# B2B Sales Intelligence Agent (`joseph99/my-actor`) Actor AI-powered B2B lead research agent. Scrapes company websites, scores ICP fit (0–100), surfaces buying signals from news, and generates personalised cold email + LinkedIn outreach copy. Powered by LangGraph + OpenRouter. - **URL**: https://apify.com/joseph99/my-actor.md - **Developed by:** [JOSEPH](https://apify.com/joseph99) (community) - **Categories:** AI, Automation, Lead generation - **Stats:** 2 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $200.00 / 1,000 lead enricheds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## B2B Sales Intelligence & Lead Qualification Agent Turn a list of company domains into a full sales intelligence dossier — complete with ICP scores, buying signals, and personalised outreach copy — in seconds. Powered by **LangGraph** + **OpenRouter** (supports GPT-4o, GPT-4o-mini, and more). --- ### What it does For each company you provide, the agent runs a 5-step research pipeline: 1. **Website scrape** — Fetches the homepage and About page to understand what the company actually does 2. **Firmographic enrichment** — Pulls tech stack data (via BuiltWith) and company details 3. **News signals** — Fetches recent headlines from Google News to surface buying triggers (fundraising, hiring, product launches, leadership changes) 4. **ICP scoring** — Scores the company 0–100 on how well it fits your Ideal Customer Profile, with detailed reasoning 5. **Outreach generation** — Writes a personalised cold email (150–200 words) and LinkedIn message (60–80 words) referencing real news signals All results are pushed to a structured dataset you can export as JSON, CSV, or Excel. --- ### Who is this for - **SDRs and AEs** who want to walk into a call knowing the company inside-out - **Sales ops teams** automating prospect research at scale - **Lead generation agencies** delivering enriched lists to clients - **Founders** doing outbound without a sales team --- ### Input | Field | Type | Required | Description | |---|---|---|---| | `leads` | Array | Yes | List of `{ domain, companyName }` objects | | `icpCriteria` | Object | No | Your ICP definition (industries, sizes, regions, pain points) | | `modelName` | String | No | LLM model: `gpt-4o-mini` (default), `gpt-4o`, `o1`, `o3-mini` | | `openaiApiKey` | String | Yes | Your **OpenRouter** API key (get one free at openrouter.ai) | #### Example input ```json { "leads": [ { "domain": "stripe.com", "companyName": "Stripe" }, { "domain": "notion.so", "companyName": "Notion" }, { "domain": "linear.app", "companyName": "Linear" } ], "icpCriteria": { "target_industries": ["SaaS", "FinTech", "Developer Tools"], "target_company_sizes": ["51-200", "201-500"], "target_regions": ["North America", "Europe"], "pain_points": ["manual workflows", "team collaboration", "scaling engineering"] }, "modelName": "gpt-4o-mini", "openaiApiKey": "YOUR_OPENROUTER_API_KEY" } ```` *** ### Output Each lead produces one row in the dataset: | Field | Type | Description | |---|---|---| | `domain` | string | Company domain | | `companyName` | string | Company name | | `icpScore` | number | ICP fit score 0–100 | | `icpReasoning` | string | Why this score was given | | `firmographics` | object | About page text + tech stack snippet | | `newsSignals` | array | Recent news headlines | | `outreachEmail` | string | Ready-to-send cold email | | `outreachLinkedIn` | string | Ready-to-send LinkedIn message | | `error` | string | Error message if a lead failed, null otherwise | *** ### Pricing This actor uses **Pay-Per-Event** (PPE) pricing: | Event | Cost | |---|---| | Actor start | $0.10 per run | | Lead enriched | $1.00 per successfully processed lead | A run with 10 leads costs roughly **$10.10** — far cheaper than Apollo, ZoomInfo, or Clay for equivalent personalised research. *** ### How to get your OpenRouter API key 1. Go to [openrouter.ai](https://openrouter.ai) 2. Sign up for a free account 3. Navigate to **Keys** → **Create Key** 4. Paste the key into the `openaiApiKey` field OpenRouter supports GPT-4o, Claude 3.5, Gemini, and many more models on a pay-as-you-go basis. *** ### Notes - The agent uses Google News RSS and public web pages only — no paid data providers required - Results quality improves with a more detailed `icpCriteria` object - For best results use `gpt-4o`; for fastest/cheapest use `gpt-4o-mini` - Each lead takes approximately 15–40 seconds depending on the model and website speed # Actor input Schema ## `leads` (type: `array`): List of companies to research. Each item must have a 'domain' field (e.g. 