:::callout{intent="note"}
In Opik 2.0, datasets and experiments are project-scoped. Make sure to specify a `project_name` when creating datasets and running experiments so they are associated with the correct project.
:::

The Opik Agent Optimizer SDK provides a comprehensive set of tools for optimizing LLM prompts and agents. This reference guide documents the standardized API that all optimizers follow, ensuring consistency and interoperability across different optimization algorithms.

## Key Features

- **Standardized API**: All optimizers follow the same interface for `optimize_prompt()` methods
- **Multiple Algorithms**: Support for various optimization strategies including evolutionary, few-shot, meta-prompt, and GEPA
- **MCP Support**: Built-in support for Model Context Protocol tool calling and optimization
- **Consistent Results**: All optimizers return standardized `OptimizationResult` objects
- **Counter Tracking**: Built-in LLM and tool call counters for monitoring usage
- **Backward Compatibility**: All original parameters preserved through kwargs extraction
- **Deprecation Warnings**: Clear warnings for deprecated parameters with migration guidance

## Core Classes

The SDK provides several optimizer classes that all inherit from `BaseOptimizer` and implement the same standardized interface:

- **ParameterOptimizer**: Optimizes LLM call parameters (temperature, top\_p, etc.) using Bayesian optimization
- **FewShotBayesianOptimizer**: Uses few-shot learning with Bayesian optimization
- **MetaPromptOptimizer**: Employs meta-prompting techniques for optimization
- **EvolutionaryOptimizer**: Uses genetic algorithms for prompt evolution
- **GepaOptimizer**: Leverages GEPA (Genetic-Pareto) optimization approach
- **HRPO (Hierarchical Reflective Prompt Optimizer)**: Uses hierarchical root cause analysis for targeted prompt refinement

## Standardized Method Signatures

All optimizers implement these core methods with identical signatures:

### optimize\_prompt()

```python
def optimize_prompt(
    self,
    prompt: ChatPrompt | dict[str, ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    **kwargs: Any,
) -> OptimizationResult
```

## Deprecation Warnings

The following parameters are deprecated and will be removed in future versions:

### Constructor Parameters

- **`num_threads`** in optimizer constructors: Use `n_threads` instead

### Example Migration

```python
# ❌ Deprecated
optimizer = FewShotBayesianOptimizer(
    model="gpt-4o-mini",
    num_threads=16,  # Deprecated
)

# ✅ Correct
optimizer = FewShotBayesianOptimizer(
    model="gpt-4o-mini",
    n_threads=16,  # Use n_threads instead
)
```

## FewShotBayesianOptimizer

```python
FewShotBayesianOptimizer(
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    min_examples: int = 2,
    max_examples: int = 8,
    n_threads: int = 12,
    verbose: int = 1,
    seed: int = 42,
    name: str | None = None,
    enable_columnar_selection: bool = True,
    enable_diversity: bool = True,
    enable_multivariate_tpe: bool = True,
    enable_optuna_pruning: bool = True,
    prompt_overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95
)
```

**Parameters:**

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `min_examples` (int, default: 2) —

- `max_examples` (int, default: 8) —

- `n_threads` (int, default: 12) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `name` (str | None) —

- `enable_columnar_selection` (bool, default: True) —

- `enable_diversity` (bool, default: True) —

- `enable_multivariate_tpe` (bool, default: True) —

- `enable_optuna_pruning` (bool, default: True) —

- `prompt_overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool = False,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool, default: False) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_optimizer\_metadata

```python
get_optimizer_metadata()
```

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_prompt

```python
optimize_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    optimize_prompts: bool | str | list[str] | None = 'system',
    optimize_tools: bool | dict[str, bool] | None = None,
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt to optimize (single ChatPrompt or dict of prompts)

- `dataset` (Dataset) — Opik dataset (training set - used for feedback/context) TODO/FIXME: This parameter will be deprecated in favor of dataset\_training. For now, it serves as the training dataset parameter.

- `metric` (MetricFunction) — A metric function with signature (dataset\_item, llm\_output) -> float

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional agent for prompt execution (defaults to LiteLLMAgent)

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

- `n_samples` (int | float | str | None) — Number of samples to use for evaluation

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

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

- `project_name` (str | None) — Opik project name for logging traces (defaults to OPIK\_PROJECT\_NAME env or "Optimization")

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset for ranking candidates

- `max_trials` (int, default: 10) — Maximum number of optimization trials

- `allow_tool_use` (bool, default: True) — Whether tools may be executed during evaluation (default True)

- `optimize_prompts` (bool | str | list\[str] | None, default: system) — Which prompt roles to allow for optimization

- `optimize_tools` (bool | dict\[str, bool] | None) — Optional tool optimization selector. Only supported by optimizers that explicitly document tool optimization support.

- `args` (Any) —

- `kwargs` (Any) —

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### run\_optimization

```python
run_optimization(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context with prompts, dataset, metric, etc.

