Generate AxGEPA optimization code for multi-objective agent-tree configurations.
npx skills add https://github.com/jadecli/researchers --skill ax-gepa
Please help me install this Agent Skill. Skill: ax-gepa Source: https://github.com/jadecli/researchers/tree/main/agentcrawls-ts/.claude/skills/ax-gepa Command: npx skills add https://github.com/jadecli/researchers --skill ax-gepa
Generates AxGEPA optimization code to efficiently optimize generators, flows, and agent trees using multi-objective criteria within the @ax-llm/ax framework.
Provide an AxGEPA-based optimization setup to optimize a given classifier or workflow.
High-intent search queries and answers about installing and using this skill.
Multi-objective agent tree optimization uses AxGEPA to explore Pareto fronts by evaluating deterministic metrics across configurations. You provide metrics, validationExamples, and maxMetricCalls to bound evaluation cost and return an optimized program.
Multi-objective optimization for LLM workflows balances competing criteria like accuracy and latency. AxGEPA applies Pareto-front exploration to generators, flows, and agent trees, returning a set of optimal trade-off programs rather than a single solution.
To configure AxGEPA.compile for flow optimization, supply your metrics, validationExamples, and maxMetricCalls. This bounds the optimization cost and returns an optimized program alongside the evaluated Pareto front for your flow or agent tree.
Yes, you can balance accuracy and latency across distributed agent systems by applying AxGEPA multi-objective optimization. It evaluates your agent-tree configurations against multiple metrics to generate a Pareto front of optimal trade-offs.
Yes, validationExamples are required alongside metrics and maxMetricCalls to bound evaluation costs during AxGEPA optimization. These inputs ensure deterministic evaluation and scalable instruction optimization for your generators and flows.
Multi-objective agent optimization returns a Pareto front because it identifies multiple trade-off solutions across competing metrics rather than a single winner. AxGEPA.compile outputs the entire Pareto front alongside the optimized program.
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