docs: graduate plugins out of experimental mode

docs/code_reference/config/analysis.md

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+# Analysis
+
+Profiling result objects and report helpers returned after generation.
+
+## Column Statistics
+
+`DataDesigner.create()` and `DataDesigner.preview()` run the dataset profiler after generation. The profiler computes statistics for each configured column; side-effect columns are recorded separately in `DatasetProfilerResults.side_effect_column_names`.
+
+Statistics result classes store computed metrics for each column type and format those metrics for reports.
+
+::: data_designer.config.analysis.column_statistics
+
+## Column Profilers
+
+Column profilers are optional analysis tools that provide deeper insights into specific column types. Currently, the only column profiler available is the Judge Score Profiler.
+
+Profiler result classes store computed profiler output and format it for reports.
+
+::: data_designer.config.analysis.column_profilers
+
+## Dataset Profiler
+
+The [DatasetProfilerResults](#data_designer.config.analysis.dataset_profiler.DatasetProfilerResults) class stores profiling results for a generated dataset. It aggregates column-level statistics, side-effect column names, and optional profiler results, and provides methods to:
+
+- Compute dataset-level metrics (completion percentage, column type summary)
+- Filter statistics by column type
+- Generate formatted analysis reports via the `to_report()` method
+
+Reports can be displayed in the console or exported to HTML/SVG formats.
+
+::: data_designer.config.analysis.dataset_profiler

docs/code_reference/config/column_configs.md

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+# Column Configurations
+
+Column configs declare Data Designer's built-in column types. Each configuration inherits from [SingleColumnConfig](#data_designer.config.base.SingleColumnConfig), which provides shared arguments like the column `name`, whether to `drop` the column after generation, and the `column_type`.
+
+For column generator implementation classes, see [column_generators](../engine/column_generators.md).
+
+!!! info "`column_type` is a discriminator field"
+    The `column_type` argument is used to identify column types when deserializing the [Data Designer Config](data_designer_config.md) from JSON/YAML. It acts as the discriminator in a [discriminated union](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions), allowing Pydantic to automatically determine which column configuration class to instantiate.
+
+## `SingleColumnConfig` {#data_designer.config.base.SingleColumnConfig}
+
+::: data_designer.config.base.SingleColumnConfig
+    options:
+      show_root_toc_entry: false
+
+## Column configurations
+
+::: data_designer.config.column_configs

docs/code_reference/config/config_builder.md

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+# Data Designer's Config Builder
+
+Use [DataDesignerConfigBuilder](#data_designer.config.config_builder.DataDesignerConfigBuilder) to construct [DataDesignerConfig](data_designer_config.md#data_designer.config.data_designer_config.DataDesignerConfig) objects. The builder accumulates model configs, tool configs, column configs, constraints, seed settings, processors, and profilers.
+
+Inputs can come from scratch, a `dict`, [BuilderConfig](#data_designer.config.config_builder.BuilderConfig), a local YAML/JSON file, or an HTTP(S) YAML/JSON URL via [`from_config()`](#data_designer.config.config_builder.DataDesignerConfigBuilder.from_config). Use [`build()`](#data_designer.config.config_builder.DataDesignerConfigBuilder.build) to create a [DataDesignerConfig](data_designer_config.md#data_designer.config.data_designer_config.DataDesignerConfig), or [`write_config()`](#data_designer.config.config_builder.DataDesignerConfigBuilder.write_config) to serialize the current builder config to YAML or JSON.
+
+!!! info "Model config loading"
+    [DataDesignerConfigBuilder](#data_designer.config.config_builder.DataDesignerConfigBuilder) accepts model configs as a list of [ModelConfig](models.md#data_designer.config.models.ModelConfig) objects, a YAML/JSON config path, or `None`. When `model_configs=None`, the builder loads default model configs if Data Designer can run locally; otherwise initialization raises BuilderConfigurationError. Model configs define the aliases referenced by model-backed columns such as [`LLMTextColumnConfig`](column_configs.md#data_designer.config.column_configs.LLMTextColumnConfig), [`LLMCodeColumnConfig`](column_configs.md#data_designer.config.column_configs.LLMCodeColumnConfig), [`LLMStructuredColumnConfig`](column_configs.md#data_designer.config.column_configs.LLMStructuredColumnConfig), [`LLMJudgeColumnConfig`](column_configs.md#data_designer.config.column_configs.LLMJudgeColumnConfig), [`EmbeddingColumnConfig`](column_configs.md#data_designer.config.column_configs.EmbeddingColumnConfig), and [`ImageColumnConfig`](column_configs.md#data_designer.config.column_configs.ImageColumnConfig).
+
+::: data_designer.config.config_builder

