genkit_llamadart 3.0.0

Genkit Dart plugin for local, on-device LLMs through llamadart: llama.cpp GGUF and LiteRT-LM chat, streaming, tool calling, JSON output, and embeddings.

genkit_llamadart

Run local LLMs in Genkit Dart apps. This plugin runs llama.cpp GGUF models and LiteRT-LM bundles in-process through llamadart, so Dart and Flutter apps get offline, on-device chat, streaming, tool calling, structured JSON output, and embeddings without an OpenAI-compatible HTTP server.

Features

• Chat generation with token streaming
• Genkit tool calling for multi-turn agent loops
• Constrained JSON output on grammar-capable backends
• Text embeddings
• Image and audio input with an optional multimodal projector
• Local model paths, or source-backed preparation from local, HTTP(S), and Hugging Face ModelSource values with caching, downloads, checksums, and progress snapshots
• Lazy model loading with queued per-model execution
• Genkit request cancellation, token usage, timings, and engine observers for OpenTelemetry instrumentation
• Android, iOS, macOS, Linux, Windows, and web

Installation

Add Genkit and the plugin to your app:

dart pub add genkit genkit_llamadart

For structured output, also add schemantic:

dart pub add schemantic

Flutter iOS/macOS apps that want Swift Package Manager-linked Apple XCFrameworks should also add the llamadart runtime companion packages for the model families they ship:

flutter pub add llamadart_llama_cpp_flutter # GGUF / llama.cpp
flutter pub add llamadart_litert_lm_flutter # .litertlm / LiteRT-LM

These companion packages provide Apple runtime packaging only. Keep importing package:genkit_llamadart/genkit_llamadart.dart for Genkit APIs.

AI agent skills

genkit_llamadart ships agent skills for setup and generation, model preparation, tool calling, and structured JSON output. Install them into your coding agent's skills directory from your app's root:

dart run skills@ get

Requirements

• Dart SDK ^3.12.0, Genkit 1.x, llamadart 0.11.x, and Schemantic 1.x
• a local model file supported by llamadart, or a ModelSource that resolves to one
• the native llamadart runtime prerequisites for your platform
• optionally, a multimodal projector file or source for image or audio input

Follow the llamadart documentation for native backends, Apple SwiftPM companion packages, and platform support.

Flutter Apple builds that use the companion SwiftPM packages require deployment targets of iOS 16.4 or newer and macOS 14.0 or newer. If an iOS app still uses CocoaPods, set the Podfile platform to 16.4 or newer too.

Quickstart

import 'package:genkit/genkit.dart';
import 'package:genkit_llamadart/genkit_llamadart.dart';

Future<void> main() async {
  final plugin = llamaDart(
    models: const <LlamaModelDefinition>[
      LlamaModelDefinition(
        name: 'local-chat',
        modelPath: '/models/qwen3.gguf',
        modelParams: ModelParams(contextSize: 8192),
        supportsEmbeddings: false,
      ),
    ],
  );

  final ai = Genkit(plugins: <LlamaDartPlugin>[plugin]);

  try {
    final response = await ai.generate(
      model: llamaDart.model('local-chat'),
      prompt: 'Say hello in one sentence.',
      config: const LlamaDartGenerationConfig(
        temperature: 0.2,
        maxTokens: 96,
        enableThinking: false,
      ),
    );

    print(response.text);
  } finally {
    await plugin.dispose();
    await ai.shutdown();
  }
}

Don't have a model file yet? Use source-backed preparation to download one, or see Choosing a model.

Choosing a Model

Pass an existing local path with LlamaModelDefinition(modelPath: ...), or let llamaDart.prepareModel(...) resolve a local, HTTP(S), or Hugging Face ModelSource into the package-managed cache. Good places to find models:

• llamadart documentation
• Hugging Face GGUF search
• LiteRT community models for .litertlm bundles, on llamadart targets that support LiteRT-LM

What to look for:

• chat and agents: an instruct or chat GGUF model
• Android LiteRT-LM chat: a .litertlm bundle such as Gemma 4 E2B
• embeddings: an embedding GGUF model
• multimodal input: a vision-capable GGUF model and, when required, a matching mmproj file

Before downloading a model, check its model card for:

• quantization level and expected RAM or CPU requirements
• chat template or instruct formatting
• context length
• whether tool calling or JSON-style output works well
• whether a separate projector file is required for image input

For tiny CPU-friendly smoke-test models, use hf://unsloth/SmolLM2-135M-Instruct-GGUF/SmolLM2-135M-Instruct-Q2_K.gguf for chat and hf://second-state/jina-embeddings-v2-small-en-GGUF/jina-embeddings-v2-small-en-Q2_K.gguf for embeddings.

