> ## Documentation Index
> Fetch the complete documentation index at: https://docs.adaptive-ml.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Load Graders

> Load existing Graders within your recipe

You can load [Graders](/v0.11/core/graders) that have been previously created in the platform automatically using the `Grader` class (excluding Recipe Graders, which only exist as code within your custom recipe).

## Include AdaptiveGrader in InputConfig

In order to do this, you must only make sure that one or more `AdaptiveGrader`s can be passed to your recipe as input:

```python theme={null}
from pydantic import Field
from typing import Annotated, Set

from adaptive_harmony.runtime import InputConfig, AdaptiveGrader

class GraderRecipeConfig(InputConfig):
    graders: Annotated[
        Set[AdaptiveGrader],
        Field(
            description="The graders to load.",
            title="Graders",
        ),
    ]
```

The `AdaptiveGrader` object will contain all the configuration necessary to instantiate a Grader in code, with the exact same properties you've defined in the Adaptive platform.

## Load with Grader.from\_config

You can then automatically load and setup your graders with `Grader.from_config()`:

```python theme={null}
from adaptive_harmony.graders import Grader
from adaptive_harmony.runtime import RecipeContext, recipe_main

@recipe_main
async def main(config: GraderRecipeConfig, ctx: RecipeContext):
    graders = []
    for grader_config in config.graders:
        grader = Grader.from_config(
            grader_config=grader_config,
            client=ctx.client,
            # the following params are optional
            # used for Binary Judge and prebuilt Graders
            tp=2,
            kv_cache_len=200_000
        )
        # setup Grader
        await grader.setup()
        graders.append(grader)
```

Now the Graders are ready to use for training, evaluation, or anything else you find use for!