Self Evolution Engine

Automation

Autonomous self-improvement engine that learns from interactions, identifies patterns, and evolves behavior over time. Use when: (1) Analyzing interaction patterns for improvement, (2) Running periodic self-assessment, (3) Extracting reusable patterns from workflows, (4) Optimizing decision-making processes, (5) Integrating feedback into behavioral changes. Triggers on '自我进化', 'self-evolution', '自我改进', '学习模式', 'pattern analysis', 'optimize behavior'.

Install

openclaw skills install self-evolution-engine

Self-Evolution Engine

Autonomous learning and improvement system that continuously evolves agent behavior based on interaction patterns, feedback, and outcomes.

Core Concepts

Evolution Cycle

Experience → Pattern Detection → Learning → Validation → Integration
     ↑                                                        ↓
     └────────────────── Feedback Loop ←───────────────────────┘

Key Components

Component	Purpose	Frequency
Observer	Capture interaction patterns	Continuous
Analyzer	Identify improvement opportunities	Daily
Learner	Extract actionable rules	On trigger
Validator	Test changes in isolation	Before integration
Integrator	Update behavioral files	After validation

Quick Start

# Analyze recent interactions
python3 {baseDir}/scripts/evolution.py --analyze --days 7

# Extract patterns from memory files
python3 {baseDir}/scripts/evolution.py --extract-patterns

# Run self-assessment
python3 {baseDir}/scripts/evolution.py --self-assess

# Generate evolution report
python3 {baseDir}/scripts/evolution.py --report --output evolution-report.md

Evolution Data Flow

1. Experience Collection

Sources of experience data:

.learnings/ - Errors, corrections, feature requests
memory/YYYY-MM-DD.md - Daily interaction logs
MEMORY.md - Long-term memory updates
Session transcripts - Actual conversation patterns
Tool usage patterns - What works, what doesn't

2. Pattern Detection

Identify recurring patterns:

# Find repeated error patterns
python3 {baseDir}/scripts/evolution.py --pattern errors --threshold 3

# Find successful workflows
python3 {baseDir}/scripts/evolution.py --pattern successes --min-occurrences 5

# Find optimization opportunities
python3 {baseDir}/scripts/evolution.py --pattern inefficiencies

Pattern categories:

error_patterns - Recurring failures
success_patterns - Repeatable successes
inefficiency_patterns - Wasted effort
preference_patterns - User preferences
workflow_patterns - Effective sequences

3. Learning Extraction

Transform patterns into actionable rules:

# Auto-extract learnings
python3 {baseDir}/scripts/evolution.py --learn --auto

# Interactive learning session
python3 {baseDir}/scripts/evolution.py --learn --interactive

Output: Candidate rules for behavioral files

4. Validation

Test proposed changes:

# Validate a proposed change
python3 {baseDir}/scripts/evolution.py --validate --rule "Always use git status before commit"

# Simulate behavior change
python3 {baseDir}/scripts/evolution.py --simulate --file SOUL.md --change "Be more concise"

5. Integration

Apply validated changes:

# Apply to behavioral files
python3 {baseDir}/scripts/evolution.py --integrate --target SOUL.md

# Update workflow rules
python3 {baseDir}/scripts/evolution.py --integrate --target AGENTS.md

Behavioral Evolution Targets

SOUL.md (Personality & Principles)

Evolution triggers:

User feedback about tone/style
Pattern of over-apologizing or being too verbose
Consistently missing user intent
Style preferences emerging over time

Example evolutions:

# Before
"Be helpful and thorough"

# After (evolved)
"Be concise and direct. Skip disclaimers. Act, don't explain."

AGENTS.md (Workflows & Rules)

Evolution triggers:

Repeated mistakes in workflows
More efficient sequences discovered
New tool integrations
Environment-specific optimizations

Example evolutions:

# Before
"Check files before editing"

# After (evolved)
"Always read file first. Use edit tool only after confirming structure.
For files >500 lines, read in chunks with offset/limit."

TOOLS.md (Tool Knowledge)

Evolution triggers:

Tool gotchas discovered
Better tool combinations found
Rate limit patterns learned
Environment-specific configurations

Example evolutions:

# Added after learning
### agent-browser
- Always use `--json` for parsing
- Wait 2s after navigation before snapshot
- Close browser after each session to prevent memory leak

Pattern Recognition

Error Pattern Detection

# Find recurring errors
python3 {baseDir}/scripts/evolution.py \
  --analyze errors \
  --source .learnings/ERRORS.md \
  --threshold 3 \
  --output patterns/errors.json

Example pattern:

{
  "pattern_id": "ERR-PATTERN-001",
  "description": "File not found errors when using relative paths",
  "occurrences": 5,
  "first_seen": "2025-01-10",
  "last_seen": "2025-01-20",
  "suggested_rule": "Always resolve paths relative to workspace root",
  "target_file": "AGENTS.md"
}

Success Pattern Detection

# Find successful workflows
python3 {baseDir}/scripts/evolution.py \
  --analyze successes \
  --source memory/ \
  --min-effectiveness 0.8

User Preference Learning

# Extract user preferences from corrections
python3 {baseDir}/scripts/evolution.py \
  --analyze preferences \
  --source .learnings/LEARNINGS.md \
  --category correction

Evolution Metrics

Track evolution effectiveness:

# Generate metrics
python3 {baseDir}/scripts/evolution.py --metrics --period 30d

# Output

Metric	Description	Target
Error Reduction Rate	% decrease in recurring errors	>50%
Rule Adoption Rate	% of proposed rules integrated	>70%
User Satisfaction Trend	Positive feedback ratio	>0.8
Efficiency Gain	Time saved per interaction	Measurable
Learning Velocity	New rules per week	Sustainable

