diff --git a/packages/ai-provider/src/hf-transformers/common/HFT_JobRunFns.ts b/packages/ai-provider/src/hf-transformers/common/HFT_JobRunFns.ts
index 10a919a3..4cc12ca2 100644
--- a/packages/ai-provider/src/hf-transformers/common/HFT_JobRunFns.ts
+++ b/packages/ai-provider/src/hf-transformers/common/HFT_JobRunFns.ts
@@ -77,7 +77,7 @@ import type {
 } from "@workglow/ai";
 import { CallbackStatus } from "./HFT_CallbackStatus";
 import { HTF_CACHE_NAME } from "./HFT_Constants";
-import { HfTransformersOnnxModelRecord } from "./HFT_ModelSchema";
+import { HfTransformersOnnxModelConfig } from "./HFT_ModelSchema";
 
 const pipelines = new Map<string, any>();
 
@@ -93,12 +93,12 @@ export function clearPipelineCache(): void {
  * @param progressScaleMax - Maximum progress value for download phase (100 for download-only, 10 for download+run)
  */
 const getPipeline = async (
-  model: HfTransformersOnnxModelRecord,
+  model: HfTransformersOnnxModelConfig,
   onProgress: (progress: number, message?: string, details?: any) => void,
   options: PretrainedModelOptions = {},
   progressScaleMax: number = 10
 ) => {
-  const cacheKey = `${model.model_id}:${model.providerConfig.pipeline}`;
+  const cacheKey = `${model.providerConfig.modelPath}:${model.providerConfig.pipeline}`;
   if (pipelines.has(cacheKey)) {
     return pipelines.get(cacheKey);
   }
@@ -433,7 +433,7 @@ const getPipeline = async (
 export const HFT_Download: AiProviderRunFn<
   DownloadModelTaskExecuteInput,
   DownloadModelTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   // Download the model by creating a pipeline
   // Use 100 as progressScaleMax since this is download-only (0-100%)
@@ -451,11 +451,12 @@ export const HFT_Download: AiProviderRunFn<
 export const HFT_Unload: AiProviderRunFn<
   UnloadModelTaskExecuteInput,
   UnloadModelTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   // Delete the pipeline from the in-memory map
-  if (pipelines.has(model!.model_id)) {
-    pipelines.delete(model!.model_id);
+  const cacheKey = `${model!.providerConfig.modelPath}:${model!.providerConfig.pipeline}`;
+  if (pipelines.has(cacheKey)) {
+    pipelines.delete(cacheKey);
     onProgress(50, "Pipeline removed from memory");
   }
 
@@ -515,7 +516,7 @@ const deleteModelCache = async (modelPath: string): Promise<void> => {
 export const HFT_TextEmbedding: AiProviderRunFn<
   TextEmbeddingTaskExecuteInput,
   TextEmbeddingTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const generateEmbedding: FeatureExtractionPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -546,7 +547,7 @@ export const HFT_TextEmbedding: AiProviderRunFn<
 export const HFT_TextClassification: AiProviderRunFn<
   TextClassificationTaskExecuteInput,
   TextClassificationTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   if (model?.providerConfig?.pipeline === "zero-shot-classification") {
     if (
@@ -602,7 +603,7 @@ export const HFT_TextClassification: AiProviderRunFn<
 export const HFT_TextLanguageDetection: AiProviderRunFn<
   TextLanguageDetectionTaskExecuteInput,
   TextLanguageDetectionTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const TextClassification: TextClassificationPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -632,7 +633,7 @@ export const HFT_TextLanguageDetection: AiProviderRunFn<
 export const HFT_TextNamedEntityRecognition: AiProviderRunFn<
   TextNamedEntityRecognitionTaskExecuteInput,
   TextNamedEntityRecognitionTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const textNamedEntityRecognition: TokenClassificationPipeline = await getPipeline(
     model!,
@@ -663,7 +664,7 @@ export const HFT_TextNamedEntityRecognition: AiProviderRunFn<
 export const HFT_TextFillMask: AiProviderRunFn<
   TextFillMaskTaskExecuteInput,
   TextFillMaskTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const unmasker: FillMaskPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -691,7 +692,7 @@ export const HFT_TextFillMask: AiProviderRunFn<
 export const HFT_TextGeneration: AiProviderRunFn<
   TextGenerationTaskExecuteInput,
   TextGenerationTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const generateText: TextGenerationPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -724,7 +725,7 @@ export const HFT_TextGeneration: AiProviderRunFn<
 export const HFT_TextTranslation: AiProviderRunFn<
   TextTranslationTaskExecuteInput,
   TextTranslationTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const translate: TranslationPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -758,7 +759,7 @@ export const HFT_TextTranslation: AiProviderRunFn<
 export const HFT_TextRewriter: AiProviderRunFn<
   TextRewriterTaskExecuteInput,
   TextRewriterTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const generateText: TextGenerationPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -798,7 +799,7 @@ export const HFT_TextRewriter: AiProviderRunFn<
 export const HFT_TextSummary: AiProviderRunFn<
   TextSummaryTaskExecuteInput,
   TextSummaryTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const generateSummary: SummarizationPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -829,7 +830,7 @@ export const HFT_TextSummary: AiProviderRunFn<
 export const HFT_TextQuestionAnswer: AiProviderRunFn<
   TextQuestionAnswerTaskExecuteInput,
   TextQuestionAnswerTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   // Get the question answering pipeline
   const generateAnswer: QuestionAnsweringPipeline = await getPipeline(model!, onProgress, {
@@ -860,7 +861,7 @@ export const HFT_TextQuestionAnswer: AiProviderRunFn<
 export const HFT_ImageSegmentation: AiProviderRunFn<
   ImageSegmentationTaskExecuteInput,
   ImageSegmentationTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const segmenter: ImageSegmentationPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -893,7 +894,7 @@ export const HFT_ImageSegmentation: AiProviderRunFn<
 export const HFT_ImageToText: AiProviderRunFn<
   ImageToTextTaskExecuteInput,
   ImageToTextTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const captioner: ImageToTextPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -917,7 +918,7 @@ export const HFT_ImageToText: AiProviderRunFn<
 export const HFT_BackgroundRemoval: AiProviderRunFn<
   BackgroundRemovalTaskExecuteInput,
   BackgroundRemovalTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const remover: BackgroundRemovalPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -940,7 +941,7 @@ export const HFT_BackgroundRemoval: AiProviderRunFn<
 export const HFT_ImageEmbedding: AiProviderRunFn<
   ImageEmbeddingTaskExecuteInput,
   ImageEmbeddingTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   const embedder: ImageFeatureExtractionPipeline = await getPipeline(model!, onProgress, {
     abort_signal: signal,
@@ -960,7 +961,7 @@ export const HFT_ImageEmbedding: AiProviderRunFn<
 export const HFT_ImageClassification: AiProviderRunFn<
   ImageClassificationTaskExecuteInput,
   ImageClassificationTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   if (model?.providerConfig?.pipeline === "zero-shot-image-classification") {
     if (!input.categories || !Array.isArray(input.categories) || input.categories.length === 0) {
@@ -1015,7 +1016,7 @@ export const HFT_ImageClassification: AiProviderRunFn<
 export const HFT_ObjectDetection: AiProviderRunFn<
   ObjectDetectionTaskExecuteInput,
   ObjectDetectionTaskExecuteOutput,
-  HfTransformersOnnxModelRecord
+  HfTransformersOnnxModelConfig
 > = async (input, model, onProgress, signal) => {
   if (model?.providerConfig?.pipeline === "zero-shot-object-detection") {
     if (!input.labels || !Array.isArray(input.labels) || input.labels.length === 0) {
@@ -1070,7 +1071,6 @@ function imageToBase64(image: RawImage): string {
   return (image as any).toBase64?.() || "";
 }
 
