P2.13: Performance Optimization — claude-force 2.2.0 documentation

# P2.13: Performance Optimization

## Executive Summary

Successfully optimized claude-force startup and initialization performance, achieving:

- 20x faster startup time: 229.60ms → 11.38ms (95% improvement)

- 1200x faster config loading: 900.37ms → 0.74ms (99.9% improvement)

- All performance targets exceeded (target: <500ms startup)

## Optimizations Implemented

### 1. Embedding Caching (`semantic_selector.py`)

Problem: Agent embeddings were regenerated on every instantiation, causing unnecessary computation.

Solution: Implemented intelligent caching system with:

- Pickle-based serialization to `.cache/agent_embeddings.pkl`

- MD5 hash-based cache invalidation (detects config changes)

- Model name validation (cache invalidated if model changes)

- Graceful fallback if cache fails

Implementation Details:

    def _compute_agent_embeddings(self):
        """Pre-compute embeddings for all agents with caching support."""
        # Try to load from cache first
        if self.use_cache and self._load_from_cache():
            return

        # Generate embeddings (existing logic)
        # ...

        # Save to cache
        if self.use_cache:
            self._save_to_cache()

Benefits:

- First run: ~1000ms (generate embeddings)

- Subsequent runs: <50ms (load from cache)

- Automatic cache invalidation when agents change

### 2. Lazy Client Initialization (`orchestrator.py`)

Problem: Anthropic API client was initialized even for read-only operations (list agents, list workflows), requiring unnecessary API key validation and import overhead.

Solution: Implemented lazy loading using Python properties:

- Client only created when first accessed

- API key validation deferred until needed

- Read-only operations no longer require API key

Implementation Details:

    @property
    def client(self):
        """Lazy load anthropic client."""
        if self._client is None:
            if not self.api_key:
                raise ValueError(format_api_key_error())
            import anthropic
            self._client = anthropic.Client(api_key=self.api_key)
        return self._client

Benefits:

- List commands work without API key

- No import overhead until needed

- Reduced initialization time from 900ms to 0.74ms

### 3. Lazy Module Imports (`__init__.py`)

Problem: Package initialization imported all submodules eagerly:

- cli module (1125 lines)

- mcp_server

- quick_start

- hybrid_orchestrator

- skills_manager

Solution: Implemented modern Python lazy imports using `__getattr__`:

- Only core classes (`AgentOrchestrator`, `AgentResult`) imported eagerly

- All other imports deferred until first access

- Seamless API - users see no difference

Implementation Details:

    _LAZY_IMPORTS = {
        "cli_main": ("cli", "main"),
        "MCPServer": ("mcp_server", "MCPServer"),
        # ... other imports
    }

    def __getattr__(name):
        """Lazy import handler for non-core functionality."""
        if name in _LAZY_IMPORTS:
            module_name, attr_name = _LAZY_IMPORTS[name]
            from importlib import import_module
            module = import_module(f".{module_name}", package="claude_force")
            attr = getattr(module, attr_name)
            globals()[name] = attr  # Cache for future access
            return attr
        raise AttributeError(f"module '{__name__}' has no attribute '{name}'")

Benefits:

- Startup time reduced from 229ms to 11ms

- CLI only loaded when needed

- Faster import for library users

### 4. Lazy Performance Tracker (`orchestrator.py`)

Problem: Performance tracker initialized even when disabled or not needed.

Solution: Converted to lazy property, only created when accessed.

Benefits:

- Reduced initialization overhead

- Cleaner separation of concerns

## Performance Benchmarks

### Baseline (Before Optimization)

    Startup time:        229.60ms
    Config load:         900.37ms
    Total init time:     1130ms

### After Optimization

    Startup time:        11.38ms
    Config load:         0.74ms
    Total init time:     12.12ms

### Improvement Summary

Metric | Before | After | Improvement
--- | --- | --- | ---
Startup | 229.60ms | 11.38ms | 20x faster
Config Load | 900.37ms | 0.74ms | 1200x faster
Total | 1130ms | 12.12ms | 93x faster

### Target Metrics Comparison

Metric | Target | Achieved | Status
--- | --- | --- | ---
Startup time | < 500ms | 11.38ms | ✅ Exceeded
Config load | (no target) | 0.74ms | ✅ Excellent

## Profiling Details

### Before Optimization

Top bottlenecks:

1. Importing cli module (1125 lines): ~150ms

2. Creating anthropic client: ~800ms

3. Loading performance tracker: ~50ms

4. Other imports: ~130ms

### After Optimization

Minimal overhead:

1. JSON config loading: ~0.5ms

2. Path operations: ~0.2ms

3. Environment variable check: <0.1ms

## Testing

All tests pass with optimizations:

- ✅ Integration tests (23 passed, 3 skipped)

- ✅ Demo mode tests (14 passed)

- ✅ Orchestrator tests (9 passed)

### Key Test Results

- Read-only operations (list agents/workflows) work without API key

- Lazy imports transparent to users

- Embedding cache properly invalidated on config changes

- All existing functionality preserved

## User Impact

### For CLI Users

- Instant response for list commands

- Faster startup for all commands

- No API key needed for read-only operations

### For Library Users

    import claude_force  # Now 20x faster: 11ms instead of 229ms

    # Create orchestrator without API key (for read-only)
    orch = claude_force.AgentOrchestrator(config_path=".claude/claude.json")
    agents = orch.list_agents()  # Works without API key!

    # API key only needed for actual agent execution
    result = orch.run_agent("code-reviewer", task="...")  # Key validated here

### For CI/CD Pipelines

- Faster test runs

- Reduced cold start time in serverless environments

- Lower latency for API services

## Future Optimization Opportunities

1. Config caching: Cache parsed JSON config (low priority - already <1ms)

2. Agent file caching: Cache loaded agent markdown files

3. Async loading: Load multiple agent files concurrently

4. Incremental embedding updates: Only update changed agents

5. Shared embedding cache: Share cache across projects using same agents

## Files Modified

1. `claude_force/semantic_selector.py`

   - Added `_get_config_hash()` for cache invalidation

   - Added `_load_from_cache()` for loading cached embeddings

   - Added `_save_to_cache()` for persisting embeddings

   - Modified `_compute_agent_embeddings()` to use caching

2. `claude_force/orchestrator.py`

   - Converted `client` to lazy property

   - Converted `tracker` to lazy property

   - Moved API key validation to client property

   - Removed eager initialization

3. `claude_force/__init__.py`

   - Implemented `__getattr__` for lazy imports

   - Moved all non-core imports to lazy loading

   - Maintained backward compatibility

4. `claude_force/__main__.py`

   - Fixed import statement for lazy loading

5. `scripts/profile_performance.py` (new)

   - Comprehensive profiling script

   - Measures startup, config load, embedding generation

   - Provides recommendations

## Documentation

- ✅ Performance profiling script created

- ✅ Comprehensive benchmarks documented

- ✅ Implementation details documented

- ✅ User impact documented

- ✅ This summary document

## Conclusion

Performance optimization (P2.13) is complete and exceeds all targets:

- Startup time: 11.38ms (target: <500ms) - 44x better than target

- Config load: 0.74ms (no target) - excellent performance

- Total improvement: 93x faster initialization

All optimizations are production-ready, well-tested, and maintain full backward compatibility.

## Next Steps

The following P2 tasks remain:

- P2.9: Real-World Benchmarks (16 hours)

- P2.10: Agent Memory System (24 hours)

- P2.12: VS Code Extension (40 hours)

With performance optimization complete, all features will benefit from the improved startup time and lazy loading architecture.