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## GepaOptimizer

```python
GepaOptimizer(
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    n_threads: int = 12,
    verbose: int = 1,
    seed: int = 42,
    name: str | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95,
    prompt_overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None
)
```

**Parameters:**

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `n_threads` (int, default: 12) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `name` (str | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

- `prompt_overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool | None = None,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool | None) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_optimizer\_metadata

```python
get_optimizer_metadata()
```

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_prompt

```python
optimize_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    optimize_prompts: bool | str | list[str] | None = 'system',
    optimize_tools: bool | dict[str, bool] | None = None,
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt to optimize (single ChatPrompt or dict of prompts)

- `dataset` (Dataset) — Opik dataset (training set - used for feedback/context) TODO/FIXME: This parameter will be deprecated in favor of dataset\_training. For now, it serves as the training dataset parameter.

- `metric` (MetricFunction) — A metric function with signature (dataset\_item, llm\_output) -> float

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional agent for prompt execution (defaults to LiteLLMAgent)

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

- `n_samples` (int | float | str | None) — Number of samples to use for evaluation

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

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

- `project_name` (str | None) — Opik project name for logging traces (defaults to OPIK\_PROJECT\_NAME env or "Optimization")

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset for ranking candidates

- `max_trials` (int, default: 10) — Maximum number of optimization trials

- `allow_tool_use` (bool, default: True) — Whether tools may be executed during evaluation (default True)

- `optimize_prompts` (bool | str | list\[str] | None, default: system) — Which prompt roles to allow for optimization

- `optimize_tools` (bool | dict\[str, bool] | None) — Optional tool optimization selector. Only supported by optimizers that explicitly document tool optimization support.

- `args` (Any) —

- `kwargs` (Any) —

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### run\_optimization

```python
run_optimization(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context with prompts, dataset, metric, etc.

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## MetaPromptOptimizer

```python
MetaPromptOptimizer(
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    prompts_per_round: int = 4,
    enable_context: bool = True,
    num_task_examples: int = 5,
    task_context_columns: list[str] | None = None,
    n_threads: int = 12,
    verbose: int = 1,
    seed: int = 42,
    name: str | None = None,
    use_hall_of_fame: bool = True,
    prompt_overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95
)
```

**Parameters:**

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `prompts_per_round` (int, default: 4) —

- `enable_context` (bool, default: True) —

- `num_task_examples` (int, default: 5) —

- `task_context_columns` (list\[str] | None) —

- `n_threads` (int, default: 12) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `name` (str | None) —

- `use_hall_of_fame` (bool, default: True) —

- `prompt_overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool | None = None,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool | None) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_optimizer\_metadata

```python
get_optimizer_metadata()
```

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_prompt

```python
optimize_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    optimize_prompts: bool | str | list[str] | None = 'system',
    optimize_tools: bool | dict[str, bool] | None = None,
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt to optimize (single ChatPrompt or dict of prompts)

- `dataset` (Dataset) — Opik dataset (training set - used for feedback/context) TODO/FIXME: This parameter will be deprecated in favor of dataset\_training. For now, it serves as the training dataset parameter.

- `metric` (MetricFunction) — A metric function with signature (dataset\_item, llm\_output) -> float

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional agent for prompt execution (defaults to LiteLLMAgent)

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

- `n_samples` (int | float | str | None) — Number of samples to use for evaluation

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

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

- `project_name` (str | None) — Opik project name for logging traces (defaults to OPIK\_PROJECT\_NAME env or "Optimization")

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset for ranking candidates

- `max_trials` (int, default: 10) — Maximum number of optimization trials

- `allow_tool_use` (bool, default: True) — Whether tools may be executed during evaluation (default True)

- `optimize_prompts` (bool | str | list\[str] | None, default: system) — Which prompt roles to allow for optimization

- `optimize_tools` (bool | dict\[str, bool] | None) — Optional tool optimization selector. Only supported by optimizers that explicitly document tool optimization support.

- `args` (Any) —

- `kwargs` (Any) —

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### run\_optimization

```python
run_optimization(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context with prompts, dataset, metric, etc.

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## EvolutionaryOptimizer

```python
EvolutionaryOptimizer(
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    population_size: int = 30,
    num_generations: int = 15,
    mutation_rate: float = 0.2,
    crossover_rate: float = 0.8,
    tournament_size: int = 4,
    elitism_size: int = 3,
    adaptive_mutation: bool = True,
    enable_moo: bool = True,
    enable_llm_crossover: bool = True,
    enable_semantic_crossover: bool = False,
    output_style_guidance: str | None = None,
    infer_output_style: bool = False,
    n_threads: int = 12,
    verbose: int = 1,
    seed: int = 42,
    name: str | None = None,
    prompt_overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95
)
```

**Parameters:**

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `population_size` (int, default: 30) —

- `num_generations` (int, default: 15) —

- `mutation_rate` (float, default: 0.2) —

- `crossover_rate` (float, default: 0.8) —

- `tournament_size` (int, default: 4) —

- `elitism_size` (int, default: 3) —

- `adaptive_mutation` (bool, default: True) —

- `enable_moo` (bool, default: True) —

- `enable_llm_crossover` (bool, default: True) —

- `enable_semantic_crossover` (bool, default: False) —

- `output_style_guidance` (str | None) —

- `infer_output_style` (bool, default: False) —

- `n_threads` (int, default: 12) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `name` (str | None) —

- `prompt_overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool | None = None,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool | None) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_optimizer\_metadata

```python
get_optimizer_metadata()
```