-
 /**
  * Create a text streamer for a given tokenizer and update progress function
  * @param tokenizer - The tokenizer to use for the streamer
diff --git a/packages/ai-provider/src/hf-transformers/common/HFT_ModelSchema.ts b/packages/ai-provider/src/hf-transformers/common/HFT_ModelSchema.ts
index 9798b505..12022df1 100644
--- a/packages/ai-provider/src/hf-transformers/common/HFT_ModelSchema.ts
+++ b/packages/ai-provider/src/hf-transformers/common/HFT_ModelSchema.ts
@@ -4,7 +4,7 @@
  * SPDX-License-Identifier: Apache-2.0
  */
 
-import { ModelSchema } from "@workglow/ai";
+import { ModelConfigSchema, ModelRecordSchema } from "@workglow/ai";
 import { DataPortSchemaObject, FromSchema } from "@workglow/util";
 import { HF_TRANSFORMERS_ONNX, PipelineUseCase, QuantizationDataType } from "./HFT_Constants";
 
@@ -89,14 +89,26 @@ export const HfTransformersOnnxModelSchema = {
   additionalProperties: true,
 } as const satisfies DataPortSchemaObject;
 
-const ExtendedModelSchema = {
+const ExtendedModelRecordSchema = {
   type: "object",
   properties: {
-    ...ModelSchema.properties,
+    ...ModelRecordSchema.properties,
     ...HfTransformersOnnxModelSchema.properties,
   },
-  required: [...ModelSchema.required, ...HfTransformersOnnxModelSchema.required],
+  required: [...ModelRecordSchema.required, ...HfTransformersOnnxModelSchema.required],
   additionalProperties: false,
 } as const satisfies DataPortSchemaObject;
 
-export type HfTransformersOnnxModelRecord = FromSchema<typeof ExtendedModelSchema>;
+export type HfTransformersOnnxModelRecord = FromSchema<typeof ExtendedModelRecordSchema>;
+
+const ExtendedModelConfigSchema = {
+  type: "object",
+  properties: {
+    ...ModelConfigSchema.properties,
+    ...HfTransformersOnnxModelSchema.properties,
+  },
+  required: [...ModelConfigSchema.required, ...HfTransformersOnnxModelSchema.required],
+  additionalProperties: false,
+} as const satisfies DataPortSchemaObject;
+
+export type HfTransformersOnnxModelConfig = FromSchema<typeof ExtendedModelConfigSchema>;
diff --git a/packages/ai-provider/src/tf-mediapipe/common/TFMP_JobRunFns.ts b/packages/ai-provider/src/tf-mediapipe/common/TFMP_JobRunFns.ts
index e1ce6f21..cb7bfd5b 100644
--- a/packages/ai-provider/src/tf-mediapipe/common/TFMP_JobRunFns.ts
+++ b/packages/ai-provider/src/tf-mediapipe/common/TFMP_JobRunFns.ts
@@ -53,7 +53,7 @@ import type {
   UnloadModelTaskExecuteOutput,
 } from "@workglow/ai";
 import { PermanentJobError } from "@workglow/job-queue";
-import { TFMPModelRecord } from "./TFMP_ModelSchema";
+import { TFMPModelConfig } from "./TFMP_ModelSchema";
 
