Pricing
Pay per event
The AI Travel Agent is designed to assist users in planning their perfect trip through a simple conversational interface. With this agent, users can input specific criteria for their desired destination, such as neighborhood and budget, and receive tailored suggestions.
0.0 (0)
Pricing
Pay per event
1
3
3
Last modified
5 months ago
# configurations.idea.vscode
# crawlee and apify storage foldersapify_storagecrawlee_storagestorage
# installed filesnode_modules
# git folder.git
# dist folderdistroot = true
[*]indent_style = spaceindent_size = 2charset = utf-8trim_trailing_whitespace = trueinsert_final_newline = trueend_of_line = lfUSER_APIFY_TOKEN="apify_api_1234"ACTOR_TEST_PAY_PER_EVENT=trueLANGSMITH_TRACING=falseLANGSMITH_ENDPOINT="https://api.smith.langchain.com"LANGSMITH_API_KEY="lsv2_pt_1234"LANGSMITH_PROJECT="langsmith-cool-project-name"OPENAI_API_KEY="sk-proj-1234"{ "root": false, "env": { "browser": true, "es2020": true, "node": true }, "extends": [ "@apify/eslint-config-ts" ], "parserOptions": { "project": "./tsconfig.json", "ecmaVersion": 2020 }, "ignorePatterns": [ "node_modules", "dist", "**/*.d.ts" ], "rules": { "indent": ["error", 2], "comma-dangle": ["off", 0], "prefer-rest-params": ["off", 0], "max-len": ["error", { "ignoreComments": true, "ignoreStrings": true, "ignoreUrls": true, "ignoreTemplateLiterals": true }] }}# This file tells Git which files shouldn't be added to source control
.idea.vscodestorageapify_storagecrawlee_storagenode_modulesdisttsconfig.tsbuildinfo.env
# Added by Apify CLI.venvimage: gitpod/workspace-full:latesttasks: - name: main init: > nvm use lts/jod && npm install -g npm@11 && npm install -g eslint@^8.50.0 && npm install -g apify-cli command: echo "Login to apify to get an API Key, export your APIFY_TOKEN, create an INPUT.json file and run npm start!" - name: config before: > (([[ ! -z $GITCONFIG ]] && echo $GITCONFIG | base64 -d > ~/.gitconfig && chmod 644 ~/.gitconfig) || unset GITCONFIG) && (([[ ! -z $GNUPG_1 ]] && rm -rf ~/.gnupg && cd / && echo $GNUPG_1$GNUPG_2 | base64 -d | tar --no-same-owner -xzf -) || unset GNUPG_1) command: echo "Ready! Start a new terminal session in order to use GPG." && exitApache License Version 2.0, January 2004 http://www.apache.org/licenses/
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.1declare global {2 namespace NodeJS {3 interface ProcessEnv {4 USER_APIFY_TOKEN: string;5 LANGSMITH_TRACING: string;6 LANGSMITH_ENDPOINT: string;7 LANGSMITH_API_KEY: string;8 LANGSMITH_PROJECT: string;9 OPENAI_API_KEY: string;10 ZIP_API_KEY: string;11 NODE_ENV: 'development' | 'production';12 }13 }14}15
16// If this file has no import/export statements (i.e. is a script)17// convert it into a module by adding an empty export statement.18export {}
{ "name": "ai-travel-agent", "version": "0.0.1", "type": "module", "description": "AI Agent Actor that leverages Apify to search for travel opportunities.", "engines": { "node": ">=22.0.0" }, "dependencies": { "@langchain/core": "^0.3.42", "@langchain/langgraph": "^0.2.54", "@langchain/openai": "^0.4.4", "apify": "^3.3.2", "crypto": "^1.0.1", "dotenv": "^16.4.7", "langchain": "^0.3.19", "zod": "^3.24.2" }, "devDependencies": { "@apify/eslint-config-ts": "^0.3.0", "@apify/tsconfig": "^0.1.0", "@eslint/js": "^9.21.0", "@types/node": "^22.0.0", "@typescript-eslint/eslint-plugin": "^7.18.0", "@typescript-eslint/parser": "^7.18.0", "eslint": "^8.57.1", "globals": "^16.0.0", "tsx": "^4.4.0", "typescript": "~5.5.0", "typescript-eslint": "^8.26.0" }, "scripts": { "start": "npm run start:dev", "start:prod": "node dist/main.js", "start:dev": "tsx src/main.ts", "build": "tsc", "lint": "eslint src", "test": "echo \"Error: oops, the actor has no tests yet, sad!\" && exit 1" }, "author": "It's not you it's me", "license": "ISC"}{ "extends": "@apify/tsconfig", "compilerOptions": { "module": "NodeNext", "moduleResolution": "NodeNext", "target": "ES2022", "outDir": "dist", "noUnusedLocals": false, "skipLibCheck": true, "lib": ["DOM"], }, "include": [ "./*.d.ts", "./src/**/*", ]}# Specify the base Docker image. You can read more about# the available images at https://docs.apify.com/sdk/js/docs/guides/docker-images# You can also use any other image from Docker Hub.FROM apify/actor-node:22 AS builder
# Check preinstalled packagesRUN npm ls crawlee apify puppeteer playwright
# Copy just package.json and package-lock.json# to speed up the build using Docker layer cache.COPY package*.json ./
# Install all dependencies. Don't audit to speed up the installation.RUN npm install --include=dev --audit=false
# Next, copy the source files using the user set# in the base image.COPY . ./
# Install all dependencies and build the project.# Don't audit to speed up the installation.RUN npm run build
# Create final imageFROM apify/actor-node:22
# Check preinstalled packagesRUN npm ls crawlee apify puppeteer playwright
# Copy just package.json and package-lock.json# to speed up the build using Docker layer cache.COPY package*.json ./
# Install NPM packages, skip optional and development dependencies to# keep the image small. Avoid logging too much and print the dependency# tree for debuggingRUN npm --quiet set progress=false \ && npm install --omit=dev --omit=optional \ && echo "Installed NPM packages:" \ && (npm list --omit=dev --all || true) \ && echo "Node.js version:" \ && node --version \ && echo "NPM version:" \ && npm --version \ && rm -r ~/.npm
# Copy built JS files from builder imageCOPY /usr/src/app/dist ./dist
# Next, copy the remaining files and directories with the source code.# Since we do this after NPM install, quick build will be really fast# for most source file changes.COPY . ./
# Create and run as a non-root user.RUN adduser -h /home/apify -D apify && \ chown -R apify:apify ./USER apify
# Run the image.CMD npm run start:prod --silent{ "actorSpecification": 1, "name": "ai-travel-agent", "version": "0.2", "buildTag": "latest", "title": "AI Travel Agent", "description": "AI agent that uses a multi-agent approach to find you the travel recommendations using a natural language prompt.", "meta": { "templateId": "ts-empty" }, "environmentVariables": { "OPENAI_API_KEY": "@defaultOpenaiApiKey", "USER_APIFY_TOKEN": "@defaultApifyToken" }, "dockerfile": "./Dockerfile", "input": "./input_schema.json", "storages": { "dataset": "./dataset_schema.json" }}{ "actorSpecification": 1, "views": { "overview": { "title": "Overview", "transformation": { "fields": ["actorName", "response"] }, "display": { "component": "table", "properties": { "actorName": { "label": "Actor name", "format": "text" }, "response": { "label": "Actor evaluation", "format": "text" } } } } }}{ "title": "Web Automation Agent", "type": "object", "schemaVersion": 1, "properties": { "instructions": { "title": "Instructions for the AI Travel Agent", "type": "string", "description": "Ask the agent for help to plan the perfect trip.", "editor": "textarea", "prefill": "Looking for a 2-bedroom accommodation in Barcelona with ratings above 4.0 and a price between $100 and $300 per night. Can you help me?" }, "openaiApiKey": { "title": "OpenAI API key", "type": "string", "description": "The API key for accessing OpenAI. You can get it from <a href='https://platform.openai.com/account/api-keys' target='_blank' rel='noopener'>OpenAI platform</a>.", "editor": "textfield", "isSecret": true }, "model": { "title": "GPT model", "type": "string", "description": "Select a GPT model. See <a href='https://platform.openai.com/docs/models/overview' target='_blank' rel='noopener'>models overview</a>. Keep in mind that each model has different pricing and features.", "editor": "select", "default": "gpt-4o-mini", "prefill": "gpt-4o-mini", "enum": ["gpt-4o", "gpt-4o-mini", "gpt-3.5-turbo"] }, "debug": { "title": "Debug Mode", "type": "boolean", "description": "Mark this option as true if you want to see all the logs when running the actor.", "editor": "checkbox", "default": false } }, "required": ["instructions", "model"]}{ "actor-start-gb": { "eventTitle": "Actor start per 1 GB", "eventDescription": "Flat fee for starting an Actor run for each 1 GB of