feat: Optimize langchain calls in batching mode (#15011)

Co-authored-by: कारतोफ्फेलस्क्रिप्ट™ <aditya@netroy.in>

packages/@n8n/nodes-langchain/nodes/agents/Agent/agents/ToolsAgent/description.ts

@@ -47,6 +47,30 @@ export const toolsAgentProperties: INodeProperties[] = [
 				description:
 					'Whether or not binary images should be automatically passed through to the agent as image type messages',
 			},
+			{
+				displayName: 'Batch Processing',
+				name: 'batching',
+				type: 'collection',
+				description: 'Batch processing options for rate limiting',
+				default: {},
+				options: [
+					{
+						displayName: 'Batch Size',
+						name: 'batchSize',
+						default: 1,
+						type: 'number',
+						description:
+							'How many items to process in parallel. This is useful for rate limiting, but will impact the ordering in the agents log output.',
+					},
+					{
+						displayName: 'Delay Between Batches',
+						name: 'delayBetweenBatches',
+						default: 0,
+						type: 'number',
+						description: 'Delay in milliseconds between batches. This is useful for rate limiting.',
+					},
+				],
+			},
 		],
 	},
 ];

packages/@n8n/nodes-langchain/nodes/agents/Agent/agents/ToolsAgent/execute.ts

@@ -11,7 +11,13 @@ import type { AgentAction, AgentFinish } from 'langchain/agents';
 import { AgentExecutor, createToolCallingAgent } from 'langchain/agents';
 import type { ToolsAgentAction } from 'langchain/dist/agents/tool_calling/output_parser';
 import { omit } from 'lodash';
-import { BINARY_ENCODING, jsonParse, NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
+import {
+	BINARY_ENCODING,
+	jsonParse,
+	NodeConnectionTypes,
+	NodeOperationError,
+	sleep,
+} from 'n8n-workflow';
 import type { IExecuteFunctions, INodeExecutionData } from 'n8n-workflow';
 import type { ZodObject } from 'zod';
 import { z } from 'zod';
@@ -406,12 +412,21 @@ export async function toolsAgentExecute(this: IExecuteFunctions): Promise<INodeE
 	const returnData: INodeExecutionData[] = [];
 	const items = this.getInputData();
 	const outputParser = await getOptionalOutputParser(this);
-	const tools = await getTools(this, outputParser);
-
-	for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
-		try {
-			const model = await getChatModel(this);
 	const memory = await getOptionalMemory(this);
+	const tools = await getTools(this, outputParser);
+	const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
+		batchSize: 1,
+		delayBetweenBatches: 0,
+	}) as {
+		batchSize: number;
+		delayBetweenBatches: number;
+	};
+
+	for (let i = 0; i < items.length; i += batchSize) {
+		const batch = items.slice(i, i + batchSize);
+		const batchPromises = batch.map(async (_item, batchItemIndex) => {
+			const itemIndex = i + batchItemIndex;
+			const model = await getChatModel(this);
 
 			const input = getPromptInputByType({
 				ctx: this,
@@ -461,7 +476,7 @@ export async function toolsAgentExecute(this: IExecuteFunctions): Promise<INodeE
 			});
 
 			// Invoke the executor with the given input and system message.
-			const response = await executor.invoke(
+			return await executor.invoke(
 				{
 					input,
 					system_message: options.systemMessage ?? SYSTEM_MESSAGE,
@@ -470,7 +485,22 @@ export async function toolsAgentExecute(this: IExecuteFunctions): Promise<INodeE
 				},
 				{ signal: this.getExecutionCancelSignal() },
 			);
+		});
+		const batchResults = await Promise.allSettled(batchPromises);
 
+		batchResults.forEach((result, index) => {
+			if (result.status === 'rejected') {
+				if (this.continueOnFail()) {
+					returnData.push({
+						json: { error: result.reason as string },
+						pairedItem: { item: index },
+					});
+					return;
+				} else {
+					throw new NodeOperationError(this.getNode(), result.reason);
+				}
+			}
+			const response = result.value;
 			// If memory and outputParser are connected, parse the output.
 			if (memory && outputParser) {
 				const parsedOutput = jsonParse<{ output: Record<string, unknown> }>(
@@ -492,15 +522,10 @@ export async function toolsAgentExecute(this: IExecuteFunctions): Promise<INodeE
 			};
 
 			returnData.push(itemResult);
-		} catch (error) {
-			if (this.continueOnFail()) {
-				returnData.push({
-					json: { error: error.message },
-					pairedItem: { item: itemIndex },
 		});
-				continue;
+
-			}
+		if (i + batchSize < items.length && delayBetweenBatches > 0) {
-			throw error;
+			await sleep(delayBetweenBatches);
 		}
 	}
 

packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/ChainLlm.node.ts

@@ -5,7 +5,7 @@ import type {
 	INodeType,
 	INodeTypeDescription,
 } from 'n8n-workflow';
-import { NodeApiError, NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
+import { NodeApiError, NodeConnectionTypes, NodeOperationError, sleep } from 'n8n-workflow';
 