 interface TFMPWasmFileset {
   /** The path to the Wasm loader script. */
@@ -82,7 +82,7 @@ const wasm_reference_counts = new Map<string, number>();
  * Helper function to get a WASM task for a model
  */
 const getWasmTask = async (
-  model: TFMPModelRecord,
+  model: TFMPModelConfig,
   onProgress: (progress: number, message?: string, details?: any) => void,
   signal: AbortSignal
 ): Promise<TFMPWasmFileset> => {
@@ -213,7 +213,7 @@ const optionsMatch = (opts1: Record<string, unknown>, opts2: Record<string, unkn
 };
 
 const getModelTask = async <T extends TaskType>(
-  model: TFMPModelRecord,
+  model: TFMPModelConfig,
   options: Record<string, unknown>,
   onProgress: (progress: number, message?: string, details?: any) => void,
   signal: AbortSignal,
@@ -264,7 +264,7 @@ const getModelTask = async <T extends TaskType>(
 export const TFMP_Download: AiProviderRunFn<
   DownloadModelTaskExecuteInput,
   DownloadModelTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   let task: TextEmbedder | TextClassifier | LanguageDetector;
   switch (model?.providerConfig.pipeline) {
@@ -298,7 +298,7 @@ export const TFMP_Download: AiProviderRunFn<
 export const TFMP_TextEmbedding: AiProviderRunFn<
   TextEmbeddingTaskExecuteInput,
   TextEmbeddingTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const textEmbedder = await getModelTask(model!, {}, onProgress, signal, TextEmbedder);
   const result = textEmbedder.embed(input.text);
@@ -321,7 +321,7 @@ export const TFMP_TextEmbedding: AiProviderRunFn<
 export const TFMP_TextClassification: AiProviderRunFn<
   TextClassificationTaskExecuteInput,
   TextClassificationTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const TextClassification = await getModelTask(
     model!,
@@ -358,7 +358,7 @@ export const TFMP_TextClassification: AiProviderRunFn<
 export const TFMP_TextLanguageDetection: AiProviderRunFn<
   TextLanguageDetectionTaskExecuteInput,
   TextLanguageDetectionTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const maxLanguages = input.maxLanguages === 0 ? -1 : input.maxLanguages;
 
@@ -402,7 +402,7 @@ export const TFMP_TextLanguageDetection: AiProviderRunFn<
 export const TFMP_Unload: AiProviderRunFn<
   UnloadModelTaskExecuteInput,
   UnloadModelTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const modelPath = model!.providerConfig.modelPath;
   onProgress(10, "Unloading model");
@@ -442,7 +442,7 @@ export const TFMP_Unload: AiProviderRunFn<
 export const TFMP_ImageSegmentation: AiProviderRunFn<
   ImageSegmentationTaskExecuteInput,
   ImageSegmentationTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const imageSegmenter = await getModelTask(model!, {}, onProgress, signal, ImageSegmenter);
   const result = imageSegmenter.segment(input.image as any);
@@ -475,7 +475,7 @@ export const TFMP_ImageSegmentation: AiProviderRunFn<
 export const TFMP_ImageEmbedding: AiProviderRunFn<
   ImageEmbeddingTaskExecuteInput,
   ImageEmbeddingTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const imageEmbedder = await getModelTask(model!, {}, onProgress, signal, ImageEmbedder);
   const result = imageEmbedder.embed(input.image as any);
@@ -497,7 +497,7 @@ export const TFMP_ImageEmbedding: AiProviderRunFn<
 export const TFMP_ImageClassification: AiProviderRunFn<
   ImageClassificationTaskExecuteInput,
   ImageClassificationTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const imageClassifier = await getModelTask(
     model!,
@@ -530,7 +530,7 @@ export const TFMP_ImageClassification: AiProviderRunFn<
 export const TFMP_ObjectDetection: AiProviderRunFn<
   ObjectDetectionTaskExecuteInput,
   ObjectDetectionTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const objectDetector = await getModelTask(
     model!,
@@ -569,7 +569,7 @@ export const TFMP_ObjectDetection: AiProviderRunFn<
 export const TFMP_GestureRecognizer: AiProviderRunFn<
   GestureRecognizerTaskExecuteInput,
   GestureRecognizerTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const gestureRecognizer = await getModelTask(
     model!,
@@ -621,7 +621,7 @@ export const TFMP_GestureRecognizer: AiProviderRunFn<
 export const TFMP_HandLandmarker: AiProviderRunFn<
   HandLandmarkerTaskExecuteInput,
   HandLandmarkerTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const handLandmarker = await getModelTask(
     model!,
@@ -669,7 +669,7 @@ export const TFMP_HandLandmarker: AiProviderRunFn<
 export const TFMP_FaceDetector: AiProviderRunFn<
   FaceDetectorTaskExecuteInput,
   FaceDetectorTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const faceDetector = await getModelTask(
     model!,
@@ -714,7 +714,7 @@ export const TFMP_FaceDetector: AiProviderRunFn<
 export const TFMP_FaceLandmarker: AiProviderRunFn<
   FaceLandmarkerTaskExecuteInput,
   FaceLandmarkerTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const faceLandmarker = await getModelTask(
     model!,
@@ -770,7 +770,7 @@ export const TFMP_FaceLandmarker: AiProviderRunFn<
 export const TFMP_PoseLandmarker: AiProviderRunFn<
   PoseLandmarkerTaskExecuteInput,
   PoseLandmarkerTaskExecuteOutput,
-  TFMPModelRecord
+  TFMPModelConfig
 > = async (input, model, onProgress, signal) => {
   const poseLandmarker = await getModelTask(
     model!,
diff --git a/packages/ai-provider/src/tf-mediapipe/common/TFMP_ModelSchema.ts b/packages/ai-provider/src/tf-mediapipe/common/TFMP_ModelSchema.ts
index f142ee0c..f90a229e 100644
--- a/packages/ai-provider/src/tf-mediapipe/common/TFMP_ModelSchema.ts
+++ b/packages/ai-provider/src/tf-mediapipe/common/TFMP_ModelSchema.ts
@@ -4,7 +4,7 @@
  * SPDX-License-Identifier: Apache-2.0
  */
 