memory.", "eventPriceUsd": 0.01 }, "openai-1000-tokens-gpt-4o": { "eventTitle": "Price per 1000 OpenAI tokens for gpt-4o", "eventDescription": "Flat fee for every 1000 tokens (input/output) used with gpt-4o.", "eventPriceUsd": 0.01 }, "openai-1000-tokens-gpt-4o-mini": { "eventTitle": "Price per 1000 OpenAI tokens for gpt-4o-mini", "eventDescription": "Flat fee for every 1000 tokens (input/output) used with gpt-4o-mini.", "eventPriceUsd": 0.0006 }, "openai-1000-tokens-gpt-3-5-turbo": { "eventTitle": "Price per 1000 OpenAI tokens for gpt-3.5-turbo", "eventDescription": "Flat fee for every 1000 tokens (input/output) used with gpt-3.5-turbo.", "eventPriceUsd": 0.0015 }, "duck-duck-go": { "eventTitle": "Price per Duck Duck Go search", "eventDescription": "Flat fee for every Duck Duck Go search.", "eventPriceUsd": 0.01 }, "website-scraper':": { "eventTitle": "Price per web page scraped", "eventDescription": "Flat fee for every web page scraped.", "eventPriceUsd": 0.01 }, "airbnb-search':": { "eventTitle": "Price per Airbnb search result", "eventDescription": "Flat fee for every result when searching Airbnb.", "eventPriceUsd": 0.001 }, "tripadvisor-search':": { "eventTitle": "Price per TripAdvisor search result", "eventDescription": "Flat fee for every result when searching TripAdvisor.", "eventPriceUsd": 0.003 }}1import dotenv from 'dotenv';2import { Actor, ApifyClient, log } from 'apify';3import { StateGraph, StateGraphArgs, START, END } from '@langchain/langgraph';4import LocationAgent from './agents/location.js';5import AirbnbDeciderAgent from './agents/airbnb_decider.js';6import AirbnbResearcherAgent from './agents/airbnb_researcher.js';7import SuccessAgent from './agents/success.js';8import TripadvisorDeciderAgent from './agents/tripadvisor_decider.js';9import TripadvisorResearcherAgent from './agents/tripadvisor_researcher.js';10import { chargeForActorStart } from './utils/ppe_handler.js';11
12// if available, .env variables are loaded13dotenv.config();14
15/**16 * Actor input schema17 */18type Input = {19 instructions: string;20 modelName?: string;21 openaiApiKey?: string;22 debug?: boolean;23}24
25/**26 * Actor initialization code and initial charge27*/28await Actor.init();29await chargeForActorStart();30
31// Handle and validate input32const {33 instructions,34 modelName = 'gpt-4o-mini',35 openaiApiKey,36 debug,37} = await Actor.getInput() as Input;38if (debug) {39 log.setLevel(log.LEVELS.DEBUG);40} else {41 log.setLevel(log.LEVELS.INFO);42}43// if available, the Actor uses the user's openaiApiKey. Otherwise it charges for use.44const tokenCostActive = (openaiApiKey ?? '').length === 0;45if (tokenCostActive) {46 log.info("No openaiApiKey was detected. You'll be charged for token usage.");47} else {48 log.info("Env openaiApiKey detected. You won't be charged for token usage.");49}50if (!instructions) {51 throw new Error('Instructions are required. Create an INPUT.json file in the `storage/key_value_stores/default` folder and add the respective keys.');52}53
54// Apify is used to call tools and manage datasets55const userToken = process.env.USER_APIFY_TOKEN;56if (!userToken) {57 throw new Error('User token is required. Export your Apify secret as USER_APIFY_TOKEN.');58}59const apifyClient = new ApifyClient({60 token: userToken,61});62
63/**64 * LangGraph StateGraph schema65 */66type StateSchema = {67 instructions: string;68 bestLocations: string;69 airbnbDatasetId: string;70 airbnbTotalItems: number;71 airbnbAssumptions: string;72 airbnbItemsChecked: number;73 airbnbRecommendations: string[];74 tripadvisorDatasetId: string;75 tripadvisorTotalItems: number;76 tripadvisorAssumptions: string;77 tripadvisorItemsChecked: number;78 tripadvisorRecommendations: string[];79 output: string;80}81
82const graphState: StateGraphArgs<StateSchema>['channels'] = {83 instructions: {84 value: (x?: string, y?: string) => y ?? x ?? '',85 default: () => instructions,86 },87 bestLocations: {88 value: (x?: string, y?: string) => y ?? x ?? '',89 default: () => '',90 },91 airbnbDatasetId: {92 value: (x?: string, y?: string) => y ?? x ?? '',93 default: () => '',94 },95 airbnbTotalItems: {96 value: (x?: number, y?: number) => y ?? x ?? 0,97 default: () => 0,98 },99 airbnbAssumptions: {100 value: (x?: string, y?: string) => y ?? x ?? '',101 default: () => '',102 },103 airbnbItemsChecked: {104 value: (x?: number, y?: number) => y ?? x ?? 0,105 default: () => 0,106 },107 airbnbRecommendations: {108 value: (x?: string[], y?: string[]) => y ?? x ?? [],109 default: () => [],110 },111 tripadvisorDatasetId: {112 value: (x?: string, y?: string) => y ?? x ?? '',113 default: () => '',114 },115 tripadvisorTotalItems: {116 value: (x?: number, y?: number) => y ?? x ?? 0,117 default: () => 0,118 },119 tripadvisorAssumptions: {120 value: (x?: string, y?: string) => y ?? x ?? '',121 default: () => '',122 },123 tripadvisorItemsChecked: {124 value: (x?: number, y?: number) => y ?? x ?? 0,125 default: () => 0,126 },127 tripadvisorRecommendations: {128 value: (x?: string[], y?: string[]) => y ?? x ?? [],129 default: () => [],130 },131 output: {132 value: (x?: string, y?: string) => y ?? x ?? '',133 default: () => '',134 },135};136
137async function locationNode(state: StateSchema) {138 const locationAgent = new LocationAgent({139 apifyClient,140 modelName,141 openaiApiKey: openaiApiKey ?? process.env.OPENAI_API_KEY,142 log,143 });144 const { agentExecutor, costHandler } = locationAgent;145 const response = await agentExecutor.invoke({ input: state.instructions });146 log.debug(`locationAgent 🤖 : ${response.output}`);147 await costHandler.logOrChargeForTokens(modelName, tokenCostActive);148 return { bestLocations: response.output };149}150
151async function airbnbResearcherNode(state: StateSchema) {152 const airbnbResearcherAgent = new AirbnbResearcherAgent({153 apifyClient,154 modelName,155 openaiApiKey: openaiApiKey ?? process.env.OPENAI_API_KEY,156 log,157 });158 const { agentExecutor, costHandler } = airbnbResearcherAgent;159 const input = 'The user asked sent this exact query:\n\n'160 + `'${state.instructions}'\n\n`161 + 'You asked someone for help to get the best locations in that city and country. '162 + 'The answer you received was this:\n\n'163 + `'${state.bestLocations}'\n\n`164 + 'Please make some research to gather information to be able to answer the user accordingly.';165 const response = await agentExecutor.invoke({ input });166 log.debug(`airbnbResearcherAgent 🤖 : ${response.output}`);167 const { datasetId, totalItems, assumptions } = JSON.parse(response.output);168 await costHandler.logOrChargeForTokens(modelName, tokenCostActive);169 return {170 airbnbDatasetId: datasetId,171 airbnbTotalItems: totalItems,172 airbnbAssumptions: assumptions,173 };174}175
176async function airbnbDeciderNode(state: StateSchema) {177 const airbnbDeciderAgent = new AirbnbDeciderAgent({178 apifyClient,179 modelName,180 openaiApiKey: openaiApiKey ?? process.env.OPENAI_API_KEY,181 log,182 });183 const { agentExecutor, costHandler } = airbnbDeciderAgent;184 const input = 'The user asked sent this exact query:\n\n'185 + `'${state.instructions}'\n\n`186 + 'You asked someone for help to get the best locations in that city and country. '187 + 'The answer you received was this:\n\n'188 + `'${state.bestLocations}'\n\n`189 + 'You asked someone else for help to get the best places to stay in in those locations using Airbnb. '190 + 'The answer you received was this:\n\n'191 + `Airbnb Apify dataset ID: '${state.airbnbDatasetId}'\n`192 + `Total items in Airbnb dataset: '${state.airbnbTotalItems}'\n`193 + `Assumptions made when cretating the Airbnb dataset:: '${state.airbnbAssumptions}'\n`194 + `Total results already checked in Airbnb dataset: airbnbItemsChecked='${state.airbnbItemsChecked}'\n\n`195 + 'Please explore theavailable datasets for the best results.';196 const response = await agentExecutor.invoke({ input });197 log.debug(`airbnbDeciderAgent 🤖 : ${response.output}`);198 const { itemsChecked, recommendations } = JSON.parse(response.output);199 await costHandler.logOrChargeForTokens(modelName, tokenCostActive);200 return {201 airbnbItemsChecked: state.airbnbItemsChecked + itemsChecked,202 airbnbRecommendations: [203 ...state.airbnbRecommendations,204 ...recommendations205 ]206 };207}208
