The Hermes Memory Loop: How an Agent Actually Learns You

This is a submission for the Hermes Agent Challenge

I stopped using AI for recurring workflows. Here's why.

I used to rely on AI assistants for everything: planning my week, debugging code, summarizing docs, drafting emails.

Then I noticed the pattern:

1. I'd start a chat.
2. I'd explain my context: my stack, my preferences, my constraints.
3. I'd get useful output.
4. I'd close the chat.
5. I'd start a new chat.
6. I'd have to explain everything again.

Every single time.

The AI had no memory of who I was, what I’d done before, or what we'd learned together. It was like talking to someone who forgot our entire conversation the moment I closed the tab.

That's fine for one-off questions. It's terrible for recurring workflows.

Then I started using Hermes Agent.

Hermes is not just another LLM chatbot. It's a generalist agent with a built-in memory loop and learning loop that let it build a model of you over time.

This article is a hands-on breakdown of how that works, why it matters, and how you can use it in your own workflows.

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What makes Hermes different: memory + learning loop

Most AI assistants rely on the context window: everything you’ve said in the current chat. Once the chat ends, that context is gone.

Hermes is different. It has:

• Persistent memory that survives across sessions.
• Memory search to retrieve past conversations and context when relevant.
• A user model that builds a deeper understanding of you over time.
• A self-improving learning loop that adapts based on feedback and patterns.
• Tool integration so memory works with files, APIs, and workflows, not just chat.

In the official Hermes materials, this is described as Hermes building a deeper model of the user and using past conversation context to improve over time.

This is what lets Hermes do things like:

• Remember your coding style from last week.
• Recall how you like your reports formatted.
• Track patterns in your daily check-ins or habits.
• Adjust suggestions based on what worked before.

That's the difference between a stateless chatbot and an agent that learns you.

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The learning loop: how Hermes actually improves

Hermes isn’t just storing data. It’s learning from experience.

The learning loop works like this:

1. Action: Hermes takes an action (generates a plan, uses a tool, writes code).
2. Feedback: You give feedback, explicitly or implicitly (accept/reject suggestions, correct outputs, continue the workflow).
3. Update: Hermes updates its internal model based on what happened.
4. Future behavior: On the next run, Hermes uses that updated model to make better decisions.

This is a continuous loop, not a one-time thing.

In practice, this means:

• If you keep rejecting certain types of suggestions, Hermes learns to avoid them.
• If you consistently use a certain style of output, Hermes starts defaulting to that style.
• If you have recurring patterns (e.g., daily check-ins, weekly reports), Hermes learns to anticipate them.

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Memory vs. context window: why it matters

Let's compare:

Feature	Context Window (Most AI)	Hermes Memory
Lifetime	Single chat session	Persistent across sessions
Search	Limited to current chat	Searchable across past conversations
User model	None	Builds a model of the user over time
Tool integration	Optional	Integrated with tools and workflows
Learning	None	Self-improving loop

This difference is huge for recurring workflows.

With context window only, you have to re-explain everything every time.

With Hermes memory, the agent remembers and adapts.

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Concrete example 1: coding workflow with memory

Session 1: First time setting up a project
You tell Hermes:

"I'm building a FastAPI app. I prefer:
type hints everywhere
Pydantic v2 models
async/await for all DB calls
tests in tests/ with pytest
minimal logging, structured JSON"

Hermes:

• Generates code following your style.
• Stores your preferences in memory.
• Logs the project structure.

Session 2: One week later
You start a new chat and say:

"Add a new endpoint for user login."

You don’t re-explain your style.

Hermes:

• Searches memory for your preferences.
• Recalls type hints, Pydantic v2, async/await, pytest.
• Generates code that matches your style automatically.

This is memory in action.

Session 3: Two weeks later
You say:

"Add password reset endpoint."

Now Hermes:

• Notices this is a pattern (you’re building auth endpoints).
• Reuses the same patterns from before.
• Possibly suggests related endpoints (e.g., "want me to add email verification too?").

This is the learning loop in action.

---

Concrete example 2: daily check-in workflow

Let's walk through a recurring workflow that's harder for normal AI but natural for Hermes.