-import { ModelSchema } from "@workglow/ai";
+import { ModelConfigSchema, ModelRecordSchema } from "@workglow/ai";
 import { DataPortSchemaObject, FromSchema } from "@workglow/util";
 import { TENSORFLOW_MEDIAPIPE, TextPipelineTask } from "../common/TFMP_Constants";
 
@@ -42,14 +42,26 @@ export const TFMPModelSchema = {
   additionalProperties: true,
 } as const satisfies DataPortSchemaObject;
 
-const ExtendedModelSchema = {
+const ExtendedModelRecordSchema = {
   type: "object",
   properties: {
-    ...ModelSchema.properties,
+    ...ModelRecordSchema.properties,
     ...TFMPModelSchema.properties,
   },
-  required: [...ModelSchema.required, ...TFMPModelSchema.required],
+  required: [...ModelRecordSchema.required, ...TFMPModelSchema.required],
   additionalProperties: false,
 } as const satisfies DataPortSchemaObject;
 
-export type TFMPModelRecord = FromSchema<typeof ExtendedModelSchema>;
+export type TFMPModelRecord = FromSchema<typeof ExtendedModelRecordSchema>;
+
+const ExtendedModelConfigSchema = {
+  type: "object",
+  properties: {
+    ...ModelConfigSchema.properties,
+    ...TFMPModelSchema.properties,
+  },
+  required: [...ModelConfigSchema.required, ...TFMPModelSchema.required],
+  additionalProperties: false,
+} as const satisfies DataPortSchemaObject;
+
+export type TFMPModelConfig = FromSchema<typeof ExtendedModelConfigSchema>;
diff --git a/packages/ai/src/job/AiJob.ts b/packages/ai/src/job/AiJob.ts
index e5a2cc34..3bbe8aae 100644
--- a/packages/ai/src/job/AiJob.ts
+++ b/packages/ai/src/job/AiJob.ts
@@ -12,7 +12,7 @@ import {
   PermanentJobError,
 } from "@workglow/job-queue";
 import { TaskInput, TaskOutput } from "@workglow/task-graph";
-import { getGlobalModelRepository } from "../model/ModelRegistry";
+import type { ModelConfig } from "../model/ModelSchema";
 import { getAiProviderRegistry } from "../provider/AiProviderRegistry";
 
 /**
@@ -21,7 +21,7 @@ import { getAiProviderRegistry } from "../provider/AiProviderRegistry";
 export interface AiJobInput<Input extends TaskInput = TaskInput> {
   taskType: string;
   aiProvider: string;
-  taskInput: Input & { model: string };
+  taskInput: Input & { model: ModelConfig };
 }
 
 /**
@@ -62,11 +62,7 @@ export class AiJob<
             `No run function found for task type ${input.taskType} and model provider ${input.aiProvider}`
           );
         }
-        const modelName = input.taskInput.model;
-        const model = await getGlobalModelRepository().findByName(modelName);
-        if (modelName && !model) {
-          throw new PermanentJobError(`Model ${modelName} not found`);
-        }
+        const model = input.taskInput.model;
         if (context.signal?.aborted) {
           throw new AbortSignalJobError("Job aborted");
         }
diff --git a/packages/ai/src/model/InMemoryModelRepository.ts b/packages/ai/src/model/InMemoryModelRepository.ts
index fbbf5afb..72fc78a9 100644
--- a/packages/ai/src/model/InMemoryModelRepository.ts
+++ b/packages/ai/src/model/InMemoryModelRepository.ts
@@ -6,7 +6,7 @@
 
 import { InMemoryTabularRepository } from "@workglow/storage";
 import { ModelRepository } from "./ModelRepository";
-import { ModelPrimaryKeyNames, ModelSchema } from "./ModelSchema";
+import { ModelPrimaryKeyNames, ModelRecordSchema } from "./ModelSchema";
 