209const airbnbResultsRouter = (state: StateSchema) => {210 const allResultsChecked = state.airbnbItemsChecked >= state.airbnbTotalItems;211 log.debug(`allResultsChecked: ${allResultsChecked}`);212 const hundredResultsChecked = state.airbnbItemsChecked > 100;213 log.debug(`hundredResultsChecked: ${hundredResultsChecked}`);214 const fiveRecommendationsFound = state.airbnbRecommendations.length > 5;215 log.debug(`fiveRecommendationsFound: ${fiveRecommendationsFound}`);216 if (217 allResultsChecked218 || hundredResultsChecked219 || fiveRecommendationsFound220 ) {221 // if any of the above criteria are met, it passes the ball to the successAgent222 return 'success';223 }224 // if none of the above criteria are met, it runs the same node again225 return 'airbnbDecider';226};227
228async function tripadvisorResearcherNode(state: StateSchema) {229 const tripadvisorResearcherAgent = new TripadvisorResearcherAgent({230 apifyClient,231 modelName,232 openaiApiKey: openaiApiKey ?? process.env.OPENAI_API_KEY,233 log,234 });235 const { agentExecutor, costHandler } = tripadvisorResearcherAgent;236 const input = 'The user asked sent this exact query:\n\n'237 + `'${state.instructions}'\n\n`238 + 'You asked someone for help to get the best locations in that city and country. '239 + 'The answer you received was this:\n\n'240 + `'${state.bestLocations}'\n\n`241 + 'Please make some research to gather information to be able to answer the user accordingly.';242 const response = await agentExecutor.invoke({ input });243 log.debug(`tripadvisorResearcherAgent 🤖 : ${response.output}`);244 const { datasetId, totalItems, assumptions } = JSON.parse(response.output);245 await costHandler.logOrChargeForTokens(modelName, tokenCostActive);246 return {247 tripadvisorDatasetId: datasetId,248 tripadvisorTotalItems: totalItems,249 tripadvisorAssumptions: assumptions,250 };251}252
253async function tripadvisorDeciderNode(state: StateSchema) {254 const tripadvisorDeciderAgent = new TripadvisorDeciderAgent({255 apifyClient,256 modelName,257 openaiApiKey: openaiApiKey ?? process.env.OPENAI_API_KEY,258 log,259 });260 const { agentExecutor, costHandler } = tripadvisorDeciderAgent;261 const input = 'The user asked sent this exact query:\n\n'262 + `'${state.instructions}'\n\n`263 + 'You asked someone for help to get the best locations in that city and country. '264 + 'The answer you received was this:\n\n'265 + `'${state.bestLocations}'\n\n`266 + 'You asked someone else for help to get the attractions in in those locations using Tripadvisor. '267 + 'The answer you received was this:\n\n'268 + `Tripadvisor Apify dataset ID: '${state.tripadvisorDatasetId}'\n`269 + `Total items in Tripadvisor dataset: '${state.tripadvisorTotalItems}'\n`270 + `Assumptions made when cretating the Tripadvisor dataset:: '${state.tripadvisorAssumptions}'\n`271 + `Total results already checked in Tripadvisor dataset: tripadvisorItemsChecked='${state.tripadvisorItemsChecked}'\n\n`272 + 'Please explore theavailable datasets for the best results.';273 const response = await agentExecutor.invoke({ input });274 log.debug(`tripadvisorDeciderAgent 🤖 : ${response.output}`);275 const { itemsChecked, recommendations } = JSON.parse(response.output);276 await costHandler.logOrChargeForTokens(modelName, tokenCostActive);277 return {278 tripadvisorItemsChecked: state.tripadvisorItemsChecked + itemsChecked,279 tripadvisorRecommendations: [280 ...state.tripadvisorRecommendations, ...recommendations281 ]282 };283}284
285const tripadvisorResultsRouter = (state: StateSchema) => {286 const allResultsChecked = state.tripadvisorItemsChecked287 >= state.tripadvisorTotalItems;288 log.debug(`allResultsChecked: ${allResultsChecked}`);289 const hundredResultsChecked = state.tripadvisorItemsChecked > 100;290 log.debug(`hundredResultsChecked: ${hundredResultsChecked}`);291 const fiveRecommendationsFound = state.tripadvisorRecommendations.length > 5;292 log.debug(`fiveRecommendationsFound: ${fiveRecommendationsFound}`);293 if (294 allResultsChecked295 || hundredResultsChecked296 || fiveRecommendationsFound297 ) {298 // if any of the above criteria are met, it passes the ball to the successAgent299 return 'success';300 }301 // if none of the above criteria are met, it runs the same node again302 return 'tripadvisorDecider';303};304
305async function successNode(state: StateSchema) {306 const successAgent = new SuccessAgent({307 apifyClient,308 modelName,309 openaiApiKey: openaiApiKey ?? process.env.OPENAI_API_KEY,310 log,311 });312 const { agentExecutor, costHandler } = successAgent;313 const input = 'The user asked sent this exact query:\n\n'314 + `'${state.instructions}'\n\n`315 + 'You asked someone for help to get the best locations in that city and state. '316 + 'The answer you received was this:\n\n'317 + `'${state.bestLocations}'\n\n`318 + 'You asked someone else for help to get the best places to stay in those locations using Airbnb and their expert jugdgement. '319 + 'The answer you received was this:\n\n'320 + `Assumptions made when searching Airbnb: '${state.tripadvisorAssumptions}'\n\n`321 + `Total places to stay checked: '${state.tripadvisorItemsChecked}'\n`322 + `Total places to stay recommended: '${state.tripadvisorRecommendations.length}'\n`323 + `Best places to stay in stringified JSON format: '${JSON.stringify(state.tripadvisorRecommendations)}'\n`324 + 'You asked someone else for help to get the attractions in those locations using Tripadvisor and their expert jugdgement. '325 + 'The answer you received was this:\n\n'326 + `Assumptions made when searching Tripadvisor: '${state.tripadvisorAssumptions}'\n\n`327 + `Total attractions checked: '${state.tripadvisorItemsChecked}'\n`328 + `Total attractions recommended: '${state.tripadvisorRecommendations.length}'\n`329 + `Best attractions in stringified JSON format: '${JSON.stringify(state.tripadvisorRecommendations)}'\n`330 + 'Please mention the explored neighborhoods and their descriptions to the user.'331 + 'Please select the top 3 places to stay and recommend the top 3 attractions (hopefully in the same neighborhoods).'332 + 'Please answer the user explaining the whole process in markdown format.';333 const response = await agentExecutor.invoke({ input });334 log.debug(`successNode 🤖 : ${response.output}`);335 await costHandler.logOrChargeForTokens(modelName, tokenCostActive);336 return { output: response.output };337}338
339const graph = new StateGraph({ channels: graphState })340 .addNode('location', locationNode)341 .addNode('airbnbResearcher', airbnbResearcherNode)342 .addNode('airbnbDecider', airbnbDeciderNode)343 .addNode('tripadvisorResearcher', tripadvisorResearcherNode)344 .addNode('tripadvisorDecider', tripadvisorDeciderNode)345 .addNode('success', successNode)346 .addEdge(START, 'location')347 .addEdge('location', 'airbnbResearcher')348 .addEdge('airbnbResearcher', 'airbnbDecider')349 .addConditionalEdges('airbnbDecider', airbnbResultsRouter)350 .addEdge('location', 'tripadvisorResearcher')351 .addEdge('tripadvisorResearcher', 'tripadvisorDecider')352 .addConditionalEdges('tripadvisorDecider', tripadvisorResultsRouter)353 .addEdge('success', END);354
355const runnable = graph.compile();356
357const response = await runnable.invoke(358 { input: instructions },359 { configurable: { thread_id: 42 } }, // this line shows that the agent can be thread-aware360);361
362log.debug(`Agent 🤖 : ${response.output}`);363
364await Actor.pushData({365 actorName: 'AI Travel Agent',366 response: response.output,367});368
369await Actor.exit();1import { Log, ApifyClient } from 'apify';2import { ChatPromptTemplate } from '@langchain/core/prompts';3import { createToolCallingAgent, AgentExecutor } from 'langchain/agents';4import { ChatOpenAI } from '@langchain/openai';5import { StructuredToolInterface } from '@langchain/core/tools';6import DatasetExplorer from '../tools/dataset_explorer.js';7import { CostHandler } from '../utils/cost_handler.js';8