docs/code_reference/config/data_designer_config.md

@@ -0,0 +1,7 @@
+# Data Designer Configuration
+
+[DataDesignerConfig](#data_designer.config.data_designer_config.DataDesignerConfig) is the top-level configuration object passed to Data Designer. It declares the columns to generate and may include model configs, tool configs, seed settings, sampler constraints, processors, and profiler configs.
+
+Prefer [DataDesignerConfigBuilder](config_builder.md#data_designer.config.config_builder.DataDesignerConfigBuilder) for programmatic construction. Direct [DataDesignerConfig](#data_designer.config.data_designer_config.DataDesignerConfig) instantiation is also supported.
+
+::: data_designer.config.data_designer_config

docs/code_reference/config/index.md

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+# Config Package
+
+The `data-designer-config` package provides `data_designer.config`, the configuration layer of Data Designer. It contains the objects used to describe dataset structure, model access, tool access, seed data, sampler parameters, validators, processors, run settings, plugin registrations, and analysis results.
+
+This package is the base of the dependency chain. Engine and interface code consume these config objects, but config objects do not execute generation directly.
+
+For programmatic configuration work, start with [config_builder](config_builder.md) and [data_designer_config](data_designer_config.md). Use the narrower pages for exact constructor fields for columns, models, MCP tools, seeds, processors, samplers, validators, or profiling results.

docs/code_reference/config/mcp.md

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+# MCP Configuration
+
+MCP config objects tell Data Designer which Model Context Protocol providers exist and which tools an LLM column may use.
+
+[MCPProvider](#data_designer.config.mcp.MCPProvider) configures remote MCP servers via SSE or Streamable HTTP transport. [LocalStdioMCPProvider](#data_designer.config.mcp.LocalStdioMCPProvider) configures local MCP servers as subprocesses via stdio transport. [ToolConfig](#data_designer.config.mcp.ToolConfig) sets which tools are available for LLM columns and how they are constrained.
+
+For MCP execution internals, see [Engine MCP](../engine/mcp.md). Related guides:
+
+- **[MCP Providers](../../concepts/mcp/mcp-providers.md)** - Configure local or remote MCP providers
+- **[Tool Configs](../../concepts/mcp/tool-configs.md)** - Define tool permissions and limits
+- **[Enabling Tools](../../concepts/mcp/enabling-tools.md)** - Use tools in LLM columns
+- **[Traces](../../concepts/traces.md)** - Capture full conversation history
+
+## API Reference
+
+::: data_designer.config.mcp

docs/code_reference/config/models.md

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+# Models
+
+[ModelProvider](#data_designer.config.models.ModelProvider) stores connection and authentication details for model providers. [ModelConfig](#data_designer.config.models.ModelConfig) stores a model alias, model identifier, provider settings, and inference parameters. [Inference Parameters](../../concepts/models/inference-parameters.md) control model behavior. Chat-completion parameters include `temperature`, `top_p`, and `max_tokens`; `temperature` and `top_p` can be fixed values or configured distributions. [ImageContext](#data_designer.config.models.ImageContext) provides image inputs to multimodal models, and [ImageInferenceParams](#data_designer.config.models.ImageInferenceParams) configures image generation models.
+
+Related guides:
+
+- **[Model Providers](../../concepts/models/model-providers.md)**
+- **[Model Configs](../../concepts/models/model-configs.md)**
+- **[Image Context](../../notebooks/4-providing-images-as-context.ipynb)**
+- **[Generating Images](../../notebooks/5-generating-images.ipynb)**
+
+::: data_designer.config.models

