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Hermes Agent Architecture Machine Learning AI Systems Learning Loops 9 min read

How Hermes Agent Learns: Architecture & The Self-Improvement Loop

Vishnu
By Vishnu
How Hermes Agent Learns: Architecture & The Self-Improvement Loop

ChatGPT forgets. Claude forgets. Every conversation starts fresh, no matter how much you’ve talked to them before.

Hermes doesn’t. It has something traditional AI assistants don’t: a closed learning loop. Every task it solves makes it smarter for the next one.

This article explains how that actually works.

The Closed Learning Loop (What Makes Hermes Different)

Here’s the magic formula:

Task → Solve → Document → Store → Improve → Next Task (Faster)

Let’s break this down with a real example.

Week 1, Monday: You ask Hermes to fetch this week’s sales data from your internal API. Hermes:

  1. Figures out the API endpoint
  2. Writes the query
  3. Gets the data
  4. Delivers the result

Hermes doesn’t just give you the answer. It documents what it did—writes a skill:

Skill: fetch_weekly_sales
When to use: "get sales data", "sales report", "this week's numbers"
Steps:
  1. Query: GET /api/sales?start=monday&end=sunday
  2. Parse JSON response
  3. Calculate total
  4. Format as report
Success: 100% (1 execution)

Friday: You ask for the same report. Hermes:

  1. Recognizes the request
  2. Retrieves the skill from memory
  3. Executes it directly (no figuring out, no trial-and-error)
  4. Completes 40% faster (per Nous Research benchmarks)

Week 2: You ask again, but this time with a twist: “Include last week too.” Hermes:

  1. Retrieves the skill
  2. Refines it (adds date range parameter)
  3. Improves the skill document
  4. Executes improved version
  5. Stores the upgrade

Over time, Hermes builds a library of refined skills specific to your workflows. That’s the learning loop.

Core Architecture Components

Hermes has five main parts that work together:

1. Inference Engine

This is where the actual LLM runs. It could be:

  • Local Ollama — runs on your machine, free
  • OpenAI’s API — cloud-based, costs money
  • Anthropic’s Claude — cloud-based
  • OpenRouter — unified API covering many models

The inference engine doesn’t do anything special—it’s just running an LLM. What’s special is what Hermes does with the LLM’s output.

2. Memory System

Everything Hermes learns lives here. The ~/.hermes/memory/ directory contains:

Conversation History

conversations/
├── user-1234/
│   ├── 2026-04-29-sales-report.txt
│   ├── 2026-04-28-api-debug.txt
│   └── ...

Hermes reads previous conversations to understand context.

Skill Documents (The secret sauce)

skills/
├── fetch_weekly_sales.md
├── create_report.md
├── fix_database_connection.md
└── ...

These are auto-generated instructions. When you ask for something similar next time, Hermes retrieves the skill instead of solving from scratch.

Preferences & Metadata

preferences/
├── user-style.json          # How you like responses formatted
├── tool-endpoints.json      # Your API keys, endpoints
├── learned-workflows.json   # What you do regularly

All stored locally. No cloud sync by default.

3. Skill Generator

When Hermes solves a task, the skill generator creates a document:

INPUT: "Create a weekly sales report from our API"
EXECUTION: (Hermes figures out: endpoint, auth, parsing, formatting)
OUTPUT SKILL:
  - What the task is
  - How to recognize it next time
  - Step-by-step execution
  - Error handling
  - Performance notes

This skill is markdown-formatted (readable by humans) and executable (Hermes can run it). It’s not hidden bytecode. You can open ~/.hermes/skills/ and read exactly what Hermes learned.

4. Platform Router

Hermes runs on multiple platforms but maintains unified memory. The platform router handles:

  • Discord: Messages in a server
  • Slack: Messages in channels or DMs
  • Telegram: Personal chats or groups
  • Email: Inbox parsing
  • CLI: Terminal input

All routes to the same memory and learning system. Ask Hermes something on Discord, and when you ask the same thing on Slack later, it’s already learned.

The router is what makes Hermes “live” where you work instead of you logging into a dashboard.

5. Tool Orchestrator

Hermes doesn’t just talk—it acts. It can:

  • Call APIs
  • Read/write files
  • Execute code
  • Use web search
  • Interact with databases

The tool orchestrator manages:

  • Which tools are available
  • How to call them safely
  • Error handling
  • Tool chaining (use tool A’s output as tool B’s input)

How a Task Flows Through the System

Here’s a concrete walkthrough:

User asks: “Create a report of this month’s unresolved tickets”

Step 1 — Recognition: Hermes checks memory: “Have I solved something like this before?”

  • Searches skill documents
  • Finds: generate_ticket_report.md
  • Confidence: 92% match

Step 2 — Retrieval or Creation: Because of high confidence, Hermes retrieves the skill. If confidence was low, it would create a new solution.

Step 3 — Execution: Hermes follows the skill:

1. Connect to ticket system API
2. Query: status != "resolved"
3. Filter: created_this_month
4. Count by category
5. Format as markdown table
6. Return to user

Step 4 — Verification: Hermes checks: Did the tool work? Was the output useful?

