Hermes AI Agent: The Agent That Learns, Remembers, and Gets Better Every Day

Imagine having to re-explain yourself every single time you open an AI tool. Who you are, what project you're working on, what output format you prefer, which terms are standard in your field. You type it all out, the session goes well, you close the tab — and tomorrow, you start from scratch again.

This is the fundamental problem with most AI tools today: impressive capabilities, but no memory.

Hermes — the open-source AI agent built by NousResearch — solves this from the ground up. Hermes doesn't just remember; it learns from past experience, builds reusable skills, and genuinely gets better with every use. This article takes a deep look at Hermes — from its language models to Hermes Agent — and explains why this open-source project is rewriting the rules.

NousResearch is an AI research collective that has spent years focused on fine-tuning open-source language models. They developed the Hermes model series on top of Llama and Mistral architectures and built a substantial following among developers who want powerful models they can run on their own infrastructure — without sending data to proprietary APIs.

The Hermes family has followed a clear evolutionary path:

This progression reflects a clear vision: a model that doesn't just answer — it actually works.

Hermes 3 was built by fine-tuning Llama 3.1, and one of its defining capabilities is precise instruction-following — the user's instructions, not a company's internal guidelines. This distinction matters: while commercial models like GPT-4o frequently refuse requests on ethical grounds, Hermes 3 is designed to do exactly what it's asked — from specialized content creation to complex function calling and structured JSON generation.

Key Hermes 3 capabilities:

To understand RAG — one of the key mechanisms in these models — see our article on RAG in AI.

Hermes 4 was released in August 2025 and introduced one core new capability: Hybrid Reasoning Mode. This means the model can switch between fast responses and deep, step-by-step reasoning. For complex problems, a <think> tag sends the model into a reasoning phase where it works through the problem before delivering a final answer — much like a human who thinks before speaking.

Hermes 4 benchmark results (405B version) are remarkable:

For comparison: GPT-4o scored 17.67% and Claude Sonnet 4 scored 17% on RefusalBench. Hermes 4 answers a dramatically higher proportion of user questions without refusal.

This connects directly to the broader topic of AI reasoning models covered on deepfa.ir.

Hermes 4.3 is a historical milestone: the first Hermes model trained on the Psyche distributed network. Psyche is a decentralized training network that uses the DisTrO optimizer to coordinate geographically dispersed compute nodes — secured by Solana blockchain consensus — in a single training run.

This is not just technically fascinating; it carries an important message: training large models no longer requires a single massive data center.

Hermes 4.3 Benchmarks (36B):

Benchmark	Hermes 4.3 (36B)	Description
MATH-500	93.8%	Complex math problems
MMLU	87.7%	Multi-domain general knowledge
BBH (Big-Bench Hard)	86.4%	Complex reasoning tasks
AIME 24	71.9%	University math olympiad
GPQA Diamond	65.5%	PhD-level expert questions
RefusalBench	74.6%	Response rate (higher = better)

A striking fact: Hermes 4.3 at 36 billion parameters outperforms Hermes 4 at 70 billion parameters on several benchmarks. The 512K token context window — made possible by advanced Attention Mechanisms — means you can feed an entire mid-sized codebase to the model in a single pass.

The difference between a chatbot and an agent, illustrated with one example:

Hermes Agent was released by NousResearch in February 2026. With over 32,000 GitHub stars, it has become one of the most popular open-source agent projects in the space.

The heart of Hermes Agent is a unique memory system. Unlike tools that reset with every session, Hermes uses three memory layers:

1. Episodic Memory

The agent stores records of past tasks and their outcomes. If it makes a mistake on a task, that failure is logged and it tries a different approach next time. Real example: if on day one it uses the wrong logic for triaging GitHub issues, by day three it has self-corrected.

2. User Model

A USER.md file builds a persistent profile of your preferences: preferred language, desired output format, domain-specific terminology, constraints you work within. This persists across every session.

3. Skill Library

When Hermes completes a complex task, it documents its approach as a reusable Markdown skill file. The next time you give it a similar task, instead of starting from scratch, it loads the saved skill. This means faster execution + lower API costs.

This architecture directly connects to the concept of Memory-Augmented Neural Networks, which addresses memory at the architectural level of AI systems.

