# GEPA Optimizer

`GepaOptimizer` wraps the external [GEPA](https://github.com/gepa-ai/gepa) package to optimize a
single system prompt for single-turn tasks. It maps Opik datasets and metrics into GEPA’s expected
format, runs GEPA’s optimization using a task model and a reflection model, and returns a standard
`OptimizationResult` compatible with the Opik SDK.

:::callout{intent="note"}
`GepaOptimizer` is ideal when you have a single-turn task (one user input → one model
response) and you want to optimize the system prompt using a reflection-driven search.
:::

## How it works

The GEPA optimizer companies two key approaches to optimize agents:

1. **Reflection**: The optimizer uses the outcomes from evaluations to improve the prompts.
2. **Evolution**: The optimizer uses an evolutionary algorithm to explore the space of prompts.

You can learn more about the algorithm in the [GEPA paper](https://arxiv.org/abs/2507.19457) but in
short, the optimizer will:

:::frame
<img src="../img/apps/opik-documentation/documentation/fern/img/agent_optimization/gepa_optimizer.png" alt="GEPA Optimizer">
:::

## Quickstart

```python
"""
Optimize a simple system prompt on the tiny_test dataset.
Requires: pip install gepa, and a valid OPENAI_API_KEY for LiteLLM-backed models.
"""
from typing import Any, Dict

from opik.evaluation.metrics import LevenshteinRatio
from opik.evaluation.metrics.score_result import ScoreResult

from opik_optimizer import ChatPrompt, datasets
from opik_optimizer.gepa_optimizer import GepaOptimizer

def levenshtein_ratio(dataset_item: Dict[str, Any], llm_output: str) -> ScoreResult:
    return LevenshteinRatio().score(reference=dataset_item["label"], output=llm_output)

dataset = datasets.tiny_test()

prompt = ChatPrompt(
    system="You are a helpful assistant. Answer concisely with the exact answer.",
    user="{text}",
)

optimizer = GepaOptimizer(
    model="openai/gpt-4o-mini",
    n_threads=6,
    model_parameters={"temperature": 0.2, "max_tokens": 200},
)

result = optimizer.optimize_prompt(
    prompt=prompt,
    dataset=dataset,
    metric=levenshtein_ratio,
    max_trials=12,
    reflection_minibatch_size=2,
    n_samples=5,
)

result.display()
```

### Determinism and tool usage

- GEPA’s seed is forwarded directly to the underlying `gepa.optimize` call, but any non-determinism in your prompt (tool calls, non-zero temperature, external APIs) will still introduce variance. To test seeding in isolation, disable tools or substitute cached responses.
- GEPA emits its own baseline evaluation inside the optimization loop. You’ll see one baseline score from Opik’s wrapper and another from GEPA before the first trial; this is expected and does not double-charge the metric budget.
- Reflection only triggers after GEPA accepts at least `reflection_minibatch_size` unique prompts. If the minibatch is larger than the trial budget, the optimizer logs a warning and skips reflection.
- Reflection calls use `GepaOptimizer.model`, are traced as Opik spans, and count toward the run's reported LLM cost. They are **not** metric calls, so `max_trials` does not bound them — a run typically makes many more reflection calls than trials, because an iteration whose candidate loses on the mini-batch costs only `2 * reflection_minibatch_size` metric calls. The run reports the total as `reflection_call_count` in the result details; set `max_reflection_calls` to cap it.
- GEPA supports **tool use during evaluation** (`allow_tool_use=True`) but does **not** support `optimize_tools=True` yet. Tool-description optimization requests are currently degraded/blocked until the adapter supports it.

### GEPA scores vs. Opik scores

- The **GEPA Score** column reflects the aggregate score GEPA computes on its train/validation split when deciding which candidates stay on the Pareto front. It is useful for understanding how GEPA’s evolutionary search ranks prompts.
- The **Opik Score** column is a fresh evaluation performed through Opik’s metric pipeline on the same dataset (respecting `n_samples`). This is the score you should use when comparing against your baseline or other optimizers.
- Because the GEPA score is based on GEPA’s internal aggregation, it can diverge from the Opik score for the same prompt. This is expected—treat the GEPA score as a hint about why GEPA kept or discarded a candidate, and rely on the Opik score for final comparisons.

### `skip_perfect_score`

- When `skip_perfect_score=True`, GEPA immediately ignores any candidate whose GEPA score meets or exceeds the `perfect_score` threshold (default `1.0`). This keeps the search moving toward imperfect prompts instead of spending budget refining already perfect ones.
- Set `skip_perfect_score=False` if your metric tops out below `1.0`, or if you still want to see how GEPA mutates a perfect-scoring prompt—for example, when you care about ties being broken by Opik’s rescoring step rather than GEPA’s aggregate.

## Configuration Options

### Optimizer parameters

The optimizer has the following parameters:

- `model` (str, default: openai/gpt-5-nano) — LiteLLM model name for the optimization algorithm

- `model_parameters` (dict\[str, typing.Any] | None) — Optional dict of LiteLLM parameters for optimizer's internal LLM calls. Common params: temperature, max\_tokens, max\_completion\_tokens, top\_p.