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_prompt

```python
optimize_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    optimize_prompts: bool | str | list[str] | None = 'system',
    optimize_tools: bool | dict[str, bool] | None = None,
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt to optimize (single ChatPrompt or dict of prompts)

- `dataset` (Dataset) — Opik dataset (training set - used for feedback/context) TODO/FIXME: This parameter will be deprecated in favor of dataset\_training. For now, it serves as the training dataset parameter.

- `metric` (MetricFunction) — A metric function with signature (dataset\_item, llm\_output) -> float

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional agent for prompt execution (defaults to LiteLLMAgent)

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

- `n_samples` (int | float | str | None) — Number of samples to use for evaluation

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

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

- `project_name` (str | None) — Opik project name for logging traces (defaults to OPIK\_PROJECT\_NAME env or "Optimization")

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset for ranking candidates

- `max_trials` (int, default: 10) — Maximum number of optimization trials

- `allow_tool_use` (bool, default: True) — Whether tools may be executed during evaluation (default True)

- `optimize_prompts` (bool | str | list\[str] | None, default: system) — Which prompt roles to allow for optimization

- `optimize_tools` (bool | dict\[str, bool] | None) — Optional tool optimization selector. Only supported by optimizers that explicitly document tool optimization support.

- `args` (Any) —

- `kwargs` (Any) —

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### run\_optimization

```python
run_optimization(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context with prompts, dataset, metric, etc.

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## HierarchicalReflectiveOptimizer

```python
HierarchicalReflectiveOptimizer(
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    reasoning_model: str | None = None,
    reasoning_model_parameters: dict[str, typing.Any] | None = None,
    max_parallel_batches: int = 5,
    batch_size: int = 25,
    convergence_threshold: float = 0.01,
    n_threads: int = 12,
    verbose: int = 1,
    seed: int = 42,
    name: str | None = None,
    prompt_overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95
)
```

**Parameters:**

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `reasoning_model` (str | None) —

- `reasoning_model_parameters` (dict\[str, typing.Any] | None) —

- `max_parallel_batches` (int, default: 5) —

- `batch_size` (int, default: 25) —

- `convergence_threshold` (float, default: 0.01) —

- `n_threads` (int, default: 12) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `name` (str | None) —

- `prompt_overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool | None = None,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool | None) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_optimizer\_metadata

```python
get_optimizer_metadata()
```

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_prompt

```python
optimize_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    optimize_prompts: bool | str | list[str] | None = 'system',
    optimize_tools: bool | dict[str, bool] | None = None,
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt to optimize (single ChatPrompt or dict of prompts)

- `dataset` (Dataset) — Opik dataset (training set - used for feedback/context) TODO/FIXME: This parameter will be deprecated in favor of dataset\_training. For now, it serves as the training dataset parameter.

- `metric` (MetricFunction) — A metric function with signature (dataset\_item, llm\_output) -> float

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional agent for prompt execution (defaults to LiteLLMAgent)

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

- `n_samples` (int | float | str | None) — Number of samples to use for evaluation

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

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

- `project_name` (str | None) — Opik project name for logging traces (defaults to OPIK\_PROJECT\_NAME env or "Optimization")

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset for ranking candidates

- `max_trials` (int, default: 10) — Maximum number of optimization trials

- `allow_tool_use` (bool, default: True) — Whether tools may be executed during evaluation (default True)

- `optimize_prompts` (bool | str | list\[str] | None, default: system) — Which prompt roles to allow for optimization

- `optimize_tools` (bool | dict\[str, bool] | None) — Optional tool optimization selector. Only supported by optimizers that explicitly document tool optimization support.

- `args` (Any) —

- `kwargs` (Any) —

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### run\_optimization

```python
run_optimization(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context with prompts, dataset, metric, etc.

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## ParameterOptimizer

```python
ParameterOptimizer(
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    default_n_trials: int = 20,
    local_search_ratio: float = 0.3,
    local_search_scale: float = 0.2,
    n_threads: int = 12,
    verbose: int = 1,
    seed: int = 42,
    name: str | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95
)
```

**Parameters:**

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `default_n_trials` (int, default: 20) —

- `local_search_ratio` (float, default: 0.3) —

- `local_search_scale` (float, default: 0.2) —

- `n_threads` (int, default: 12) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `name` (str | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool | None = None,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool | None) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_optimizer\_metadata

```python
get_optimizer_metadata()
```

