dspy-gepa-reflective

# DSPy GEPA Optimizer ## Goal Optimize complex agentic systems using LLM reflection on full execution traces with Pareto-based evolutionary search. ## When to Use - **Agentic systems** with tool use - When you have **rich textual feedback** on failures - Complex multi-step workflows - Instruction-only optimization needed ## Related Skills - For non-agentic programs: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md), [dspy-bootstrap-fewshot](../dspy-bootstrap-fewshot/SKILL.md) - Measure improvements: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Agent or complex program | | `trainset` | `list[dspy.Example]` | Training examples | | `metric` | `callable` | Accepts five arguments and returns `dspy.Prediction(score=..., feedback=...)` | | `reflection_lm` | `dspy.LM` | Strong LM for reflection (GPT-4) | | `auto` | `str` | "light", "medium", "heavy" | ## Outputs | Output | Type | Description | |--------|------|-------------| | `compiled_program` | `dspy.Module` | Reflectively optimized program | ## Workflow ### Phase 1: Define Feedback Metric GEPA requires metrics that return *textual feedback*: ```python def gepa_metric(example, pred, trace=None, pred_name=None, pred_trace=None): """Return score and actionable feedback for GEPA reflection.""" is_correct = example.answer.lower() in pred.answer.lower() if is_correct: feed...