Genetic evolution for AI personalities. Self-optimizing agents with liquid memory, ML analytics, and evolutionary optimization. Beyond prompt engineering.
Agents evolve through genetic algorithms, inheriting and mutating personality traits across generations for optimal behavior.
Dynamic memory systems that adapt in real-time, storing and retrieving context efficiently using liquid neural network principles.
Built-in machine learning analytics for tracking agent performance, behavior patterns, and optimization opportunities.
Agents autonomously improve their responses and behaviors based on feedback loops and evolutionary pressure.
Rich personality modeling with traits, preferences, and behavioral modifiers that evolve over time.
Seamless coordination between multiple agents with shared genetic pools and collaborative evolution.
LLM Integration • Prompt Management • Response Generation
Mutation • Crossover • Selection
Liquid Memory • Context Store
Metrics • Predictions • ML
Agent State • Evolution History • Trait Genealogy
Phylogenic AI is a framework that applies genetic algorithms to evolve AI agent personalities over multiple generations. Unlike static prompt engineering, agents inherit traits, mutate behaviors, and undergo fitness-based selection to optimize their performance for specific tasks. This approach enables autonomous self-improvement without manual intervention.
The framework uses four evolutionary phases: (1) Genesis - initialize base traits, (2) Mutation - apply random genetic variations, (3) Selection - evaluate fitness scores based on task performance, and (4) Inheritance - pass successful traits to the next generation. This mimics biological evolution to create optimized AI personalities.
Liquid memory is a dynamic memory system inspired by Liquid Neural Networks (LNNs). It adapts in real-time, efficiently storing and retrieving context while automatically discarding outdated information based on usage patterns. This enables agents to maintain relevant long-term context without memory overflow.
While prompt engineering manually crafts static instructions, Phylogenic AI automatically evolves optimal agent behaviors through genetic algorithms. Agents self-optimize based on feedback loops, requiring less manual intervention and discovering non-obvious behavioral improvements that humans might not anticipate.
Phylogenic AI is built with Python 3.8+ and integrates with major LLM providers including OpenAI, Anthropic, and local models via Ollama. It uses NumPy for genetic operations, supports async execution, and includes comprehensive type hints for IDE support.
Join the evolution. Build self-optimizing agents that grow smarter with every interaction.