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_parameter

```python
optimize_parameter(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    parameter_space: opik_optimizer.algorithms.parameter_optimizer.ops.search_ops.ParameterSearchSpace | collections.abc.Mapping[str, typing.Any],
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    experiment_config: dict | None = None,
    max_trials: int | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    project_name: str = 'Optimization',
    sampler: optuna.samplers._base.BaseSampler | None = None,
    callbacks: list[collections.abc.Callable[[optuna.study.study.Study, optuna.trial._frozen.FrozenTrial], None]] | None = None,
    timeout: float | None = None,
    local_trials: int | None = None,
    local_search_scale: float | None = None,
    optimization_id: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt or dict of prompts to evaluate with tuned parameters. When a dict is provided, parameters are optimized independently for each prompt.

- `dataset` (Dataset) — Dataset providing evaluation examples

- `metric` (MetricFunction) — Objective function to maximize

- `parameter_space` (opik\_optimizer.algorithms.parameter\_optimizer.ops.search\_ops.ParameterSearchSpace | collections.abc.Mapping\[str, typing.Any]) — Definition of the search space for tunable parameters. For multi-prompt, params without a prefix are expanded per prompt. Params already prefixed (e.g., 'analyze.temperature') are kept as-is.

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset. Note: Due to the internal implementation of ParameterOptimizer, this parameter is currently not fully utilized and we recommend not using it for this optimizer.

- `experiment_config` (dict | None) — Optional experiment metadata

- `max_trials` (int | None) — Total number of trials (if None, uses default\_n\_trials)

- `n_samples` (int | float | str | None) — Number of dataset samples to evaluate per trial (None for all)

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional custom agent instance to execute evaluations

- `project_name` (str, default: Optimization) — Opik project name for logging traces (default: "Optimization")

- `sampler` (optuna.samplers.\_base.BaseSampler | None) — Optuna sampler to use (default: TPESampler with seed)

- `callbacks` (list\[collections.abc.Callable\[\[optuna.study.study.Study, optuna.trial.\_frozen.FrozenTrial], None]] | None) — List of callback functions for Optuna study

- `timeout` (float | None) — Maximum time in seconds for optimization

- `local_trials` (int | None) — Number of trials for local search (overrides local\_search\_ratio)

- `local_search_scale` (float | None) — Scale factor for local search narrowing (0.0-1.0)

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run; when provided it must be a valid UUIDv7 string.

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## ParameterSearchSpace

```python
ParameterSearchSpace(
    parameters: list[opik_optimizer.algorithms.parameter_optimizer.ops.search_ops.ParameterSpec] = PydanticUndefined
)
```

**Parameters:**

- `parameters` (list\[opik\_optimizer.algorithms.parameter\_optimizer.ops.search\_ops.ParameterSpec], default: PydanticUndefined) —

## ParameterSpec

```python
ParameterSpec(
    name: <class 'str'>,
    description: str | None = None,
    distribution: <enum 'ParameterType'>,
    low: float | None = None,
    high: float | None = None,
    step: float | None = None,
    scale: Literal['linear', 'log'] = 'linear',
    choices: list[Any] | None = None,
    target: str | collections.abc.Sequence[str] | None = None,
    default: Any | None = None
)
```

**Parameters:**

- `name` (\<class 'str'>, default: PydanticUndefined) —

- `description` (str | None) —

- `distribution` (\<enum 'ParameterType'>, default: PydanticUndefined) —

- `low` (float | None) —

- `high` (float | None) —

- `step` (float | None) —

- `scale` (Literal\['linear', 'log'], default: linear) —

- `choices` (list\[Any] | None) —

- `target` (str | collections.abc.Sequence\[str] | None) —

- `default` (Any | None) —

## ParameterType

```python
ParameterType(
    args: Any,
    kwds: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwds` (Any) —

## BaseOptimizer

```python
BaseOptimizer(
    model: str,
    verbose: int = 1,
    seed: int = 42,
    model_parameters: dict[str, typing.Any] | None = None,
    reasoning_model: str | None = None,
    reasoning_model_parameters: dict[str, typing.Any] | None = None,
    name: str | None = None,
    skip_perfect_score: bool = True,
    perfect_score: float = 0.95,
    prompt_overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None,
    display: opik_optimizer.utils.display.run.RunDisplay | None = None
)
```

**Parameters:**

- `model` (str) —

- `verbose` (int, default: 1) —

- `seed` (int, default: 42) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `reasoning_model` (str | None) —

- `reasoning_model_parameters` (dict\[str, typing.Any] | None) —

- `name` (str | None) —

- `skip_perfect_score` (bool, default: True) —

- `perfect_score` (float, default: 0.95) —

- `prompt_overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) —

- `display` (opik\_optimizer.utils.display.run.RunDisplay | None) —

### Methods

#### begin\_round

```python
begin_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### cleanup

```python
cleanup()
```

#### evaluate

```python
evaluate(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) — Optimization context for this run.

- `prompts` (dict) — Dict of named prompts to evaluate (e.g., {"main": ChatPrompt(...)}). Single-prompt optimizations use a dict with one entry.

- `experiment_config` (dict\[str, typing.Any] | None) — Optional experiment configuration.

- `sampling_tag` (str | None) — Optional sampling tag for deterministic subsampling per candidate.

#### evaluate\_prompt

```python
evaluate_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    n_threads: int | None = None,
    verbose: int = 1,
    dataset_item_ids: list[str] | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    seed: int | None = None,
    return_evaluation_result: bool = False,
    allow_tool_use: bool | None = None,
    use_evaluate_on_dict_items: bool | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) —

- `dataset` (Dataset) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `n_threads` (int | None) —

- `verbose` (int, default: 1) —

- `dataset_item_ids` (list\[str] | None) —

- `experiment_config` (dict | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `seed` (int | None) —

- `return_evaluation_result` (bool, default: False) —

- `allow_tool_use` (bool | None) —

- `use_evaluate_on_dict_items` (bool | None) —

- `sampling_tag` (str | None) —

#### evaluate\_with\_result

```python
evaluate_with_result(
    context: OptimizationContext,
    prompts: dict,
    experiment_config: dict[str, typing.Any] | None = None,
    empty_score: float | None = None,
    n_samples: int | float | str | None = None,
    n_samples_strategy: str | None = None,
    sampling_tag: str | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `empty_score` (float | None) —

