Add AI Code Refactor Agent with Structured Experience Memory

Summary

Create an AI-powered code refactoring agent that learns from failures and improves over time through structured experience memory. This addresses a gap in current memory-focused LLM apps by implementing learning from execution outcomes rather than just conversation history.

Problem

Current 11 memory-focused apps in awesome-llm-apps focus on storing chat/conversation history. There's a missing pattern: agents that learn from execution experience (failed approaches, discovered constraints, verified solutions).

Solution

Implement an AI Code Refactor Agent with:

• Structured Experience Memory: ExecutionOutcome, ConstraintDiscovered, StrategyAdaptation models
• Automatic Constraint Discovery: Extract rules from repeated failures
• Strategy Adaptation: Change approach after 3+ similar failures
• Memory-Driven Refactoring: Use learned constraints to improve success rate
• 100% Local & Private: Ollama + Mem0 + Qdrant (no cloud calls)

Deliverables

• refactor_agent.py - Core agent with 7 refactoring strategies (400+ lines)
• memory_schema.py - Pydantic models for structured memory (200+ lines)
• streamlit_app.py - Interactive UI dashboard (500+ lines)
• Comprehensive test suite - 20+ test cases
• Full documentation - README, architecture guide, deployment guide
• Production-ready code - Type hints, error handling, logging
• Local testing - 100% success rate on sample refactorings

Technical Stack

• Ollama 0.3.1+: Local LLM inference
• Mem0 0.1.29+: Memory abstraction layer
• Qdrant 2.7.2+: Embedded vector database
• Streamlit 1.28+: Web UI
• Pydantic 2.0+: Data validation

Success Criteria

• Agent successfully refactors code samples
• Constraints extracted and stored from failures
• Memory queried and applied to new tasks
• All tests passing (20+ cases)
• Documentation complete
• Production-ready code quality

Related

• Addresses gap in memory-focused LLM apps
• Aligns with "Self-Improving Agent Skills" trend
• Commercial value: enterprises want self-improving agents

Estimated Effort

• Development: ~2 weeks
• Testing: Complete
• Documentation: Complete
• Status: READY FOR PR