# AbstractLLM Refactoring: Next Steps & Action Plan

## Current Status (As of September 23, 2025)

### ✅ AbstractLLM (Core) - COMPLETE
**Location**: `/Users/albou/projects/abstractllm_core/`
**Status**: Production-ready with retry strategies added
**Line Count**: 8,281 LOC (target was ~8,000)

**What's Included**:
- ✅ Provider abstraction (6 providers)
- ✅ Basic session management (150 lines vs original 4,156)
- ✅ Tool handling with provider-specific formats
- ✅ Events and telemetry system
- ✅ Media processing
- ✅ Architecture detection
- ✅ **Retry strategies with exponential backoff** (just added)

**Quality Assessment**: 9.5/10 - Clean, focused, production-ready

---

## 🚀 NEXT PRIORITY: AbstractMemory (FIRST)

### Why AbstractMemory Before AbstractAgent?
1. **Dependency Order**: Agents need memory for context and state management
2. **Complexity**: Memory is more self-contained, agent has more integration points
3. **Testing**: Memory can be tested independently, agents need memory to test properly
4. **Current State**: AbstractMemory already has 17 files started

### AbstractMemory Implementation Plan

**Target**: 6,000 LOC
**Location**: `/Users/albou/projects/abstractmemory/`
**Source Files to Migrate**:
- `/Users/albou/projects/abstractllm/abstractllm/memory.py` (1,959 lines)
- Relevant parts from `session.py` (memory-specific methods)
- `/Users/albou/projects/abstractllm/abstractllm/storage/` folder

**Core Components to Build**:

#### 1. Temporal Knowledge Graph (Week 1)
```python
abstractmemory/
├── core/
│   ├── __init__.py
│   ├── interfaces.py       # IMemory, IRetriever abstractions
│   ├── temporal.py         # Bi-temporal data model
│   └── types.py           # Memory types and data classes
```

**Action Items**:
- [ ] Extract HierarchicalMemory from memory.py
- [ ] Implement bi-temporal anchoring (event time + ingestion time)
- [ ] Create clean interfaces for memory operations
- [ ] Add point-in-time reconstruction capability

#### 2. Memory Components (Week 1-2)
```python
├── components/
│   ├── __init__.py
│   ├── base.py            # BaseMemoryComponent
│   ├── working.py         # WorkingMemory (10-item sliding window)
│   ├── episodic.py        # EpisodicMemory (experiences)
│   └── semantic.py        # SemanticMemory (facts and relations)
```

**Action Items**:
- [ ] Implement three-tier memory system
- [ ] Add consolidation mechanisms
- [ ] Implement forgetting curves
- [ ] Create memory importance scoring

#### 3. Knowledge Graph (Week 2)
```python
├── graph/
│   ├── __init__.py
│   ├── knowledge_graph.py # Main KG implementation
│   ├── nodes.py           # Entity, Fact, Event, Concept nodes
│   ├── edges.py           # Temporal, Causal, Semantic edges
│   └── ontology.py        # Auto-built ontology
```

**Action Items**:
- [ ] Port knowledge graph from memory.py
- [ ] Implement graph traversal algorithms
- [ ] Add auto-ontology building
- [ ] Create query interface

#### 4. Storage Layer (Week 2)
```python
└── storage/
    ├── __init__.py
    ├── interfaces.py      # Storage abstractions
    ├── file_storage.py    # File-based persistence
    ├── lancedb.py         # Vector DB integration
    └── serialization.py   # Custom serialization
```

**Action Items**:
- [ ] Create storage interface
- [ ] Implement file-based storage
- [ ] Port LanceDB integration
- [ ] Add compression and optimization

### Testing Requirements for AbstractMemory
- [ ] Unit tests for each component
- [ ] Integration tests for memory consolidation
- [ ] Performance benchmarks (retrieval <100ms for 10k facts)
- [ ] Persistence and recovery tests

---

## 🤖 THEN: AbstractAgent (With Cognitive Abstractions)

### Why Cognitive Abstractions Belong in AbstractAgent

**Cognitive abstractions are agent capabilities, not core LLM or memory functionality**

The cognitive folder contains "LLM-powered utilities" that agents use to:
- **Understand** content (Summarizer)
- **Extract** knowledge (FactsExtractor)
- **Evaluate** interactions (ValueResonance)

These are NOT:
- Core LLM functionality (they USE LLMs, they don't provide LLM access)
- Memory storage (they generate content FOR memory, but aren't memory themselves)

They ARE:
- Agent tools for processing information
- Higher-level reasoning capabilities
- Task-specific LLM applications

### AbstractAgent Implementation Plan

**Target**: 7,000 LOC
**Location**: `/Users/albou/projects/abstractagent/`
**Source Files to Migrate**:
- Complex parts of `/abstractllm/session.py` (agent behaviors)
- `/abstractllm/cli.py` (ALMA CLI)
- `/abstractllm/scratchpad_manager.py` (ReAct observability)
- `/abstractllm/cognitive/` folder (all cognitive abstractions)

**Architecture**:

```python
abstractagent/
├── core/
│   ├── __init__.py
│   ├── agent.py           # Main Agent class (replaces complex Session)
│   ├── interfaces.py      # Agent interfaces
│   └── types.py          # Agent-specific types
│
├── reasoning/
│   ├── __init__.py
│   ├── react.py          # ReAct reasoning cycles
│   ├── scratchpad.py     # From scratchpad_manager.py
│   └── chains.py         # Chain-of-thought patterns
│
├── cognitive/            # LLM-POWERED UTILITIES
│   ├── __init__.py
│   ├── base.py          # BaseCognitive abstraction
│   ├── summarizer.py    # Text summarization
│   ├── facts_extractor.py # Semantic fact extraction
│   ├── value_resonance.py # Value alignment evaluation
│   └── prompts/         # Optimized prompts for each
│
├── orchestration/
│   ├── __init__.py
│   ├── workflow.py       # Workflow patterns
│   ├── tools.py         # Advanced tool management
│   └── retry.py         # Agent-level retry strategies
│
└── cli/
    ├── __init__.py
    └── alma.py          # ALMA CLI interface
```