Examples

Example	Model	Shows
genkit_llamadart_example.dart	chat	streaming chat generation
genkit_llamadart_source_prepare_example.dart	any source	resolving a ModelSource through the package-managed cache
genkit_llamadart_preparation_task_example.dart	any source	preparation snapshots, warm-up, then generation
genkit_llamadart_agent_example.dart	chat	multi-turn tool loop; interactive when LLAMADART_PROMPT is unset
genkit_llamadart_json_example.dart	grammar-capable chat	constrained JSON output with streaming
genkit_llamadart_embedding_example.dart	embedding	vector dimensions and sample values
genkit_llamadart_observability_example.dart	chat	OpenTelemetry spans and metrics through genkit_otel

The table lists examples in the recommended order to try them. Examples that take a model path read it from LLAMADART_MODEL_PATH; add LLAMADART_MMPROJ_PATH to the chat and agent examples when the model requires a projector file.

LLAMADART_MODEL_PATH=/models/Qwen_Qwen3.5-9B-Q4_K_M.gguf \
dart run example/genkit_llamadart_example.dart

The same form runs the agent, JSON, and embedding examples. Use an embedding model for the embedding example. The observability example also needs an OpenTelemetry collector; see Observability.

The preparation examples take a local path, HTTP(S) URL, or Hugging Face source and default to the tiny SmolLM2 model:

dart run -DLLAMADART_MODEL_SOURCE=hf://owner/repo/model.gguf \
  example/genkit_llamadart_source_prepare_example.dart

Use a .litertlm source the same way on supported llamadart targets:

dart run \
  -DLLAMADART_MODEL_SOURCE='https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm/resolve/main/gemma-4-E2B-it.litertlm?download=true' \
  example/genkit_llamadart_source_prepare_example.dart

Source-Backed Model Preparation

If your app does not already manage model files itself, use llamaDart.prepareModel(...) with llamadart's ModelSource and package-managed cache/download options. The helper resolves the source to a local file, builds the normal LlamaModelDefinition, and returns a plugin plus typed model/embedder refs for standard Genkit calls.

import 'package:genkit/genkit.dart';
import 'package:genkit_llamadart/genkit_llamadart.dart';

Future<void> main() async {
  final prepared = await llamaDart.prepareModel(
    name: 'local-chat',
    source: ModelSource.parse(
      'hf://unsloth/SmolLM2-135M-Instruct-GGUF/SmolLM2-135M-Instruct-Q2_K.gguf',
    ),
    modelParams: const ModelParams(contextSize: 4096),
    options: ModelLoadOptions(
      cachePolicy: ModelCachePolicy.preferCached,
      cacheDirectory: '/path/to/app/model-cache',
      sha256: null, // set to a 64-character SHA-256 digest when available
      bearerToken: null, // set for private remote sources
    ),
    supportsEmbeddings: false,
  );

  final ai = Genkit(plugins: <LlamaDartPlugin>[prepared.plugin]);
  try {
    final response = await ai.generate(
      model: prepared.modelRef,
      prompt: 'Say hello in one sentence.',
    );
    print(response.text);
  } finally {
    await prepared.dispose();
    await ai.shutdown();
  }
}

Use this path for HTTP(S), Hugging Face, or local ModelSource values when you want llamadart to own cache lookup, download, checksum verification, and private-token/header plumbing. Keep constructing LlamaModelDefinition manually when your application already has a local filesystem path and owns all download or cache policy itself.

For multimodal models, pass mmprojSource and optional mmprojOptions; the resolved projector file path is wired into LlamaModelDefinition.mmprojPath. Local ModelSource.path(...) values use llamadart's local-path semantics: remote-only options such as cache policy overrides, cache directories, bearer tokens, headers, resume, and retry settings are rejected instead of silently ignored.