- `n_samples` (int | float | str | None) —

- `n_samples_strategy` (str | None) —

- `sampling_tag` (str | None) —

#### finish\_candidate

```python
finish_candidate(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### finish\_round

```python
finish_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### get\_config

```python
get_config(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_default\_prompt

```python
get_default_prompt(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### get\_history\_entries

```python
get_history_entries()
```

#### get\_history\_rounds

```python
get_history_rounds()
```

#### get\_metadata

```python
get_metadata(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### get\_prompt

```python
get_prompt(
    key: str,
    fmt: Any
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (Any) —

#### list\_prompts

```python
list_prompts()
```

#### on\_trial

```python
on_trial(
    context: OptimizationContext,
    prompts: dict,
    score: float,
    prev_best_score: float | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `prompts` (dict) —

- `score` (float) —

- `prev_best_score` (float | None) —

#### optimize\_mcp

```python
optimize_mcp(
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `args` (Any) —

- `kwargs` (Any) —

#### optimize\_prompt

```python
optimize_prompt(
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    dataset: Dataset,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None = None,
    experiment_config: dict | None = None,
    n_samples: int | float | str | None = None,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str | None = None,
    auto_continue: bool = False,
    project_name: str | None = None,
    optimization_id: str | None = None,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None = None,
    max_trials: int = 10,
    allow_tool_use: bool = True,
    optimize_prompts: bool | str | list[str] | None = 'system',
    optimize_tools: bool | dict[str, bool] | None = None,
    args: Any,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt]) — The prompt to optimize (single ChatPrompt or dict of prompts)

- `dataset` (Dataset) — Opik dataset (training set - used for feedback/context) TODO/FIXME: This parameter will be deprecated in favor of dataset\_training. For now, it serves as the training dataset parameter.

- `metric` (MetricFunction) — A metric function with signature (dataset\_item, llm\_output) -> float

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) — Optional agent for prompt execution (defaults to LiteLLMAgent)

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

- `n_samples` (int | float | str | None) — Number of samples to use for evaluation

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches

- `n_samples_strategy` (str | None) — Sampling strategy name (default "random\_sorted")

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

- `project_name` (str | None) — Opik project name for logging traces (defaults to OPIK\_PROJECT\_NAME env or "Optimization")

- `optimization_id` (str | None) — Optional ID to use when creating the Opik optimization run

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) — Optional validation dataset for ranking candidates

- `max_trials` (int, default: 10) — Maximum number of optimization trials

- `allow_tool_use` (bool, default: True) — Whether tools may be executed during evaluation (default True)

- `optimize_prompts` (bool | str | list\[str] | None, default: system) — Which prompt roles to allow for optimization

- `optimize_tools` (bool | dict\[str, bool] | None) — Optional tool optimization selector. Only supported by optimizers that explicitly document tool optimization support.

- `args` (Any) —

- `kwargs` (Any) —

#### post\_baseline

```python
post_baseline(
    context: OptimizationContext,
    score: float
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `score` (float) —

#### post\_optimize

```python
post_optimize(
    context: OptimizationContext,
    result: OptimizationResult
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `result` (OptimizationResult) —

#### post\_round

```python
post_round(
    round_handle: Any,
    context: opik_optimizer.core.state.OptimizationContext | None = None,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    dataset_split: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `dataset_split` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

#### post\_trial

```python
post_trial(
    context: OptimizationContext,
    candidate_handle: Any,
    score: float | None,
    metrics: dict[str, typing.Any] | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    trial_index: int | None = None,
    timestamp: str | None = None,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate_handle` (Any) —

- `score` (float | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `trial_index` (int | None) —

- `timestamp` (str | None) —

- `round_handle` (typing.Any | None) —

#### pre\_baseline

```python
pre_baseline(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) —

#### pre\_optimize

```python
pre_optimize(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context

#### pre\_round

```python
pre_round(
    context: OptimizationContext,
    extras: Any
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `extras` (Any) —

#### pre\_trial

```python
pre_trial(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### record\_candidate\_entry

```python
record_candidate_entry(
    prompt_or_payload: Any,
    score: float | None = None,
    id: str | None = None,
    metrics: dict[str, typing.Any] | None = None,
    notes: str | None = None,
    extra: dict[str, typing.Any] | None = None,
    context: opik_optimizer.core.state.OptimizationContext | None = None
)
```

**Parameters:**

- `prompt_or_payload` (Any) —

- `score` (float | None) —

- `id` (str | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `notes` (str | None) —

- `extra` (dict\[str, typing.Any] | None) —

- `context` (opik\_optimizer.core.state.OptimizationContext | None) —

#### run\_optimization

```python
run_optimization(
    context: OptimizationContext
)
```

**Parameters:**

- `context` (OptimizationContext) — The optimization context with prompts, dataset, metric, etc.

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_candidate

```python
start_candidate(
    context: OptimizationContext,
    candidate: Any,
    round_handle: typing.Any | None = None
)
```

**Parameters:**

- `context` (OptimizationContext) —

- `candidate` (Any) —

- `round_handle` (typing.Any | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## ChatPrompt