'stripe.com') and optionally a 'companyName' field. ## `icpCriteria` (type: `object`): Your Ideal Customer Profile definition. Used to score each lead 0-100. ## `modelName` (type: `string`): The OpenRouter model to use. gpt-4o gives best results; gpt-4o-mini is faster and cheaper. ## `openaiApiKey` (type: `string`): Your OpenRouter API key. It is stored as a secret input and decrypted only for the Actor run. ## Actor input object example ```json { "leads": [ { "domain": "stripe.com", "companyName": "Stripe" }, { "domain": "notion.so", "companyName": "Notion" } ], "icpCriteria": { "target_industries": [ "SaaS", "FinTech", "E-commerce", "MarTech" ], "employee_range": "50-5000", "revenue_range": "$5M-$500M", "target_titles": [ "VP Sales", "Head of Revenue", "CRO", "CEO" ], "pain_points": [ "scaling sales team", "lead quality", "CRM hygiene", "pipeline visibility" ], "tech_stack_must_have": [], "tech_stack_nice_to_have": [ "Salesforce", "HubSpot", "Outreach" ] }, "modelName": "openai/gpt-4o-mini" } ``` # Actor output Schema ## `companyName` (type: `string`): Name of the researched company ## `icpScore` (type: `string`): Ideal Customer Profile fit score 0-100 ## `outreachEmail` (type: `string`): Ready-to-send personalised cold email # 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 = { "leads": [ { "domain": "stripe.com", "companyName": "Stripe" }, { "domain": "notion.so", "companyName": "Notion" } ], "icpCriteria": { "target_industries": [ "SaaS", "FinTech", "E-commerce", "MarTech" ], "employee_range": "50-5000", "revenue_range": "$5M-$500M", "target_titles": [ "VP Sales", "Head of Revenue", "CRO", "CEO" ], "pain_points": [ "scaling sales team", "lead quality", "CRM hygiene", "pipeline visibility" ], "tech_stack_must_have": [], "tech_stack_nice_to_have": [ "Salesforce", "HubSpot", "Outreach" ] }, "modelName": "openai/gpt-4o-mini" }; // Run the Actor and wait for it to finish const run = await client.actor("joseph99/my-actor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "leads": [ { "domain": "stripe.com", "companyName": "Stripe", }, { "domain": "notion.so", "companyName": "Notion", }, ], "icpCriteria": { "target_industries": [ "SaaS", "FinTech", "E-commerce", "MarTech", ], "employee_range": "50-5000", "revenue_range": "$5M-$500M", "target_titles": [ "VP Sales", "Head of Revenue", "CRO", "CEO", ], "pain_points": [ "scaling sales team", "lead quality", "CRM hygiene", "pipeline visibility", ], "tech_stack_must_have": [], "tech_stack_nice_to_have": [ "Salesforce", "HubSpot", "Outreach", ], }, "modelName": "openai/gpt-4o-mini", } # Run the Actor and wait for it to finish run = client.actor("joseph99/my-actor").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 '{ "leads": [ { "domain": "stripe.com", "companyName": "Stripe" }, { "domain": "notion.so", "companyName": "Notion" } ], "icpCriteria": { "target_industries": [ "SaaS", "FinTech", "E-commerce", "MarTech" ], "employee_range": "50-5000", "revenue_range": "$5M-$500M", "target_titles": [ "VP Sales", "Head of Revenue", "CRO", "CEO" ], "pain_points": [ "scaling sales team", "lead quality", "CRM hygiene", "pipeline visibility" ], "tech_stack_must_have": [], "tech_stack_nice_to_have": [ "Salesforce", "HubSpot", "Outreach" ] }, "modelName": "openai/gpt-4o-mini" }' | apify call joseph99/my-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=joseph99/my-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "B2B Sales Intelligence Agent", "description": "AI-powered B2B lead research agent. Scrapes company websites, scores ICP fit (0–100), surfaces buying signals from news, and generates personalised cold email + LinkedIn outreach copy. Powered by LangGraph + OpenRouter.", "version": "0.0", "x-build-id": "XmG4q1fhPWpwqeJIR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/joseph99~my-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-joseph99-my-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/joseph99~my-actor/runs": { "post": { "operationId": "runs-sync-joseph99-my-actor", "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/joseph99~my-actor/run-sync": { "post": { "operationId": "run-sync-joseph99-my-actor", "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": [ "leads", "openaiApiKey" ], "properties": { "leads": { "title": "Leads to enrich", "type": "array", "description": "List of companies to research. Each item must have a 'domain' field (e.g. 'stripe.com') and optionally a 'companyName' field.", "default": [] }, "icpCriteria": { "title": "ICP Criteria", "type": "object", "description": "Your Ideal Customer Profile definition. Used to score each lead 0-100." }, "modelName": { "title": "OpenRouter model", "enum": [ "openai/gpt-4o", "openai/gpt-4o-mini", "openai/o1", "openai/o3-mini" ], "type": "string", "description": "The OpenRouter model to use. gpt-4o gives best results; gpt-4o-mini is faster and cheaper.", "default": "openai/gpt-4o-mini" }, "openaiApiKey": { "title": "OpenRouter API Key", "type": "string", "description": "Your OpenRouter API key. It is stored as a secret input and decrypted only for the Actor run." } } }, "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 } } } } } } } } } } ```