evolving-ai-agents

Automated agent evolution uses LLM-guided mutation cycles to iteratively optimize prompts, skills, and memory for higher benchmark scores. It runs solve, observe, evolve, gate, and reload cycles, using benchmark feedback to guide targeted mutations instead of manual trial-and-error.

File-system-based agent optimization represents evolving agent state—including prompts, skills, memory, and tools—as versioned workspace files. This approach ensures all mutations are explicit, inspectable, and rollbackable using git snapshot gating to prevent harmful changes from lingering.

Automated skill discovery learns reusable procedures by proposing, curating, merging, and updating skills based on observed agent failures and performance changes. It operates within a benchmark-driven evolution loop to continuously refine agent capabilities across any domain.

Yes, a git-versioned workspace contract is required to run benchmark regression fixing and agent evaluation loops. You also need A-Evolve-compatible agent and benchmark adapters that implement solve and evaluate functions for iterative optimization.

Install the a-evolve package and instantiate the Evolver with your specified agent and benchmark parameters. Run the evolution cycles by calling the run method with your desired cycle count to receive the final improved benchmark score.

Agent evolution loops reject mutations through git snapshot gating, a safety mechanism that rolls back harmful or unhelpful changes. This ensures only mutations that improve benchmark performance are retained in the versioned workspace.