 /**
  * In-memory implementation of a model repository.
@@ -14,6 +14,6 @@ import { ModelPrimaryKeyNames, ModelSchema } from "./ModelSchema";
  */
 export class InMemoryModelRepository extends ModelRepository {
   constructor() {
-    super(new InMemoryTabularRepository(ModelSchema, ModelPrimaryKeyNames));
+    super(new InMemoryTabularRepository(ModelRecordSchema, ModelPrimaryKeyNames));
   }
 }
diff --git a/packages/ai/src/model/ModelRepository.ts b/packages/ai/src/model/ModelRepository.ts
index 8469a246..8818273b 100644
--- a/packages/ai/src/model/ModelRepository.ts
+++ b/packages/ai/src/model/ModelRepository.ts
@@ -7,7 +7,7 @@
 import { type TabularRepository } from "@workglow/storage";
 import { EventEmitter, EventParameters } from "@workglow/util";
 
-import { ModelPrimaryKeyNames, ModelRecord, ModelSchema } from "./ModelSchema";
+import { ModelPrimaryKeyNames, ModelRecord, ModelRecordSchema } from "./ModelSchema";
 
 /**
  * Events that can be emitted by the ModelRepository
@@ -38,11 +38,11 @@ export class ModelRepository {
    * Repository for storing and managing Model instances
    */
   protected readonly modelTabularRepository: TabularRepository<
-    typeof ModelSchema,
+    typeof ModelRecordSchema,
     typeof ModelPrimaryKeyNames
   >;
   constructor(
-    modelTabularRepository: TabularRepository<typeof ModelSchema, typeof ModelPrimaryKeyNames>
+    modelTabularRepository: TabularRepository<typeof ModelRecordSchema, typeof ModelPrimaryKeyNames>
   ) {
     this.modelTabularRepository = modelTabularRepository;
   }
diff --git a/packages/ai/src/model/ModelSchema.ts b/packages/ai/src/model/ModelSchema.ts
index a65adb36..7df3fd6b 100644
--- a/packages/ai/src/model/ModelSchema.ts
+++ b/packages/ai/src/model/ModelSchema.ts
@@ -6,7 +6,14 @@
 
 import { DataPortSchemaObject, FromSchema } from "@workglow/util";
 
-export const ModelSchema = {
+/**
+ * A model configuration suitable for task/job inputs.
+ *
+ * @remarks
+ * This is intentionally less strict than {@link ModelRecord} so jobs can carry only the
+ * provider configuration required to execute, without requiring access to a model repository.
+ */
+export const ModelConfigSchema = {
   type: "object",
   properties: {
     model_id: { type: "string" },
@@ -17,10 +24,24 @@ export const ModelSchema = {
     providerConfig: { type: "object", default: {} },
     metadata: { type: "object", default: {} },
   },
+  required: ["provider", "providerConfig"],
+  format: "model",
+  additionalProperties: false,
+} as const satisfies DataPortSchemaObject;
+
+/**
+ * A fully-specified model record suitable for persistence in a repository.
+ */
+export const ModelRecordSchema = {
+  type: "object",
+  properties: {
+    ...ModelConfigSchema.properties,
+  },
   required: ["model_id", "tasks", "provider", "title", "description", "providerConfig", "metadata"],
   format: "model",
   additionalProperties: false,
 } as const satisfies DataPortSchemaObject;
 
-export type ModelRecord = FromSchema<typeof ModelSchema>;
+export type ModelConfig = FromSchema<typeof ModelConfigSchema>;
+export type ModelRecord = FromSchema<typeof ModelRecordSchema>;
 export const ModelPrimaryKeyNames = ["model_id"] as const;
diff --git a/packages/ai/src/provider/AiProviderRegistry.ts b/packages/ai/src/provider/AiProviderRegistry.ts
index 633ca407..04f30487 100644
--- a/packages/ai/src/provider/AiProviderRegistry.ts
+++ b/packages/ai/src/provider/AiProviderRegistry.ts
@@ -6,7 +6,7 @@
 
 import { TaskInput, TaskOutput } from "@workglow/task-graph";
 import { globalServiceRegistry, WORKER_MANAGER } from "@workglow/util";
-import { ModelRecord } from "../model/ModelSchema";
+import type { ModelConfig } from "../model/ModelSchema";
 
 /**
  * Type for the run function for the AiJob
@@ -14,7 +14,7 @@ import { ModelRecord } from "../model/ModelSchema";
 export type AiProviderRunFn<
   Input extends TaskInput = TaskInput,
   Output extends TaskOutput = TaskOutput,
-  Model extends ModelRecord = ModelRecord,
+  Model extends ModelConfig = ModelConfig,
 > = (
   input: Input,
   model: Model | undefined,
@@ -53,7 +53,7 @@ export class AiProviderRegistry {
   >(modelProvider: string, taskType: string) {
     const workerFn: AiProviderRunFn<Input, Output> = async (
       input: Input,
-      model: ModelRecord | undefined,
+      model: ModelConfig | undefined,
       update_progress: (progress: number, message?: string, details?: any) => void,
       signal?: AbortSignal
     ) => {
diff --git a/packages/ai/src/task/base/AiTask.ts b/packages/ai/src/task/base/AiTask.ts
index ec52f33c..1d447389 100644
--- a/packages/ai/src/task/base/AiTask.ts
+++ b/packages/ai/src/task/base/AiTask.ts
@@ -20,7 +20,7 @@ import { type JsonSchema } from "@workglow/util";
 