Observable Preparation and Warm-Up

Flutter and other client apps can use prepareModelTask(...) when they need deterministic loading UI for source resolution, cache checks, downloads, verification, Genkit setup, failures, and cancellation.

import 'package:genkit/genkit.dart';
import 'package:genkit_llamadart/genkit_llamadart.dart';

Future<void> main() async {
  final task = llamaDart.prepareModelTask(
    name: 'local-chat',
    source: ModelSource.parse(
      'hf://unsloth/SmolLM2-135M-Instruct-GGUF/SmolLM2-135M-Instruct-Q2_K.gguf',
    ),
    modelParams: const ModelParams(contextSize: 4096),
    options: ModelLoadOptions(
      cachePolicy: ModelCachePolicy.preferCached,
      cacheDirectory: '/path/to/app/model-cache',
    ),
    supportsEmbeddings: false,
  );

  final subscription = task.snapshots.listen((snapshot) {
    // Bind these fields into your UI state, ChangeNotifier, Bloc, Riverpod, etc.
    final stage = snapshot.stage;
    final fraction = snapshot.fraction;
    final modelPath = snapshot.modelEntry?.filePath;
    final errorText = snapshot.errorMessage;
    print('$stage ${fraction ?? '-'} ${modelPath ?? errorText ?? ''}');
  });

  LlamaPreparedModel? prepared;
  Genkit? ai;
  try {
    prepared = await task.result;
    ai = prepared.createGenkit();

    await prepared.warmUp(
      ai,
      systemPrompt: 'Use terse, app-friendly answers.',
      prompt: 'Reply with one token: ready',
      config: const LlamaDartGenerationConfig(
        maxTokens: 1,
        temperature: 0.0,
        enableThinking: false,
      ),
    );

    final response = await ai.generate(
      model: prepared.modelRef,
      prompt: 'Say hello in one sentence.',
    );
    print(response.text);
  } finally {
    await subscription.cancel();
    await task.dispose();
    if (prepared != null) {
      await prepared.dispose();
    }
    if (ai != null) {
      await ai.shutdown();
    }
  }
}

Call task.cancel() to request cooperative cancellation while preparation is in flight. Disposing the task closes snapshot resources; disposing the returned LlamaPreparedModel releases plugin/runtime resources owned by this package. prepared.createGenkit() is a convenience for registering the plugin, but the returned Genkit instance remains caller-owned and should still be shut down by the app.

GenUI and Server Integration

UI frameworks such as GenUI should adapt through normal Genkit model refs and backends. Once your app has a prepared model, pass prepared.modelRef and prepared.plugin into the Genkit-facing adapter instead of depending on a provider-specific GenUI llamadart bridge:

final prepared = await llamaDart.prepareModel(...);
final ai = prepared.createGenkit();

final session = GenkitGenUiSession(
  backend: GenkitBackend<LlamaDartGenerationConfig>(
    ai: ai,
    model: prepared.modelRef,
    config: const LlamaDartGenerationConfig(maxTokens: 512),
  ),
  catalog: appCatalog,
);

Use genkit_llamadart directly when the app wants source-backed local model preparation, progress snapshots, typed Genkit refs, warm-up, and lifecycle helpers. Manually construct LlamaModelDefinition(modelPath: ...) when another part of the app already owns file resolution and caching. Provider-specific packages such as genui_genkit_llamadart should be treated as transitional UI wiring once the GenUI docs can point at the direct Genkit model-ref path above.

The same prepared-model API works in backend/server apps. A server package can add genkit_shelf 1.0 and shelf, and expose a Genkit flow while keeping model preparation in one place:

import 'package:genkit/io.dart';
import 'package:genkit_llamadart/genkit_llamadart.dart';
import 'package:genkit_shelf/genkit_shelf.dart';
import 'package:shelf/shelf_io.dart' as shelf_io;

Future<void> main() async {
  final prepared = await llamaDart.prepareModel(
    name: 'server-chat',
    source: ModelSource.parse('/models/server-chat.gguf'),
    modelParams: const ModelParams(contextSize: 8192),
    supportsEmbeddings: false,
  );
  final ai = prepared.createGenkit();

  final flow = ai.defineFlow<String, String, String, void>(
    name: 'localChat',
    fn: (prompt, context) async {
      final stream = ai.generateStream<Object?, LlamaDartGenerationConfig>(
        model: prepared.modelRef,
        prompt: prompt,
        config: const LlamaDartGenerationConfig(maxTokens: 512),
      );

      await for (final chunk in stream) {
        if (chunk.text.isNotEmpty) {
          context.sendChunk(chunk.text);
        }
      }

      return (await stream.onResult).text;
    },
  );

  final router = GenkitRouter()..addAction(flow);
  await shelf_io.serve(router.asShelfHandler(), '127.0.0.1', 8080);
}

