# Profile Analysis Agent (`ai_automata/social-media-analysis-agent`) Actor Input Instagram, YouTube, Twitter or other social media profile URL and get an analysis of the profile. - **URL**: https://apify.com/ai\_automata/social-media-analysis-agent.md - **Developed by:** [Sam Automan](https://apify.com/ai_automata) (community) - **Categories:** Agents, Social media, SEO tools - **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 ### Python LangGraph template A template for [LangGraph](https://www.langchain.com/langgraph) projects in Python for building AI agents with [Apify Actors](https://apify.com/actors). The template provides a basic structure and an example [LangGraph](https://www.langchain.com/langgraph) [ReAct agent](https://react-lm.github.io/) that calls [Actors](https://apify.com/actors) as tools in a workflow. ### How it works A [ReAct agent](https://react-lm.github.io/) is created and given a set of tools to accomplish a task. The agent receives a query from the user and decides which tools to use and in what order to complete the task. In this case, the agent is provided with an [Instagram Scraper Actor](https://apify.com/apify/instagram-scraper) to scrape Instagram profile posts and a calculator tool to sum a list of numbers to calculate the total number of likes and comments. The agent is configured to also output structured data, which is pushed to the dataset, while textual output is stored in the key-value store as a `response.txt` file. ### How to use Add or modify the agent tools in the `src/tools.py` file, and make sure to include new tools in the agent tools list in `src/main.py`. Additionally, you can update the agent system prompt in `src/main.py`. For more information, refer to the [LangChain agents documentation](https://docs.langchain.com/oss/python/langchain/agents) and the [LangChain tools documentation](https://python.langchain.com/docs/concepts/tools/). For a more advanced multi-agent example, see the [Finance Monitoring Agent actor](https://github.com/apify/actor-finance-monitoring-agent) or visit the [LangGraph documentation](https://langchain-ai.github.io/langgraph/concepts/multi_agent/). ##### Pay Per Event This template uses the [Pay Per Event (PPE)](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) monetization model, which provides flexible pricing based on defined events. To charge users, define events in JSON format and save them on the Apify platform. Here is an example schema with the `task-completed` event: ```json [ { "task-completed": { "eventTitle": "Task completed", "eventDescription": "Cost per query answered.", "eventPriceUsd": 0.1 } } ] ```` In the Actor, trigger the event with: ```python await Actor.charge(event_name='task-completed') ``` This approach allows you to programmatically charge users directly from your Actor, covering the costs of execution and related services, such as LLM input/output tokens. To set up the PPE model for this Actor: - **Configure the OpenAI API key environment variable**: provide your OpenAI API key to the `OPENAI_API_KEY` in the Actor's **Environment variables**. - **Configure Pay Per Event**: establish the Pay Per Event pricing schema in the Actor's **Monetization settings**. First, set the **Pricing model** to `Pay per event` and add the schema. An example schema can be found in [pay\_per\_event.json](.actor/pay_per_event.json). ### Included features - **[Apify SDK](https://docs.apify.com/sdk/python/)** for Python - a toolkit for building Apify [Actors](https://apify.com/actors) and scrapers in Python - **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your Actor's input - **[Dataset](https://docs.apify.com/sdk/python/docs/concepts/storages#working-with-datasets)** - store structured data where each object stored has the same attributes - **[Key-value store](https://docs.apify.com/platform/storage/key-value-store)** - store any kind of data, such as JSON documents, images, or text files ### Resources - [What are AI agents?](https://blog.apify.com/what-are-ai-agents/) - [Python tutorials in Academy](https://docs.apify.com/academy/python) - [Apify Python SDK documentation](https://docs.apify.com/sdk/python/) - [LangChain documentation](https://python.langchain.com/docs/introduction/) - [LangGraph documentation](https://langchain-ai.github.io/langgraph/tutorials/introduction/) - [Integration with Make, GitHub, Zapier, Google Drive, and other apps](https://apify.com/integrations) ### 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 ## `query` (type: `string`): Query for the agent. ## `modelName` (type: `string`): The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini, and the reasoning models o1 and o3-mini. ## `debug` (type: `boolean`): If enabled, the Actor will run in debug mode and produce more output. ## Actor input object example ```json { "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.", "modelName": "gpt-4o-mini", "debug": false } ``` # Actor output Schema ## `results` (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 = { "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.", "modelName": "gpt-4o-mini" }; // Run the Actor and wait for it to finish const run = await client.actor("ai_automata/social-media-analysis-agent").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 = { "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.", "modelName": "gpt-4o-mini", } # Run the Actor and wait for it to finish run = client.actor("ai_automata/social-media-analysis-agent").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 '{ "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.", "modelName": "gpt-4o-mini" }' | apify call ai_automata/social-media-analysis-agent --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ai_automata/social-media-analysis-agent", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Profile Analysis Agent", "description": "Input Instagram, YouTube, Twitter or other social media profile URL and get an analysis of the profile.", "version": "0.0", "x-build-id": "ZoSXfaQgnCA9DLxqz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ai_automata~social-media-analysis-agent/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ai_automata-social-media-analysis-agent", "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/ai_automata~social-media-analysis-agent/runs": { "post": { "operationId": "runs-sync-ai_automata-social-media-analysis-agent", "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/ai_automata~social-media-analysis-agent/run-sync": { "post": { "operationId": "run-sync-ai_automata-social-media-analysis-agent", "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", "required": [ "query" ], "properties": { "query": { "title": "Query", "type": "string", "description": "Query for the agent.", "default": "This is a fallback test query, do nothing and !!do not call any tools!!. If asked to generate structured response, create a dummy one without optional fields - minimal as possible." }, "modelName": { "title": "OpenAI model", "enum": [ "gpt-4o", "gpt-4o-mini", "o1", "o3-mini" ], "type": "string", "description": "The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini, and the reasoning models o1 and o3-mini.", "default": "gpt-4o-mini" }, "debug": { "title": "Debug", "type": "boolean", "description": "If enabled, the Actor will run in debug mode and produce more output.", "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 } } } } } } } } } } ```