 import { getPromptInputByType } from '@utils/helpers';
 import { getOptionalOutputParser } from '@utils/output_parsers/N8nOutputParser';
@@ -67,19 +67,28 @@ export class ChainLlm implements INodeType {
 		this.logger.debug('Executing Basic LLM Chain');
 		const items = this.getInputData();
 		const returnData: INodeExecutionData[] = [];
+		const { batchSize, delayBetweenBatches } = this.getNodeParameter('batching', 0, {
+			batchSize: 100,
+			delayBetweenBatches: 0,
+		}) as {
+			batchSize: number;
+			delayBetweenBatches: number;
+		};
+		// Get output parser if configured
+		const outputParser = await getOptionalOutputParser(this);
+
+		// Process items in batches
+		for (let i = 0; i < items.length; i += batchSize) {
+			const batch = items.slice(i, i + batchSize);
+			const batchPromises = batch.map(async (_item, batchItemIndex) => {
+				const itemIndex = i + batchItemIndex;
 
-		// Process each input item
-		for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
-			try {
 				// Get the language model
 				const llm = (await this.getInputConnectionData(
 					NodeConnectionTypes.AiLanguageModel,
 					0,
 				)) as BaseLanguageModel;
 
-				// Get output parser if configured
-				const outputParser = await getOptionalOutputParser(this);
-
 				// Get user prompt based on node version
 				let prompt: string;
 
@@ -106,26 +115,22 @@ export class ChainLlm implements INodeType {
 					[],
 				) as MessageTemplate[];
 
-				// Execute the chain
+				return (await executeChain({
-				const responses = await executeChain({
 					context: this,
 					itemIndex,
 					query: prompt,
 					llm,
 					outputParser,
 					messages,
+				})) as object[];
 			});
 
-				// If the node version is 1.6(and LLM is using `response_format: json_object`) or higher or an output parser is configured,
+			const batchResults = await Promise.allSettled(batchPromises);
-				//  we unwrap the response and return the object directly as JSON
+
-				const shouldUnwrapObjects = this.getNode().typeVersion >= 1.6 || !!outputParser;
+			batchResults.forEach((promiseResult, batchItemIndex) => {
-				// Process each response and add to return data
+				const itemIndex = i + batchItemIndex;
-				responses.forEach((response) => {
+				if (promiseResult.status === 'rejected') {
-					returnData.push({
+					const error = promiseResult.reason as Error;
-						json: formatResponse(response, shouldUnwrapObjects),
-					});
-				});
-			} catch (error) {
 					// Handle OpenAI specific rate limit errors
 					if (error instanceof NodeApiError && isOpenAiError(error.cause)) {
 						const openAiErrorCode: string | undefined = (error.cause as any).error?.code;
@@ -137,13 +142,26 @@ export class ChainLlm implements INodeType {
 						}
 					}
 
-				// Continue on failure if configured
 					if (this.continueOnFail()) {
-					returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
+						returnData.push({
-					continue;
+							json: { error: error.message },
+							pairedItem: { item: itemIndex },
+						});
+						return;
+					}
+					throw new NodeOperationError(this.getNode(), error);
 				}
 
-				throw error;
+				const responses = promiseResult.value;
+				responses.forEach((response: object) => {
+					returnData.push({
+						json: formatResponse(response, this.getNode().typeVersion >= 1.6 || !!outputParser),
+					});
+				});
+			});
+
+			if (i + batchSize < items.length && delayBetweenBatches > 0) {
+				await sleep(delayBetweenBatches);
 			}
 		}
 

packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/methods/config.ts

@@ -270,4 +270,29 @@ export const nodeProperties: INodeProperties[] = [
 			},
 		},
 	},
+	{
+		displayName: 'Batch Processing',
+		name: 'batching',
+		type: 'collection',
+		placeholder: 'Add Batch Processing Option',
+		description: 'Batch processing options for rate limiting',
+		default: {},
+		options: [
+			{
+				displayName: 'Batch Size',
+				name: 'batchSize',
+				default: 100,
+				type: 'number',
+				description:
+					'How many items to process in parallel. This is useful for rate limiting, but will impact the agents log output.',
+			},
+			{
+				displayName: 'Delay Between Batches',
+				name: 'delayBetweenBatches',
+				default: 1000,
+				type: 'number',
+				description: 'Delay in milliseconds between batches. This is useful for rate limiting.',
+			},
+		],
+	},
 ];

packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/test/ChainLlm.node.test.ts

@@ -3,7 +3,7 @@
 import { FakeChatModel } from '@langchain/core/utils/testing';
 import { mock } from 'jest-mock-extended';
 import type { IExecuteFunctions, INode } from 'n8n-workflow';
-import { NodeConnectionTypes } from 'n8n-workflow';
+import { NodeConnectionTypes, UnexpectedError } from 'n8n-workflow';
 
 import * as helperModule from '@utils/helpers';
 import * as outputParserModule from '@utils/output_parsers/N8nOutputParser';
@@ -12,6 +12,11 @@ import { ChainLlm } from '../ChainLlm.node';
 import * as executeChainModule from '../methods/chainExecutor';
 import * as responseFormatterModule from '../methods/responseFormatter';
 