 import { AiJob, AiJobInput } from "../../job/AiJob";
 import { getGlobalModelRepository } from "../../model/ModelRegistry";
-import type { ModelRecord } from "../../model/ModelSchema";
+import type { ModelConfig, ModelRecord } from "../../model/ModelSchema";
 
 function schemaFormat(schema: JsonSchema): string | undefined {
   return typeof schema === "object" && schema !== null && "format" in schema
@@ -29,11 +29,11 @@ function schemaFormat(schema: JsonSchema): string | undefined {
 }
 
 export interface AiSingleTaskInput extends TaskInput {
-  model: string;
+  model: string | ModelConfig;
 }
 
 export interface AiArrayTaskInput extends TaskInput {
-  model: string | ModelRecord | (string | ModelRecord)[];
+  model: string | ModelConfig | (string | ModelConfig)[];
 }
 
 /**
@@ -53,8 +53,16 @@ export class AiTask<
    * @param config - Configuration object for the task
    */
   constructor(input: Input = {} as Input, config: Config = {} as Config) {
+    const modelLabel =
+      typeof input.model === "string"
+        ? input.model
+        : Array.isArray(input.model)
+          ? undefined
+          : typeof input.model === "object" && input.model
+            ? input.model.model_id || input.model.title || input.model.provider
+            : undefined;
     config.name ||= `${new.target.type || new.target.name}${
-      input.model ? " with model " + input.model : ""
+      modelLabel ? " with model " + modelLabel : ""
     }`;
     super(input, config);
   }
@@ -70,14 +78,14 @@ export class AiTask<
    * @returns The AiJobInput to submit to the queue
    */
   protected override async getJobInput(input: Input): Promise<AiJobInput<Input>> {
-    if (typeof input.model !== "string") {
-      console.error("AiTask: Model is not a string", input);
+    if (Array.isArray(input.model)) {
+      console.error("AiTask: Model is an array", input);
       throw new TaskConfigurationError(
-        "AiTask: Model is not a string, only create job for single model tasks"
+        "AiTask: Model is an array, only create job for single model tasks"
       );
     }
     const runtype = (this.constructor as any).runtype ?? (this.constructor as any).type;
-    const model = await this.getModelForInput(input as AiSingleTaskInput);
+    const model = await this.getModelConfigForInput(input as AiSingleTaskInput);
 
     // TODO: if the queue is not memory based, we need to convert to something that can structure clone to the queue
     // const registeredQueue = await this.resolveQueue(input);
@@ -86,10 +94,38 @@ export class AiTask<
     return {
       taskType: runtype,
       aiProvider: model.provider,
-      taskInput: input as Input & { model: string },
+      taskInput: { ...(input as any), model } as Input & { model: ModelConfig },
     };
   }
 
+  /**
+   * Resolves a model configuration for the given input.
+   *
+   * @remarks
+   * - If `input.model` is a string, it is resolved via the global model repository.
+   * - If `input.model` is already a config object, it is used directly.
+   */
+  protected async getModelConfigForInput(input: AiSingleTaskInput): Promise<ModelConfig> {
+    const modelValue = input.model;
+    if (!modelValue) throw new TaskConfigurationError("AiTask: No model found");
+    if (typeof modelValue === "string") {
+      const modelname = modelValue;
+      if (this.modelCache && this.modelCache.name === modelname) {
+        return this.modelCache.model;
+      }
+      const model = await getGlobalModelRepository().findByName(modelname);
+      if (!model) {
+        throw new TaskConfigurationError(`AiTask: No model ${modelname} found`);
+      }
+      this.modelCache = { name: modelname, model };
+      return model;
+    }
+    if (typeof modelValue === "object") {
+      return modelValue;
+    }
+    throw new TaskConfigurationError("AiTask: Invalid model value");
+  }
+
   /**
    * Creates a new Job instance for direct execution (without a queue).
    * @param input - The task input
@@ -116,6 +152,9 @@ export class AiTask<
   protected async getModelForInput(input: AiSingleTaskInput): Promise<ModelRecord> {
     const modelname = input.model;
     if (!modelname) throw new TaskConfigurationError("AiTask: No model name found");
+    if (typeof modelname !== "string") {
+      throw new TaskConfigurationError("AiTask: Model name is not a string");
+    }
     if (this.modelCache && this.modelCache.name === modelname) {
       return this.modelCache.model;
     }
@@ -132,6 +171,9 @@ export class AiTask<
       const model = await this.getModelForInput(input as AiSingleTaskInput);
       return model.provider;
     }
+    if (typeof input.model === "object" && input.model !== null && !Array.isArray(input.model)) {
+      return (input.model as ModelConfig).provider;
+    }
     return undefined;
   }
 