Session 1: First check-in
You tell Hermes:

"I'm tracking my daily energy and focus. I prefer to log:

• sleep hours
• main stressors
• focus score (1–10)
• one thing I’m grateful for”

Hermes:

• Stores your logging format.
• Creates a daily check-in template.
• Logs the first entry.

Session 2: One week later
You say:

"Sleep: 6 hours. Stressors: work deadline. Focus: 6/10. Grateful for: coffee with a friend."

You don't re-explain the format.

Hermes:

• Searches memory for your logging preferences.
• Recalls your format.
• Logs the entry in the same structure.
• Compares with past entries.

Session 3: One month later
You say the same thing again.

Now Hermes:

• Notices patterns: “Your focus is lower on days with <7 hours sleep.”
• Anticipates needs: “You mentioned work stress 3 times this week. Want to adjust your schedule?”
• Generates a weekly summary: “Average focus: 6.5/10. Best days: 8+ hours sleep.”

This is memory + learning loop working together.

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How to use Hermes memory in your workflows (step-by-step)

You don’t need a complex app to use Hermes memory. Here’s how to start.

Step 1: Define what you want Hermes to remember
Think about:

• Your preferences (coding style, tone, format).
• Your recurring workflows (daily check-ins, weekly reports).
• Your constraints (budget, time zone, tools you use).
• Your patterns (e.g., “I always struggle with X on Mondays”).

Write these down as a profile or context file.

Example profile (JSON):

{
  "name": "Developer Profile",
  "stack": ["FastAPI", "Python", "PostgreSQL"],
  "preferences": {
    "type_hints": true,
    "pydantic_version": "v2",
    "async_db_calls": true,
    "test_framework": "pytest",
    "logging": "structured JSON"
  },
  "workflows": [
    "daily check-ins",
    "weekly reports",
    "PR reviews"
  ]
}

Step 2: Use structured prompts that invoke memory
Instead of:

"Help me with this."

Use:

"You are a [role]. Use the profile and today’s input to:
Recall past patterns (from memory).
Plan actions.
Use tools to log results.
Update memory for next time."

This tells Hermes to use memory explicitly.

Step 3: Log and update memory intentionally
After each run, save:

• The input.
• The plan.
• The output.
• Any updates to preferences or patterns.

Example log (JSON):

{
  "date": "2026-05-30",
  "input": "Sleep: 6 hours. Stressors: work deadline. Focus: 6/10.",
  "plan": ["Log entry", "Compare with past", "Suggest adjustments"],
  "output": "Logged. Focus lower on <7h sleep days.",
  "memory_update": "Focus correlates with sleep duration."
}

Step 4: Give feedback
Hermes learns from feedback. Give it explicitly:

• "This suggestion was helpful."
• "Don’t do it this way again." = "I prefer X over Y."

Or implicitly:

• Accepting/rejecting suggestions.
• Continuing or stopping a workflow.
• Correcting outputs.

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What this enables: real-world use cases

Once you have memory and a learning loop, you can do things that were impossible before.

1. Recurring workflows that improve over time
   Daily check-ins, weekly reports, habit tracking: Hermes remembers what worked and adapts.

2. Personalized assistance
   Hermes learns your style, preferences, and constraints, so outputs feel tailored to you.

3. Long-term pattern recognition
   Hermes can notice patterns across weeks or months:

• "You always struggle with X on Mondays."
• "Appetite is lower on period days."
• "Focus is higher after 8+ hours sleep."

1. Multi-session projects
   You can start a project, step away, come back later, and Hermes remembers where you left off.

2. Context-aware code generation

   Hermes remembers your coding style, so every new file matches your existing codebase.

Why this changes everything

Most AI assistants are stateless. They forget you immediately.

Hermes is stateful. It remembers you, learns from you, and adapts to you.

That’s the difference between:

• "Helpful chatbot" and

• "Operational partner that learns me over time."

For recurring workflows, that’s a game-changer.

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Key takeaways

Hermes Agent has a memory loop that persists across sessions.

Hermes has a learning loop that improves over time based on feedback and patterns.

Memory is not just context window; it’s searchable, persistent, and user-modeling.

This enables recurring workflows that improve over time.

To use it: define what to remember, use structured prompts, log intentionally, and give feedback.

If you’re building with Hermes, think less about “what can I ask this once?” and more about “what recurring workflow can I delegate to an agent that learns me over time?”