+jest.mock('n8n-workflow', () => ({
+	...jest.requireActual('n8n-workflow'),
+	sleep: jest.fn(),
+}));
+
 jest.mock('@utils/helpers', () => ({
 	getPromptInputByType: jest.fn(),
 }));
@@ -25,12 +30,7 @@ jest.mock('../methods/chainExecutor', () => ({
 }));
 
 jest.mock('../methods/responseFormatter', () => ({
-	formatResponse: jest.fn().mockImplementation((response) => {
+	formatResponse: jest.fn(),
-		if (typeof response === 'string') {
-			return { text: response.trim() };
-		}
-		return response;
-	}),
 }));
 
 describe('ChainLlm Node', () => {
@@ -38,6 +38,8 @@ describe('ChainLlm Node', () => {
 	let mockExecuteFunction: jest.Mocked<IExecuteFunctions>;
 
 	beforeEach(() => {
+		jest.resetAllMocks();
+
 		node = new ChainLlm();
 		mockExecuteFunction = mock<IExecuteFunctions>();
 
@@ -63,7 +65,12 @@ describe('ChainLlm Node', () => {
 		const fakeLLM = new FakeChatModel({});
 		mockExecuteFunction.getInputConnectionData.mockResolvedValue(fakeLLM);
 
-		jest.clearAllMocks();
+		(responseFormatterModule.formatResponse as jest.Mock).mockImplementation((response) => {
+			if (typeof response === 'string') {
+				return { text: response.trim() };
+			}
+			return response;
+		});
 	});
 
 	describe('description', () => {
@@ -164,15 +171,14 @@ describe('ChainLlm Node', () => {
 		});
 
 		it('should continue on failure when configured', async () => {
+			mockExecuteFunction.continueOnFail.mockReturnValue(true);
 			(helperModule.getPromptInputByType as jest.Mock).mockReturnValue('Test prompt');
 
-			const error = new Error('Test error');
+			(executeChainModule.executeChain as jest.Mock).mockRejectedValueOnce(
-			(executeChainModule.executeChain as jest.Mock).mockRejectedValue(error);
+				new UnexpectedError('Test error'),
-
+			);
-			mockExecuteFunction.continueOnFail.mockReturnValue(true);
 
 			const result = await node.execute.call(mockExecuteFunction);
-
 			expect(result).toEqual([[{ json: { error: 'Test error' }, pairedItem: { item: 0 } }]]);
 		});
 

packages/@n8n/nodes-langchain/nodes/chains/ChainRetrievalQA/ChainRetrievalQa.node.ts

@@ -8,7 +8,7 @@ import {
 import type { BaseRetriever } from '@langchain/core/retrievers';
 import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';
 import { createRetrievalChain } from 'langchain/chains/retrieval';
-import { NodeConnectionTypes, NodeOperationError, parseErrorMetadata } from 'n8n-workflow';
+import { NodeConnectionTypes, NodeOperationError, parseErrorMetadata, sleep } from 'n8n-workflow';
 import {
 	type INodeProperties,
 	type IExecuteFunctions,
@@ -177,6 +177,31 @@ export class ChainRetrievalQa implements INodeType {
 							},
 						},
 					},
+					{
+						displayName: 'Batch Processing',
+						name: 'batching',
+						type: 'collection',
+						description: 'Batch processing options for rate limiting',
+						default: {},
+						options: [
+							{
+								displayName: 'Batch Size',
+								name: 'batchSize',
+								default: 100,
+								type: 'number',
+								description:
+									'How many items to process in parallel. This is useful for rate limiting.',
+							},
+							{
+								displayName: 'Delay Between Batches',
+								name: 'delayBetweenBatches',
+								default: 0,
+								type: 'number',
+								description:
+									'Delay in milliseconds between batches. This is useful for rate limiting.',
+							},
+						],
+					},
 				],
 			},
 		],
@@ -186,11 +211,20 @@ export class ChainRetrievalQa implements INodeType {
 		this.logger.debug('Executing Retrieval QA Chain');
 
 		const items = this.getInputData();
+		const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
+			batchSize: 100,
+			delayBetweenBatches: 0,
+		}) as {
+			batchSize: number;
+			delayBetweenBatches: number;
+		};
 		const returnData: INodeExecutionData[] = [];
 
-		// Run for each item
+		for (let i = 0; i < items.length; i += batchSize) {
-		for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
+			const batch = items.slice(i, i + batchSize);
-			try {
+			const batchPromises = batch.map(async (_item, batchIndex) => {
+				const itemIndex = i + batchIndex;
+
 				const model = (await this.getInputConnectionData(
 					NodeConnectionTypes.AiLanguageModel,
 					0,
@@ -266,32 +300,47 @@ export class ChainRetrievalQa implements INodeType {
 
 				// Execute the chain with tracing config
 				const tracingConfig = getTracingConfig(this);
-				const response = await retrievalChain
+
+				const result = await retrievalChain
 					.withConfig(tracingConfig)
 					.invoke({ input: query }, { signal: this.getExecutionCancelSignal() });
 
