Hermes Agent: the open source AI that remembers everything and gets smarter over time

An AI agent that grows with you – what Nous Research built, why it matters, and what it means for autonomous agents

Two weeks ago, Nous Research released Hermes Agent – an open-source autonomous AI agent that lives on your server, remembers what it learns, and builds its own skills over time. It went from 44 GitHub stars to over 5,500 in fourteen days.

We analyzed 8 sources: the official documentation, GitHub repository, 4 independent articles, Twitter discussions from the founding team and major tech influencers, and competitive data covering 6 major agent frameworks. Here’s what we found.

What Hermes Agent actually is

Hermes Agent is not a chatbot. It’s not a coding copilot tethered to an IDE. It’s an autonomous agent designed to live on your infrastructure permanently, learning your preferences, building its own skills, and reaching you wherever you are.

The install takes 60 seconds:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

From there, you get a full interactive CLI with 40+ built-in tools, persistent memory across sessions, and an optional messaging gateway that connects to Telegram, Discord, Slack, WhatsApp, and Signal simultaneously.

Co-founder Teknium described it as “a very good blend between coding agents like Claude Code and generalist agents like Clawdbot.” Nous Research positions it “between a Claude Code style CLI and an OpenClaw style messaging platform agent.”

The three features that set it apart

1. Multi-level memory

Here’s the core idea. Most AI agents have the memory of a goldfish – every session starts from zero. Hermes Agent maintains three layers of memory:

• Session memory: standard conversation context within a single interaction
• Persistent memory: facts, preferences, and project knowledge that carries across sessions, stored in transparent markdown files (MEMORY.md, USER.md, SOUL.md)
• Skill memory: auto-generated SKILL.md documents that encode solutions to hard problems

Skill memory is the interesting part. When Hermes Agent solves a hard problem – debugging a microservice, optimizing a data pipeline, configuring a deployment – it writes a skill document describing the approach. Next time a similar task comes up, it loads the skill automatically.

What you get is procedural learning. The agent genuinely gets more capable the longer it runs.

2. Six sandboxing backends

Most agents give you one or two execution environments. Hermes Agent supports six:

That range maps to real deployment scenarios. A developer testing locally. A researcher running batch jobs on an HPC cluster. A startup deploying on a $5 VPS. A team running GPU-intensive tasks on Modal. Same tool, different scales.

3. Research-grade infrastructure

Hermes Agent started as an internal research tool. Teknium admitted it began as “a way for us to have agentic primitives for datagen and RL and got [bigger].” The research features remain:

• Batch processing: generate thousands of tool-calling trajectories in parallel with automatic checkpointing
• Atropos RL: reinforcement learning integration with 11 tool-call parsers for training any model architecture
• ShareGPT export: export conversations for fine-tuning with trajectory compression

The dual nature here is unusual – personal assistant and training data factory in one. For AI researchers, it’s a daily driver that also generates the training data to make future models better.

How it compares

We mapped Hermes Agent against the major players in the 2026 AI agent market:

Claude Code dominates reasoning-heavy coding tasks, scoring 80.9% on SWE-bench Verified. But it’s closed source, Claude-only, and costs $20-200/month depending on usage. No messaging platform support.

Aider (39K stars) owns git-first workflows where every edit is a commit. Focused, excellent at what it does, but it’s a coding tool, not a general agent.

OpenClaw (160K+ stars) leads managed deployment with one-click cloud hosting through platforms like Augmi. It supports Telegram, Discord, and Slack, with always-on agents and persistent state. Where OpenClaw is cloud-managed, Hermes is self-hosted.

OpenCode (95K stars) is the terminal-native power tool with 75+ LLM providers. Focused on coding though, not general agent tasks.

Hermes Agent (5,500+ stars) occupies that middle ground: self-hosted, model-agnostic, multi-platform messaging, persistent memory, research features. Good at everything, best-in-class at memory and research.

The crypto angle

Here’s where it gets interesting if you’re watching the AI x crypto intersection.

Nous Research raised $50 million in Series A funding from Paradigm – one of crypto’s largest venture capital firms – at a $1 billion token valuation. Co-founder Jeffrey Quesnelle is a former MEV (Maximal Extractable Value) engineer. The company plans to launch a token for decentralized AI training coordination.

Connect the dots: an always-on AI agent with persistent memory, multi-platform messaging, scheduled automation, and 6 execution backends is one wallet integration away from becoming an autonomous economic actor.

An agent that can hold tokens, execute transactions, and manage its own finances on a schedule. We’re building toward exactly this with Augmi’s Phase 2 roadmap – crypto wallets for AI agents – and it’s the logical evolution of what Nous Research is doing too.

The convergence of open-source AI agents and crypto infrastructure feels inevitable. Both communities value decentralization, permissionlessness, and composability. The team that merges them first wins a massive market.

The SKILL.md standard

One development worth calling out: the agentskills.io open format for agent capabilities.

The SKILL.md format – YAML frontmatter plus markdown instructions – has been adopted by Claude Code, OpenAI Codex, OpenClaw, Hermes Agent, and VS Code Copilot. It’s becoming the common language for agent skills.

Why does that matter? Skills become portable. A skill written for Claude Code works in Hermes Agent. A community skill from ClawHub works in VS Code Copilot. The ecosystem becomes composable the same way npm packages made JavaScript composable.

Hermes Agent goes further with quarantine and audit systems for community skills, recognizing that an “agent skill marketplace” needs the same security review as a mobile app store.

What we think happens next

Memory becomes the moat. The agent that remembers best wins long-term engagement. You won’t switch away from an agent that knows your preferences, projects, and solutions accumulated over months.

Self-hosted and cloud will coexist. Hermes Agent is self-hosted. OpenClaw (via Augmi) is cloud-managed. Both have real advantages. The market is big enough for both, the same way AWS and on-premises infrastructure both thrive.

Research tools become products. The best developer tools often start as internal infrastructure. Hermes Agent’s origin as a research tool for training data generation gives it a robustness that purpose-built consumer products often lack.

Agent economies are coming. Persistent, autonomous agents with crypto capabilities will create new economic models. Agents that earn, spend, and transact independently. Markets where agents hire other agents. I know that sounds far-fetched until you realize the infrastructure is 80% built.

Takeaways

1. Hermes Agent is the real deal. 5,500+ stars in 2 weeks, $50M in funding, genuine technical differentiation. Not vaporware.

2. Memory is the next battleground. Every agent framework is racing to solve persistence. Hermes Agent’s multi-level approach, especially auto-generated skills, is the most sophisticated we’ve seen.

3. Coding agents and messaging agents are converging. Coding agents add messaging. Messaging agents add coding. Hermes Agent launched as both. Expect that convergence to accelerate.

4. Crypto x AI is infrastructure, not hype. Nous Research’s Paradigm funding and MEV-engineer founder make the crypto connection organic. Agent wallets are the obvious next step.

5. The SKILL.md standard is winning. Five major platforms adopted it. Skill portability is becoming table stakes.

Whether you’re a developer evaluating agent tools, a researcher exploring training pipelines, or a builder at the intersection of AI and crypto, Hermes Agent deserves your attention. Not because it’s perfect – tool reliability across models is still a real concern – but because it represents where the entire agent market is heading: persistent, autonomous, multi-platform, and increasingly economically independent.

We analyzed 8 sources across web articles, GitHub data, social media discussions, and competitive research. Full analysis available in our research archive.

Building the future of crypto-native AI agents at Augmi.world.