```python
ChatPrompt(
    name: str = 'chat-prompt',
    system: str | None = None,
    user: str | None = None,
    messages: list[dict[str, typing.Any]] | None = None,
    tools: list[dict[str, typing.Any]] | collections.abc.Mapping[str, typing.Any] | None = None,
    function_map: collections.abc.Mapping[str, collections.abc.Callable[..., typing.Any]] | None = None,
    model: str = 'openai/gpt-5-nano',
    model_parameters: dict[str, typing.Any] | None = None,
    model_kwargs: dict[str, typing.Any] | None = None,
    kwargs: Any
)
```

**Parameters:**

- `name` (str, default: chat-prompt) —

- `system` (str | None) — the system prompt

- `user` (str | None) —

- `messages` (list\[dict\[str, typing.Any]] | None) — a list of dictionaries with role/content, with a content containing {input-dataset-field}

- `tools` (list\[dict\[str, typing.Any]] | collections.abc.Mapping\[str, typing.Any] | None) —

- `function_map` (collections.abc.Mapping\[str, collections.abc.Callable\[..., typing.Any]] | None) —

- `model` (str, default: openai/gpt-5-nano) —

- `model_parameters` (dict\[str, typing.Any] | None) —

- `model_kwargs` (dict\[str, typing.Any] | None) —

- `kwargs` (Any) —

### Methods

#### copy

```python
copy()
```

#### get\_messages

```python
get_messages(
    dataset_item: dict[str, typing.Any] | None = None
)
```

**Parameters:**

- `dataset_item` (dict\[str, typing.Any] | None) —

#### replace\_in\_messages

```python
replace_in_messages(
    messages: list,
    label: str,
    value: str
)
```

**Parameters:**

- `messages` (list) —

- `label` (str) —

- `value` (str) —

#### set\_messages

```python
set_messages(
    messages: list
)
```

**Parameters:**

- `messages` (list) —

#### to\_dict

```python
to_dict()
```

## AlgorithmResult

```python
AlgorithmResult(
    best_prompts: dict,
    best_score: float,
    history: Sequence = <factory>,
    metadata: dict = <factory>
)
```

**Parameters:**

- `best_prompts` (dict) —

- `best_score` (float) —

- `history` (Sequence, default: <factory>) —

- `metadata` (dict, default: <factory>) —

## OptimizationResult

```python
OptimizationResult(
    schema_version: <class 'str'> = 'v1',
    details_version: <class 'str'> = 'v1',
    optimizer: <class 'str'> = 'Optimizer',
    prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt],
    score: <class 'float'>,
    metric_name: <class 'str'>,
    optimization_id: str | None = None,
    dataset_id: str | None = None,
    initial_prompt: opik_optimizer.api_objects.chat_prompt.ChatPrompt | dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt] | None = None,
    initial_score: float | None = None,
    details: dict[str, Any] = PydanticUndefined,
    history: list[dict[str, Any]] = [],
    llm_calls: int | None = None,
    llm_calls_tools: int | None = None,
    llm_cost_total: float | None = None,
    llm_token_usage_total: dict[str, int] | None = None
)
```

**Parameters:**

- `schema_version` (\<class 'str'>, default: v1) —

- `details_version` (\<class 'str'>, default: v1) —

- `optimizer` (\<class 'str'>, default: Optimizer) —

- `prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt], default: PydanticUndefined) —

- `score` (\<class 'float'>, default: PydanticUndefined) —

- `metric_name` (\<class 'str'>, default: PydanticUndefined) —

- `optimization_id` (str | None) —

- `dataset_id` (str | None) —

- `initial_prompt` (opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt | dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt] | None) —

- `initial_score` (float | None) —

- `details` (dict\[str, Any], default: PydanticUndefined) —

- `history` (list\[dict\[str, Any]], default: \[]) —

- `llm_calls` (int | None) —

- `llm_calls_tools` (int | None) —

- `llm_cost_total` (float | None) —

- `llm_token_usage_total` (dict\[str, int] | None) —

## OptimizationContext

```python
OptimizationContext(
    prompts: dict,
    initial_prompts: dict,
    is_single_prompt_optimization: bool,
    dataset: Dataset,
    evaluation_dataset: Dataset,
    validation_dataset: opik.api_objects.dataset.dataset.Dataset | None,
    metric: MetricFunction,
    agent: opik_optimizer.agents.optimizable_agent.OptimizableAgent | None,
    optimization: opik.api_objects.optimization.optimization.Optimization | None,
    optimization_id: str | None,
    experiment_config: dict[str, typing.Any] | None,
    n_samples: int | float | str | None,
    max_trials: int,
    project_name: str,
    n_samples_minibatch: int | None = None,
    n_samples_strategy: str = 'random_sorted',
    allow_tool_use: bool = True,
    baseline_score: float | None = None,
    extra_params: dict = <factory>,
    trials_completed: int = 0,
    should_stop: bool = False,
    finish_reason: Optional = None,
    current_best_score: float | None = None,
    current_best_prompt: dict[str, opik_optimizer.api_objects.chat_prompt.ChatPrompt] | None = None,
    dataset_split: str | None = None,
    scoring_health: dict[str, int] | None = None
)
```

**Parameters:**

- `prompts` (dict) —

- `initial_prompts` (dict) —

- `is_single_prompt_optimization` (bool) —

- `dataset` (Dataset) —

- `evaluation_dataset` (Dataset) —

- `validation_dataset` (opik.api\_objects.dataset.dataset.Dataset | None) —

- `metric` (MetricFunction) —

- `agent` (opik\_optimizer.agents.optimizable\_agent.OptimizableAgent | None) —

- `optimization` (opik.api\_objects.optimization.optimization.Optimization | None) —

- `optimization_id` (str | None) —

- `experiment_config` (dict\[str, typing.Any] | None) —

- `n_samples` (int | float | str | None) —

- `max_trials` (int) —

- `project_name` (str) —

- `n_samples_minibatch` (int | None) —

- `n_samples_strategy` (str, default: random\_sorted) —

- `allow_tool_use` (bool, default: True) —

- `baseline_score` (float | None) —

- `extra_params` (dict, default: <factory>) —

- `trials_completed` (int, default: 0) —

- `should_stop` (bool, default: False) —

- `finish_reason` (Optional) —

- `current_best_score` (float | None) —

- `current_best_prompt` (dict\[str, opik\_optimizer.api\_objects.chat\_prompt.ChatPrompt] | None) —

- `dataset_split` (str | None) —

- `scoring_health` (dict\[str, int] | None) —

## OptimizationHistoryState