-				// Get the answer from the response
+				return result;
-				const answer: string = response.answer;
+			});
+
+			const batchResults = await Promise.allSettled(batchPromises);
+
+			batchResults.forEach((response, index) => {
+				if (response.status === 'rejected') {
+					const error = response.reason;
+					if (this.continueOnFail()) {
+						const metadata = parseErrorMetadata(error);
+						returnData.push({
+							json: { error: error.message },
+							pairedItem: { item: index },
+							metadata,
+						});
+						return;
+					} else {
+						throw error;
+					}
+				}
+				const output = response.value;
+				const answer: string = output.answer;
 				if (this.getNode().typeVersion >= 1.5) {
 					returnData.push({ json: { response: answer } });
 				} else {
 					// Legacy format for versions 1.4 and below is { text: string }
 					returnData.push({ json: { response: { text: answer } } });
 				}
-			} catch (error) {
-				if (this.continueOnFail()) {
-					const metadata = parseErrorMetadata(error);
-					returnData.push({
-						json: { error: error.message },
-						pairedItem: { item: itemIndex },
-						metadata,
 			});
-					continue;
+
+			// Add delay between batches if not the last batch
+			if (i + batchSize < items.length && delayBetweenBatches > 0) {
+				await sleep(delayBetweenBatches);
+			}
 		}
 
-				throw error;
-			}
-		}
 		return [returnData];
 	}
 }

packages/@n8n/nodes-langchain/nodes/chains/ChainRetrievalQA/test/ChainRetrievalQa.node.test.ts

@@ -8,6 +8,11 @@ import { NodeConnectionTypes, NodeOperationError, UnexpectedError } from 'n8n-wo
 
 import { ChainRetrievalQa } from '../ChainRetrievalQa.node';
 
+jest.mock('n8n-workflow', () => ({
+	...jest.requireActual('n8n-workflow'),
+	sleep: jest.fn(),
+}));
+
 const createExecuteFunctionsMock = (
 	parameters: IDataObject,
 	fakeLlm: BaseLanguageModel,
@@ -80,7 +85,12 @@ describe('ChainRetrievalQa', () => {
 			const params = {
 				promptType: 'define',
 				text: 'What is the capital of France?',
-				options: {},
+				options: {
+					batching: {
+						batchSize: 1,
+						delayBetweenBatches: 100,
+					},
+				},
 			};
 
 			const result = await node.execute.call(
@@ -114,7 +124,12 @@ describe('ChainRetrievalQa', () => {
 			const params = {
 				promptType: 'define',
 				text: 'What is the capital of France?',
-				options: {},
+				options: {
+					batching: {
+						batchSize: 1,
+						delayBetweenBatches: 100,
+					},
+				},
 			};
 
 			const result = await node.execute.call(
@@ -158,6 +173,10 @@ describe('ChainRetrievalQa', () => {
 				text: 'What is the capital of France?',
 				options: {
 					systemPromptTemplate: customSystemPrompt,
+					batching: {
+						batchSize: 1,
+						delayBetweenBatches: 100,
+					},
 				},
 			};
 
@@ -185,7 +204,12 @@ describe('ChainRetrievalQa', () => {
 			const params = {
 				promptType: 'define',
 				text: undefined, // undefined query
-				options: {},
+				options: {
+					batching: {
+						batchSize: 1,
+						delayBetweenBatches: 100,
+					},
+				},
 			};
 
 			await expect(
@@ -211,7 +235,12 @@ describe('ChainRetrievalQa', () => {
 			const params = {
 				promptType: 'define',
 				text: 'What is the capital of France?',
-				options: {},
+				options: {
+					batching: {
+						batchSize: 1,
+						delayBetweenBatches: 100,
+					},
+				},
 			};
 
 			// Override continueOnFail to return true

packages/@n8n/nodes-langchain/nodes/chains/ChainSummarization/V2/ChainSummarizationV2.node.ts

@@ -1,5 +1,6 @@
 import type { Document } from '@langchain/core/documents';
 import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+import type { ChainValues } from '@langchain/core/utils/types';
 import type { TextSplitter } from '@langchain/textsplitters';
 import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
 import { loadSummarizationChain } from 'langchain/chains';
@@ -12,7 +13,7 @@ import type {
 	IDataObject,
 	INodeInputConfiguration,
 } from 'n8n-workflow';
-import { NodeConnectionTypes } from 'n8n-workflow';
+import { NodeConnectionTypes, sleep } from 'n8n-workflow';
 
 import { N8nBinaryLoader } from '@utils/N8nBinaryLoader';
 import { N8nJsonLoader } from '@utils/N8nJsonLoader';
@@ -306,6 +307,31 @@ export class ChainSummarizationV2 implements INodeType {
 								},
 							],
 						},
+						{
+							displayName: 'Batch Processing',
+							name: 'batching',
+							type: 'collection',
+							description: 'Batch processing options for rate limiting',
+							default: {},
+							options: [
+								{
+									displayName: 'Batch Size',
+									name: 'batchSize',
+									default: 100,
+									type: 'number',
+									description:
+										'How many items to process in parallel. This is useful for rate limiting.',
+								},
+								{
+									displayName: 'Delay Between Batches',
+									name: 'delayBetweenBatches',
+									default: 0,
+									type: 'number',
+									description:
+										'Delay in milliseconds between batches. This is useful for rate limiting.',
+								},
+							],
+						},
 					],
 				},
 			],
@@ -324,9 +350,19 @@ export class ChainSummarizationV2 implements INodeType {
 
 		const items = this.getInputData();
 		const returnData: INodeExecutionData[] = [];
+		const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
+			batchSize: 100,
+			delayBetweenBatches: 0,
+		}) as {
+			batchSize: number;
+			delayBetweenBatches: number;
+		};
+
+		for (let i = 0; i < items.length; i += batchSize) {
+			const batch = items.slice(i, i + batchSize);
+			const batchPromises = batch.map(async (_item, batchIndex) => {
+				const itemIndex = i + batchIndex;
 