Step 5 — Learning: If execution was perfect: skill confidence increases. If execution had issues: skill gets refined and rewritten.

Step 6 — Storage: Updated skill saved to ~/.hermes/skills/ with:

  • Execution count: 3
  • Success rate: 100%
  • Last improved: 2026-04-29
  • Performance: 0.8 seconds average

Next time you ask, Hermes starts with this refined version.

Memory Architecture in Detail

Hermes uses a hierarchical memory system:

Long-Term Memory (Persistent)
├── Skills (reusable solutions)
├── Conversation history (learning about you)
└── Learned preferences (how you like things)

Short-Term Memory (This conversation)
├── Context window (recent messages)
└── Current task state

Working Memory
├── Tools being used
├── Variables in execution
└── Current reasoning

Long-term memory persists across restarts. Short-term and working memory reset.

This is why Hermes gets smarter over months. It builds up a real understanding of your workflows.

Self-Improvement Mechanisms

Hermes improves skills in three ways:

1. Frequency-Based Improvement

The more you use a skill, the more Hermes refines it:

  • First use: Functional but verbose
  • Fifth use: Optimized, stripped down, faster

2. Feedback-Based Improvement

If you say “that wasn’t quite right” or refine the result, Hermes updates the skill.

3. Comparison-Based Improvement

If Hermes finds a faster or more reliable way to do something, it updates previous skills.

Example: Week 1, Hermes learns to fetch sales data via REST API. Week 3, it discovers a GraphQL endpoint that’s 10x faster. It updates all related skills to use GraphQL instead.

Comparison: Agent-First vs. Gateway-First Architecture

Hermes is agent-first. It’s built around this learning loop.

OpenClaw is gateway-first. It’s built around routing and tool access.

AspectHermesOpenClaw
Design goalSelf-improvementTool coverage
MemoryPersistent, learnableSession-based
SkillsAuto-generated, self-improvedCommunity marketplace (risky)
CVEsZero reported341 malicious skills found (2026)
LearningOver time, per-userNo learning across sessions
ScalingBetter with useScales with tool breadth

Both are valid architectures. Different goals. Hermes is better if you want personalization. OpenClaw is better if you need broad tool coverage.

Why This Architecture Matters

For You:

  • Your AI assistant gets better the more you use it
  • Less “prompt engineering”—Hermes learns what you mean
  • Better privacy (local memory, no telemetry)

For Performance:

  • Repeated tasks execute faster (40% faster per benchmarks)
  • Memory reduces inference overhead
  • Skill reuse beats solving from scratch

For Security:

  • No community skill marketplace = no supply chain attacks
  • All learnings stay local (unless you opt into cloud)
  • Zero CVEs (as of April 2026)

For Cost:

  • If using Ollama: infrastructure cost only (no API calls)
  • If using cloud APIs: fewer tokens used (skills are efficient)

The Hidden Benefit: Personalization Without Creepiness

Traditional cloud AI learns about users at the company level. Hermes learns about you specifically.

Your preferences. Your workflows. Your terminology. Over time, Hermes becomes genuinely useful because it’s tailored to your exact context.

No algorithmic feed optimization. No selling your data. Just genuine personalization.

Real-World Scenario: How This Plays Out

Day 1: You connect Hermes to your Slack workspace. It can answer basic questions. Generic, but functional.

Week 2: You’ve asked it to fetch data, create reports, debug issues. It’s learned 7 skills specific to your stack.

Week 4: A new person joins your team and asks Hermes the same questions. Hermes answers in 3 seconds (skills retrieval) instead of 30 seconds (first-time solving). Team productivity up.

Month 2: Hermes has learned your team’s code patterns, API conventions, error messages. It proactively suggests optimizations based on what it’s seen fail before.

Month 4: You barely prompt Hermes anymore. It knows what you need and does it.

That’s the architecture in action.

What’s Actually Running in ~/.hermes/

If you want to peek under the hood:

ls -la ~/.hermes/

# You'll see:
memory/           # Everything it learned
  skills/         # Auto-generated solutions
  conversations/  # Chat history
  preferences/    # Your settings
config.yml        # Your setup
logs/            # Debug logs
cache/           # Temporary data

It’s all readable files. No proprietary formats. You can audit what Hermes learned about you.

FAQ

Q: Can Hermes hallucinate in skills? Yes. If it solves a task incorrectly and stores the skill, it’ll repeat the mistake. Feedback and refinement fix this, but early skills can be buggy.

Q: How much disk space does memory use? Depends on usage. Typical: 50MB-500MB after months of use. Conversations are stored as text (efficient).

Q: Can I reset Hermes’s learning? Yes. Delete ~/.hermes/memory/ and it starts fresh. Alternatively, selective skill deletion is supported.

Q: Does Hermes waste time on skills that are rarely used? No. Skills are indexed and pruned. Unused skills don’t slow down retrieval.

Q: Why no cloud learning sync? Privacy by default. You can configure cloud storage, but Hermes doesn’t push to servers by default.

That’s how Hermes learns and improves. It’s not magic. It’s systematic documentation and reuse of what works. Over time, that compounds into genuinely useful personalization.

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