Hermes Agent: Memory System

Last updated: 2026-08-31

The memory system is Hermes's core differentiator — it's not a "forget after chatting" chatbot, but an intelligent assistant that remembers everything across sessions.

📋 Prerequisites: Lesson 3 — Configuration Files

1. What You Will Learn

2. Story

(1) Pain Point: Re-introducing Yourself Every Time

Bob chats with AI every day and always has to repeat: "I'm a frontend dev, I use React + TypeScript, prefer ESLint..." The AI never remembers.

(2) Solution: Agent Truly Remembers You

# Day 1
me: I'm Alice, backend developer, Python + FastAPI
Agent: ✅ Remembered! I'll keep your tech stack and preferences long-term.

# Day 30
me: Help me write an API
Agent: [Recall] You use Python + FastAPI, prefer type hints...
       Here's the FastAPI version:
       ```python
       from fastapi import FastAPI
       app = FastAPI()
       ```

3. Three-Layer Memory Architecture

graph TD
    A[User Input] --> B[Working Memory]
    B -->|Auto-extract| C[Long-term Memory]
    C -->|Deep modeling| D[User Model - Honcho]
    D -->|Personalize| E[Response Output]

graph TD
    A[User Input] --> B[Working Memory]
    B -->|Auto-extract| C[Long-term Memory]
    C -->|Deep modeling| D[User Model - Honcho]
    D -->|Personalize| E[Response Output]

4. Working Memory

Working memory stores active context for the current conversation:

{
  "session_id": "abc123",
  "entities": [
    {"name": "React Dashboard", "type": "project"},
    {"name": "TypeScript", "type": "tech_stack"}
  ],
  "intent": "code_review",
  "context_window": [
    "User asked to review React component",
    "Component uses TypeScript"
  ]
}

5. Long-term Memory

(1) Auto Memory Extraction

me: I'm working on an e-commerce project using Next.js 14 + Prisma + PostgreSQL

# Hermes auto-extracts:
📝 Long-term memory updated:
  - Project: E-commerce
  - Tech stack: Next.js 14, Prisma, PostgreSQL

(2) Semantic Retrieval

me: Add a product listing page

Agent: [Memory retrieval] E-commerce project (Next.js 14 + Prisma)
       Using your existing tech stack:
       ```tsx
       // app/products/page.tsx
       import { prisma } from '@/lib/prisma'
       ```

(3) /remember Command

/remember I need to submit a weekly report every Friday
/remember list
/remember delete 3
/remember export > my_memories.json

6. Honcho User Modeling

(1) What is Honcho?

Honcho is Hermes's user understanding engine — it doesn't just store preferences, it models behavioral patterns:

graph LR
    A[Conversation History] --> B[Honcho Engine]
    B --> C[Skill Assessment]
    B --> D[Communication Style]
    B --> E[Decision Preferences]
    B --> F[Knowledge Graph]

graph LR
    A[Conversation History] --> B[Honcho Engine]
    B --> C[Skill Assessment]
    B --> D[Communication Style]
    B --> E[Decision Preferences]
    B --> F[Knowledge Graph]

(2) User Profile Dimensions

(3) View Your Profile

/honcho profile

# 🧑‍💻 User Profile
# Technical skill: ⭐⭐⭐⭐⭐ (Senior developer)
# Preferred languages: TypeScript > Python > Go
# Code style: Strict typing + functional
# Communication: Concise, no fluff

7. Memory Management Commands

❓ FAQ

📖 Summary

• Three layers: Working Memory (session) → Long-term Memory (cross-session) → User Model (deep profile)
• /remember for manual saves; Agent auto-extracts important information
• Semantic retrieval, not keyword matching
• Honcho auto-models user behavior, continuously updated
• All data stored locally, zero privacy leakage

📝 Exercises

1. Basic (⭐): Save 5 preferences with /remember, verify recall in a new conversation.
2. Intermediate (⭐⭐): Observe how Honcho auto-updates your user profile, document 3 auto-update instances.
3. Advanced (⭐⭐⭐): Design a memory classification system (by project/preference/habits/team norms), import into Hermes and verify retrieval.

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