-		for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
-			try {
 				const model = (await this.getInputConnectionData(
 					NodeConnectionTypes.AiLanguageModel,
 					0,
@@ -353,6 +389,7 @@ export class ChainSummarizationV2 implements INodeType {
 				const item = items[itemIndex];
 
 				let processedDocuments: Document[];
+				let output: ChainValues = {};
 
 				// Use dedicated document loader input to load documents
 				if (operationMode === 'documentLoader') {
@@ -368,11 +405,9 @@ export class ChainSummarizationV2 implements INodeType {
 						? await documentInput.processItem(item, itemIndex)
 						: documentInput;
 
-					const response = await chain.withConfig(getTracingConfig(this)).invoke({
+					output = await chain.withConfig(getTracingConfig(this)).invoke({
 						input_documents: processedDocuments,
 					});
-
-					returnData.push({ json: { response } });
 				}
 
 				// Take the input and use binary or json loader
@@ -412,22 +447,38 @@ export class ChainSummarizationV2 implements INodeType {
 					}
 
 					const processedItem = await processor.processItem(item, itemIndex);
-					const response = await chain.invoke(
+					output = await chain.invoke(
 						{
 							input_documents: processedItem,
 						},
 						{ signal: this.getExecutionCancelSignal() },
 					);
-					returnData.push({ json: { response } });
-				}
-			} catch (error) {
-				if (this.continueOnFail()) {
-					returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
-					continue;
 				}
+				return output;
+			});
 
+			const batchResults = await Promise.allSettled(batchPromises);
+			batchResults.forEach((response, index) => {
+				if (response.status === 'rejected') {
+					const error = response.reason as Error;
+					if (this.continueOnFail()) {
+						returnData.push({
+							json: { error: error.message },
+							pairedItem: { item: i + index },
+						});
+					} else {
 						throw error;
 					}
+				} else {
+					const output = response.value;
+					returnData.push({ json: { output } });
+				}
+			});
+
+			// Add delay between batches if not the last batch
+			if (i + batchSize < items.length && delayBetweenBatches > 0) {
+				await sleep(delayBetweenBatches);
+			}
 		}
 
 		return [returnData];

packages/@n8n/nodes-langchain/nodes/chains/InformationExtractor/InformationExtractor.node.ts

@@ -3,7 +3,7 @@ import { HumanMessage } from '@langchain/core/messages';
 import { ChatPromptTemplate, SystemMessagePromptTemplate } from '@langchain/core/prompts';
 import type { JSONSchema7 } from 'json-schema';
 import { OutputFixingParser, StructuredOutputParser } from 'langchain/output_parsers';
-import { jsonParse, NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
+import { jsonParse, NodeConnectionTypes, NodeOperationError, sleep } from 'n8n-workflow';
 import type {
 	INodeType,
 	INodeTypeDescription,
@@ -213,6 +213,31 @@ export class InformationExtractor implements INodeType {
 							rows: 6,
 						},
 					},
+					{
+						displayName: 'Batch Processing',
+						name: 'batching',
+						type: 'collection',
+						description: 'Batch processing options for rate limiting',
+						default: {},
+						options: [
+							{
+								displayName: 'Batch Size',
+								name: 'batchSize',
+								default: 100,
+								type: 'number',
+								description:
+									'How many items to process in parallel. This is useful for rate limiting, but will impact the agents log output.',
+							},
+							{
+								displayName: 'Delay Between Batches',
+								name: 'delayBetweenBatches',
+								default: 0,
+								type: 'number',
+								description:
+									'Delay in milliseconds between batches. This is useful for rate limiting.',
+							},
+						],
+					},
 				],
 			},
 		],
@@ -220,6 +245,13 @@ export class InformationExtractor implements INodeType {
 
 	async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
 		const items = this.getInputData();
+		const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
+			batchSize: 100,
+			delayBetweenBatches: 0,
+		}) as {
+			batchSize: number;
+			delayBetweenBatches: number;
+		};
 
 		const llm = (await this.getInputConnectionData(
 			NodeConnectionTypes.AiLanguageModel,
@@ -265,7 +297,13 @@ export class InformationExtractor implements INodeType {
 		}
 
 		const resultData: INodeExecutionData[] = [];
-		for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
+
+		for (let i = 0; i < items.length; i += batchSize) {
+			const batch = items.slice(i, i + batchSize);
+
+			const batchPromises = batch.map(async (_item, batchItemIndex) => {
+				const itemIndex = i + batchItemIndex;
+
 				const input = this.getNodeParameter('text', itemIndex) as string;
 				const inputPrompt = new HumanMessage(input);
 