9/**10 * Interface for parameters required by PropertyAgent class.11 */12export interface PropertyAgentParams {13 apifyClient: ApifyClient,14 modelName: string,15 openaiApiKey: string,16 log: Log | Console;17}18
19/**20 * An AI Agent that explores an Apify dataset in search for the perfect results.21 */22export class PropertyAgent {23 protected log: Log | Console;24 protected apifyClient: ApifyClient;25 public agentExecutor: AgentExecutor;26 public costHandler: CostHandler;27
28 constructor(fields?: PropertyAgentParams) {29 this.log = fields?.log ?? console;30 this.apifyClient = fields?.apifyClient ?? new ApifyClient();31 this.costHandler = new CostHandler(fields?.modelName ?? 'gpt-4o-mini', this.log);32 const llm = new ChatOpenAI({33 model: fields?.modelName,34 apiKey: fields?.openaiApiKey,35 temperature: 0,36 callbacks: [37 this.costHandler,38 ],39 });40 const tools = this.buildTools(this.apifyClient, this.log);41 const prompt = this.buildPrompt();42 const agent = createToolCallingAgent({43 llm,44 tools,45 prompt,46 });47 this.agentExecutor = new AgentExecutor({48 agent,49 tools,50 verbose: false,51 maxIterations: 5,52 });53 }54
55 protected buildTools(56 apifyClient: ApifyClient, log: Log | Console57 ): StructuredToolInterface[] {58 // Tools are initialized to be passed to the agent59 const datasetExplorer = new DatasetExplorer({ apifyClient, log });60 return [61 datasetExplorer,62 ];63 }64
65 protected buildPrompt(): ChatPromptTemplate {66 return ChatPromptTemplate.fromMessages([67 ['system',68 'You are a experienced travel agent that wants to help the user plan the perfect trip. '69 + "You'll have a datasetId and the total amount of items available to explore. "70 + 'Explore only one batch of 25 results (using limit=25). '71 + 'Skip the first items based on the number of items that were already checked (using offset=itemsChecked). '72 + "When exploring the dataset, include only these fields exactly as written here: ['id','name','title','rating','pricing','url','images']"73 + 'Remember: do not call the DatasetExplorer tool more than once, or you will die. \n\n'74 + 'The user may have specified other requests like pets, bedrooms, beds, baths, etc. '75 + 'Filter the results based on this information or notify the user if you are unable to do so. '76 + 'Use your expertise to recommend the best results that you can find that matches the user criteria. '77 + 'If the total amount of items (totalItems) is over 100, select up to 2 results otherwise pick up to 3. Try to at least pick one. '78 + "As your response, return only a JSON object (and nothing more! not even a ```json or ``` wrapper) with the key 'itemsChecked' as a number with the amount of results that you received from the dataset_explorer and the key 'recommendations' with your recommendations in JSON format, using the same fields that you specified when calling the dataset_explorer."79 ],80 ['placeholder', '{chat_history}'],81 ['human', '{input}'],82 ['placeholder', '{agent_scratchpad}'],83 ]);84 }85}86
87export default PropertyAgent;1import { Log, ApifyClient } from 'apify';2import { ChatPromptTemplate } from '@langchain/core/prompts';3import { createToolCallingAgent, AgentExecutor } from 'langchain/agents';4import { ChatOpenAI } from '@langchain/openai';5import { StructuredToolInterface } from '@langchain/core/tools';6import AirbnbSearch from '../tools/airbnb_search.js';7import { CostHandler } from '../utils/cost_handler.js';8
9/**10 * Interface for parameters required by ResearcherAgent class.11 */12export interface ResearcherAgentParams {13 apifyClient: ApifyClient,14 modelName: string,15 openaiApiKey: string,16 log: Log | Console;17}18
19/**20 * An AI Agent that searches Researcher for specific locations and stores the results in a dataset.21 */22export class ResearcherAgent {23 protected log: Log | Console;24 protected apifyClient: ApifyClient;25 public agentExecutor: AgentExecutor;26 public costHandler: CostHandler;27
28 constructor(fields?: ResearcherAgentParams) {29 this.log = fields?.log ?? console;30 this.apifyClient = fields?.apifyClient ?? new ApifyClient();31 this.costHandler = new CostHandler(fields?.modelName ?? 'gpt-4o-mini', this.log);32 const llm = new ChatOpenAI({33 model: fields?.modelName,34 apiKey: fields?.openaiApiKey,35 temperature: 0,36 callbacks: [37 this.costHandler,38 ],39 });40 const tools = this.buildTools(this.apifyClient, this.log);41 const prompt = this.buildPrompt();42 const agent = createToolCallingAgent({43 llm,44 tools,45 prompt,46 });47 this.agentExecutor = new AgentExecutor({48 agent,49 tools,50 verbose: false,51 maxIterations: 3,52 });53 }54
55 protected buildTools(56 apifyClient: ApifyClient, log: Log | Console57 ): StructuredToolInterface[] {58 // Tools are initialized to be passed to the agent59 const airbnbSearch = new AirbnbSearch({ apifyClient, log });60 return [61 airbnbSearch,62 ];63 }64
65 protected buildPrompt(): ChatPromptTemplate {66 const today = new Date().toISOString().slice(0, 10);67 return ChatPromptTemplate.fromMessages([68 ['system',69 'You are a experienced travel agent that knows that Airbnb is a website where you can search for places to stay based on criteria like location, dates and price. '70 + 'Using the recommended locations, search on Airbnb for places that match the user price, date and travelers (adults, children and pets) requirements. '71 + 'The user must specify who is traveling (adults, children, infants, pets) (default to 2 adults if the information is not given). '72 + `When managing dates, you always try to use the best format in the world, which is: yyy-mm-dd. For any calculation assume that today is ${today}.`73 + 'The user can specify if the travel has a start date (default to a trip of one week if the information is not given and choose today if no start date is available). '74 + 'The user can specify if the travel has an end date (default to a trip of one week if the information is not given and choose today if no start date is available). '75 + 'The user can specify if the travel destination has a minimum value (default to 1 if the information is not given). '76 + 'The user can specify if the travel destination has a maximum value (default to 250 if the information is not given). '77 + 'The user can specify a minimum number of baths, beds or bedrooms (omit these search parameters if the information is not given). '78 + 'Inform the user of the assumptions you made to make the Airbnb search.'79 + 'Inform the user the datasetId and the amount of items you received from the airbnb_search tool in order to explore the results later. '80 + "As your response, return only a JSON object (and nothing more! not even a ```json or ``` wrapper) with the keys 'datasetId' and 'assumptions', with their corresponding values in string format, along with the key 'totalItems' with the number of items in the dataset in number format."81 ],82 ['placeholder', '{chat_history}'],83 ['human', '{input}'],84 ['placeholder', '{agent_scratchpad}'],85 ]);86 }87}88
89export default ResearcherAgent;1import { Log, ApifyClient } from 'apify';2import { ChatPromptTemplate } from '@langchain/core/prompts';3import { createToolCallingAgent, AgentExecutor } from 'langchain/agents';4import { ChatOpenAI } from '@langchain/openai';5import { StructuredToolInterface } from '@langchain/core/tools';6import DuckDuckGo from '../tools/duck_duck_go.js';7import WebsiteScraper from '../tools/website_scraper.js';8import { CostHandler } from '../utils/cost_handler.js';9
10/**11 * Interface for parameters required by LocationAgent class.12 */13export interface LocationAgentParams {14 apifyClient: ApifyClient,15 modelName: string,16 openaiApiKey: string,17 log: Log | Console;18}19
20/**21 * AI Agent that takes the user input and finds the best Zip Codes in a city to live in.22 */23export class LocationAgent {24 protected log: Log | Console;25 protected apifyClient: ApifyClient;26 public agentExecutor: AgentExecutor;27 public costHandler: CostHandler;28
