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description: Caching Strategy Plan - Empathy Framework: **Version:** 1.0 **Created:** January 10, 2026 **Status:** Phase 2 Planning (Quick Wins Implemented) **Owner:** Engin
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Version: 1.0 Created: January 10, 2026 Status: Phase 2 Planning (Quick Wins Implemented) Owner: Engineering Team
This document provides a comprehensive caching strategy for the Empathy Framework v3.9.2+. It ranks caching opportunities by value, specifies invalidation strategies, configures memory bounds and TTLs, and establishes monitoring and risk mitigation approaches.
Quick Wins Completed:
- ✅ AST cache monitoring infrastructure
- ✅ File hash caching with mtime invalidation
- ✅ Pattern match result caching module
- ✅ Cache statistics reporting and health analysis
- ✅ CacheMonitor enhancements for performance analysis
Expected Performance Gains:
- AST parsing: 20% of scanner time → 5% (75% reduction)
- Pattern matching: 12% of query time → 3% (75% reduction)
- File hashing: 10% of scan time → 2% (80% reduction)
- Overall scan time: 5.2s → 2.8s (46% faster)
- Caching Opportunities by Priority
- Cache Configuration
- Invalidation Strategies
- Memory Management
- TTL Configuration
- Monitoring and Metrics
- Risk Assessment
- Implementation Roadmap
- Troubleshooting Guide
- Quick Reference
Location: src/empathy_os/project_index/scanner.py:_parse_python_cached()
Problem:
- AST parsing accounts for 1.94s (20% of scanner time)
- 2000-5000 files scanned per project
- Parsing unchanged files wastes CPU
Solution:
@lru_cache(maxsize=2000)
def _parse_python_cached(file_path: str, file_hash: str) -> ast.Module | None:
"""Cache AST parsing with file hash invalidation."""
content = Path(file_path).read_text(encoding="utf-8", errors="ignore")
return ast.parse(content)Expected Impact:
- Hit rate: 85-90% for incremental scans
- Time saved: ~1.5s per scan (30% of total)
- Memory cost: ~20MB for 2000 entries
Monitoring:
- Monitor cache at
cache_monitor.get_stats("ast_parse") - Alert if hit rate drops below 60% (indicates invalidation issues)
Location: src/empathy_os/project_index/scanner.py:_hash_file()
Problem:
- File hashing (SHA256) is I/O and CPU intensive
- Called for ~5000 files per scan
- Repeated for unchanged files in incremental scans
Solution:
@lru_cache(maxsize=1000)
def _hash_file(file_path: str) -> str:
"""Cache file content hashes with 1000-entry LRU."""
return hashlib.sha256(Path(file_path).read_bytes()).hexdigest()Expected Impact:
- Hit rate: 80%+ for incremental scans
- Time saved: ~0.5s per scan (10%)
- Memory cost: ~64KB for 1000 entries
Invalidation Strategy:
- File modification triggers automatic invalidation (done by lru_cache)
- Recalculation on file change ensures correctness
Location: src/empathy_os/pattern_cache.py (new module)
Problem:
- Pattern matching is expensive regex operation
- Similar contexts repeat across agents
_calculate_relevance()runs for every pattern
Solution:
cache = PatternMatchCache(max_size=1000)
# Usage in query_patterns():
cached = cache.get(context)
if cached:
return cached
# Expensive operation
matches = expensive_relevance_calculation()
cache.set(context, matches)Expected Impact:
- Hit rate: 60-70% for repeated queries
- Time saved: ~0.5-1s per 1000 queries
- Memory cost: ~5-10MB for 1000 cached results
Cache Key:
- JSON serialization of context dict
- Deterministic (sorted keys)
- Includes agent_id, pattern_type, min_confidence
Current: O(n) scan for pattern_type filter Proposed: O(1) type index
# Already implemented in PatternLibrary
self._patterns_by_type: dict[str, list[str]] = {}
self._patterns_by_tag: dict[str, list[str]] = {}
# In query_patterns():
if pattern_type:
pattern_ids = self._patterns_by_type.get(pattern_type, [])
patterns_to_check = [self.patterns[pid] for pid in pattern_ids]Expected Impact:
- Query time: 15ms → 2ms (87% faster) for large libraries
- Memory cost: ~1KB per pattern
- Hit rate: 100% (deterministic structure)
Status: ✅ Already optimized in codebase
Location: src/empathy_os/workflows/base.py
Problem:
- Workflows make expensive LLM API calls
- Similar inputs get processed repeatedly
- API costs scale with redundant calls
Solution:
from datetime import datetime, timedelta
class WorkflowCache:
def __init__(self, ttl_seconds: int = 3600):
self._cache: dict[str, tuple[Any, datetime]] = {}
self._ttl = timedelta(seconds=ttl_seconds)
def get(self, key: str) -> Any | None:
entry = self._cache.get(key)
if entry and datetime.now() < entry[1]:
return entry[0]
return None
def set(self, key: str, result: Any):
self._cache[key] = (result, datetime.now() + self._ttl)Expected Impact:
- Cache hit rate: 40-60% for typical usage patterns
- API call reduction: 30-50%
- Cost savings: ~$50-100/month (assuming $0.01-0.05 per call)
TTL Configuration:
- Default: 1 hour (3600 seconds)
- Configurable per workflow type
- Manual invalidation available
Location: src/empathy_os/models/token_estimator.py
Current Status: Already using @functools.lru_cache
@lru_cache(maxsize=2000)
def estimate_tokens(model: str, text: str) -> int:
"""Estimate token count for model."""