@@ -275,7 +313,7 @@ export class InformationExtractor implements INodeType {
 
 				const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(
 					`${options.systemPromptTemplate ?? SYSTEM_PROMPT_TEMPLATE}
-{format_instructions}`,
+	{format_instructions}`,
 				);
 
 				const messages = [
@@ -287,16 +325,30 @@ export class InformationExtractor implements INodeType {
 				const prompt = ChatPromptTemplate.fromMessages(messages);
 				const chain = prompt.pipe(llm).pipe(parser).withConfig(getTracingConfig(this));
 
-			try {
+				return await chain.invoke(messages);
-				const output = await chain.invoke(messages);
+			});
-				resultData.push({ json: { output } });
+			const batchResults = await Promise.allSettled(batchPromises);
-			} catch (error) {
-				if (this.continueOnFail()) {
-					resultData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
-					continue;
-				}
 
-				throw error;
+			batchResults.forEach((response, index) => {
+				if (response.status === 'rejected') {
+					const error = response.reason as Error;
+					if (this.continueOnFail()) {
+						resultData.push({
+							json: { error: response.reason as string },
+							pairedItem: { item: i + index },
+						});
+						return;
+					} else {
+						throw new NodeOperationError(this.getNode(), error.message);
+					}
+				}
+				const output = response.value;
+				resultData.push({ json: { output } });
+			});
+
+			// Add delay between batches if not the last batch
+			if (i + batchSize < items.length && delayBetweenBatches > 0) {
+				await sleep(delayBetweenBatches);
 			}
 		}
 

packages/@n8n/nodes-langchain/nodes/chains/InformationExtractor/test/InformationExtraction.node.test.ts

@@ -7,6 +7,11 @@ import { makeZodSchemaFromAttributes } from '../helpers';
 import { InformationExtractor } from '../InformationExtractor.node';
 import type { AttributeDefinition } from '../types';
 
+jest.mock('n8n-workflow', () => ({
+	...jest.requireActual('n8n-workflow'),
+	sleep: jest.fn(),
+}));
+
 const mockPersonAttributes: AttributeDefinition[] = [
 	{
 		name: 'name',
@@ -91,7 +96,12 @@ describe('InformationExtractor', () => {
 						attributes: {
 							attributes: mockPersonAttributes,
 						},
-						options: {},
+						options: {
+							batching: {
+								batchSize: 1,
+								delayBetweenBatches: 100,
+							},
+						},
 						schemaType: 'fromAttributes',
 					},
 					new FakeLLM({ response: formatFakeLlmResponse({ name: 'John', age: 30 }) }),
@@ -111,7 +121,12 @@ describe('InformationExtractor', () => {
 						attributes: {
 							attributes: mockPersonAttributes,
 						},
-						options: {},
+						options: {
+							batching: {
+								batchSize: 1,
+								delayBetweenBatches: 100,
+							},
+						},
 						schemaType: 'fromAttributes',
 					},
 					new FakeLLM({ response: formatFakeLlmResponse({ name: 'John' }) }),
@@ -132,7 +147,12 @@ describe('InformationExtractor', () => {
 							attributes: {
 								attributes: mockPersonAttributesRequired,
 							},
-							options: {},
+							options: {
+								batching: {
+									batchSize: 1,
+									delayBetweenBatches: 100,
+								},
+							},
 							schemaType: 'fromAttributes',
 						},
 						new FakeLLM({ response: formatFakeLlmResponse({ name: 'John' }) }),
@@ -154,7 +174,12 @@ describe('InformationExtractor', () => {
 							attributes: {
 								attributes: mockPersonAttributes,
 							},
-							options: {},
+							options: {
+								batching: {
+									batchSize: 1,
+									delayBetweenBatches: 100,
+								},
+							},
 							schemaType: 'fromAttributes',
 						},
 						new FakeLLM({ response: formatFakeLlmResponse({ name: 'John', age: '30' }) }),
@@ -175,7 +200,12 @@ describe('InformationExtractor', () => {
 						attributes: {
 							attributes: mockPersonAttributesRequired,
 						},
-						options: {},
+						options: {
+							batching: {
+								batchSize: 1,
+								delayBetweenBatches: 100,
+							},
+						},
 						schemaType: 'fromAttributes',
 					},
 					new FakeListChatModel({
@@ -200,7 +230,12 @@ describe('InformationExtractor', () => {
 						attributes: {
 							attributes: mockPersonAttributesRequired,
 						},
-						options: {},
+						options: {
+							batching: {
+								batchSize: 1,
+								delayBetweenBatches: 100,
+							},
+						},
 						schemaType: 'fromAttributes',
 					},
 					new FakeListChatModel({

packages/@n8n/nodes-langchain/nodes/chains/SentimentAnalysis/SentimentAnalysis.node.ts

