsletter describes it as working with Fable 5 and HyperFrames.
A coding tool or interface used to connect Kimi K3 to Polymarket data in a trading workflow. It functions as the orchestration layer for market analysis and execution.
Google's suite of productivity applications used for email, documents, spreadsheets, and calendaring. It is mentioned here as the environment Cursor agents can now operate across.
OpenAI's chat model optimized for more engaging conversation, better intent understanding, and improved handling of complex constraints. It is described as rolling out to paid users first and then free users.
A communications platform used here as a runtime/connection endpoint for personal AI demos. It is mentioned alongside WebRTC in a quick setup workflow.
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