docs/code_reference/config/plugins.md

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+# Plugins
+
+Plugin packages register [Plugin](#data_designer.plugins.plugin.Plugin) objects through entry points in the `data_designer.plugins` group. A plugin registration ties a config class to its implementation class and declares its [PluginType](#data_designer.plugins.plugin.PluginType).
+
+Related pages: [Build Your Own](../../plugins/build_your_own.md), [Column Generators](../engine/column_generators.md), [Seed Readers](../engine/seed_readers.md), [Engine Processors](../engine/processors.md), and [Processor Configurations](processors.md).
+
+## `Plugin` {#data_designer.plugins.plugin.Plugin}
+
+::: data_designer.plugins.plugin.Plugin
+    options:
+      show_root_toc_entry: false
+
+## `PluginType` {#data_designer.plugins.plugin.PluginType}
+
+::: data_designer.plugins.plugin.PluginType
+    options:
+      show_root_toc_entry: false

docs/code_reference/config/processors.md

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+# Processor Configurations
+
+Processor configs request data transformations after generation. Add them to a `DataDesignerConfig` or `DataDesignerConfigBuilder`; the engine later compiles them into runtime processor implementations.
+
+Related pages: [engine processors](../engine/processors.md) and [Build Your Own](../../plugins/build_your_own.md).
+
+::: data_designer.config.processors

docs/code_reference/run_config.md → docs/code_reference/config/run_config.md

@@ -1,14 +1,14 @@
 # Run Config
 
-The `run_config` module defines runtime settings that control dataset generation behavior,
-including early shutdown thresholds, batch sizing, non-inference worker concurrency,
-and the Jinja rendering engine used by the runtime.
+`RunConfig` controls dataset generation behavior, including early shutdown thresholds,
+batch sizing, non-inference worker concurrency, and the Jinja rendering engine used by
+the runtime.
 
 `JinjaRenderingEngine.SECURE` is the default. Set `JinjaRenderingEngine.NATIVE`
 when you want Jinja2's broader built-in sandbox behavior instead of Data Designer's
 hardened renderer.
 
-For guidance on when to use each mode, see [Security](../concepts/security.md).
+For guidance on when to use each mode, see [Security](../../concepts/security.md).
 
 ## Usage
 

docs/code_reference/sampler_params.md → docs/code_reference/config/sampler_params.md

@@ -1,6 +1,6 @@
 # Sampler Parameters
 
-The `sampler_params` module defines parameter configuration objects for all Data Designer sampler types. Sampler parameters are used within the [SamplerColumnConfig](column_configs.md#data_designer.config.column_configs.SamplerColumnConfig) to specify how values should be generated for sampled columns.
+Sampler parameter classes configure Data Designer's built-in samplers. Use them in [SamplerColumnConfig](column_configs.md#data_designer.config.column_configs.SamplerColumnConfig) to specify how sampled column values are generated.
 
 !!! tip "Displaying available samplers and their parameters"
     The config builder has an `info` attribute that can be used to display the

docs/code_reference/config/seeds.md

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+# Seeds
+
+Seed configs declare existing data used as input during generation. A [SeedConfig](#data_designer.config.seed.SeedConfig) combines a seed source with optional row sampling and selection settings. Seed source objects declare where seed data comes from; the engine reads them through seed readers.
+
+Use these objects with `DataDesignerConfigBuilder.with_seed_dataset()`. Related pages: [Seed Datasets](../../concepts/seed-datasets.md) and [seed readers](../engine/seed_readers.md).
+
+Built-in seed sources include local files, Hugging Face paths, in-memory DataFrames, directories, file contents, and agent rollout traces. Plugin seed sources can extend the same discriminated union through the plugin system.
+
+## Seed Config
+
+::: data_designer.config.seed
+
+## Built-In Seed Sources
+
+::: data_designer.config.seed_source
+
+## DataFrame Seed Source
+
+::: data_designer.config.seed_source_dataframe

docs/code_reference/config/validator_params.md

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+# Validator Parameters
+
+`ValidationColumnConfig` selects a validator with `validator_type` and configures it with `validator_params`.
+The `validator_type` field can be `code`, `local_callable`, or `remote`. The matching `validator_params` objects are:
+
+::: data_designer.config.validator_params