Self-Evolution Engine

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

Core Concepts

Evolution Cycle

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Key Components

Quick Start

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Evolution Data Flow

1. Experience Collection

Sources of experience data:

• - .learnings/ - Errors, corrections, feature requests
• INLINECODE1 - Daily interaction logs
• INLINECODE2 - Long-term memory updates
• Session transcripts - Actual conversation patterns
• Tool usage patterns - What works, what doesn't

2. Pattern Detection

Identify recurring patterns:

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Pattern categories:

• - errorpatterns - Recurring failures
• successpatterns - Repeatable successes
• inefficiencypatterns - Wasted effort
• preferencepatterns - User preferences
• workflow_patterns - Effective sequences

3. Learning Extraction

Transform patterns into actionable rules:

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Output: Candidate rules for behavioral files

4. Validation

Test proposed changes:

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5. Integration

Apply validated changes:

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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:
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AGENTS.md (Workflows & Rules)

Evolution triggers:

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

Example evolutions:
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TOOLS.md (Tool Knowledge)

Evolution triggers:

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

Example evolutions:
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Pattern Recognition

Error Pattern Detection

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Example pattern:
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Success Pattern Detection

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User Preference Learning

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Evolution Metrics

Track evolution effectiveness:

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Automated Evolution

Periodic Self-Assessment

Add to heartbeat or cron:

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Integration with Self-Improvement Skill

This skill builds on self-improvement:

1. 1. self-improvement logs individual learnings
2. INLINECODE5 analyzes patterns across learnings
3. INLINECODE6 proposes behavioral changes
4. INLINECODE7 tracks the change as a learning

Workflow:
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Evolution Rules

When to Evolve

Trigger evolution when:

What to Evolve

Evolution Safeguards

Before any evolution:

1. 1. Validate - Test in isolation
2. Review - Check for conflicts
3. Backup - Save current state
4. Reversible - Ensure can rollback
5. Log - Track all changes

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Reports

Evolution Report

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Diff Report

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Advanced Usage

Custom Pattern Detectors

Create custom detectors in scripts/detectors/:

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Register:
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Evolution Hooks

Trigger evolution on specific events:

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Integration Points

With longterm-memory skill

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With self-improvement skill

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Best Practices

1. 1. Run analysis regularly - Weekly or bi-weekly
2. Validate before integrating - Never auto-integrate without validation
3. Keep evolution log - Track all changes and reasons
4. Measure impact - Track metrics before/after changes
5. Human oversight - Significant changes should be reviewed
6. Rollback ready - Always maintain ability to revert
7. 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