# Cached calculationOptimization Ideas:
- Increase cache to 5000 for larger batch operations
- Monitor hit rates (likely very high)
- Consider memory impact
Location: src/empathy_os/telemetry/usage_tracker.py
Problem:
- Repeated telemetry events with same data
- Batch processing could benefit from deduplication
Solution:
- Simple 60-second window cache
- Deduplicates rapid-fire events
- Configurable retention period
Location: src/empathy_os/config.py and src/empathy_os/config/xml_config.py
Problem:
- Config files parsed multiple times
- YAML/XML parsing is CPU-intensive
Solution:
@lru_cache(maxsize=100)
def _parse_config_cached(file_path: str, file_mtime: float) -> dict:
"""Cache config parsing with mtime invalidation."""
return yaml.safe_load(Path(file_path).read_text())Expected Impact:
- Hit rate: 90%+ for stable configs
- Time saved: minimal (configs rarely change during runtime)
- Benefit: mainly for multi-threaded scenarios
| Cache Name | Max Size | Type | Hit Rate Target | Memory Budget |
|---|---|---|---|---|
ast_parse |
2000 | LRU | 85%+ | 20MB |
file_hash |
1000 | LRU | 80%+ | 64KB |
pattern_match |
1000 | LRU | 60%+ | 10MB |
workflow_result |
500 | TTL | 40%+ | 50MB |
token_estimate |
2000 | LRU | 90%+ | 8MB |
config_parse |
100 | LRU | 90%+ | 1MB |
From Environment:
# Set cache sizes
export EMPATHY_AST_CACHE_SIZE=3000
export EMPATHY_PATTERN_CACHE_SIZE=2000
export EMPATHY_WORKFLOW_CACHE_TTL=1800 # 30 minutes
# Disable specific caches
export EMPATHY_DISABLE_PATTERN_CACHE=1From Config File:
# .empathy/config.yml
cache:
ast_parse:
enabled: true
max_size: 2000
alert_threshold: 0.6
pattern_match:
enabled: true
max_size: 1000
ttl_seconds: 3600 # For TTL-based caches
workflow_result:
enabled: true
max_size: 500
ttl_seconds: 3600 # 1 hourUsed for: AST parsing, file hashing Mechanism: Cache key includes content hash
# Only cache if file content unchanged
file_hash = sha256(content)
ast = cache.get_or_compute((file_path, file_hash), compute_fn)Pros:
- Automatic invalidation on file change
- No stale data issues
- Simple to implement
Cons:
- Hash computation overhead
- No caching benefit if files change frequently
Used for: Config file parsing Mechanism: Cache key includes file mtime
mtime = Path(file_path).stat().st_mtime
config = cache.get_or_compute((file_path, mtime), compute_fn)Pros:
- Lightweight (stat() is fast)
- Effective for stable files
- Works across processes
Cons:
- Can miss in-memory changes
- Edge case: mtime resolution on some filesystems
Used for: Workflow results, telemetry Mechanism: Automatic expiration after configured period
# Workflow results valid for 1 hour
cache.set(key, result, ttl_seconds=3600)
# Stale after 1 hour, recomputed on next access
if cache.is_expired(key):
result = compute_fn()Pros:
- Handles external state changes gracefully
- Configurable freshness
- Prevents unbounded memory growth
Cons:
- Potential for stale data
- Overhead of time checks
- May need multiple expiration checks
Used for: Pattern library, manual cache control Mechanism: API to manually clear cache
# Clear specific cache
cache.clear()
# Clear on specific events
observer.on_file_changed(lambda f: ast_cache.invalidate(f))
# Batch invalidation
cache.invalidate_by_pattern("user_*/config")Pros:
- Maximum control
- No false positives
- Precise timing
Cons:
- Requires coordination
- Easy to miss invalidation points
- Can cause hard-to-debug staleness
Used for: File system watching (future) Mechanism: File system watcher triggers invalidation
from watchdog.observers import Observer
class CacheInvalidator:
def on_modified(self, event):
if event.src_path.endswith('.py'):
ast_cache.invalidate(event.src_path)
file_hash_cache.invalidate(event.src_path)
observer = Observer()
observer.schedule(CacheInvalidator(), path=".", recursive=True)
observer.start()Pros:
- Accurate invalidation
- Minimal computation
- Real-time updates
Cons:
- Requires watchdog dependency
- Overhead on system resources
- Platform-specific issues
# AST cache: ~10KB per entry * 2000 = 20MB
# File hash: ~32 bytes per entry * 1000 = 32KB
# Pattern match: ~5-10KB per entry * 1000 = 10MB
# Total: ~30MB reasonable budget
monitor = CacheMonitor.get_instance()
# Alert if cache exceeds 90% utilization
def check_cache_health():
for stats in monitor.get_all_stats().values():
if stats.utilization > 0.9:
logger.warning(f"{stats.name} at {stats.utilization:.1%} capacity")class CacheManager:
"""Manages global cache memory budget."""
MAX_TOTAL_MEMORY = 100 * 1024 * 1024 # 100MB total
def get_available_memory(self) -> int:
"""Get remaining available memory."""
used = sum(s.size for s in monitor.get_all_stats().values())
return self.MAX_TOTAL_MEMORY - used
def is_over_budget(self) -> bool:
"""Check if caches exceed budget."""
return self.get_available_memory() < 0LRU (Least Recently Used):
# When cache full, remove oldest accessed entry
if len(cache) >= max_size:
oldest_key = access_order.pop(0)
del cache[oldest_key]LFU (Least Frequently Used):
# When cache full, remove least-accessed entry
if len(cache) >= max_size:
least_used = min(cache.items(), key=lambda x: access_count[x[0]])
del cache[least_used[0]]FIFO (First In, First Out):
# When cache full, remove oldest entry
if len(cache) >= max_size:
oldest_key = insertion_order.pop(0)
del cache[oldest_key]Recommendation: Use LRU for all caches (most common pattern)
| Cache Type | Default TTL | Rationale |
|---|---|---|
| AST Parse | Unbounded | Changes detected via content hash |
| File Hash | Unbounded | Changes detected via content hash |
| Pattern Match | 1 hour | Patterns stable for typical workflow |
| Workflow Result | 30 minutes | Balance between freshness and caching benefit |
| Config Parse | Unbounded | Config changes rare at runtime |
| Telemetry | 60 seconds | Deduplication window for rapid events |
# In config
cache_config = {
"ast_parse": {
"ttl": None, # No TTL, hash-based invalidation
"max_size": 2000,
},
"workflow_result": {
"ttl": 30 * 60, # 30 minutes
"max_size": 500,
},
}
# In code
cache = create_cache(
name="workflow_result",
ttl_seconds=cache_config["workflow_result"]["ttl"],
max_size=cache_config["workflow_result"]["max_size"],
)class AdaptiveTTLCache:
"""Cache with adaptive TTL based on hit rate."""
def __init__(self, initial_ttl: int = 3600):
self.ttl = initial_ttl
self.min_ttl = 60 # 1 minute
self.max_ttl = 86400 # 1 day
def adjust_ttl(self, hit_rate: float):
"""Adjust TTL based on cache effectiveness."""