@@ -2,7 +2,7 @@ import type { BaseLanguageModel } from '@langchain/core/language_models/base';
 import { HumanMessage } from '@langchain/core/messages';
 import { SystemMessagePromptTemplate, ChatPromptTemplate } from '@langchain/core/prompts';
 import { OutputFixingParser, StructuredOutputParser } from 'langchain/output_parsers';
-import { NodeConnectionTypes, NodeOperationError } from 'n8n-workflow';
+import { NodeConnectionTypes, NodeOperationError, sleep } from 'n8n-workflow';
 import type {
 	IDataObject,
 	IExecuteFunctions,
@@ -131,6 +131,31 @@ export class SentimentAnalysis implements INodeType {
 						description:
 							'Whether to enable auto-fixing (may trigger an additional LLM call if output is broken)',
 					},
+					{
+						displayName: 'Batch Processing',
+						name: 'batching',
+						type: 'collection',
+						description: 'Batch processing options for rate limiting',
+						default: {},
+						options: [
+							{
+								displayName: 'Batch Size',
+								name: 'batchSize',
+								default: 100,
+								type: 'number',
+								description:
+									'How many items to process in parallel. This is useful for rate limiting.',
+							},
+							{
+								displayName: 'Delay Between Batches',
+								name: 'delayBetweenBatches',
+								default: 0,
+								type: 'number',
+								description:
+									'Delay in milliseconds between batches. This is useful for rate limiting.',
+							},
+						],
+					},
 				],
 			},
 		],
@@ -145,12 +170,21 @@ export class SentimentAnalysis implements INodeType {
 		)) as BaseLanguageModel;
 
 		const returnData: INodeExecutionData[][] = [];
+		const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
+			batchSize: 100,
+			delayBetweenBatches: 0,
+		}) as {
+			batchSize: number;
+			delayBetweenBatches: number;
+		};
 
-		for (let i = 0; i < items.length; i++) {
+		for (let i = 0; i < items.length; i += batchSize) {
-			try {
+			const batch = items.slice(i, i + batchSize);
+			const batchPromises = batch.map(async (_item, batchItemIndex) => {
+				const itemIndex = i + batchItemIndex;
 				const sentimentCategories = this.getNodeParameter(
 					'options.categories',
-					i,
+					itemIndex,
 					DEFAULT_CATEGORIES,
 				) as string;
 
@@ -160,9 +194,13 @@ export class SentimentAnalysis implements INodeType {
 					.filter(Boolean);
 
 				if (categories.length === 0) {
-					throw new NodeOperationError(this.getNode(), 'No sentiment categories provided', {
+					return {
-						itemIndex: i,
+						result: null,
-					});
+						itemIndex,
+						error: new NodeOperationError(this.getNode(), 'No sentiment categories provided', {
+							itemIndex,
+						}),
+					};
 				}
 
 				// Initialize returnData with empty arrays for each category
@@ -170,7 +208,7 @@ export class SentimentAnalysis implements INodeType {
 					returnData.push(...Array.from({ length: categories.length }, () => []));
 				}
 
-				const options = this.getNodeParameter('options', i, {}) as {
+				const options = this.getNodeParameter('options', itemIndex, {}) as {
 					systemPromptTemplate?: string;
 					includeDetailedResults?: boolean;
 					enableAutoFixing?: boolean;
@@ -197,7 +235,7 @@ export class SentimentAnalysis implements INodeType {
 			{format_instructions}`,
 				);
 
-				const input = this.getNodeParameter('inputText', i) as string;
+				const input = this.getNodeParameter('inputText', itemIndex) as string;
 				const inputPrompt = new HumanMessage(input);
 				const messages = [
 					await systemPromptTemplate.format({
@@ -217,7 +255,7 @@ export class SentimentAnalysis implements INodeType {
 					);
 
 					if (sentimentIndex !== -1) {
-						const resultItem = { ...items[i] };
+						const resultItem = { ...items[itemIndex] };
 						const sentimentAnalysis: IDataObject = {
 							category: output.sentiment,
 						};
@@ -229,28 +267,60 @@ export class SentimentAnalysis implements INodeType {
 							...resultItem.json,
 							sentimentAnalysis,
 						};
-						returnData[sentimentIndex].push(resultItem);
+
+						return {
+							result: {
+								resultItem,
+								sentimentIndex,
+							},
+							itemIndex,
+						};
 					}
+
+					return {
+						result: {},
+						itemIndex,
+					};
 				} catch (error) {
-					throw new NodeOperationError(
+					return {
+						result: null,
+						itemIndex,
+						error: new NodeOperationError(
 							this.getNode(),
 							'Error during parsing of LLM output, please check your LLM model and configuration',
 							{
-							itemIndex: i,
+								itemIndex,
 							},
-					);
+						),
+					};
 				}
-			} catch (error) {
+			});
+			const batchResults = await Promise.all(batchPromises);
+
+			batchResults.forEach(({ result, itemIndex, error }) => {
+				if (error) {
 					if (this.continueOnFail()) {
 						const executionErrorData = this.helpers.constructExecutionMetaData(
 							this.helpers.returnJsonArray({ error: error.message }),
-						{ itemData: { item: i } },
+							{ itemData: { item: itemIndex } },
 						);
+
 						returnData[0].push(...executionErrorData);
-					continue;
+						return;
-				}
+					} else {
 						throw error;
 					}
+				} else if (result.resultItem && result.sentimentIndex) {
+					const sentimentIndex = result.sentimentIndex;
+					const resultItem = result.resultItem;
+					returnData[sentimentIndex].push(resultItem);
+				}
+			});
+
+			// Add delay between batches if not the last batch
+			if (i + batchSize < items.length && delayBetweenBatches > 0) {
+				await sleep(delayBetweenBatches);
+			}
 		}
 		return returnData;
 	}

