Hermes Learning Loop

Hermes Learning Loop for OpenClaw

Inspired by: NousResearch/hermes-agent (Self-improving AI agent)

Implements Hermes Agent's core learning loop in OpenClaw — automatically extracts successful workflows, creates reusable skills, and persists curated knowledge across sessions.

When to Use

Features

Quick Start

Install

clawhub install hermes-learning-loop

Manual Trigger

# After completing a task
node learning-loop.js extract --session=
Create skill from workflow
node learning-loop.js create-skill --name="my-skill" --description="What it does"
Periodic nudge (heartbeat)
node learning-loop.js nudge

Auto-Trigger (Integration)

Add to HEARTBEAT.md:

## Learning Loop - Periodic Nudge
Frequency: Every 5 tasks or 30 minutes
Task:
1. Run node learning-loop.js nudge
2. Review extracted memories
3. Approve/reject skill creations

Architecture

Learning Loop Cycle

┌─────────────────────────────────────────────────────────┐
│  1. Task Execution                                      │
│     - Agent completes task                              │
│     - Track tool calls, decisions, outcomes             │
└──────────────┬──────────────────────────────────────────┘
               │
               ▼
┌─────────────────────────────────────────────────────────┐
│  2. Periodic Nudge (every 5 tasks)                      │
│     - System prompt: "Reflect on recent activity"       │
│     - Agent evaluates: Is this worth persisting?        │
└──────────────┬──────────────────────────────────────────┘
               │
               ▼
┌─────────────────────────────────────────────────────────┐
│  3. Memory Extraction                                   │
│     - If valuable → Write to MEMORY.md / USER.md        │
│     - If workflow → Create skill file                   │
│     - If context → Index in session archive             │
└──────────────┬──────────────────────────────────────────┘
               │
               ▼
┌─────────────────────────────────────────────────────────┐
│  4. Skill Creation                                      │
│     - Extract steps, tool calls, file references        │
│     - Write to ~/.openclaw/skills///   │
│     - Follow agentskills.io specification               │
└─────────────────────────────────────────────────────────┘

4-Layer Memory System

| Layer | Purpose | Location | Load Timing | |-------|---------|----------|-------------| | Prompt Memory | Always-on context | MEMORY.md, USER.md | Every session start | | Skills | Procedural memory (how-to) | ~/.openclaw/skills/ | On-demand (summary → full) | | Session Archive | Episodic memory (what happened) | SQLite + FTS5 | Deliberate retrieval | | User Model | Behavioral patterns | Optional (Honcho-like) | Continuous passive |

Skill Triggers

Automatically create skill when:

Usage Examples

Example 1: Post-Task Skill Extraction

# After completing complex deployment
export SESSION_ID="2026-04-03-deploy"
export TASK_OUTCOME="success"
export TOOL_CALLS="15"
node learning-loop.js extract

Output:

📊 Learning Loop Analysis
✅ Task completed successfully
📈 Tool calls: 15
⚠️  Error recovery: 2 instances
💡  User corrections: 1
🎯 Skill-worthy detected: YES
Proposed skill:
  Name: deploy-to-k8s
  Description: Deploy application to Kubernetes with health checks
  Steps: 7
  Tool calls: 15
📄 Created: ~/.openclaw/skills/devops/deploy-to-k8s/SKILL.md

Example 2: Periodic Nudge

# Heartbeat triggers this
node learning-loop.js nudge

Output:

🔔 Periodic Nudge - Task #5
Looking back at tasks 1-5...
Task 1: ✅ Simple (no extraction needed)
Task 2: ✅ Complex workflow detected
  → Extracted: "github-pr-workflow"
Task 3: ✅ Error recovery pattern
  → Extracted: "debug-python-import"
Task 4: ✅ Simple
Task 5: ✅ User correction applied
  → Updated: "github-pr-workflow" (v1.0.1)
📊 Summary:
  - New skills: 2
  - Updated skills: 1
  - Memory entries: 3

Example 3: Memory Curation

# Review what's worth keeping
node learning-loop.js curate --session="2026-04-03"

Decision Framework:

┌──────────────────────────────────────────────────────┐
│ Should this be persisted?                            │
├──────────────────────────────────────────────────────┤
│ ❌ Chat pleasantries → Discard                       │
│ ❌ Failed attempts → Discard (keep only success)     │
│ ✅ Successful workflows → Extract as skill           │
│ ✅ User preferences → Add to USER.md                 │
│ ✅ Project context → Add to MEMORY.md (project/)     │
│ ✅ Corrections/feedback → Add to MEMORY.md (feedback/)│
└──────────────────────────────────────────────────────┘