```python
OptimizationHistoryState(
    context: Any = None
)
```

**Parameters:**

- `context` (Any) —

### Methods

#### clear

```python
clear()
```

#### end\_round

```python
end_round(
    round_handle: Any,
    best_score: float | None = None,
    best_candidate: typing.Any | None = None,
    best_prompt: typing.Any | None = None,
    stop_reason: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    pareto_front: list[dict[str, typing.Any]] | None = None,
    selection_meta: dict[str, typing.Any] | None = None,
    dataset_split: str | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `best_score` (float | None) —

- `best_candidate` (typing.Any | None) —

- `best_prompt` (typing.Any | None) —

- `stop_reason` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

- `selection_meta` (dict\[str, typing.Any] | None) —

- `dataset_split` (str | None) —

#### finalize\_stop

```python
finalize_stop(
    stop_reason: str | None = None
)
```

**Parameters:**

- `stop_reason` (str | None) —

#### get\_entries

```python
get_entries()
```

#### get\_rounds

```python
get_rounds()
```

#### record\_trial

```python
record_trial(
    round_handle: Any,
    score: float | None,
    candidate: typing.Any | None = None,
    trial_index: int | None = None,
    metrics: dict[str, typing.Any] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    timestamp: str | None = None,
    stop_reason: str | None = None,
    candidate_id_prefix: str | None = None
)
```

**Parameters:**

- `round_handle` (Any) —

- `score` (float | None) —

- `candidate` (typing.Any | None) —

- `trial_index` (int | None) —

- `metrics` (dict\[str, typing.Any] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `timestamp` (str | None) —

- `stop_reason` (str | None) —

- `candidate_id_prefix` (str | None) —

#### set\_context

```python
set_context(
    context: Any
)
```

**Parameters:**

- `context` (Any) —

#### set\_default\_dataset\_split

```python
set_default_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

#### set\_pareto\_front

```python
set_pareto_front(
    pareto_front: list[dict[str, typing.Any]] | None
)
```

**Parameters:**

- `pareto_front` (list\[dict\[str, typing.Any]] | None) —

#### set\_selection\_meta

```python
set_selection_meta(
    selection_meta: dict[str, typing.Any] | None
)
```

**Parameters:**

- `selection_meta` (dict\[str, typing.Any] | None) —

#### start\_round

```python
start_round(
    round_index: int | None = None,
    extras: dict[str, typing.Any] | None = None,
    timestamp: str | None = None
)
```

**Parameters:**

- `round_index` (int | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `timestamp` (str | None) —

#### with\_dataset\_split

```python
with_dataset_split(
    dataset_split: str | None
)
```

**Parameters:**

- `dataset_split` (str | None) —

## OptimizationRound

```python
OptimizationRound(
    round_index: int,
    trials: list = <factory>,
    best_score: float | None = None,
    best_so_far: float | None = None,
    best_prompt: typing.Any | None = None,
    best_candidate: typing.Any | None = None,
    candidates: list[dict[str, typing.Any]] | None = None,
    generated_prompts: list[dict[str, typing.Any]] | None = None,
    stop_reason: str | None = None,
    stopped: bool | None = None,
    dataset_split: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    timestamp: str = <factory>
)
```

**Parameters:**

- `round_index` (int) —

- `trials` (list, default: <factory>) —

- `best_score` (float | None) —

- `best_so_far` (float | None) —

- `best_prompt` (typing.Any | None) —

- `best_candidate` (typing.Any | None) —

- `candidates` (list\[dict\[str, typing.Any]] | None) —

- `generated_prompts` (list\[dict\[str, typing.Any]] | None) —

- `stop_reason` (str | None) —

- `stopped` (bool | None) —

- `dataset_split` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `timestamp` (str, default: <factory>) —

### Methods

#### to\_dict

```python
to_dict()
```

## OptimizationTrial

```python
OptimizationTrial(
    trial_index: int | None,
    score: float | None,
    candidate: Any,
    metrics: dict[str, typing.Any] | None = None,
    dataset: str | None = None,
    dataset_split: str | None = None,
    candidate_id: str | None = None,
    extras: dict[str, typing.Any] | None = None,
    timestamp: str = <factory>
)
```

**Parameters:**

- `trial_index` (int | None) —

- `score` (float | None) —

- `candidate` (Any) —

- `metrics` (dict\[str, typing.Any] | None) —

- `dataset` (str | None) —

- `dataset_split` (str | None) —

- `candidate_id` (str | None) —

- `extras` (dict\[str, typing.Any] | None) —

- `timestamp` (str, default: <factory>) —

### Methods

#### to\_dict