Embeddings

Use llamaDart.embedder(...) with ai.embed(...) for single or multiple documents. Embeddings currently accept text-only documents.

import 'package:genkit/genkit.dart';
import 'package:genkit_llamadart/genkit_llamadart.dart';

Future<void> main() async {
  final plugin = llamaDart(
    models: const <LlamaModelDefinition>[
      LlamaModelDefinition(
        name: 'local-embed',
        modelPath: '/models/embed.gguf',
        supportsTools: false,
        supportsConstrainedOutput: false,
      ),
    ],
  );
  final ai = Genkit(plugins: <LlamaDartPlugin>[plugin]);

  try {
    final embeddings = await ai.embed(
      embedder: llamaDart.embedder('local-embed'),
      document: DocumentData(
        content: <Part>[TextPart(text: 'hello world from llamadart')],
      ),
      options: const LlamaDartEmbedConfig(normalize: true),
    );

    print(embeddings.single.embedding.length);
  } finally {
    await plugin.dispose();
    await ai.shutdown();
  }
}

Structured JSON Output

Constrained JSON mode works with Genkit output schemas. This is useful when you need machine-readable output from a local model.

import 'dart:convert';

import 'package:genkit/genkit.dart';
import 'package:genkit_llamadart/genkit_llamadart.dart';
import 'package:schemantic/schemantic.dart';

final answerSchema = SchemanticType.from<Map<String, dynamic>>(
  jsonSchema: <String, Object?>{
    'type': 'object',
    'properties': <String, Object?>{
      'summary': <String, Object?>{'type': 'string'},
      'sentiment': <String, Object?>{'type': 'string'},
    },
    'required': <String>['summary', 'sentiment'],
    'additionalProperties': false,
  },
  parse: (json) {
    if (json is Map<String, dynamic>) {
      return json;
    }
    if (json is Map) {
      return json.cast<String, dynamic>();
    }
    throw FormatException('Expected a JSON object.');
  },
);

Future<void> main() async {
  final plugin = llamaDart(
    models: const <LlamaModelDefinition>[
      LlamaModelDefinition(
        name: 'local-json',
        modelPath: '/models/chat.gguf',
        supportsEmbeddings: false,
        supportsTools: false,
      ),
    ],
  );
  final ai = Genkit(plugins: <LlamaDartPlugin>[plugin]);

  try {
    final response = await ai.generate<
      Map<String, dynamic>,
      LlamaDartGenerationConfig
    >(
      model: llamaDart.model('local-json'),
      prompt: 'Summarize this review as JSON: The battery life is great.',
      outputSchema: answerSchema,
      outputFormat: 'json',
      outputConstrained: true,
      config: const LlamaDartGenerationConfig(enableThinking: false),
    );

    print(jsonEncode(response.output));
  } finally {
    await plugin.dispose();
    await ai.shutdown();
  }
}

Multimodal Requests

If your model needs a multimodal projector, set mmprojPath on the model definition. Requests can include Genkit Media parts alongside text.

final plugin = llamaDart(
  models: const <LlamaModelDefinition>[
    LlamaModelDefinition(
      name: 'local-vision',
      modelPath: '/models/vision.gguf',
      mmprojPath: '/models/mmproj.gguf',
      supportsEmbeddings: false,
    ),
  ],
);

final response = await ai.generate(
  model: llamaDart.model('local-vision'),
  messages: <Message>[
    Message(
      role: Role.user,
      content: <Part>[
        TextPart(text: 'Describe this image in one sentence.'),
        MediaPart(
          media: Media(
            url: 'file:///tmp/example.png',
            contentType: 'image/png',
          ),
        ),
      ],
    ),
  ],
);

Supported media inputs:

• images from local paths, file://, and data: URLs on compatible multimodal backends
• http(s) image URLs on backends that consume remote URLs directly (currently WebGPU)
• audio from local paths, file://, and data: URLs

For local files and HTTP(S) URLs with a recognized extension, the media type is inferred from the URI path. This includes signed HTTP(S) URLs with query parameters or fragments, although remote fetching remains backend-dependent. Local file:// URLs cannot include query parameters or fragments. Provide contentType when the path has no recognizable media extension.

