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Add documentation for the GEPARootAgentOptimizer #1847

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41 changes: 39 additions & 2 deletions docs/optimize/index.md
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Expand Up @@ -255,6 +255,43 @@ Defaults to 3.
optimization results if desired.
Facilitates warm starts.

### `GEPARootAgentOptimizer` {#geparootagentoptimizer}

The
[`GEPARootAgentOptimizer`](https://github.com/google/adk-python/blob/main/src/google/adk/optimization/gepa_root_agent_optimizer.py)
improves both the instructions of the root agent and the instructions of skills
provided to it via a
[`SkillToolset`](https://github.com/google/adk-python/blob/main/src/google/adk/tools/skill_toolset.py)
using the [GEPA](https://gepa-ai.github.io/gepa/) optimizer.
In many ways it can be considered to be an extension of the
[`GEPARootAgentPromptOptimizer`](#geparootagentpromptoptimizer).
It expects the sampler to provide eval results as an
[`UnstructuredSamplingResult`](#sampler-results).
Its output is a subclass of [`OptimizerResult`](#agent-optimizer-results) which
specifies a list of [optimized agents with scores](#agent-optimizer-results) and
additional metrics collected during optimization.

Note: The `GEPARootAgentOptimizer` does not improve any sub-agents or agent
tools.

You can configure the `GEPARootAgentOptimizer` with a
`GEPARootAgentOptimizerConfig` that contains the following fields:

* `optimizer_model` (optional): The model used to analyze evaluation results and
optimize the agent.
Defaults to `"gemini-3.5-flash"`.
* `model_configuration` (optional): The configuration for the optimizer model.
Defaults to a config with a `ThinkingLevel` of `HIGH`.
* `max_metric_calls` (optional): The maximum number of evaluations to run during
optimization.
Defaults to 100.
* `reflection_minibatch_size` (optional): The number of examples to use at a
time to update the instructions.
Defaults to 3.
* `run_dir` (optional): The directory to save intermediate and final
optimization results if desired.
Facilitates warm starts.

### `SimplePromptOptimizer` {#simplepromptoptimizer}

The `SimplePromptOptimizer` is an automated, iterative prompt-tuning component designed
Expand All @@ -272,7 +309,7 @@ The optimizer automatically executes an asynchronous, four-stage feedback loop:

**Note:** The optimization loop does not mutate your initial agent instance in place. Upon completion, it returns an `OptimizerResult` containing the highest-scoring agent variation extracted during the process.

### Configuration
#### Configuration

Configure the behavior of the loop by passing a `SimplePromptOptimizerConfig` instance to the optimizer.

Expand All @@ -281,7 +318,7 @@ Configure the behavior of the loop by passing a `SimplePromptOptimizerConfig` in
| `num_iterations` | int | *Required* | The total number of optimization rounds to execute. |
| `batch_size` | int | *Required* | The number of evaluation sample cases processed by the sampler during each individual iteration. |

### Implementation Example
#### Implementation Example

Once your configuration is defined, run the optimization with:

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