Hermes Agent ships with 40+ built-in tools including:

One of the smartest design decisions in Hermes Agent is being model-agnostic. A single change to the .env file switches you between GPT-5, Claude Opus 4.6, local Ollama models, or any OpenAI-compatible endpoint — without changing anything else. Skills, memory, and user model carry over completely.

This flexibility means you can route cheap tasks to local models and complex reasoning to frontier APIs — optimizing cost as you go.

For an introduction to the concept of AI agents, our article on AI Agents provides useful context.

Hermes Agent supports the MCP (Model Context Protocol), which enables AI connection to real-world tools. This means Hermes can communicate directly with external platforms like Asana, Slack, GitHub, and other services.

Hermes connects to CLI, Telegram, Discord, Slack, WhatsApp, Signal, and Email — all through a shared session architecture. You can give a task via Telegram, Hermes executes it, and returns the result to the same channel.

One developer set up this workflow:

This task is defined once and runs automatically via cron. If the classification logic is wrong on day one, episodic memory logs it and by day three it's self-corrected.

A researcher uses Hermes for literature review:

One user reported giving Hermes the instruction: "Build a full-stack todo app with authentication and deploy it." Hermes wrote the code, ran tests, debugged issues, handled deployment — and stored the skills acquired throughout the process for similar future projects.

A user runs Hermes on a Raspberry Pi 4 as a central brain across all their devices. User preferences are shared across devices, and Hermes performs tasks with full awareness of the user's complete digital context.

This type of usage aligns closely with Edge AI, which brings processing to the network edge.

To be honest: Hermes Agent is still in early stages.

Feature	Hermes Agent	LangChain/CrewAI	Claude Code / Cursor
Persistent cross-session memory	✅ Built-in, three-layer	⚠️ Requires manual implementation	Limited
Automatic learning from mistakes	✅ Built-in learning loop	❌ No	❌ No
Model agnostic	✅ 200+ models	✅ Yes	❌ Locked to specific model
Setup complexity	Single curl command	⚠️ Complex configuration	✅ Simple
Monthly cost	$5 VPS + API usage	Variable	Fixed monthly subscription
Messaging platforms	✅ Telegram, Slack, Discord, ...	⚠️ Requires development	❌ No
Open source	✅ Apache 2.0	✅ Yes	❌ Proprietary

One of the most fascinating aspects of NousResearch is the Psyche network. It demonstrates that large models can be trained across globally distributed nodes — without a single centralized data center. This concept connects directly to Federated Learning and privacy-preserving AI.

If Psyche can prove itself at larger scale, it could permanently shift how large models are developed — from the monopoly of big tech companies to a genuinely distributed model of AI development.

The growing trajectory of autonomous AI agents also suggests Hermes Agent is moving in exactly the right direction.

Hermes — both as a language model family and as Hermes Agent — is a project that demonstrates open source and commercial-grade capability don't have to be at odds.

Hermes 4 with benchmark scores that rival expensive proprietary systems, and Hermes Agent with its three-layer memory architecture that genuinely learns — this model family offers something you can't find elsewhere: an AI agent that becomes more capable the more you work with it.

If you're a developer, a researcher, or simply someone who wants an AI assistant that actually remembers context — Hermes Agent is worth trying.

For a broader perspective on the AI and large language model landscape, check out our article on AI Language Models on DeepFA.

Hermes AI Agent: The Agent That Learns, Remembers, and Gets Better Every Day

Introduction

Who Is NousResearch and Where Did Hermes Come From?

Hermes Language Models: From Hermes 3 to Hermes 4.3

Hermes 3: The Foundation That's Still Relevant

Hermes 4: When Hybrid Reasoning Changes Everything

Hermes 4.3: The First Model Trained on a Decentralized Network

Hermes Agent: When a Model Becomes a Real Assistant

Three-Layer Memory Architecture

Hermes Agent Capabilities That Genuinely Differentiate It

40+ Built-In Tools

Works With Any Major AI Model

MCP Protocol Support

Messaging Platform Integration

Real Examples: Hermes Agent in Practice

Example 1: Daily Developer Automation

Example 2: Research Literature Review

Example 3: Full Software Project Build

Example 4: Personal Assistant on Raspberry Pi

Hermes vs. the Competition

Limitations You Should Know

The Future: Psyche and Decentralized Training

Conclusion

Where innovation and AI come together