Tool Calling

• Genkit can drive multi-turn tool loops through this plugin; see the agent example.
• Tool callbacks return ToolResult.response(value). Multipart tool-result content is rejected with UNIMPLEMENTED; return text or structured output instead.
• Tool input schemas and tool-request inputs must be JSON objects. Primitive or list root inputs cannot be represented by llamadart's map argument API and fail with INVALID_ARGUMENT.
• Named Schemantic object schemas are supported; local $ref and $defs wrappers emitted by Genkit are resolved before tool parameters are mapped.
• Local models may vary in how reliably they emit structured tool arguments. If a model emits empty or weak tool arguments, use strong tool descriptions, prompt guidance, and app context to stabilize behavior.

Observability

Configure a production instrumentation provider with configureInstrumentation(...) from package:genkit/telemetry.dart before creating Genkit. Genkit's development provider activates when a telemetry server is configured; installing this plugin alone does not export telemetry.

The model response supplies backend-reported usage.inputTokens, outputTokens, totalTokens, and cachedContentTokens. Missing measurements remain absent; zero counts remain zero. Native llama.cpp and capable WebGPU bridges report usage; do not assume every backend provides it.

response.raw additionally includes:

• usage: llamadart's usage map, with time_to_first_token_ms and duration_ms when reported. These backend timings exclude the plugin queue and model load.
• queueMs: time waiting for the model's serialized execution slot.
• initializationMs: time obtaining the runtime, including lazy model/projector loading on its first request.
• totalLatencyMs: elapsed time in the model action, including queue and setup.

response.latencyMs retains its existing generation-path timing after runtime initialization. Usage is captured for ordinary chat and constrained JSON output. These are per-model-call measurements. In Genkit Dart 1.0, GenerateResult.usage reports the final model turn rather than the sum of all tool-loop turns. Genkit instrumentation records each model call separately; aggregate those measurements in your application or collector when you need whole-run usage.

Pass observers: [yourObserver] to llamaDart(...), prepareModel(...), or prepareModelTask(...) to observe engine model loads, chat/text generation, and embeddings, including failures and cancellation. Extend LlamaEngineObserver and LlamaOperationObserver. Callbacks execute in the request's Dart zone, allowing your instrumentation provider to correlate them with the surrounding Genkit span, including after queue waits. Observer errors are logged by llamadart without breaking inference. With a custom runtime factory, that factory is responsible for installing its observers.

Observers receive request content. Export prompts, responses and tool data only with an explicit application opt-in. Count token usage once at the model level; engine diagnostics can carry the same measurements and should not increment a second model-usage counter.

See the observability example for genkit_otel's GenAiInstrumentation and correlated engine spans. The example initializes and shuts down the OpenTelemetry SDK, exporting model token and duration metrics once. Message capture and raw action I/O are explicitly disabled. Engine spans include only operation names and outcomes; upstream instrumentation may still export exception details on failures.

The telemetry packages are development dependencies here; applications adopting this example should add genkit_otel and dartastic_opentelemetry to their own dependencies. They are not required by the plugin itself.

Start an OpenTelemetry collector accepting OTLP HTTP on port 4318, then run:

OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 \
OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf \
LLAMADART_MODEL_PATH=/models/chat.gguf \
dart run example/genkit_llamadart_observability_example.dart

Configuration

Model Capability Flags

Use LlamaModelDefinition to control what each registered model advertises and accepts:

• supportsEmbeddings: only register an embedder when the model should expose one
• supportsTools: disable Genkit tool use for models or templates that should not use tools
• supportsConstrainedOutput: disable constrained JSON output for models that should not advertise it, including .litertlm LiteRT-LM bundles until that backend supports grammar constraints

These flags default to true for backward compatibility. Set them explicitly for single-purpose chat, embedding, and structured-output models so Genkit does not advertise actions the selected model should not serve.

Use modelInfo to customize Genkit metadata such as the label, versions, configuration schema, stage, or additional support metadata. modelInfo.supports is merged after the derived capability flags and therefore overrides advertised metadata only; it does not bypass runtime enforcement by supportsTools, supportsConstrainedOutput, or supportsEmbeddings. Keep overridden metadata aligned with those flags.