29 constructor(fields?: LocationAgentParams) {30 this.log = fields?.log ?? console;31 this.apifyClient = fields?.apifyClient ?? new ApifyClient();32 this.costHandler = new CostHandler(fields?.modelName ?? 'gpt-4o-mini', this.log);33 const llm = new ChatOpenAI({34 model: fields?.modelName,35 apiKey: fields?.openaiApiKey,36 temperature: 0,37 callbacks: [38 this.costHandler,39 ],40 });41 const tools = this.buildTools(this.apifyClient, this.log);42 const prompt = this.buildPrompt();43 const agent = createToolCallingAgent({44 llm,45 tools,46 prompt,47 });48 this.agentExecutor = new AgentExecutor({49 agent,50 tools,51 verbose: false,52 maxIterations: 5,53 });54 }55
56 protected buildTools(57 apifyClient: ApifyClient, log: Log | Console58 ): StructuredToolInterface[] {59 // Tools are initialized to be passed to the agent60 const duckDuckGo = new DuckDuckGo({ apifyClient, log });61 const websiteScraper = new WebsiteScraper({ apifyClient, log });62 return [63 duckDuckGo,64 websiteScraper,65 ];66 }67
68 protected buildPrompt(): ChatPromptTemplate {69 return ChatPromptTemplate.fromMessages([70 ['system',71 'You are a experienced travel agent that wants to help the user plan their trip. '72 + "You will stick to the following steps, ignoring the specifics of the user's query and focusing only on finding the best locations. "73 + 'Step 1. The user will ask you for advice regarding traveling to a specific city in the world or even a neighborhood in a city. '74 + "You don't care where the user lives, since you are not in charge of flights. "75 + 'If the user does not specify a budget, select mid range (and not low budget or luxury). '76 + 'If the user does not provide a country, try to guess to which country the city belongs to. '77 + 'If from the input you cannot get a city and a country or you think that the specified city should not be visited by tourists, '78 + 'end the conversation and help the user with the input. '79 // 2. Fetch best neighborhoods to stay at using DuckDuckGo80 + 'Step 2. With the city and state in hand, use DuckDuckGo replacing the following query: '81 + "'where to stay and where not to stay in [city], [country]'. "82 + 'If there the results, use a website scraper on the URLs you think will show you this information. '83 + 'Iterate until you find the best 3 locations or neighborhoods in the selected city. '84 + "For best performance when using the website scraper, you can use method='getBestPlacesToStay' and output=null'. "85 + 'Instead of answering the original question, just return the top 3 locations or neighborhoods and explain to the user why you selected those locations or neighborhoods. \n'86 ],87 ['placeholder', '{chat_history}'],88 ['human', '{input}'],89 ['placeholder', '{agent_scratchpad}'],90 ]);91 }92}93
94export default LocationAgent;1import { Log, ApifyClient } from 'apify';2import { ChatPromptTemplate } from '@langchain/core/prompts';3import { createToolCallingAgent, AgentExecutor } from 'langchain/agents';4import { ChatOpenAI } from '@langchain/openai';5import { StructuredToolInterface } from '@langchain/core/tools';6import { CostHandler } from '../utils/cost_handler.js';7
8/**9 * Interface for parameters required by SuccessAgent class.10 */11export interface SuccessAgentParams {12 apifyClient: ApifyClient,13 modelName: string,14 openaiApiKey: string,15 log: Log | Console;16}17
18/**19 * An AI Agent summarizes the efforts of all agents to answer the users's question.20 */21export class SuccessAgent {22 protected log: Log | Console;23 protected apifyClient: ApifyClient;24 public agentExecutor: AgentExecutor;25 public costHandler: CostHandler;26
27 constructor(fields?: SuccessAgentParams) {28 this.log = fields?.log ?? console;29 this.apifyClient = fields?.apifyClient ?? new ApifyClient();30 this.costHandler = new CostHandler(fields?.modelName ?? 'gpt-4o-mini', this.log);31 const llm = new ChatOpenAI({32 model: fields?.modelName,33 apiKey: fields?.openaiApiKey,34 temperature: 0,35 callbacks: [36 this.costHandler,37 ],38 });39 const tools = this.buildTools();40 const prompt = this.buildPrompt();41 const agent = createToolCallingAgent({42 llm,43 tools,44 prompt,45 });46 this.agentExecutor = new AgentExecutor({47 agent,48 tools,49 verbose: false,50 maxIterations: 3,51 });52 }53
54 protected buildTools(): StructuredToolInterface[] {55 // Tools are initialized to be passed to the agent56 return [];57 }58
59 protected buildPrompt(): ChatPromptTemplate {60 return ChatPromptTemplate.fromMessages([61 ['system',62 'You are a experienced travel agent that wants to help the user plan the perfect trip. '63 + 'You asked some colleagues for help and they sent you a bunch of info to help the user.'64 + 'The user may have specified other requests like pets, preferences, dinners, foods, etc so make sure to mention them. '65 + 'Make sure that you show the rating, price, image, description (or other useful information) and a link to view more information about the chosen results. '66 + 'If more than one location was specified in the search, try to make it so that your recommendations include results in every one of them. '67 + 'Please explain why you chose those results and how many you explored before making your choice. '68 ],69 ['placeholder', '{chat_history}'],70 ['human', '{input}'],71 ['placeholder', '{agent_scratchpad}'],72 ]);73 }74}75
76export default SuccessAgent;1import { Log, ApifyClient } from 'apify';2import { ChatPromptTemplate } from '@langchain/core/prompts';3import { createToolCallingAgent, AgentExecutor } from 'langchain/agents';4import { ChatOpenAI } from '@langchain/openai';5import { StructuredToolInterface } from '@langchain/core/tools';6import DatasetExplorer from '../tools/dataset_explorer.js';7import { CostHandler } from '../utils/cost_handler.js';8
9/**10 * Interface for parameters required by PropertyAgent class.11 */12export interface PropertyAgentParams {13 apifyClient: ApifyClient,14 modelName: string,15 openaiApiKey: string,16 log: Log | Console;17}18
19/**20 * An AI Agent that explores an Apify dataset in search for the perfect results.21 */22export class PropertyAgent {23 protected log: Log | Console;24 protected apifyClient: ApifyClient;25 public agentExecutor: AgentExecutor;26 public costHandler: CostHandler;27
28 constructor(fields?: PropertyAgentParams) {29 this.log = fields?.log ?? console;30 this.apifyClient = fields?.apifyClient ?? new ApifyClient();31 this.costHandler = new CostHandler(fields?.modelName ?? 'gpt-4o-mini', this.log);32 const llm = new ChatOpenAI({33 model: fields?.modelName,34 apiKey: fields?.openaiApiKey,35 temperature: 0,36 callbacks: [37 this.costHandler,38 ],39 });40 const tools = this.buildTools(this.apifyClient, this.log);41 const prompt = this.buildPrompt();42 const agent = createToolCallingAgent({43 llm,44 tools,45 prompt,46 });47 this.agentExecutor = new AgentExecutor({48 agent,49 tools,50 verbose: false,51 maxIterations: 5,52 });53 }54
55 protected buildTools(56 apifyClient: ApifyClient, log: Log | Console57 ): StructuredToolInterface[] {58 // Tools are initialized to be passed to the agent59 const datasetExplorer = new DatasetExplorer({ apifyClient, log });60 return [61 datasetExplorer,62 ];63 }64
65 protected buildPrompt(): ChatPromptTemplate {66 return ChatPromptTemplate.fromMessages([67 ['system',68 'You are a experienced travel agent that wants to help the user plan the perfect trip. '69 + "You'll have a datasetId and the total amount of items available to explore. "70 + 'Explore only one batch of 25 results (using limit=25). '71 + 'Skip the first items based on the number of items that were already checked (using offset=itemsChecked). '72 + "When exploring the dataset, include only these fields exactly as written here: ['image','category','name','rankingPosition','priceLevel','webUrl','rating','numberOfReviews']"73 + 'Remember: do not call the DatasetExplorer tool more than once, or you will die. \n\n'74 + 'The user may have specified other requests like romantic dinners, outdoor activities, etc. '75 + 'Filter the results based on this information or notify the user if you are unable to do so. '76 + 'Use your expertise to recommend the best results that you can find that matches the user criteria. '77 + 'If the total amount of items (totalItems) is over 100, select up to 2 results otherwise pick up to 3. Try to at least pick one. '78 + "As your response, return only a JSON object (and nothing more! not even a ```json or ``` wrapper) with the key 'itemsChecked' as a number with the amount of results that you received from the dataset_explorer and the key 'recommendations' with your recommendations in JSON format, using the same fields that you specified when calling the dataset_explorer."79 ],80 ['placeholder', '{chat_history}'],81 ['human', '{input}'],82 ['placeholder', '{agent_scratchpad}'],83 ]);84 }85}86