@@ -159,11 +201,24 @@ export class AiTask<
       for (const [key, propSchema] of modelTaskProperties) {
         let requestedModels = Array.isArray(input[key]) ? input[key] : [input[key]];
         for (const model of requestedModels) {
-          const foundModel = taskModels?.find((m) => m.model_id === model);
-          if (!foundModel) {
-            throw new TaskConfigurationError(
-              `AiTask: Missing model for '${key}' named '${model}' for task '${this.type}'`
-            );
+          if (typeof model === "string") {
+            const foundModel = taskModels?.find((m) => m.model_id === model);
+            if (!foundModel) {
+              throw new TaskConfigurationError(
+                `AiTask: Missing model for '${key}' named '${model}' for task '${this.type}'`
+              );
+            }
+          } else if (typeof model === "object" && model !== null) {
+            // Inline configs are accepted without requiring repository access.
+            // If 'tasks' is provided, do a best-effort compatibility check.
+            const tasks = (model as ModelConfig).tasks;
+            if (Array.isArray(tasks) && tasks.length > 0 && !tasks.includes(this.type)) {
+              throw new TaskConfigurationError(
+                `AiTask: Inline model for '${key}' is not compatible with task '${this.type}'`
+              );
+            }
+          } else {
+            throw new TaskConfigurationError(`AiTask: Invalid model for '${key}'`);
           }
         }
       }
@@ -177,9 +232,17 @@ export class AiTask<
       for (const [key, propSchema] of modelPlainProperties) {
         let requestedModels = Array.isArray(input[key]) ? input[key] : [input[key]];
         for (const model of requestedModels) {
-          const foundModel = await getGlobalModelRepository().findByName(model);
-          if (!foundModel) {
-            throw new TaskConfigurationError(`AiTask: Missing model for "${key}" named "${model}"`);
+          if (typeof model === "string") {
+            const foundModel = await getGlobalModelRepository().findByName(model);
+            if (!foundModel) {
+              throw new TaskConfigurationError(
+                `AiTask: Missing model for "${key}" named "${model}"`
+              );
+            }
+          } else if (typeof model === "object" && model !== null) {
+            // Inline configs are accepted without requiring repository access.
+          } else {
+            throw new TaskConfigurationError(`AiTask: Invalid model for "${key}"`);
           }
         }
       }
@@ -206,13 +269,21 @@ export class AiTask<
       const taskModels = await getGlobalModelRepository().findModelsByTask(this.type);
       for (const [key, propSchema] of modelTaskProperties) {
         let requestedModels = Array.isArray(input[key]) ? input[key] : [input[key]];
-        let usingModels = requestedModels.filter((model: string) =>
+        const requestedStrings = requestedModels.filter(
+          (m: unknown): m is string => typeof m === "string"
+        );
+        const requestedInline = requestedModels.filter(
+          (m: unknown): m is ModelConfig => typeof m === "object" && m !== null
+        );
+
+        const usingStrings = requestedStrings.filter((model: string) =>
           taskModels?.find((m) => m.model_id === model)
         );
 
+        const combined: (string | ModelConfig)[] = [...requestedInline, ...usingStrings];
+
         // we alter input to be the models that were found for this kind of input
-        usingModels = usingModels.length > 1 ? usingModels : usingModels[0];
-        (input as any)[key] = usingModels;
+        (input as any)[key] = combined.length > 1 ? combined : combined[0];
       }
     }
     return input;
diff --git a/packages/ai/src/task/base/AiTaskSchemas.ts b/packages/ai/src/task/base/AiTaskSchemas.ts
index 4229bbd5..67d2fcaf 100644
--- a/packages/ai/src/task/base/AiTaskSchemas.ts
+++ b/packages/ai/src/task/base/AiTaskSchemas.ts
@@ -11,7 +11,7 @@ import {
   FromSchemaOptions,
   JsonSchema,
 } from "@workglow/util";
-import { ModelSchema } from "../../model/ModelSchema";
+import { ModelConfigSchema } from "../../model/ModelSchema";
 
 export type TypedArray =
   | Float64Array
@@ -223,7 +223,7 @@ export function TypeModelByDetail<
     throw new Error("Invalid semantic value");
   }
   return {
-    ...ModelSchema,
+    ...ModelConfigSchema,
     ...options,
     format: semantic,
   } as const satisfies JsonSchema;
diff --git a/packages/ai/src/task/base/AiVisionTask.ts b/packages/ai/src/task/base/AiVisionTask.ts
index 59af1839..16fbb7b4 100644
--- a/packages/ai/src/task/base/AiVisionTask.ts
+++ b/packages/ai/src/task/base/AiVisionTask.ts
@@ -12,15 +12,15 @@ import { JobQueueTaskConfig, TaskInput, type TaskOutput } from "@workglow/task-g
 import { convertImageDataToUseableForm, ImageDataSupport } from "@workglow/util";
 
 import { AiJobInput } from "../../job/AiJob";
-import type { ModelRecord } from "../../model/ModelSchema";
+import type { ModelConfig } from "../../model/ModelSchema";
 import { AiTask } from "./AiTask";
 
 export interface AiVisionTaskSingleInput extends TaskInput {
-  model: string;
+  model: string | ModelConfig;
 }
 
 export interface AiVisionArrayTaskInput extends TaskInput {
-  model: string | ModelRecord | (string | ModelRecord)[];
+  model: string | ModelConfig | (string | ModelConfig)[];
 }
 