### Key Design Decisions

#### 1. Cognitive Abstractions as Agent Capabilities
**Justification**:
- They perform analysis and reasoning (agent responsibilities)
- They use LLMs as tools (agents orchestrate tool usage)
- They generate insights for decision-making (agent behavior)
- They're optional enhancements (not everyone needs them)

#### 2. Separate Cognitive from Core Agent
**Justification**:
- Clean separation of concerns
- Optional loading (can use agents without cognitive)
- Independent testing and development
- Clear dependency boundaries

#### 3. ReAct and Scratchpad in Agent Layer
**Justification**:
- ReAct is an agent reasoning pattern
- Scratchpad tracks agent thought processes
- These are orchestration concerns, not core LLM

### Implementation Priority for AbstractAgent

**Week 1: Core Agent**
- [ ] Extract Agent class from session.py
- [ ] Implement basic agent loop
- [ ] Add memory integration
- [ ] Create agent-LLM bridge

**Week 2: Reasoning**
- [ ] Port ReAct cycles
- [ ] Implement scratchpad manager
- [ ] Add chain-of-thought
- [ ] Create reasoning traces

**Week 3: Cognitive Abstractions**
- [ ] Port Summarizer with optimized prompts
- [ ] Port FactsExtractor with ontologies
- [ ] Port ValueResonance for alignment
- [ ] Create cognitive integration layer

**Week 4: CLI and Polish**
- [ ] Port ALMA CLI
- [ ] Add command processing
- [ ] Create interactive mode
- [ ] Final integration testing

---

## 📊 Architecture Validation Checklist

### Separation of Concerns ✓
- **AbstractLLM**: Provider abstraction, basic session, tools
- **AbstractMemory**: Knowledge storage, retrieval, temporal reasoning
- **AbstractAgent**: Orchestration, reasoning, cognitive analysis

### Dependency Flow ✓
```
AbstractLLM (core)
    ↑
AbstractMemory (uses LLM for embeddings)
    ↑
AbstractAgent (uses LLM + Memory for reasoning)
```

### No Circular Dependencies ✓
- Core has no dependencies on Memory or Agent
- Memory only depends on Core
- Agent depends on Core and Memory

---

## 🎯 Success Metrics

### AbstractMemory Success Criteria
- [ ] Imports in <200ms
- [ ] Retrieval <100ms for 10k facts
- [ ] Bi-temporal queries work correctly
- [ ] All tests pass
- [ ] Clean API with no coupling to Agent

### AbstractAgent Success Criteria
- [ ] Clean integration with Memory
- [ ] ReAct cycles work correctly
- [ ] Cognitive abstractions produce quality output
- [ ] ALMA CLI maintains all features
- [ ] <500ms overhead for cognitive processing

---

## 📅 Recommended Timeline

### Phase 1: AbstractMemory (2 weeks)
- Week 1: Core implementation and components
- Week 2: Knowledge graph and storage

### Phase 2: AbstractAgent (3 weeks)
- Week 1: Core agent and reasoning
- Week 2: Cognitive abstractions
- Week 3: CLI and integration

### Phase 3: Testing & Documentation (1 week)
- Integration testing across all packages
- Documentation and examples
- Migration guides

---

## 🚨 Critical Path Items

1. **AbstractMemory MUST be complete before AbstractAgent**
   - Agents need memory for context
   - Testing agents requires working memory

2. **Cognitive abstractions MUST go in AbstractAgent**
   - They are agent capabilities, not core functionality
   - They use LLMs as tools, not provide LLM access

3. **Maintain backward compatibility**
   - Create migration scripts
   - Provide compatibility layers
   - Document all breaking changes

---

## 📝 Next Immediate Actions

1. **Start AbstractMemory Implementation**
   ```bash
   cd /Users/albou/projects/abstractmemory
   # Begin with core/interfaces.py
   ```

2. **Extract HierarchicalMemory from memory.py**
   ```bash
   # Source: /Users/albou/projects/abstractllm/abstractllm/memory.py
   # Destination: /Users/albou/projects/abstractmemory/abstractmemory/
   ```

3. **Create test harness for AbstractMemory**
   ```bash
   # Set up pytest with memory-specific tests
   ```

4. **Document Memory API design**
   ```bash
   # Create abstractmemory/README.md with API docs
   ```

---

## 💡 Key Insights

1. **Cognitive abstractions are "LLM Applications"**: They USE language models to perform specific tasks (summarization, extraction, evaluation). They don't provide LLM capabilities themselves.

2. **Memory is infrastructure, Agent is orchestration**: Memory provides the storage and retrieval mechanisms. Agents orchestrate the use of LLMs, memory, and tools to accomplish tasks.

3. **Clean boundaries enable parallel development**: With clear interfaces, AbstractMemory and AbstractAgent can be developed by different teams once Memory's API is defined.

---

## 📚 References

- Original Refactoring Plan: `/docs/final-refactoring/00-refactoring-summary.md`
- Architecture Document: `/docs/final-refactoring/01-architecture-final.md`
- AbstractLLM Core: `/Users/albou/projects/abstractllm_core/`
- Original Codebase: `/Users/albou/projects/abstractllm/`

---

*Last Updated: September 23, 2025*
*Next Review: After AbstractMemory Phase 1 Complete*