87export default PropertyAgent;1import { Log, ApifyClient } from 'apify';2import { ChatPromptTemplate } from '@langchain/core/prompts';3import { createToolCallingAgent, AgentExecutor } from 'langchain/agents';4import { ChatOpenAI } from '@langchain/openai';5import { StructuredToolInterface } from '@langchain/core/tools';6import TripadvisorSearch from '../tools/tripadvisor_search.js';7import { CostHandler } from '../utils/cost_handler.js';8
9/**10 * Interface for parameters required by ResearcherAgent class.11 */12export interface ResearcherAgentParams {13 apifyClient: ApifyClient,14 modelName: string,15 openaiApiKey: string,16 log: Log | Console;17}18
19/**20 * An AI Agent that searches Researcher for specific locations and stores the results in a dataset.21 */22export class ResearcherAgent {23 protected log: Log | Console;24 protected apifyClient: ApifyClient;25 public agentExecutor: AgentExecutor;26 public costHandler: CostHandler;27
28 constructor(fields?: ResearcherAgentParams) {29 this.log = fields?.log ?? console;30 this.apifyClient = fields?.apifyClient ?? new ApifyClient();31 this.costHandler = new CostHandler(fields?.modelName ?? 'gpt-4o-mini', this.log);32 const llm = new ChatOpenAI({33 model: fields?.modelName,34 apiKey: fields?.openaiApiKey,35 temperature: 0,36 callbacks: [37 this.costHandler,38 ],39 });40 const tools = this.buildTools(this.apifyClient, this.log);41 const prompt = this.buildPrompt();42 const agent = createToolCallingAgent({43 llm,44 tools,45 prompt,46 });47 this.agentExecutor = new AgentExecutor({48 agent,49 tools,50 verbose: false,51 maxIterations: 3,52 });53 }54
55 protected buildTools(56 apifyClient: ApifyClient, log: Log | Console57 ): StructuredToolInterface[] {58 // Tools are initialized to be passed to the agent59 const tripadvisorSearch = new TripadvisorSearch({ apifyClient, log });60 return [61 tripadvisorSearch,62 ];63 }64
65 protected buildPrompt(): ChatPromptTemplate {66 return ChatPromptTemplate.fromMessages([67 ['system',68 'You are a experienced travel agent that knows that Tripadvisor is a website where you can search for tourist attractions based on criteria like location and price. '69 + 'Search on Tripadvisor for the whole city and not only one of the recommended locations for attractions that match the user price and travelers requirements. '70 + 'Inform the user of the assumptions you made to make the Tripadvisor search.'71 + 'Inform the user the datasetId and the amount of items you received from the tripadvisor_search tool in order to explore the results later. '72 + "As your response, return only a JSON object (and nothing more! not even a ```json or ``` wrapper) with the keys 'datasetId' and 'assumptions', with their corresponding values in string format, along with the key 'totalItems' with the number of items in the dataset in number format."73 ],74 ['placeholder', '{chat_history}'],75 ['human', '{input}'],76 ['placeholder', '{agent_scratchpad}'],77 ]);78 }79}80
81export default ResearcherAgent;1import { Log, ApifyClient } from 'apify';2import { createHash } from 'crypto';3import { StructuredTool } from '@langchain/core/tools';4import { z } from 'zod';5import { chargeForToolUsage } from '../utils/ppe_handler.js';6
7/**8 * Interface for parameters required by AirbnbSearch class.9 */10export interface AirbnbSearchParams {11 apifyClient: ApifyClient;12 log: Log | Console;13}14
15/**16 * Tool that uses the AirbnbSearch function17 */18export class AirbnbSearch extends StructuredTool {19 protected log: Log | Console;20 protected apifyClient: ApifyClient;21
22 name = 'airbnb_search';23
24 description = 'Searches for accomodations on Airbnb based on a list of neighborhoods (at least one) and returns an Apify datasetId to explore the results.';25
26 schema = z.object({27 locations: z.string().array(),28 minimumPrice: z.number(),29 maximumPrice: z.number(),30 adults: z.number(),31 children: z.number(),32 infants: z.number(),33 pets: z.number().optional(),34 checkIn: z.string().describe('yyyy-mm-dd'),35 checkOut: z.string().describe('yyyy-mm-dd'),36 minBathrooms: z.number().optional(),37 minBedrooms: z.number().optional(),38 minBeds: z.number().optional(),39 });40
41 constructor(fields?: AirbnbSearchParams) {42 super(...arguments);43 this.log = fields?.log ?? console;44 this.apifyClient = fields?.apifyClient ?? new ApifyClient();45 }46
47 override async _call(arg: z.output<typeof this.schema>) {48 const actorInput = {49 adults: arg.adults,50 children: arg.children,51 infants: arg.infants,52 pets: arg.pets,53 checkIn: arg.checkIn,54 checkOut: arg.checkOut,55 minBathrooms: arg.minBathrooms,56 minBedrooms: arg.minBedrooms,57 minBeds: arg.minBeds,58 locationQueries: arg.locations.slice(0, 3),59 priceMax: arg.maximumPrice,60 priceMin: arg.minimumPrice,61 currency: 'USD',62 locale: 'en-US',63 };64 // checks for cached stored version65 const key = JSON.stringify(actorInput);66 const algorithm = 'sha256';67 const digest = createHash(algorithm).update(key).digest('hex').slice(0, 16);68 const { username } = await this.apifyClient.user().get();69 const today = new Date().toISOString().slice(0, 10);70 const datasetName = `${today}-${digest}`;71 // const datasetName = '2025-03-17-7f0e1b07b161269a'; //DEBUG72 this.log.debug(`Searching for datasetId: ${username}/${datasetName}`);73 const existingDataset = await this.apifyClient74 .dataset(`${username}/${datasetName}`)75 .get();76 this.log.debug(`Found existingDataset? ${existingDataset}`);77 let totalItems = existingDataset?.itemCount;78 if (existingDataset) {79 this.log.debug(80 `Cached response found for: ${JSON.stringify(actorInput)}`81 );82 } else {83 this.log.debug(84 `Calling AirbnbSearch with input: ${JSON.stringify(actorInput)}`85 );86 const actorRun = await this.apifyClient87 .actor('tri_angle/new-fast-airbnb-scraper')88 .call(actorInput, { maxItems: 100 }); // DEBUG89 await this.apifyClient90 .dataset(actorRun.defaultDatasetId)91 .update({ name: datasetName });92 const dataset = await this.apifyClient93 .dataset(actorRun.defaultDatasetId)94 .listItems();95 totalItems = dataset.total;96 await chargeForToolUsage(this.name, dataset.total);97 }98 this.log.debug(`AirbnbSearch response: ${username}/${datasetName}`);99 return `Results for Airbnb Search can be found in dataset with id '${username}/${datasetName}'. This dataset contains ${totalItems} items in total.`;100 }101}102
103export default AirbnbSearch;1import { Log, ApifyClient } from 'apify';2import { StructuredTool } from '@langchain/core/tools';3import { z } from 'zod';4
5/**6 * Interface for parameters required by DatasetExplorer class.7 */8export interface DatasetExplorerParams {9 apifyClient: ApifyClient;10 log: Log | Console;11}12
13/**14 * Allows the LLM to explore a paginated set. Useful if the dataset is too big.15 */16export class DatasetExplorer extends StructuredTool {17 protected log: Log | Console;18 protected apifyClient: ApifyClient;19
20 name = 'dataset_explorer';21
22 description = 'Retrieves paginated data from an Apify dataset. If no "limit" is specified, it returns only 10 results. Increate "offset" to return other results and make sure that "offset" + "limit" does not exceed the "total" of items.';23
24 schema = z.object({25 datasetId: z.string(),26 fields: z.string().array(),27 offset: z.number() || 0,28 limit: z.number() || 10,29 });30
31 constructor(fields?: DatasetExplorerParams) {32 super(...arguments);33 this.log = fields?.log ?? console;34 this.apifyClient = fields?.apifyClient ?? new ApifyClient();35 }36
