Hermes Agent Hits #1 on OpenRouter Global Rankings — What 271 Billion Tokens Tells Us
Hermes Agent by Nous Research topped OpenRouter's global rankings across all AI apps with 271 billion tokens, not just CLI tools. We unpack what that usage means, how open-source adaptability is winning the agent race, and why persistent memory and skills matter more than peak demo performance.
On May 6, 2026, Nous Research announced that Hermes Agent had reached #1 on OpenRouter's global token rankings with 271 billion tokens processed—not just among coding agents or CLI tools, but across all AI applications on the platform. OpenClaw sat at #2, watching from below.
This is a milestone for open-source in the agent race: a self-hosted, MIT-licensed agent with persistent memory and a skills system that learns from experience is now processing more tokens than proprietary chat apps, productivity tools, and every other category on one of the largest model routing platforms in the world.
This post unpacks what that ranking means, how Hermes Agent actually works, why open-source adaptability is winning, and what product teams should watch as the agent ecosystem matures.
What OpenRouter is (and why #1 matters)
OpenRouter is a unified API gateway that routes requests to 200+ language models from providers like OpenAI, Anthropic, Google, Nous Research, Xiaomi, Moonshot, and dozens of others. Developers and apps use OpenRouter to:
Fallback to alternative models when one is down or rate-limited
Track usage across all models in one dashboard
The App & Agent Rankings page shows which applications are processing the most tokens through OpenRouter's gateway. It is a usage leaderboard, not a quality benchmark—but usage at scale is a proxy for real-world adoption.
When Hermes Agent hit #1 with 271 billion tokens, it meant:
More developers and teams are routing production workloads through Hermes than any other app
The open-source agent is competing with (and beating) proprietary tools in live use
Persistent memory, skills, and multi-platform access are proving more valuable than single-session chat or IDE-only copilots
What Hermes Agent is
Hermes Agent is an open-source (MIT), self-hosted agent runtime built by Nous Research—the lab behind Hermes, Nomos, and Psyche models. Launched February 25, 2026, it is designed to run persistently on a machine you control (VPS, home server, laptop, or serverless host) and stay on, remember, and meet you where you already talk—terminal, Telegram, Discord, Slack, and more.
Core architecture:
One or more language models you select (Nous Portal, OpenRouter, OpenAI, Anthropic, MiniMax, Xiaomi MiMo, Kimi from Moonshot, or your own endpoint)
A big bag of tools—file access, terminals, browsers, platform integrations, MCP servers, and more—governed by what you turn on and how you scope it
Persistence: memory, skills, session search, and user modeling so the agent is not reinventing you from scratch every Monday
It is infrastructure-shaped, not chat-shaped: you optimize for uptime, secrets handling, and who is allowed to talk to the bot—not just "token quality on this one prompt."
Key features (why it's winning)
1. Persistent memory across sessions
Where stateless chat forgets, Hermes accumulates:
Memory that persists and can be nudged back into attention
FTS5 full-text search + LLM summarization so the agent can find past work instead of pretending the last message is the whole world
User modeling so preferences and context can deepen across sessions
Why it matters: A chat that remembers your last 10 projects, coding style, and preferences is 10x more useful than one that asks "What can I help you with today?" every Monday.
2. Skills system: learning from experience
When Hermes solves a hard problem, it writes a reusable skill document so it never forgets how. Skills are:
Searchable (FTS5-indexed locally)
Shareable (compatible with agentskills.io open standard)
Improvable (refined during use based on outcomes)
Hermes ships with 40+ bundled skills covering MLOps, GitHub workflows, diagramming, note-taking, and more. You can also create custom skills from your own workflows.
Why it matters: Most agents start from zero context every turn. Hermes builds expertise over time, like a junior engineer who gets better the longer they work with your codebase.
3. 40+ built-in tools
Out of the box, Hermes can:
Read/write files (with approval gates)
Run shell commands (local, Docker, SSH, Singularity, Modal backends)
Browse the web (search, fetch, vision)
Interact with platforms (Telegram, Discord, Slack, WhatsApp, Signal, email)
Connect MCP servers (Model Context Protocol for external integrations)
Schedule automations (cron-style delivery to any platform)
Spawn subagents (isolated workers for parallel tracks)
Why it matters: A broad tool set means Hermes can handle research, coding, ops, communication, and automation without context-switching across apps.
hermes gateway — connects Telegram, Discord, Slack, WhatsApp, Signal, and CLI—all from a single gateway process
Why it matters: You can ask questions from your phone while commuting, review code from Slack during standup, and dive deep in the terminal when you're at your desk—same agent, same memory, same skills.
5. Data privacy: local SQLite, no cloud middleman
All data—memories, skills, conversation history—is stored in a local SQLite database on your machine. Nothing passes through third-party cloud services. You retain complete ownership and control.
Why it matters: For sensitive projects, proprietary code, or regulated industries, self-hosted is the only acceptable architecture. Hermes ships that by default.