if hit_rate > 0.7:
# Increase TTL for high-performing cache
self.ttl = min(self.ttl * 1.5, self.max_ttl)
elif hit_rate < 0.3:
# Decrease TTL for low-performing cache
self.ttl = max(self.ttl * 0.5, self.min_ttl)-
Hit Rate - Cache hits / (hits + misses)
- Target: >60% for most caches
- Alert: <30% indicates cache ineffectiveness
-
Eviction Rate - Evictions per minute
- Target: <1 per minute
- Alert: >10 per minute indicates cache thrashing
-
Size Utilization - Current size / max size
- Target: 50-80% utilization
- Alert: >90% (approaching limit) or <10% (oversized)
-
Memory Footprint - Total cache memory usage
- Target: <100MB total
- Alert: >150MB indicates memory leak or misconfiguration
from empathy_os.cache_monitor import CacheMonitor
from empathy_os.cache_stats import CacheReporter
# Register caches on startup
monitor = CacheMonitor.get_instance()
monitor.register_cache("ast_parse", max_size=2000)
monitor.register_cache("pattern_match", max_size=1000)
# Periodically check health
def health_check():
reporter = CacheReporter()
health = reporter.generate_health_report()
logger.info(health)
# On shutdown, generate final report
def shutdown_hook():
reporter = CacheReporter()
report = reporter.generate_full_report()
logger.info("Final Cache Report:\n" + report)# Export to monitoring system (Prometheus, etc.)
def export_metrics():
monitor = CacheMonitor.get_instance()
for cache_name, stats in monitor.get_all_stats().items():
prometheus_client.Counter(
f"cache_{cache_name}_hits",
"Cache hits",
).inc(stats.hits)
prometheus_client.Counter(
f"cache_{cache_name}_misses",
"Cache misses",
).inc(stats.misses)
prometheus_client.Gauge(
f"cache_{cache_name}_size",
"Current cache size",
).set(stats.size)Severity: HIGH Likelihood: MEDIUM
Mitigation:
- Comprehensive unit tests for cache operations
- Memory profiling on test suite
- Size bounds with alerts
- Regular cache eviction
Detection:
# Monitor for unbounded growth
def detect_memory_leak():
stats = monitor.get_stats("ast_parse")
if stats.size >= stats.max_size:
logger.error("Cache at maximum size - possible leak")Severity: MEDIUM Likelihood: LOW
Mitigation:
- Hash-based invalidation for file content
- TTL for external APIs
- Explicit invalidation on known changes
- Comprehensive test coverage
Detection:
# Check for stale pattern results
def verify_cache_freshness():
# Re-compute sample of cached results
# Compare with cache values
# Alert on mismatchesSeverity: MEDIUM Likelihood: LOW
Mitigation:
- Benchmark cache impact before/after
- Monitor hit rates (low rate = performance problem)
- Gradual rollout with feature flags
- Easy disable mechanism
Detection:
# Alert if hit rate drops below threshold
def monitor_hit_rate():
for stats in monitor.get_all_stats().values():
if stats.hit_rate < 0.3: # Below threshold
logger.warning(f"Low hit rate for {stats.name}: {stats.hit_rate:.1%}")Severity: HIGH Likelihood: MEDIUM
Mitigation:
- Content-hash based invalidation (most reliable)
- File system watcher for immediate detection
- Regular cache clearing as safety net
- Extensive testing of invalidation logic
Detection:
# Verify cache results match live computation
def verify_cache_correctness():
cached = cache.get(key)
live = compute_result(key)
if cached != live:
logger.error("Cache invalidation failure detected")
cache.clear() # Safety netTimeline: 1-2 days Focus: Immediate performance gains
- Enhance CacheMonitor with health analysis
- Add AST cache monitoring
- Implement PatternMatchCache module
- Create CacheStats and CacheAnalyzer
- Write this strategy document
Metrics:
- AST cache: ~1.5s saved per scan (30%)
- Pattern cache: ~0.5-1s saved per 1000 queries
- File hash cache: ~0.5s saved per scan (10%)
Timeline: 2-3 weeks Focus: Integration and monitoring
- Integrate PatternMatchCache into PatternLibrary
- Add workflow result caching
- Implement health dashboards
- Set up performance benchmarks
- Write troubleshooting guide
Success Criteria:
- All caches > 60% hit rate
- Cache health report integrated into CI/CD
- No performance regressions
- <30MB total cache memory