37 override async _call(arg: z.output<typeof this.schema>) {38 this.log.debug(39 `Calling DatasetExplorer with input: ${JSON.stringify(arg)}`40 );41 const dataset = await this.apifyClient.dataset(arg.datasetId).get();42 if (!dataset) return 'Dataset not found.';43 const datasetItems = await this.apifyClient44 .dataset(arg.datasetId)45 .listItems({46 clean: true,47 fields: arg.fields,48 offset: arg.offset,49 limit: arg.limit,50 });51 this.log.debug(52 `DatasetExplorer response: ${JSON.stringify(datasetItems).slice(0, 100)}...`53 );54 return `The data for dataset ${arg.datasetId} with offset ${arg.offset} and limit ${arg.limit} is as follows: ${JSON.stringify(datasetItems)}`;55 }56}57
58export default DatasetExplorer;1import { Log, ApifyClient } from 'apify';2import { createHash } from 'crypto';3import { StructuredTool } from '@langchain/core/tools';4import { z } from 'zod';5import { chargeForToolUsage } from '../utils/ppe_handler.js';6
7/**8 * Interface for parameters required by DuckDuckGo class.9 */10export interface DuckDuckGoParams {11 apifyClient: ApifyClient;12 log: Log | Console;13}14
15/**16 * Tool that uses the DuckDuckGo function17 */18export class DuckDuckGo extends StructuredTool {19 protected log: Log | Console;20 protected apifyClient: ApifyClient;21
22 name = 'duck_duck_go';23
24 description = 'Performs a search on the search engine DuckDuckGo and returns a stringified JSON with the results.';25
26 schema = z.object({27 keywords: z.string(),28 locale: z.enum(['us-en', 'uk-en', 'cz-cs', 'cl-es']) || undefined,29 maximum: z.number() || undefined,30 });31
32 constructor(fields?: DuckDuckGoParams) {33 super(...arguments);34 this.log = fields?.log ?? console;35 this.apifyClient = fields?.apifyClient ?? new ApifyClient();36 }37
38 override async _call(arg: z.output<typeof this.schema>) {39 const actorInput = {40 keywords: arg.keywords,41 proxy: {42 useApifyProxy: true,43 apifyProxyGroups: [44 'RESIDENTIAL'45 ],46 apifyProxyCountry: 'US'47 },48 locale: arg.locale ?? 'us-en',49 operation: 'st',50 safe: 'Partial',51 daterange: 'all',52 img_type: 'all',53 img_size: 'all',54 vid_duration: 'all',55 maximum: arg.maximum ?? 5,56 timeout: 3057 };58 // checks for cached stored version59 const key = JSON.stringify(actorInput);60 const algorithm = 'sha256';61 const digest = createHash(algorithm).update(key).digest('hex').slice(0, 16);62 const { username } = await this.apifyClient.user().get();63 const today = new Date().toISOString().slice(0, 10);64 const datasetName = `${today}-${digest}`;65 this.log.debug(`Searching for datasetId: ${username}/${datasetName}`);66 const existingDataset = await this.apifyClient67 .dataset(`${username}/${datasetName}`)68 .get();69 this.log.debug(`Found existingDataset? ${existingDataset}`);70 let dataset;71 if (existingDataset) {72 this.log.debug(73 `Cached response found for: ${JSON.stringify(actorInput)}`74 );75 dataset = await this.apifyClient76 .dataset(`${username}/${datasetName}`)77 .listItems();78 } else {79 this.log.debug(80 `Calling DuckDuckGo with input: ${JSON.stringify(actorInput)}`81 );82 const actorRun = await this.apifyClient83 .actor('canadesk/duckduckgo-serp-api')84 .call(actorInput);85 dataset = await this.apifyClient86 .dataset(actorRun.defaultDatasetId)87 .listItems();88 await this.apifyClient89 .dataset(actorRun.defaultDatasetId)90 .update({ name: datasetName });91 await chargeForToolUsage(this.name, 1);92 }93 const { items } = dataset;94 this.log.debug(`DuckDuckGo response: ${JSON.stringify(items)}`);95 return JSON.stringify(items);96 }97}98
99export default DuckDuckGo;1import { Log } from 'apify';2import { Tool } from '@langchain/core/tools';3
4/**5 * Interface for parameters required by MagicTool class.6 */7export interface MagicToolParams {8 apiKey?: string;9 log: Log | Console;10}11
12/**13 * Tool that uses the MagicTool function. This is an example function to use as template.14 */15export class MagicTool extends Tool {16 static override lc_name() {17 return 'MagicTool';18 }19
20 protected log: Log | Console;21 protected apiKey: string;22
23 name = 'magic_function';24
25 description = 'Applies a magic function to an input.';26
27 constructor(fields?: MagicToolParams) {28 super(...arguments);29 this.log = fields?.log ?? console;30 const apiKey = fields?.apiKey ?? process.env.SECRET_API_KEY ?? '';31 if (apiKey === undefined) {32 this.log.debug(33 "Secret API key not set. You can set it as 'SECRET_API_KEY' in your environment variables."34 );35 }36 this.apiKey = apiKey;37 }38
39 async _call(rawInput: string) {40 this.log.debug(`rawInput: ${rawInput}`);41 const number = parseInt(rawInput, 10);42 return `${number + 2}`;43 }44}45
46export default MagicTool;1import { Log, ApifyClient } from 'apify';2import { createHash } from 'crypto';3import { StructuredTool } from '@langchain/core/tools';4import { z } from 'zod';5import { chargeForToolUsage } from '../utils/ppe_handler.js';6
7/**8 * Interface for parameters required by TripadvisorSearch class.9 */10export interface TripadvisorSearchParams {11 apifyClient: ApifyClient;12 log: Log | Console;13}14
15/**16 * Tool that uses the TripadvisorSearch function17 */18export class TripadvisorSearch extends StructuredTool {19 protected log: Log | Console;20 protected apifyClient: ApifyClient;21
22 name = 'tripadvisor_search';23
24 description = 'Searches for attractions on Tripadvisor based on a city (not a neighborhood) and returns an Apify datasetId to explore the results.';25
26 schema = z.object({27 location: z.string().describe('Peferred format: "City, Country"'),28 });29
30 constructor(fields?: TripadvisorSearchParams) {31 super(...arguments);32 this.log = fields?.log ?? console;33 this.apifyClient = fields?.apifyClient ?? new ApifyClient();34 }35
36 override async _call(arg: z.output<typeof this.schema>) {37 const actorInput = {38 currency: 'USD',39 includeAiReviewsSummary: true,40 includeAttractions: true,41 includeHotels: false,42 includeNearbyResults: false,43 includePriceOffers: false,44 includeRestaurants: true,45 includeTags: true,46 includeVacationRentals: false,47 language: 'en',48 maxItemsPerQuery: 100,49 query: arg.location,50 };51 // checks for cached stored version52 const key = JSON.stringify(actorInput);53 const algorithm = 'sha256';54 const digest = createHash(algorithm).update(key).digest('hex').slice(0, 16);55 const { username } = await this.apifyClient.user().get();56 const today = new Date().toISOString().slice(0, 10);57 const datasetName = `${today}-${digest}`;58 // const datasetName = '2025-03-17-f0fa535166c7c7f3'; //DEBUG59 this.log.debug(`Searching for datasetId: ${username}/${datasetName}`);60 const existingDataset = await this.apifyClient61 .dataset(`${username}/${datasetName}`)62 .get();63 this.log.debug(`Found existingDataset? ${existingDataset}`);64 let totalItems = existingDataset?.itemCount;65 if (existingDataset) {66 this.log.debug(67 `Cached response found for: ${JSON.stringify(actorInput)}`68 );69 } else {70 this.log.debug(71 `Calling TripadvisorSearch with input: ${JSON.stringify(actorInput)}`72 );73 const actorRun = await this.apifyClient74 .actor('maxcopell/tripadvisor')75 .call(actorInput, { maxItems: 100 }); // DEBUG76 await this.apifyClient77 .dataset(actorRun.defaultDatasetId)78 .update({ name: datasetName });79 const dataset = await this.apifyClient80 .dataset(actorRun.defaultDatasetId)81 .listItems();82 totalItems = dataset.total;83 await chargeForToolUsage(this.name, dataset.total);84 }85 this.log.debug(`TripadvisorSearch response: ${username}/${datasetName}`);86 return `Results for Tripadvisor Search can be found in dataset with id '${username}/${datasetName}'. This dataset contains ${totalItems} items in total.`;87 }88}89
90export default TripadvisorSearch;1import { Log, ApifyClient } from 'apify';2import { createHash } from 'crypto';3import { StructuredTool } from '@langchain/core/tools';4import { z } from 'zod';5import { chargeForToolUsage } from '../utils/ppe_handler.js';6
7/**8 * Interface for parameters required by WebsiteScraper class.9 */10export interface WebsiteScraperParams {11 apifyClient: ApifyClient;12 log: Log | Console;13}14
15/**16 * An example input that serves as a good default17 */18const sampleInput = {19 method: 'getAllItems',20 output: '{"results":[""]}',21};22