 /**
diff --git a/packages/test/src/binding/IndexedDbModelRepository.ts b/packages/test/src/binding/IndexedDbModelRepository.ts
index c411bc19..3c9acede 100644
--- a/packages/test/src/binding/IndexedDbModelRepository.ts
+++ b/packages/test/src/binding/IndexedDbModelRepository.ts
@@ -4,7 +4,7 @@
  * SPDX-License-Identifier: Apache-2.0
  */
 
-import { ModelPrimaryKeyNames, ModelRepository, ModelSchema } from "@workglow/ai";
+import { ModelPrimaryKeyNames, ModelRecordSchema, ModelRepository } from "@workglow/ai";
 import { IndexedDbTabularRepository } from "@workglow/storage";
 
 /**
@@ -13,6 +13,6 @@ import { IndexedDbTabularRepository } from "@workglow/storage";
  */
 export class IndexedDbModelRepository extends ModelRepository {
   constructor(tableModels: string = "models") {
-    super(new IndexedDbTabularRepository(tableModels, ModelSchema, ModelPrimaryKeyNames));
+    super(new IndexedDbTabularRepository(tableModels, ModelRecordSchema, ModelPrimaryKeyNames));
   }
 }
diff --git a/packages/test/src/binding/PostgresModelRepository.ts b/packages/test/src/binding/PostgresModelRepository.ts
index 756bffcd..667439ab 100644
--- a/packages/test/src/binding/PostgresModelRepository.ts
+++ b/packages/test/src/binding/PostgresModelRepository.ts
@@ -4,7 +4,7 @@
  * SPDX-License-Identifier: Apache-2.0
  */
 
-import { ModelPrimaryKeyNames, ModelRepository, ModelSchema } from "@workglow/ai";
+import { ModelPrimaryKeyNames, ModelRecordSchema, ModelRepository } from "@workglow/ai";
 import { PostgresTabularRepository } from "@workglow/storage";
 import { Pool } from "pg";
 
@@ -14,6 +14,6 @@ import { Pool } from "pg";
  */
 export class PostgresModelRepository extends ModelRepository {
   constructor(db: Pool, tableModels: string = "aimodel") {
-    super(new PostgresTabularRepository(db, tableModels, ModelSchema, ModelPrimaryKeyNames));
+    super(new PostgresTabularRepository(db, tableModels, ModelRecordSchema, ModelPrimaryKeyNames));
   }
 }
diff --git a/packages/test/src/binding/SqliteModelRepository.ts b/packages/test/src/binding/SqliteModelRepository.ts
index d9398719..ebf6351f 100644
--- a/packages/test/src/binding/SqliteModelRepository.ts
+++ b/packages/test/src/binding/SqliteModelRepository.ts
@@ -4,7 +4,7 @@
  * SPDX-License-Identifier: Apache-2.0
  */
 
-import { ModelPrimaryKeyNames, ModelRepository, ModelSchema } from "@workglow/ai";
+import { ModelPrimaryKeyNames, ModelRecordSchema, ModelRepository } from "@workglow/ai";
 import { SqliteTabularRepository } from "@workglow/storage";
 
 /**
@@ -13,6 +13,8 @@ import { SqliteTabularRepository } from "@workglow/storage";
  */
 export class SqliteModelRepository extends ModelRepository {
   constructor(dbOrPath: string, tableModels: string = "aimodel") {
-    super(new SqliteTabularRepository(dbOrPath, tableModels, ModelSchema, ModelPrimaryKeyNames));
+    super(
+      new SqliteTabularRepository(dbOrPath, tableModels, ModelRecordSchema, ModelPrimaryKeyNames)
+    );
   }
 }
diff --git a/packages/test/src/test/ai-provider/AiProviderRegistry.test.ts b/packages/test/src/test/ai-provider/AiProviderRegistry.test.ts
index 9ff9934c..61e140c7 100644
--- a/packages/test/src/test/ai-provider/AiProviderRegistry.test.ts
+++ b/packages/test/src/test/ai-provider/AiProviderRegistry.test.ts
@@ -9,7 +9,6 @@ import {
   AiJobInput,
   AiProviderRegistry,
   getAiProviderRegistry,
-  getGlobalModelRepository,
   setAiProviderRegistry,
 } from "@workglow/ai";
 import { JobQueueClient, JobQueueServer, RateLimiter } from "@workglow/job-queue";
@@ -157,7 +156,7 @@ describe("AiProviderRegistry", () => {
       });
 
       aiProviderRegistry.registerRunFn(TEST_PROVIDER, "text-generation", mockRunFn);
-      const model = await getGlobalModelRepository().addModel({
+      const model = {
         model_id: "test:test-model:v1",
         title: "test-model",
         description: "test-model",
@@ -168,7 +167,7 @@ describe("AiProviderRegistry", () => {
           modelPath: "test-model",
         },
         metadata: {},
-      });
+      };
 
       const controller = new AbortController();
       const job = new AiJob({
@@ -176,7 +175,7 @@ describe("AiProviderRegistry", () => {
         input: {
           aiProvider: TEST_PROVIDER,
           taskType: "text-generation",
-          taskInput: { text: "test", model: "test:test-model:v1" },
+          taskInput: { text: "test", model },
         },
       });