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Performance Optimizations

Performance Optimizations

ModelSEEDagent includes several performance optimizations to improve analysis speed and reduce resource usage.

Connection Pooling

HTTP connection pooling has been implemented to reduce the overhead of creating new connections for each LLM request:

  • Benefit: Eliminates 40+ redundant ArgoLLM initializations per session
  • Implementation: Session-level HTTP client reuse via LLMConnectionPool
  • Configuration: Automatic - no user configuration required

Model Caching

Intelligent COBRA model caching reduces file I/O overhead and improves performance.

How It Works

File Modification Tracking: - Models are cached with file modification timestamps - Cache is invalidated when source files change - Automatic cleanup prevents memory bloat

Cache Benefits: - 4.7x speedup for repeated model access - Eliminates redundant SBML parsing - Reduces disk I/O during analysis workflows

Cache Configuration

Automatic Management: - Cache size is automatically managed - LRU eviction for memory efficiency - Debug logging shows cache hit/miss rates

Cache Statistics (in debug logs): 

Loading model from disk: /path/to/model.xml
Using cached model: /path/to/model.xml
Cached model: /path/to/model.xml (ID: model_id)

Memory Usage

Cache Efficiency: - Models are deep-copied when retrieved - Original cached models remain unmodified - Memory usage scales with model complexity

No user configuration required - caching is automatic and transparent.

Last updated: 3003b76c - Model caching implementation detected### COBRA Multiprocessing Control

COBRA tools now default to single-process mode to prevent connection pool fragmentation:

  • Environment Variables:
  • COBRA_DISABLE_MULTIPROCESSING=1 - Force single process mode
  • COBRA_PROCESSES=N - Set process count for all COBRA tools
  • COBRA_FVA_PROCESSES=N - Set process count for flux variability analysis
  • COBRA_SAMPLING_PROCESSES=N - Set process count for flux sampling

Performance Monitoring

Built-in performance monitoring tracks optimization effectiveness:

  • Session metrics: Total runtime, tool execution times
  • Connection tracking: LLM initialization counts
  • Model access patterns: Cache hit rates and load times

Last updated: 3003b76c - Performance optimization work detected

Additional Information

For more details on ModelSEEDagent configuration and usage, see the main documentation.