Hermes Agent: Self-Improving AI with Persistent Memory

Key Takeaway

Hermes Agent is a self-improving AI agent that maintains persistent memory across sessions and autonomously creates new skills from experience. Created by Nous Research, it transforms AI assistants from stateless tools into genuine learning collaborators that evolve over time.

What is Hermes Agent?

Hermes Agent is an advanced AI agent framework that solves the fundamental amnesia problem we all face with current AI assistants. The project Hermes Agent by Nous Research enables AI agents to remember previous sessions, learn from interactions, and autonomously develop new capabilities - creating a continuous improvement loop rather than starting fresh each time.

The Problem We All Know

Anyone who has worked extensively with AI assistants knows this frustration: we spend hours building up context, teaching preferences, and refining our workflows. Then we close the session, and it's all gone. The next conversation starts from absolute zero.

This isn't just inconvenient - it fundamentally limits what we can accomplish with AI assistants. We can't build long-term working relationships, can't leverage accumulated knowledge, and certainly can't expect our assistants to improve based on past experiences. Every interaction is essentially a one-night stand with our AI tools.

Existing solutions like RAG (Retrieval Augmented Generation) help with retrieving relevant information, but they don't create genuine learning loops. Our assistants remain fundamentally reactive rather than proactive, unable to synthesize learnings into new capabilities.

How Hermes Agent Works

Hermes Agent creates a continuous learning system through three core mechanisms that work together to enable genuine memory and skill development.

FTS5 Session Search with LLM Summarization - meaning the agent maintains a searchable database of all past interactions. Instead of just storing raw conversation logs, it uses Full-Text Search (FTS5) combined with LLM-powered summarization. Think of it like having a brilliant assistant who not only remembers everything but can instantly recall the relevant parts when needed.

Honcho for User Modeling - this is where it gets interesting. Hermes doesn't just remember what we said; it builds an actual model of who we are, how we work, and what we care about. Honcho creates a persistent representation of our preferences, work style, and domain knowledge that evolves over time.

Autonomous Skill Creation - here's the game-changer. Based on repeated patterns and successful interactions, Hermes can write new functions for itself. If we frequently ask it to perform a specific type of analysis or transformation, it can encode that as a reusable skill. The agent literally programs itself to better serve our needs.

Quick Start

Here's how we get started with Hermes Agent:

# Clone the repository
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent

# Install dependencies
pip install -r requirements.txt

# Configure your LLM backend
export OPENAI_API_KEY="your-key-here"
# or use local models

# Run the agent
python main.py

A Real Example

Let's say we want to create an agent that learns our code review preferences over time:

from hermes_agent import Agent, Memory

# Initialize agent with persistent memory
agent = Agent(
memory=Memory(session_store="./sessions"),
user_model="./user_profile",
skill_library="./skills"
)

# First interaction - teaching preferences
response = agent.process(
"Review this PR focusing on security and performance"
)

# Later sessions - agent remembers and applies learned preferences
response = agent.process(
"Review this new PR" # Automatically applies security/performance focus
)

# Agent may have created a custom skill
print(agent.skills.list()) # Shows auto-generated review skills

Key Features

Cross-Session Memory - Think of it like working with a colleague who never forgets. All context, preferences, and learnings persist across sessions and even across different deployment environments.

Platform-Agnostic Deployment - We can run Hermes on a $5 VPS, scale it to GPU clusters, or deploy serverless. The architecture separates the learning system from the compute layer, giving us total flexibility.

Multi-Platform Integration - Hermes integrates with Telegram, Discord, and other messaging platforms. We interact with our AI assistant where we already work, not through yet another interface.

Transparent Skill Evolution - Unlike black-box systems, we can actually see what skills the agent develops. The skill library is human-readable, allowing us to audit, modify, or remove capabilities as needed.

User Modeling Engine - Honcho builds a sophisticated model of our working style, domain knowledge, and preferences that informs every interaction, making the assistant genuinely personalized over time.

When to Use Hermes Agent vs. Alternatives

Hermes Agent excels when we need genuine long-term AI collaboration. If our use case involves building up domain knowledge over weeks or months, teaching complex preferences, or wanting an assistant that actively improves its capabilities, Hermes is purpose-built for this.

For simpler use cases - one-off queries, stateless operations, or situations where we prefer the assistant to remain exactly the same - traditional AI assistants or API-based solutions like OpenAI's Assistants API might be more appropriate. They're simpler to set up and don't require managing persistent state.

AutoGPT and similar autonomous agents focus more on task decomposition and web interaction. Hermes instead focuses on memory, learning, and skill development. They're complementary approaches - we could even use AutoGPT-style planning with Hermes-style memory.

LangChain and LlamaIndex provide excellent tools for building AI applications with memory components. Hermes builds on similar concepts but integrates them into a complete agent framework with autonomous skill creation, which those libraries leave to us to implement.

My Take - Will I Use This?

In my view, Hermes Agent represents one of the most important developments in practical AI agents. The autonomous skill creation is particularly impressive - it's not just remembering, it's genuinely evolving.

I'm definitely integrating this into my workflow for long-term projects where I want an AI collaborator that grows with the project. The ability to deploy anywhere - from cheap VPS to enterprise infrastructure - makes it viable for both personal experiments and production systems.

The main limitation: this is bleeding-edge technology from Nous Research. Expect some rough edges, and be prepared for a more technical setup compared to plug-and-play SaaS solutions. We'll need to manage our own infrastructure and handle the complexity of persistent state.

But for those of us building serious AI-powered workflows, that trade-off is absolutely worth it. Check out the project: Hermes Agent on GitHub

Frequently Asked Questions

What is Hermes Agent?

Hermes Agent is a self-improving AI agent framework by Nous Research that maintains persistent memory across sessions and autonomously creates new skills based on user interactions and patterns.

Who created Hermes Agent?

Hermes Agent was created by Nous Research, an AI research organization known for their work on open-source language models and agent frameworks.

When should we use Hermes Agent?

Use Hermes Agent when you need an AI assistant that learns and improves over time, maintains context across sessions, and can develop custom capabilities based on your specific workflows and preferences.

What are the alternatives to Hermes Agent?

Alternatives include OpenAI's Assistants API for simpler persistent assistants, AutoGPT for autonomous task execution, LangChain/LlamaIndex for building custom agent workflows, and traditional chatbots for stateless interactions. Hermes is unique in combining persistent memory with autonomous skill creation.

What are the limitations of Hermes Agent?

As cutting-edge technology, Hermes requires technical setup and infrastructure management. It's not a plug-and-play SaaS solution. You'll need to handle persistent storage, manage state, and potentially deal with evolving APIs as the project matures.