# LangChain.js template (`ellustar/my-actor-17`) Actor The LangChain.js Template Actor is a ready-to-use foundation for building AI agents with LangChain.js. It supports LLM integration, chains, tools, and memory for fast prototyping and scalable deployment of chatbots, automation workflows, and AI apps. - **URL**: https://apify.com/ellustar/my-actor-17.md - **Developed by:** [Ellustar](https://apify.com/ellustar) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### LangChain.js template > LangChain is a framework for developing applications powered by language models. This example template illustrates how to use LangChain.js with Apify to crawl the web data, vectorize them, and prompt the OpenAI model. All of this is within a single Apify Actor and slightly over a hundred lines of code. ### Included features - **[Apify SDK](https://docs.apify.com/sdk/js/)** - a toolkit for building [Actors](https://apify.com/actors) - **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your Actor's input - **[Langchain.js](https://github.com/hwchase17/langchainjs)** - a framework for developing applications powered by language models - **[OpenAI](https://openai.com/)** - a powerful language model ### How it works The code contains the following steps: 1. Crawls given website using [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor. 2. Vectorizes the data using the [OpenAI](https://openai.com/) API. 3. Caches the vector index in the [key-value store](https://docs.apify.com/platform/storage/key-value-store) so that when you run Actor for the same website again, the cached data are used to speed it up. 4. Data are fed to the OpenAI model using [Langchain.js](https://github.com/hwchase17/langchainjs), and a given query is asked. ### Before you start To be able to run this template both locally and on the Apify platform, you need to: - Have an [Apify account](https://console.apify.com/) and sign into it using `apify login` command in your terminal. Without this, you won't be able to run the required [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor to gather the data. - Have an [OpenAI](https://openai.com/) account and an API key. This is needed for vectorizing the data and also to be able to prompt the OpenAI model. - When running locally store this as OPENAI_API_KEY environment variable (https://docs.apify.com/cli/docs/vars#set-up-environment-variables-in-apify-console). - When running on Apify platform, you can simply paste this into the input field in the input UI. ### Production use > This serves purely as an example of the whole pipeline. For production use, we recommend you to: - Separate crawling, data vectorization, and prompting into separate Actors. This way, you can run them independently and scale them separately. - Replace the local vector store with [Pinecone](https://www.pinecone.io/) or a similar database. See the [LangChain.js docs](https://js.langchain.com/docs/) for more information. ### Resources - [Pinecone integration](https://apify.com/jan.turon/pinecone-integration) Actor - [How to use Pinecone with LLMs](https://blog.apify.com/what-is-pinecone-why-use-it-with-llms/) - [How to use LangChain with OpenAI, Pinecone, and Apify](https://blog.apify.com/how-to-use-langchain/) - [Integration with Zapier](https://apify.com/integrations), Make, Google Drive and others - [Video guide on getting data using Apify API](https://www.youtube.com/watch?v=ViYYDHSBAKM) - A short guide on [how to create web scrapers using code templates](https://www.youtube.com/watch?v=u-i-Korzf8w) [Web Scraping Data for Generative AI](https://www.youtube.com/watch?v=8uvHH-ocSes) ### Getting started For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-at-apify-console). In short, you will: 1. Build the Actor 2. Run the Actor ### Pull the Actor for local development If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI: 1. Install `apify-cli` **Using Homebrew** ```bash brew install apify-cli ``` **Using NPM** ```bash npm -g install apify-cli ``` 2. Pull the Actor by its unique ``, which is one of the following: - unique name of the Actor to pull (e.g. "apify/hello-world") - or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb") You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID. This command will copy the Actor into the current directory on your local machine. ```bash apify pull ``` ### Documentation reference To learn more about Apify and Actors, take a look at the following resources: - [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js) - [Apify SDK for Python documentation](https://docs.apify.com/sdk/python) - [Apify Platform documentation](https://docs.apify.com/platform) - [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU) # Actor input Schema ## `startUrls` (type: `array`): One or more URLs of pages where the crawler will start. Note that the Actor will additionally only crawl sub-pages of these URLs. For example, for start URL `https://www.example.com/blog`, it will crawl pages like `https://example.com/blog/article-1`, but will skip `https://example.com/docs/something-else`. ## `maxCrawlPages` (type: `integer`): The maximum number pages to crawl. It includes the start URLs, pagination pages, pages with no content, etc. The crawler will automatically finish after reaching this number. This setting is useful to prevent accidental crawler runaway. ## `openAIApiKey` (type: `string`): Enter your [OpenAI](https://openai.com/) account and an API key. This is needed for vectorizing the data and also to be able to prompt the OpenAI model. ## `query` (type: `string`): The query you want to ask the model about the crawled data. ## `forceRecrawl` (type: `boolean`): If enabled, the data will be re-crawled even if cached vector index is available. ## Actor input object example ```json { "startUrls": [ { "url": "https://wikipedia.com" } ], "maxCrawlPages": 3, "query": "What is Wikipedia?", "forceRecrawl": false } ```` # Actor output Schema ## `answer` (type: `string`): No description ## `vectorIndexCache` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://wikipedia.com" } ], "maxCrawlPages": 3, "query": "What is Wikipedia?" }; // Run the Actor and wait for it to finish const run = await client.actor("ellustar/my-actor-17").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [{ "url": "https://wikipedia.com" }], "maxCrawlPages": 3, "query": "What is Wikipedia?", } # Run the Actor and wait for it to finish run = client.actor("ellustar/my-actor-17").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://wikipedia.com" } ], "maxCrawlPages": 3, "query": "What is Wikipedia?" }' | apify call ellustar/my-actor-17 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ellustar/my-actor-17", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LangChain.js template", "description": "The LangChain.js Template Actor is a ready-to-use foundation for building AI agents with LangChain.js. It supports LLM integration, chains, tools, and memory for fast prototyping and scalable deployment of chatbots, automation workflows, and AI apps.", "version": "0.0", "x-build-id": "OlDCs2MRuWVMoF4hG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ellustar~my-actor-17/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ellustar-my-actor-17", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ellustar~my-actor-17/runs": { "post": { "operationId": "runs-sync-ellustar-my-actor-17", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ellustar~my-actor-17/run-sync": { "post": { "operationId": "run-sync-ellustar-my-actor-17", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "One or more URLs of pages where the crawler will start. Note that the Actor will additionally only crawl sub-pages of these URLs. For example, for start URL `https://www.example.com/blog`, it will crawl pages like `https://example.com/blog/article-1`, but will skip `https://example.com/docs/something-else`.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxCrawlPages": { "title": "Max pages", "minimum": 0, "type": "integer", "description": "The maximum number pages to crawl. It includes the start URLs, pagination pages, pages with no content, etc. The crawler will automatically finish after reaching this number. This setting is useful to prevent accidental crawler runaway.", "default": 9999999 }, "openAIApiKey": { "title": "OpenAI API key", "type": "string", "description": "Enter your [OpenAI](https://openai.com/) account and an API key. This is needed for vectorizing the data and also to be able to prompt the OpenAI model." }, "query": { "title": "Query", "type": "string", "description": "The query you want to ask the model about the crawled data." }, "forceRecrawl": { "title": "Re-crawl the data", "type": "boolean", "description": "If enabled, the data will be re-crawled even if cached vector index is available.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```