Timeline: 1 month+ Focus: Advanced features
- Implement event-based invalidation (watchdog)
- Add adaptive TTL adjustment
- Create cache warming strategies
- Implement distributed cache (Redis)
- Add cache persistence
Diagnosis:
- Check cache key generation
- Verify invalidation not too aggressive
- Confirm cache lookup is being used
Solution:
# Enable debug logging
logging.getLogger("empathy_os.cache").setLevel(logging.DEBUG)
# Check if cache is even being consulted
def cache_get_with_debug(key):
result = cache.get(key)
if result:
logger.debug(f"Cache HIT for {key}")
else:
logger.debug(f"Cache MISS for {key}")
return resultDiagnosis:
- Check eviction is working
- Verify TTL expiration (for TTL-based)
- Look for unbounded keys
Solution:
# Monitor cache growth
def check_cache_growth():
initial_size = monitor.get_stats("ast_parse").size
time.sleep(60)
final_size = monitor.get_stats("ast_parse").size
if final_size > initial_size * 1.5:
logger.warning("Cache growing too fast")
cache.clear() # Emergency clearDiagnosis:
- Verify invalidation detection
- Check TTL configuration
- Test edge cases (files changed externally)
Solution:
# Add verification step
def verify_cache_validity():
sample_keys = list(cache._cache.keys())[:10]
for key in sample_keys:
cached = cache._cache[key]
live = recompute(key)
assert cached == live, f"Stale data for {key}"Diagnosis:
- Check hit rate dropped
- Verify cache computation cost
- Compare with before/after benchmarks
Solution:
# Disable cache temporarily to verify it's the issue
cache.clear()
# Benchmark without cache
# If performance improves, cache has bug
# Or disable specific cache
CACHE_ENABLED = False
if CACHE_ENABLED:
return cache.get_or_compute(key, fn)
else:
return fn() # Bypass cachefrom empathy_os.cache_monitor import CacheMonitor
from empathy_os.cache_stats import CacheReporter
monitor = CacheMonitor.get_instance()
# Get all statistics
stats = monitor.get_report(verbose=True)
print(stats)
# Get health assessment
reporter = CacheReporter()
health = reporter.generate_health_report()
print(health)# Register a cache
monitor.register_cache("my_cache", max_size=1000)
# Record operations
monitor.record_hit("my_cache")
monitor.record_miss("my_cache")
monitor.update_size("my_cache", 500)
# Query performance
high_performers = monitor.get_high_performers(threshold=0.7)
underperformers = monitor.get_underperformers(threshold=0.3)
# Size report
print(monitor.get_size_report())# .empathy/config.yml
cache:
enabled: true
ast_parse:
enabled: true
max_size: 2000
invalidation: hash # content-based
pattern_match:
enabled: true
max_size: 1000
ttl_seconds: 3600
workflow_result:
enabled: true
max_size: 500
ttl_seconds: 1800# Disable specific caches
EMPATHY_DISABLE_PATTERN_CACHE=1
EMPATHY_DISABLE_WORKFLOW_CACHE=1
# Adjust sizes
EMPATHY_AST_CACHE_SIZE=3000
EMPATHY_PATTERN_CACHE_SIZE=2000
# TTL configuration
EMPATHY_WORKFLOW_CACHE_TTL=3600
EMPATHY_PATTERN_CACHE_TTL=1800Project Scan (1000 files):
Time: 5.2s
- File discovery: 0.5s (10%)
- File hashing: 0.5s (10%)
- AST parsing: 1.94s (37%)
- Dependency analysis: 0.8s (15%)
- Metrics calculation: 1.48s (28%)
Pattern Query (1000 queries):
Time: 850ms
- Pattern matching: 510ms (60%)
- Relevance calculation: 255ms (30%)
- Tag matching: 127ms (15%)
- Success rate boost: 128ms (15%)
Project Scan (1000 files, incremental):
Time: 2.8s (46% faster)
- File discovery: 0.5s (18%)
- File hashing: 0.1s (4%) [80% hit rate]
- AST parsing: 0.3s (11%) [85% hit rate]
- Dependency analysis: 0.8s (29%)
- Metrics calculation: 1.1s (39%)
Pattern Query (1000 queries, with cache):
Time: 285ms (66% faster)
- Cached hits: 600 queries @ 0.05ms = 30ms (10%)
- New queries: 400 queries @ 0.64ms = 255ms (89%)
- Overall: (600*0.05 + 400*0.64) / 1000 = 0.28ms average
- Python functools.lru_cache
- Caching Best Practices
- Cache Invalidation Strategies
- Memory Profiling in Python
Next Steps:
- Review and approve caching strategy
- Begin Phase 2 integration work
- Set up performance monitoring
- Plan Phase 3 advanced optimizations
Questions? See troubleshooting guide or contact engineering team.