🤝 Related Work & Acknowledgments¶

SuperOpt builds on and extends foundational work in agent optimization. We acknowledge the inspiration and methodologies from related approaches while highlighting how SuperOpt provides a more comprehensive framework.

📚 Key Research Papers¶

🎯 Building on Established Foundations¶

GEPA Integration¶

SuperOpt incorporates GEPA's evolutionary prompt optimization techniques within the SuperPrompt component. We appreciate the foundational work by GEPA that demonstrated how evolutionary algorithms can effectively optimize prompt configurations for better task performance.

# SuperOpt can use GEPA-style optimization for prompts
from superopt import SuperOpt
from superopt.components.superprompt import SuperPrompt

# Configure SuperPrompt with GEPA-inspired parameters
superprompt = SuperPrompt(
    population_size=20,      # GEPA-style population approach
    mutation_rate=0.3,       # Evolutionary parameters
    use_pareto_selection=True
)

optimizer = SuperOpt(
    environment=environment,
    superprompt=superprompt   # Uses GEPA methodology
)

ACE Integration¶

The SuperMem component draws inspiration from ACE's context accumulation approaches, extending them with typed memory hierarchies, exponential decay, and conflict resolution.

# SuperOpt extends ACE with additional memory features
from superopt import SuperOpt
from superopt.components.supermem import SuperMem

# Enhanced memory with ACE-inspired accumulation + more
supermem = SuperMem(
    min_confidence=0.1,
    type_hierarchy_enabled=True,  # Beyond basic ACE
    conflict_resolution=True,     # Additional SuperOpt features
    exponential_decay=True
)

optimizer = SuperOpt(
    environment=environment,
    supermem=supermem
)

🔄 Complementary Approaches¶

Rather than competing with these approaches, SuperOpt is designed to work alongside and extend them:

optimizer = SuperOpt(
    superprompt=SuperPrompt(use_gepa_methodology=True),
    # Plus SuperReflexion, SuperRAG, SuperMem
)

optimizer = SuperOpt(
    supermem=SuperMem(use_ace_accumulation=True),
    # Plus SuperPrompt, SuperReflexion, SuperRAG
)

# DSPy pipelines optimized by SuperOpt
dspy_pipeline = DSPyPipeline()
optimizer = SuperOpt(
    environment=dspy_pipeline.get_environment()
)

🏗️ Additional Foundations¶

TextGrad (Textual Differentiation)¶

The concept of Natural Language Gradients in SuperOpt builds on TextGrad's textual differentiation approach, extending it to multi-component environment optimization.

Meta-ACE Extensions¶

SuperOpt's meta-reasoning capabilities are informed by Romero's Meta-ACE framework, particularly in how we handle hierarchical optimization decisions and stability constraints.

🙏 Acknowledgments¶

We gratefully acknowledge the foundational contributions that inspired SuperOpt:

🎯 SuperOpt's Unique Contribution¶

SuperOpt represents an evolution in agent optimization research, moving from single-component approaches to comprehensive environment optimization:

This collaborative approach allows researchers and practitioners to leverage the best of all methodologies within a unified system.