- `n_threads` (int, default: 6) — Number of parallel threads for evaluation

- `verbose` (int, default: 1) — Controls internal logging/progress bars (0=off, 1=on)

- `seed` (int, default: 42) — Random seed for reproducibility

### `optimize_prompt` parameters

The `optimize_prompt` method has the following parameters:

- `prompt` (ChatPrompt) — The prompt to optimize

- `dataset` (Dataset) — Opik Dataset to optimize on

- `metric` (Callable) — Metric function to evaluate on

- `experiment_config` (dict | None) — Optional configuration for the experiment

- `n_samples` (int | float | str | None) — Number of dataset items to use per evaluation. Use counts (e.g., `50`), fractions (e.g., `0.1`), percentages (e.g., "10%"), or "all"/"full"/None for the full dataset.

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches (defaults to n\_samples).

- `n_samples_strategy` (str | None) — Sampling strategy for subsampling (default: "random\_sorted").

- `auto_continue` (bool, default: False) — Whether to auto-continue optimization

- `agent_class` (type\[opik\_optimizer.optimizable\_agent.OptimizableAgent] | None) — Optional agent class to use

- `project_name` (str, default: Optimization) —

- `max_trials` (int, default: 10) — Maximum number of different prompts to test (default: 10)

- `reflection_minibatch_size` (int, default: 3) — Size of reflection minibatches (default: 3)

- `max_reflection_calls` (int, default: 0) — Cap on reflection-LLM calls — the calls GEPA makes to propose new candidates, which are not metric calls and are billed on top of them. Defaults to `0`, meaning no cap: GEPA reflects as often as its search requires. Set a positive value to bound reflection spend; the run then stops once the cap is reached and reports `finish_reason="reflection_budget"` (a run that spent its full metric budget at the same time reports `max_trials`, since the trials are the reason it ended). Reflection calls scale with engine iterations rather than trials, so pick this from the run's reported `reflection_call_count`, not from `max_trials`.

- `candidate_selection_strategy` (str, default: pareto) — Strategy for candidate selection (choose from "pareto", "current\_best", or "epsilon\_greedy"; default: "pareto")

- `skip_perfect_score` (bool, default: True) — Skip candidates with perfect scores (default: True)

- `perfect_score` (float, default: 1.0) — Score considered perfect (default: 1.0)

- `use_merge` (bool, default: False) — Enable merge operations (default: False)

- `max_merge_invocations` (int, default: 5) — Maximum merge invocations (default: 5)

- `run_dir` (str | None) — Directory for run outputs (default: None)

- `track_best_outputs` (bool, default: False) — Track best outputs during optimization (default: False)

- `display_progress_bar` (bool, default: False) — Display progress bar (default: False)

- `seed` (int, default: 42) — Random seed for reproducibility (default: 42)

- `raise_on_exception` (bool, default: True) — Raise exceptions instead of continuing (default: True)

- `kwargs` (Any) —

### Model Support

GEPA coordinates two model contexts:

- `GepaOptimizer.model`: LiteLLM model string the optimizer uses for internal reasoning (reflection, mutation prompts, etc.).
- `ChatPrompt.model`: The model evaluated against your dataset—this should match what you run in production.

Set `model` to any LiteLLM-supported provider (e.g., `"gpt-4o"`, `"azure/gpt-4"`, `"anthropic/claude-3-opus"`, `"gemini/gemini-1.5-pro"`) and pass extra parameters via `model_parameters` when you need to tune temperature, max tokens, or other limits:

```python
optimizer = GepaOptimizer(
    model="anthropic/claude-3-opus-20240229",
    model_parameters={
        "temperature": 0.7,
        "max_tokens": 4096
    }
)
```

Reflection is handled internally; there is no separate `reflection_model` argument to set.

## Limitations & tips

- **Instruction-focused**: The current wrapper optimizes the instruction/system portion of your prompt. If you rely heavily on few-shot exemplars, consider pairing GEPA with the Few-Shot Bayesian optimizer or an Evolutionary run.
- **Reflection can misfire**: GEPA’s reflective mutations are only as good as the metric reasons you supply. If `ScoreResult.reason` is vague, the optimizer may reinforce bad behaviors. Invest in descriptive metrics before running GEPA at scale.
- **Cost-aware**: Although GEPA is more sample-efficient than some RL-based methods, reflection and Pareto scoring still consume multiple LLM calls per trial. Start with small `max_trials` and monitor API usage.

## Next Steps

1. Explore specific [Optimizers](https://www.comet.com/development/optimization-runs/algorithms/overview) for algorithm details.
2. Refer to the [FAQ](https://www.comet.com/development/optimization-runs/faq) for common questions and troubleshooting.
3. Refer to the [API Reference](https://www.comet.com/development/optimization-runs/advanced/api_reference) for detailed configuration options.

## Related pages

- [Optimization algorithms overview](./development-optimization-runs-algorithms-overview.md)
- [Optimizer benchmarks](./development-optimization-runs-algorithms-benchmarks.md)
- [MetaPrompt Optimizer](./development-optimization-runs-algorithms-metaprompt-optimizer.md)
- [HRPO (Hierarchical Reflective Prompt Optimizer)](./development-optimization-runs-algorithms-hierarchical-adaptive-optimizer.md)
- [Few-Shot Bayesian Optimizer](./development-optimization-runs-algorithms-fewshot-bayesian-optimizer.md)
- [Evolutionary Optimizer: Genetic Algorithms](./development-optimization-runs-algorithms-evolutionary-optimizer.md)
- [Parameter Optimizer: Bayesian Parameter Tuning](./development-optimization-runs-algorithms-parameter-optimizer.md)
- [Tool Optimization (MCP & Function Calling)](./development-optimization-runs-algorithms-tool-optimization.md)

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