Optimization

Optimization means measuring a program and improving the parts that affect quality: instructions, demos, tool descriptions, templates, component maps, or saved optimizer artifacts.

For TypeScript, use the top-level optimize(...) helper for normal AxGen and Flow tuning, and agent.optimize(...) for agent-specific tuning. Generated languages expose the AxIR-supported optimizer surface, usually around AxGEPA and artifact application.

from axllm import AxGEPA

engine = AxGEPA(reflection_client)
result = engine.optimize(request, evaluator)

GEPA is useful when accuracy, cost, latency, brevity, tool-use quality, or policy quality are real tradeoffs. The output can be a Pareto frontier instead of one fake “best” prompt.

What You Provide

• A program to tune.
• Training examples with the same input/output shape as the signature.
• A metric or judge that scores predictions.
• Optional validation examples for holdout selection.
• Student and teacher model settings where the language surface supports them.
• A maxMetricCalls bound so the optimizer cannot spend without limit.

flowchart LR
  A[Program] --> D[Optimizer]
  B[Train examples] --> D
  C[Metric or judge] --> D
  E[Validation examples] --> D
  D --> F[Optimized artifact]
  D --> G[Pareto frontier]

AxGen Example

Use this for a single structured generator. Keep the metric deterministic when the expected output is easy to score.

from axllm import AxGEPA

gepa = AxGEPA({"maxMetricCalls": 40})
artifact = gepa.optimize({"program": program, "train": train}, evaluator)

Flow Example

Flows expose multiple optimizable components. Use multi-objective metrics when a workflow must balance accuracy with brevity, cost, or latency.

from axllm import AxGEPA

gepa = AxGEPA({"maxMetricCalls": 40})
artifact = gepa.optimize({"program": program, "train": train}, evaluator)

Agent Example

Use agent.optimize(...) for tool-use, clarification, delegation, and final-response behavior. The normal path starts with task records containing input, criteria, and optional expectedActions or forbiddenActions.

from axllm import AxGEPA

gepa = AxGEPA({"maxMetricCalls": 40})
artifact = gepa.optimize({"program": program, "train": train}, evaluator)

Metrics And Judges

Normalize scores to 0..1 when possible. Keep objective names stable across calls.

Bootstrap And GEPA Together

Bootstrap demos are useful for small starter sets because they seed the model with concrete successful examples before GEPA mutates instructions/components. TypeScript optimize(...) composes the practical bootstrap-plus-GEPA path. Generated languages expose the optimizer primitives supported by their AxIR contract.

Artifacts

Optimization output is model-adjacent configuration. Save it, version it, record the examples and metrics used, and apply it through the program or agent API rather than manually patching instructions.

from axllm import AxGEPA

gepa = AxGEPA({"maxMetricCalls": 40})
artifact = gepa.optimize({"program": program, "train": train}, evaluator)

Budget Discipline

• Always set maxMetricCalls in docs and examples.
• Use distinct validation examples when selecting a best candidate.
• Start with small numTrials and scale once the metric is stable.
• For trees, inspect optimized component keys so you know what changed.
• Persist artifacts only after a held-out or smoke run proves they help.

See AxGEPA GEPA and optimize() API.