6. MIT license: truly open-source
Unlike source-available projects with restrictive licenses, Hermes is MIT—you can fork, modify, sell, or embed it in commercial products without royalties or approval.
Why it matters:Open-source adaptability is how you win in infrastructure. Teams can customize Hermes for internal workflows, contribute improvements upstream, and trust it won't get rug-pulled by a vendor pivot.
140,000+ stars (in the top 0.1% of all GitHub repos)
Nearly 1,000 contributors (rare for a 2.5-month-old project)
Active daily commits (not abandonware)
Why it matters:Stars can be gamed, but contributors and commit velocity signal real adoption. Hermes is not a weekend hack—it is a thriving ecosystem.
How Hermes compares to other agents
Feature
Hermes Agent
Claude Code (CLI)
ChatGPT Plus
OpenClaw
Persistent memory
✅ FTS5 + LLM summarization
❌ Session-based
❌ Session-based
✅ Session continuity
Skills system
✅ Learns from experience
❌ Skills via marketplace
❌ No skills
✅ Skills support
Multi-platform (Telegram, Slack, etc.)
✅ Gateway built-in
❌ CLI only
❌ Web/app only
✅ Gateway support
Self-hosted
✅ VPS, Pi, laptop, cloud
❌ Cloud-only
❌ Cloud-only
✅ Self-hosted
Open-source (MIT)
✅ True open-source
❌ Proprietary
❌ Proprietary
✅ Open-source
Scheduled automations
✅ Cron-style delivery
❌ No scheduling
❌ No scheduling
✅ Scheduling
Subagents (parallel work)
✅ Isolated workers
⚠️ Limited
❌ No subagents
✅ Subagents
Data privacy
✅ Local SQLite
⚠️ Cloud transit
❌ Cloud-only
✅ Local
Key takeaway: Hermes wins on persistence, multi-platform, and ownership. Claude Code wins on polish and IDE integration. ChatGPT wins on zero setup for casual users.
Why open-source is winning the agent race
The Hermes #1 ranking is not an outlier—it is part of a pattern:
Hermes Agent — #1 on OpenRouter (271B tokens)
OpenClaw — #2 on OpenRouter
Claude Code — Popular but not in OpenRouter's top apps (because it routes through Anthropic's API, not OpenRouter)
What the proprietary tools have:
Polish (better onboarding, fewer sharp edges)
Brand (Anthropic, OpenAI have distribution and trust)
Integrated hosting (no VPS setup required)
What the open-source tools have:
Customization (fork it, modify it, run it your way)
Ownership (data stays local, no vendor lock-in)
Community velocity (1000 contributors ship features faster than a 10-person team)
Cost control (swap models, use local LLMs, optimize spend)
In infrastructure, ownership beats convenience when the product gets serious. That is why Linux, Postgres, Kubernetes, and VS Code (open-core) won—not because they were easier, but because teams needed control.
Hermes is following the same playbook for agents.
What 271 billion tokens actually means
271 billion tokens is roughly:
203 million words (at ~750 tokens = 1000 words)
Equivalent to 406,000 novels (500 words/page, 250 pages/novel)
Or 135 million API calls at 2000 tokens/call (conversational turns)
Context: OpenRouter serves hundreds of apps—chat UIs, coding copilots, productivity tools, creative writing assistants. For Hermes to process more tokens than all of them means it is being used heavily in production workflows, not just demos.
What drives that usage:
Long-running sessions (agents that stay on for days/weeks process more tokens than 5-minute chats)
Tool-heavy workflows (file reads, shell commands, web searches all add tokens)
Multi-user teams (one Hermes instance can serve multiple people via gateway)
Scheduled automations (cron jobs running reports, summaries, monitoring)
Bottom line: Hermes is not a toy—it is infrastructure that teams are relying on for real work.
Choose your model provider via hermes model (Nous Portal, OpenRouter, OpenAI, Anthropic, etc.)
Enable tools you trust via hermes tools
Start the terminal UI with hermes or gateway with hermes gateway
For production:
Security: Read the docs on command approval, DM pairing, and isolation patterns
Hosting: VPS for stable public presence, home lab for custody, laptop for experiments
Networking: Use Tailscale, WireGuard, or plain SSH keys—don't expose god-mode shell to the internet
Bottom line
Hermes Agent hitting #1 on OpenRouter's global rankings with 271 billion tokens is a signal that open-source is winning the agent race—not because it is easier, but because it offers ownership, customization, and long-running state that browser-based and session-limited tools cannot match.
With persistent memory, a skills system that learns from experience, 40+ tools, multi-platform gateway, and MIT license, Hermes is proving that agents are infrastructure, not chat apps—and teams want control over their infrastructure.
For product teams, the takeaway is clear: provenance, adaptability, and long-term memory matter more than peak demo performance. The tools that let users own their data, customize workflows, and build expertise over time will win—regardless of how polished the onboarding is.