Default Request Settings

Unless you override them in LlamaDartGenerationConfig, the plugin uses these defaults:

• temperature: 0.8
• topP: 0.9
• topK: 40
• minP: 0.0
• penalty: 1.1
• maxTokens: 4096
• enableThinking: false
• parallelToolCalls: false

Additional request controls include:

• stop: custom stop sequences
• seed: an optional deterministic sampling seed
• sourceLangCode and targetLangCode: language hints for translation-style templates
• chatTemplateKwargs: extra globals passed through to the model chat template

Lifecycle and Cancellation

• models load lazily on first use
• requests for the same model are queued through a single runtime instance
• different model names get separate runtime instances
• pass cancel: controller.token to Genkit generation for cooperative cancellation; queued cancelled requests are checked before loading or generation, without interrupting another active request, and settle when their queue slot is reached
• call prepared.cancelActiveGeneration() to implement a stop button for a prepared model
• call plugin.cancelActiveGeneration(name) for one registered model, or plugin.cancelActiveGenerations() to stop all currently loaded models
• these helpers cancel only the active generation; a separately queued request can still start afterward
• Genkit ai.generate() can return finishReason: failed or aborted; inspect the outcome and error/cause before using the result
• LlamaPreparedModel.warmUp() still throws on failure or cancellation
• call await plugin.dispose() before process shutdown to release native state
• plugin disposal is terminal; create a new plugin or prepared-model handle instead of reusing it
• if multiple runtimes fail during disposal, the returned ParallelWaitError retains every underlying failure
• call await ai.shutdown() when your Genkit app is done

Limitations

• model paths are local filesystem paths after ModelSource resolution
• embeddings are text-only
• LiteRT-LM .litertlm bundles can be used for chat and tool-call flows, but constrained JSON output currently requires a backend with grammar constraints; set supportsConstrainedOutput: false for .litertlm model definitions.
• constrained structured output with active tool calling is not supported yet
• some models may need prompt tuning for reliable tool arguments
• multimodal requests require a compatible model and projector file
• direct HTTP(S) image inputs are backend-dependent and currently supported by WebGPU; download the image to a local path or provide data: bytes for other compatible multimodal backends

Upgrading to Genkit 1.0

The next release requires Dart 3.12, Genkit 1.0, llamadart 0.11.1, and Schemantic 1.0. For applications using the previous dependency versions:

• Put the output type first in explicit generation type arguments: ai.generate<T, LlamaDartGenerationConfig>(...) and ai.generateStream<T, LlamaDartGenerationConfig>(...).
• Replace GenerateResponseHelper<T> with GenerateResult<T>, including explicit types for results returned by prepared.warmUp<T>(ai).
• Use ToolChoice.auto, ToolChoice.required, or ToolChoice.none for Genkit's toolChoice; use StatusCode.invalidArgument and other lowerCamelCase status values when checking errors.
• Replace ai.embedMany(...) with ai.embed(...) for multiple documents.
• Define custom Schemantic JSON schemas with plain maps instead of $Schema.
• Use ModelParams(device: ComputeDevice.cpu) (or auto, gpu, or npu) for device selection. The deprecated LiteRtLmBackendPreference export has been removed; replace it with ComputeDevice.
• Use genkit_otel 0.2.0 or newer with Genkit 1.0.
• Flutter Apple apps using runtime companions must upgrade them alongside llamadart: core 0.11.1 pairs with llamadart_llama_cpp_flutter 0.0.21 and llamadart_litert_lm_flutter 0.0.13. See the llamadart 0.11.1 installation notes for the matching runtime packaging.

Upgrading to 2.0

Version 2.0 requires Dart 3.12, Genkit 0.17, llamadart 0.9, and Schemantic 0.2.3. When upgrading from 1.x (Genkit 0.13–0.15):

• Return ToolResult.response(value) from tool callbacks; see Tool Calling.
• Check generation results for failed or aborted finish reasons, and optionally pass Genkit cancellation tokens; see Lifecycle and Cancellation.

See the changelog for the full list of changes.

Contributing

See CONTRIBUTING.md for local checks, real-model smoke tests, and the release process, and ARCHITECTURE.md for the package layering rules. Report bugs and request features in the issue tracker.