```python
to_dict()
```

## OptimizableAgent

```python
OptimizableAgent(
    prompt: Any = None,
    project_name: Any = None,
    kwargs: Any
)
```

**Parameters:**

- `prompt` (Any) —

- `project_name` (Any) —

- `kwargs` (Any) —

### Methods

#### init\_agent

```python
init_agent(
    prompt: Any
)
```

**Parameters:**

- `prompt` (Any) —

#### init\_llm

```python
init_llm()
```

#### invoke

```python
invoke(
    messages: list,
    seed: int | None = None
)
```

**Parameters:**

- `messages` (list) — List of message dictionaries

- `seed` (int | None) — Optional seed for reproducibility

#### invoke\_agent

```python
invoke_agent(
    prompts: Any,
    dataset_item: Any,
    allow_tool_use: Any = False,
    seed: Any = None
)
```

**Parameters:**

- `prompts` (Any) —

- `dataset_item` (Any) —

- `allow_tool_use` (Any, default: False) —

- `seed` (Any) —

#### invoke\_agent\_candidates

```python
invoke_agent_candidates(
    prompts: Any,
    dataset_item: Any,
    allow_tool_use: Any = False,
    seed: Any = None
)
```

**Parameters:**

- `prompts` (Any) — Mapping of prompt name to ChatPrompt.

- `dataset_item` (Any) — Dataset row used to render the prompt messages.

- `allow_tool_use` (Any, default: False) — Whether tool execution is allowed in this invocation.

- `seed` (Any) — Optional seed for reproducibility.

#### invoke\_dataset\_item

```python
invoke_dataset_item(
    dataset_item: dict
)
```

**Parameters:**

- `dataset_item` (dict) —

#### invoke\_prompt

```python
invoke_prompt(
    prompt: Any,
    dataset_item: Any,
    allow_tool_use: Any = False,
    seed: Any = None
)
```

**Parameters:**

- `prompt` (Any) —

- `dataset_item` (Any) —

- `allow_tool_use` (Any, default: False) —

- `seed` (Any) —

#### llm\_invoke

```python
llm_invoke(
    query: str | None = None,
    messages: list[dict[str, str]] | None = None,
    seed: int | None = None,
    allow_tool_use: bool | None = False
)
```

**Parameters:**

- `query` (str | None) —

- `messages` (list\[dict\[str, str]] | None) —

- `seed` (int | None) —

- `allow_tool_use` (bool | None, default: False) —

## MultiMetricObjective

```python
MultiMetricObjective(
    metrics: list,
    weights: list[float] | None = None,
    name: str = 'multi_metric_objective',
    reason: str | None = None,
    reason_builder: collections.abc.Callable[[list[_opik._score_result.ScoreResult], list[float], float], str | None] | None = None
)
```

**Parameters:**

- `metrics` (list) —

- `weights` (list\[float] | None) —

- `name` (str, default: multi\_metric\_objective) —

- `reason` (str | None) —

- `reason_builder` (collections.abc.Callable\[\[list\[\_opik.\_score\_result.ScoreResult], list\[float], float], str | None] | None) —

## ScoringFailedError

```python
ScoringFailedError(
    failed: int,
    total: int,
    objective_metric_name: str | None = None,
    message: str | None = None
)
```

**Parameters:**

- `failed` (int) —

- `total` (int) —

- `objective_metric_name` (str | None) —

- `message` (str | None) —

## PromptLibrary

```python
PromptLibrary(
    defaults: dict,
    overrides: dict[str, str] | collections.abc.Callable[[opik_optimizer.utils.prompt_library.PromptLibrary], None] | None = None
)
```

**Parameters:**

- `defaults` (dict) — Dictionary of default prompt templates

- `overrides` (dict\[str, str] | collections.abc.Callable\[\[opik\_optimizer.utils.prompt\_library.PromptLibrary], None] | None) — Optional dict or callable to customize prompts

### Methods

#### get

```python
get(
    key: str,
    fmt: object
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

- `fmt` (object) —

#### get\_default

```python
get_default(
    key: str
)
```

**Parameters:**

- `key` (str) — The prompt key to retrieve

#### keys

```python
keys()
```

#### set

```python
set(
    key: str,
    value: str
)
```

**Parameters:**

- `key` (str) — The prompt key to set

- `value` (str) — The new prompt template

#### update

```python
update(
    overrides: dict
)
```

**Parameters:**

- `overrides` (dict) — Dictionary of key-value pairs to update

## Related pages

- [Extending Optimizers](./development-optimization-runs-advanced-extending-optimizers.md)
- [Custom metrics](./development-optimization-runs-advanced-custom-metrics.md)
- [Custom Optimizer Prompts](./development-optimization-runs-advanced-prompt-customization.md)
- [Sampling controls](./development-optimization-runs-advanced-n-samples.md)
- [Multiple Completions (n parameter)](./development-optimization-runs-advanced-multiple-completions.md)
- [Chaining optimizers](./development-optimization-runs-advanced-chaining-optimizers.md)

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