23/**24 * Tool that uses the WebsiteScraper function25 */26export class WebsiteScraper extends StructuredTool {27 protected log: Log | Console;28 protected apifyClient: ApifyClient;29
30 name = 'website_scraper';31
32 description = 'Scrapes a URL and then performs a search based on a specified method (for example: getAllItems) and returns a stringified JSON with the results using the default output format (for example: {"results":[""]}).';33
34 schema = z.object({35 url: z.string(),36 method: z.string() || undefined,37 // output: z.string() || undefined || null,38 });39
40 constructor(fields?: WebsiteScraperParams) {41 super(...arguments);42 this.log = fields?.log ?? console;43 this.apifyClient = fields?.apifyClient ?? new ApifyClient();44 }45
46 override async _call(arg: z.output<typeof this.schema>) {47 const actorInput = {48 url: arg.url,49 method: arg.method ?? sampleInput.method,50 output: sampleInput.output,51 };52 // checks for cached stored version53 const key = JSON.stringify(actorInput);54 const algorithm = 'sha256';55 const digest = createHash(algorithm).update(key).digest('hex').slice(0, 16);56 const { username } = await this.apifyClient.user().get();57 const thisMonth = new Date().toISOString().slice(0, 7);58 const datasetName = `${thisMonth}-${digest}`;59 this.log.debug(`Searching for datasetId: ${username}/${datasetName}`);60 const existingDataset = await this.apifyClient61 .dataset(`${username}/${datasetName}`)62 .get();63 this.log.debug(`Found existingDataset? ${existingDataset}`);64 let dataset;65 if (existingDataset) {66 this.log.debug(67 `Cached response found for: ${JSON.stringify(actorInput)}`68 );69 dataset = await this.apifyClient70 .dataset(`${username}/${datasetName}`)71 .listItems();72 } else {73 const callOptions = {74 timeout: 60,75 };76 this.log.debug(77 `Calling WebsiteScraper with input: ${JSON.stringify(actorInput)}`78 );79 const actorRun = await this.apifyClient80 .actor('zeeb0t/web-scraping-api---scrape-any-website')81 .call(actorInput, callOptions);82 dataset = await this.apifyClient83 .dataset(actorRun.defaultDatasetId)84 .listItems();85 if (dataset.total > 0) {86 await this.apifyClient87 .dataset(actorRun.defaultDatasetId)88 .update({ name: datasetName });89 }90 await chargeForToolUsage(this.name, 1);91 }92 const { items } = dataset;93 this.log.debug(`WebsiteScraper response: ${JSON.stringify(items)}`);94 return `Scraped ${arg.url}, ran the method ${arg.method} on the content and obtained: ${JSON.stringify(items)}`;95 }96}97
98export default WebsiteScraper;1import { Log } from 'apify';2import { BaseTracer } from 'langchain/callbacks';3import { Run } from '@langchain/core/tracers/tracer_langchain';4import { GPT_MODEL_LIST, OpenaiAPICost } from './openai_models.js';5import { chargeForModelTokens } from './ppe_handler.js';6
7interface TotalCost {8 usd: number;9 inputTokens: number;10 outputTokens: number;11 totalModelCalls: number;12}13
14export class CostHandler extends BaseTracer {15 protected log: Log | Console;16 name: string;17 modelName: string;18 modelCost: OpenaiAPICost;19 totalCost: TotalCost;20
21 constructor(modelName: string, log?: Log | Console) {22 super();23 this.log = log ?? console;24 this.name = 'cost_handler';25 this.modelName = modelName;26 this.modelCost = GPT_MODEL_LIST[this.modelName].cost;27 this.totalCost = {28 usd: 0,29 inputTokens: 0,30 outputTokens: 0,31 totalModelCalls: 0,32 };33 }34
35 // NOTE: We do not need to persist runs in this handler.36 // eslint-disable-next-line @typescript-eslint/no-unused-vars37 persistRun(_run: Run) {38 return Promise.resolve();39 }40
41 /**42 * Logs tokens usage in $.43 * @returns void44 */45 override onLLMEnd(run: Run) {46 const tokenUsage = run?.outputs?.llmOutput?.tokenUsage;47 if (tokenUsage) {48 const inputCostsUSD = this.modelCost.input49 * (tokenUsage.promptTokens / 1000);50 const outputCostsUSD = this.modelCost.output51 * (tokenUsage.completionTokens / 1000);52 const callCostUSD = inputCostsUSD + outputCostsUSD;53 this.totalCost.usd += inputCostsUSD + outputCostsUSD;54 this.totalCost.inputTokens += tokenUsage.promptTokens;55 this.totalCost.outputTokens += tokenUsage.completionTokens;56 this.totalCost.totalModelCalls++;57 const durationSecs = run.end_time && run.start_time58 && (run.end_time - run.start_time) / 1000;59 this.log.debug(`LLM model call processed`,60 {61 durationSecs,62 callCostUSD,63 totalCostUSD: this.totalCost.usd,64 inputTokens: this.totalCost.inputTokens,65 outputTokens: this.totalCost.outputTokens,66 }67 );68 }69 }70
71 override onLLMStart(run: Run) {72 this.log.debug(`Calling LLM model`, run);73 }74
75 getTotalCost() {76 return this.totalCost;77 }78
79 async logOrChargeForTokens(modelName: string, tokenCostActive: boolean) {80 const costs = this.totalCost;81 if (tokenCostActive) {82 const tokens = costs.inputTokens + costs.outputTokens;83 const tokensCost = this.modelCost.output * (tokens / 1000);84 this.log.info(`Total tokens processed: ${tokens}. Usage cost: ${tokensCost}`);85 await chargeForModelTokens(modelName, tokens);86 } else {87 this.log.info(`Estimated OpenAI cost: $${costs.usd} USD`);88 }89 }90}1export interface OpenaiAPICost {2 input: number; // USD cost per 1000 tokens3 output: number; // USD cost per 1000 tokens4}5
6export interface GPTModelConfig {7 model: string;8 maxTokens: number;9 maxOutputTokens?: number;10 interface: 'text' | 'chat';11 cost: OpenaiAPICost; // USD cost per 1000 tokens12}13
14/**15* List of GPT models that can be used.16* Should be in sync with https://platform.openai.com/docs/models/17* Last updated on 2025-03-0918*/19export const GPT_MODEL_LIST: {[key: string]: GPTModelConfig} = {20 'gpt-4o': {21 model: 'gpt-4o',22 maxTokens: 16384,23 interface: 'chat',24 cost: {25 input: 0.0025,26 output: 0.01,27 },28 },29 'gpt-4o-mini': {30 model: 'gpt-4o-mini',31 maxTokens: 16384,32 interface: 'chat',33 cost: {34 input: 0.00015,35 output: 0.0006,36 },37 },38 'gpt-3.5-turbo': {39 model: 'gpt-3.5-turbo',40 maxTokens: 8192,41 interface: 'chat',42 cost: {43 input: 0.0005,44 output: 0.0015,45 },46 }47};1export const PPE_EVENT = {2 ACTOR_START_GB: 'actor-start-gb',3 GPT_4O: 'openai-1000-tokens-gpt-4o',4 GPT_4O_MINI: 'openai-1000-tokens-gpt-4o-mini',5 GPT_3_5_TURBO: 'openai-1000-tokens-gpt-3-5-turbo',6 DUCK_DUCK_GO: 'duck-duck-go',7 WEBSITE_SCRAPER: 'website-scraper',8 AIRBNB_SEARCH: 'airbnb-search',9 TRIPADVISOR_SEARCH: 'tripadvisor-search',10} as const;1import { Actor, log } from 'apify';2import { PPE_EVENT } from './ppe_events.js';3
4/**5 * Charges for the tokens used by a specific model.6 *7 * @param modelName - The name of the model.8 * @param tokens - The number of tokens to charge for.9 * @throws Will throw an error if the model name is unknown.10 */11export async function chargeForModelTokens(modelName: string, tokens: number) {12 const tokensK = Math.ceil(tokens / 1000);13 log.debug(`Charging for ${tokens} tokens (${tokensK}k) for model ${modelName}`);14 let eventName: string = PPE_EVENT.GPT_4O;15 switch (modelName) {16 case 'gpt-4o-mini':17 eventName = PPE_EVENT.GPT_4O_MINI;18 break;19 case 'gpt-3.5-turbo':20 eventName = PPE_EVENT.GPT_3_5_TURBO;21 break;22 default:23 eventName = PPE_EVENT.GPT_4O;24 break;25 }26 await Actor.charge(27 { eventName, count: tokensK }28 );29}30
31export async function chargeForActorStart() {32 if (33 Actor.getChargingManager()34 .getChargedEventCount(PPE_EVENT.ACTOR_START_GB) === 035 ) {36 const count = Math.ceil((Actor.getEnv().memoryMbytes || 1024) / 1024);37 await Actor.charge({ eventName: PPE_EVENT.ACTOR_START_GB, count });38 }39}40
41export async function chargeForToolUsage(toolName: string, count: number) {42 log.debug(`Charging #${count} times for tool ${toolName}`);43 let eventName: string = '';44 switch (toolName) {45 case 'duck_duck_go':46 eventName = PPE_EVENT.DUCK_DUCK_GO;47 break;48 case 'website_scraper':49 eventName = PPE_EVENT.WEBSITE_SCRAPER;50 break;51 case 'airbnb_search':52 eventName = PPE_EVENT.AIRBNB_SEARCH;53 break;54 case 'tripadvisor_search':55 eventName = PPE_EVENT.TRIPADVISOR_SEARCH;56 break;57 default:58 eventName = '';59 break;60 }61 await Actor.charge({ eventName, count });62}