packages/@n8n/nodes-langchain/nodes/chains/TextClassifier/TextClassifier.node.ts

@@ -2,7 +2,7 @@ import type { BaseLanguageModel } from '@langchain/core/language_models/base';
 import { HumanMessage } from '@langchain/core/messages';
 import { SystemMessagePromptTemplate, ChatPromptTemplate } from '@langchain/core/prompts';
 import { OutputFixingParser, StructuredOutputParser } from 'langchain/output_parsers';
-import { NodeOperationError, NodeConnectionTypes } from 'n8n-workflow';
+import { NodeOperationError, NodeConnectionTypes, sleep } from 'n8n-workflow';
 import type {
 	IDataObject,
 	IExecuteFunctions,
@@ -158,6 +158,31 @@ export class TextClassifier implements INodeType {
 						description:
 							'Whether to enable auto-fixing (may trigger an additional LLM call if output is broken)',
 					},
+					{
+						displayName: 'Batch Processing',
+						name: 'batching',
+						type: 'collection',
+						description: 'Batch processing options for rate limiting',
+						default: {},
+						options: [
+							{
+								displayName: 'Batch Size',
+								name: 'batchSize',
+								default: 100,
+								type: 'number',
+								description:
+									'How many items to process in parallel. This is useful for rate limiting.',
+							},
+							{
+								displayName: 'Delay Between Batches',
+								name: 'delayBetweenBatches',
+								default: 0,
+								type: 'number',
+								description:
+									'Delay in milliseconds between batches. This is useful for rate limiting.',
+							},
+						],
+					},
 				],
 			},
 		],
@@ -165,6 +190,13 @@ export class TextClassifier implements INodeType {
 
 	async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
 		const items = this.getInputData();
+		const { batchSize, delayBetweenBatches } = this.getNodeParameter('options.batching', 0, {
+			batchSize: 100,
+			delayBetweenBatches: 0,
+		}) as {
+			batchSize: number;
+			delayBetweenBatches: number;
+		};
 
 		const llm = (await this.getInputConnectionData(
 			NodeConnectionTypes.AiLanguageModel,
@@ -223,25 +255,21 @@ export class TextClassifier implements INodeType {
 			{ length: categories.length + (fallback === 'other' ? 1 : 0) },
 			(_) => [],
 		);
-		for (let itemIdx = 0; itemIdx < items.length; itemIdx++) {
+
+		for (let i = 0; i < items.length; i += batchSize) {
+			const batch = items.slice(i, i + batchSize);
+			const batchPromises = batch.map(async (_item, batchItemIndex) => {
+				const itemIdx = i + batchItemIndex;
 				const item = items[itemIdx];
 				item.pairedItem = { item: itemIdx };
 				const input = this.getNodeParameter('inputText', itemIdx) as string;
 
 				if (input === undefined || input === null) {
-				if (this.continueOnFail()) {
-					returnData[0].push({
-						json: { error: 'Text to classify is not defined' },
-						pairedItem: { item: itemIdx },
-					});
-					continue;
-				} else {
 					throw new NodeOperationError(
 						this.getNode(),
 						`Text to classify for item ${itemIdx} is not defined`,
 					);
 				}
-			}
 
 				const inputPrompt = new HumanMessage(input);
 
@@ -252,9 +280,9 @@ export class TextClassifier implements INodeType {
 				) as string;
 				const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(
 					`${systemPromptTemplateOpt ?? SYSTEM_PROMPT_TEMPLATE}
-{format_instructions}
+	{format_instructions}
-${multiClassPrompt}
+	${multiClassPrompt}
-${fallbackPrompt}`,
+	${fallbackPrompt}`,
 				);
 
 				const messages = [
@@ -267,24 +295,39 @@ ${fallbackPrompt}`,
 				const prompt = ChatPromptTemplate.fromMessages(messages);
 				const chain = prompt.pipe(llm).pipe(parser).withConfig(getTracingConfig(this));
 
-			try {
+				return await chain.invoke(messages);
-				const output = await chain.invoke(messages);
+			});
+
+			const batchResults = await Promise.allSettled(batchPromises);
+
+			batchResults.forEach((response, batchItemIndex) => {
+				const index = i + batchItemIndex;
+				if (response.status === 'rejected') {
+					const error = response.reason as Error;
+					if (this.continueOnFail()) {
+						returnData[0].push({
+							json: { error: error.message },
+							pairedItem: { item: index },
+						});
+						return;
+					} else {
+						throw new NodeOperationError(this.getNode(), error.message);
+					}
+				} else {
+					const output = response.value;
+					const item = items[index];
 
 					categories.forEach((cat, idx) => {
 						if (output[cat.category]) returnData[idx].push(item);
 					});
+
 					if (fallback === 'other' && output.fallback) returnData[returnData.length - 1].push(item);
-			} catch (error) {
+				}
-				if (this.continueOnFail()) {
-					returnData[0].push({
-						json: { error: error.message },
-						pairedItem: { item: itemIdx },
 			});
 
-					continue;
+			// Add delay between batches if not the last batch
-				}
+			if (i + batchSize < items.length && delayBetweenBatches > 0) {
-
+				await sleep(delayBetweenBatches);
-				throw error;
 			}
 		}