Configuration

Environment Variables

| Variable | Description | Default | |----------|-------------|---------| | LEARNING_NUDGE_INTERVAL | Tasks between nudges | 5 | | LEARNING_MIN_TOOL_CALLS | Min tool calls for skill | 5 | | LEARNING_AUTO_CREATE | Auto-create skills (vs approve) | false | | LEARNING_SKILLS_DIR | Skills directory | ~/.openclaw/skills/ | | LEARNING_MEMORY_DIR | Memory directory | ~/.openclaw/memory/ |

Config File

# ~/.openclaw/learning-loop.yaml
learning:
  nudge:
    enabled: true
    interval: 5  # tasks
    
  skill_creation:
    auto_approve: false  # Review before creating
    min_tool_calls: 5
    categories:
      - devops
      - research
      - productivity
      
  memory:
    max_prompt_chars: 3575  # Hermes-style tight limit
    archive_enabled: true
    fts5_enabled: true

Skill File Format

Follows agentskills.io specification:

---
name: deploy-to-k8s
description: Deploy application to Kubernetes with health checks
version: 1.0.0
platforms: [linux, macos]
metadata:
  tags: [kubernetes, devops, deployment]
  category: devops
  created_from: session-2026-04-03
deploy-to-k8s
Overview
Deploy any application to Kubernetes cluster with automated health checks and rollback.
Prerequisites
kubectl configured
Kubernetes cluster access
Docker image ready
Steps
1. Validate cluster connection
   

2. Apply deployment manifest
   

3. Wait for rollout
   

4. Health check
   

5. Verify service
   

Tool Calls Used
exec: kubectl commands
read: deployment.yaml
web_search: Kubernetes docs (if errors)
Related Skills
docker-build-optimization
k8s-troubleshooting

Integration with OpenClaw

Heartbeat Integration

## HEARTBEAT.md Update
Learning Loop - Periodic Nudge
Frequency: Every 5 tasks
Steps:
1. Check task counter
2. If counter % 5 == 0 → Run nudge
3. Review proposed skills/memories
4. Approve/reject
5. Reset counter

PUA Integration

# Task PUA can trigger learning loop
if task.failed && task.recovered:
  node learning-loop.js extract --reason="error-recovery"
  
if task.pua_level >= "L2":
  # Complex task → likely skill-worthy
  node learning-loop.js nudge

Memory PUA Integration

# Memory health check includes learning loop status
node memory-pua.js audit
Output includes:
- Skills created this week
- Memories curated
- Nudge effectiveness

Comparison: Hermes vs This Implementation

| Feature | Hermes Agent | This Skill | |---------|--------------|------------| | Periodic Nudge | ✅ Built-in | ✅ Via heartbeat | | Skill Auto-Creation | ✅ Full auto | ⚠️ Opt-in approval | | 4-Layer Memory | ✅ SQLite + FTS5 | ⚠️ Markdown + optional SQLite | | Progressive Disclosure | ✅ Summary → Full | ✅ Same pattern | | User Modeling (Honcho) | ✅ Optional | ❌ Not implemented | | Gateway Integration | ✅ Multi-platform | ⚠️ OpenClaw channels only | | Context Compression | ✅ Lineage-aware | ⚠️ Basic summarization |

Benefits

Related Skills

References

License

MIT-0

*Version 1.0.0: Initial implementation inspired by Hermes Agent learning loop*

Install

clawhub install hermes-learning-loop

Manual Trigger

# After completing a task
node learning-loop.js extract --session=
Create skill from workflow
node learning-loop.js create-skill --name="my-skill" --description="What it does"
Periodic nudge (heartbeat)
node learning-loop.js nudge

Auto-Trigger (Integration)

Add to HEARTBEAT.md:

```markdown

Environment Variables

| Variable | Description | Default | |----------|-------------|---------| | LEARNING_NUDGE_INTERVAL | Tasks between nudges | 5 | | LEARNING_MIN_TOOL_CALLS | Min tool calls for skill | 5 | | LEARNING_AUTO_CREATE | Auto-create skills (vs approve) | false | | LEARNING_SKILLS_DIR | Skills directory | ~/.openclaw/skills/ | | LEARNING_MEMORY_DIR | Memory directory | ~/.openclaw/memory/ |

Config File

# ~/.openclaw/learning-loop.yaml
learning:
  nudge:
    enabled: true
    interval: 5  # tasks
    
  skill_creation:
    auto_approve: false  # Review before creating
    min_tool_calls: 5
    categories:
      - devops
      - research
      - productivity
      
  memory:
    max_prompt_chars: 3575  # Hermes-style tight limit
    archive_enabled: true
    fts5_enabled: true