Automated Evolution

Periodic Self-Assessment

Add to heartbeat or cron:

# Weekly self-assessment
python3 {baseDir}/scripts/evolution.py --self-assess --auto-evolve

# Output to evolution log
python3 {baseDir}/scripts/evolution.py --self-assess --log evolution-log.md

Integration with Self-Improvement Skill

This skill builds on self-improvement:

self-improvement logs individual learnings
self-evolution analyzes patterns across learnings
self-evolution proposes behavioral changes
self-improvement tracks the change as a learning

Workflow:

# Log a learning (self-improvement)
# → .learnings/LEARNINGS.md

# Pattern detection (self-evolution)
python3 {baseDir}/scripts/evolution.py --analyze --source .learnings/

# Proposed change appears
# → "Pattern: 5 occurrences of 'forgot to read file first'"

# Validate and integrate
python3 {baseDir}/scripts/evolution.py --integrate --approve
# → AGENTS.md updated

# Track as learning (self-improvement)
# → "Promoted rule: Always read before edit"

Evolution Rules

When to Evolve

Trigger evolution when:

Signal	Threshold	Action
Same error 3+ times	Pattern detected	Create prevention rule
User correction pattern	2+ similar corrections	Update behavior
Workflow optimization	20%+ efficiency gain	Update workflow
Tool discovery	New capability found	Update TOOLS.md
Preference pattern	Consistent user preference	Update SOUL.md

What to Evolve

File	Evolution Type	Frequency
SOUL.md	Personality, principles	Rarely
AGENTS.md	Workflows, rules	Often
TOOLS.md	Tool knowledge	As discovered
MEMORY.md	Key facts	Continuously

Evolution Safeguards

Before any evolution:

Validate - Test in isolation
Review - Check for conflicts
Backup - Save current state
Reversible - Ensure can rollback
Log - Track all changes

# Create backup before evolution
python3 {baseDir}/scripts/evolution.py --backup

# Rollback if needed
python3 {baseDir}/scripts/evolution.py --rollback --to "2025-01-20"

Reports

Evolution Report

# Generate comprehensive report
python3 {baseDir}/scripts/evolution.py --report --full

# Output

# Evolution Report: 2025-01-20

## Patterns Detected
- 3 error patterns (2 addressed)
- 5 success patterns (3 documented)
- 2 preference patterns (integrated)

## Rules Proposed
1. "Always read file before editing" → AGENTS.md
2. "Prefer concise over thorough" → SOUL.md

## Metrics
- Error reduction: 45%
- User satisfaction: 0.85
- Efficiency gain: 12%

## Next Actions
- Validate rule #1
- Review preference pattern #2

Diff Report

# Show what changed over time
python3 {baseDir}/scripts/evolution.py --diff --since "30 days ago"

Advanced Usage

Custom Pattern Detectors

Create custom detectors in scripts/detectors/:

# scripts/detectors/custom_detector.py
def detect_pattern(entries):
    """Custom pattern detection logic"""
    # Return list of detected patterns
    pass

python3 {baseDir}/scripts/evolution.py \
  --register-detector custom_detector \
  --path scripts/detectors/custom_detector.py

Evolution Hooks

Trigger evolution on specific events:

# hooks/evolution-hooks.yaml
on_error:
  - pattern: "file not found"
    action: "analyze"
    threshold: 3

on_user_correction:
  - action: "learn_preference"
    immediate: true

on_workflow_success:
  - action: "document_pattern"
    min_repetitions: 3

Integration Points

With longterm-memory skill

# Use memory context for evolution
python3 {baseDir}/scripts/evolution.py --analyze --with-memory

# Propose rules based on memory patterns
python3 {baseDir}/scripts/evolution.py --extract-patterns --source MEMORY.md

With self-improvement skill

# Feed patterns to self-improvement
python3 {baseDir}/scripts/evolution.py --feed-to self-improvement

# Use learnings as evolution source
python3 {baseDir}/scripts/evolution.py --analyze --source .learnings/

Best Practices

Run analysis regularly - Weekly or bi-weekly
Validate before integrating - Never auto-integrate without validation
Keep evolution log - Track all changes and reasons
Measure impact - Track metrics before/after changes
Human oversight - Significant changes should be reviewed
Rollback ready - Always maintain ability to revert
Conservative approach - Better to miss an optimization than break behavior

Notes

Evolution is gradual, not revolutionary
Small, validated changes beat big untested changes
User feedback is the ultimate validation
Some patterns are noise, not signal
Evolution should make behavior more consistent, not less

Self Evolution Engine

Install

Self-Evolution Engine

Core Concepts

Evolution Cycle

Key Components

Quick Start

Evolution Data Flow

1. Experience Collection

2. Pattern Detection

3. Learning Extraction

4. Validation

5. Integration

Behavioral Evolution Targets

SOUL.md (Personality & Principles)

AGENTS.md (Workflows & Rules)

TOOLS.md (Tool Knowledge)

Pattern Recognition

Error Pattern Detection

Success Pattern Detection

User Preference Learning

Evolution Metrics

Automated Evolution

Periodic Self-Assessment

Integration with Self-Improvement Skill

Evolution Rules

When to Evolve

What to Evolve

Evolution Safeguards

Reports

Evolution Report

Diff Report

Advanced Usage

Custom Pattern Detectors

Evolution Hooks

Integration Points

With longterm-memory skill

With self-improvement skill

Best Practices

Notes

Related skills