Enables evolutionary solution generation and optimization using genetic algorithm principles, allowing LLMs to evolve solutions across multiple generations with consistency check evaluations and convergence detection.
Implements the Genetic-Evolutionary Prompt Architecture for automatic prompt optimization, providing tools to optimize, quickly improve, and contextually adapt prompts.
MCP server for agent-native evolutionary optimization. Enables agents to join runs, submit gate-checked mutations, and collaborate on evolving code toward a stated goal.
Objective-driven cognitive architecture engine that builds single experts, councils, or full autonomous organizations from user goals, generating deployment-ready superprompts and configurations.
Enables multi-strategy AI orchestration including council decision review, debate, brainstorming, evaluation, and spec review, with support for multiple LLM providers and advisor personas.
