# LinkedIn Lead AI Outreach Message Generator (`shahabuddin38/linkedin-lead-ai-outreach-message-generator`) Actor Generate personalized LinkedIn outreach messages, follow-ups, and email versions from lead data. Score prospects, detect pain points, and create reply-focused sequences that feel human and relevant. No scraping or automation, just smarter outreach to connect and convert better leads. - **URL**: https://apify.com/shahabuddin38/linkedin-lead-ai-outreach-message-generator.md - **Developed by:** [Shahab Uddin](https://apify.com/shahabuddin38) (community) - **Categories:** Lead generation, Social media, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## LinkedIn Lead & AI Outreach Message Generator Generate personalized LinkedIn outreach messages, follow-ups, email versions, pain-point angles, and lead scores from B2B prospect data. Turn simple lead details into reply-focused outreach sequences without scraping or automating LinkedIn. This Actor is a LinkedIn outreach generator and LinkedIn message generator built for compliant B2B lead outreach. It helps teams turn lead notes, pasted CSVs, public company details, and LinkedIn-style profile data into personalized outreach messages, a follow-up message generator flow, and a sales outreach sequence that is easy to review before sending. If you need an AI sales message generator or LinkedIn prospecting tool for freelancers, agencies, recruiters, consultants, and SaaS teams, this Actor focuses on qualification, messaging quality, and practical prioritization instead of risky automation. ### What this Actor does - Accepts single leads, arrays of leads, or pasted CSV text. - Normalizes and deduplicates lead data. - Scores lead quality, decision-maker likelihood, personalization strength, reply probability, and overall priority. - Generates likely pain-point angles based on the lead, offer, and company context. - Creates a full message set: - LinkedIn connection request - First message - Follow-up 1 - Follow-up 2 - Soft breakup message - Email subject - Email version - DM version - Builds a simple 4-step outreach sequence for Day 0, Day 1, Day 3, and Day 7. - Adds objection handling suggestions and a final outreach strategy report. - Works without AI keys using rule-based templates, and can optionally improve wording with `OPENAI_API_KEY` or `GEMINI_API_KEY`. ### Who it is for - B2B lead generation agencies - LinkedIn outreach freelancers - SEO agencies - Web design agencies - Recruiters - SaaS sales teams - Consultants - Upwork freelancers doing outreach ### Use cases - Prioritize founder and decision-maker leads before outreach. - Turn messy lead spreadsheets into outreach-ready records. - Generate short, human LinkedIn messages for SaaS, agency, recruiting, and consulting offers. - Create email versions from the same lead context. - Produce follow-up sequences that stay pain-point focused instead of sounding generic. - Give junior SDRs or freelancers a review-ready draft to personalize before sending. ### Input The Actor accepts: - `leads`: array of lead objects - `csvText`: pasted CSV text - `offerOrService` - `targetAudience` - `messageGoal` - `messageStyle` - `tone` - `language` - `includeEmailVersion` - `includeObjectionHandling` - `includeSequence` - `includeLeadScoring` - `maxLeads` - `exportFormat` Lead objects can include: - `name` - `jobTitle` - `companyName` - `industry` - `location` - `linkedinProfileUrl` - `companyUrl` - `website` - `companyDescription` - `recentPostText` - `painPoint` - `offerOrService` ### Output Billable dataset rows contain outreach-ready lead records with: - Lead details - Pain angles - Lead and reply scoring - LinkedIn message assets - Email assets - Outreach sequence - Objection handling - CTA recommendation - Compliance note The Actor also saves `OUTPUT.json` with: - Summary stats - Top leads - Best messages - Outreach strategy recommendations - Limitations ### Message generation Messages are built to stay: - Short - Human - Specific - Pain-point focused - Permission-based - Under the required character limits Supported styles: - `soft` - `direct` - `consultative` - `founder_style` - `agency_style` - `recruiter_style` Character limits enforced: - Connection request: `<= 280` - First message: `<= 600` - Follow-ups: `<= 500` - Email version: `<= 120 words` ### Lead scoring The Actor scores each lead using: - Decision-maker title signals - Industry fit - Pain signal clarity - Company description presence - Website presence - Location presence - Recent post or contextual signal presence It also estimates: - `leadQualityScore` - `decisionMakerScore` - `personalizationScore` - `replyProbabilityScore` - `priorityScore` ### Compliance notes This Actor does not send messages, automate LinkedIn, or bypass LinkedIn protections. Users must review and send messages manually or through compliant workflows. This Actor is not positioned as a scraper, LinkedIn bot, inbox automation tool, or spam tool. It is a lead qualification and personalized outreach generation engine. ### Billing notes Only outreach-ready lead/message rows are written as billable dataset results. Summaries and strategy reports are saved to `OUTPUT.json`. Recommended launch pricing: - `$0.01` per outreach-ready lead/message row - `$0.00005` actor start fee Suggested post-validation pricing: - `$0.02` per generated sequence ### Limitations - The Actor does not log into LinkedIn. - The Actor does not scrape private LinkedIn data. - The Actor does not automate sending or follow-up delivery. - The quality of personalization depends on the quality of the lead data provided. - Optional AI enhancement is best-effort only and never replaces the rule-based fallback. ### FAQ #### Does this Actor scrape LinkedIn? No. It only processes user-provided lead data, pasted CSVs, manual inputs, and public context the user already provides. #### Does it send connection requests or DMs? No. It generates messages and recommendations only. #### Do I need an AI API key? No. The Actor works without AI keys using rule-based templates. If `OPENAI_API_KEY` or `GEMINI_API_KEY` is available, the Actor can refine wording without blocking the run. #### What dataset rows are billable? Only high-value outreach rows are pushed to the dataset. Summary-only information is written to `OUTPUT.json` instead of the dataset. #### What should the Store thumbnail say? Suggested thumbnail copy: - `LINKEDIN OUTREACH` - `AI MESSAGES` - `Lead -> Message -> Reply` # Actor input Schema ## `leads` (type: `array`): Lead objects with name, job title, company, LinkedIn URL, website, industry, and pain point. ## `csvText` (type: `string`): Optional pasted CSV text with columns like name, jobTitle, companyName, industry, location, website, linkedinProfileUrl. ## `offerOrService` (type: `string`): What you want to sell or pitch. ## `targetAudience` (type: `string`): Who you want to contact. ## `messageGoal` (type: `string`): What action the outreach should aim to create. ## `messageStyle` (type: `string`): The messaging style to use for the generated outreach. ## `tone` (type: `string`): Tone of voice for the generated messages. ## `language` (type: `string`): Language for message generation. ## `includeEmailVersion` (type: `boolean`): Whether to include an email subject and email version for each lead. ## `includeObjectionHandling` (type: `boolean`): Whether to generate common objections and suggested replies. ## `includeSequence` (type: `boolean`): Whether to generate the 4-step outreach sequence. ## `includeLeadScoring` (type: `boolean`): Whether to include lead qualification and reply scoring in the output. ## `maxLeads` (type: `integer`): Maximum number of leads to process and return. ## `exportFormat` (type: `string`): How to save the generated results in storage. ## Actor input object example ```json { "leads": [ { "name": "Alex Morgan", "jobTitle": "Founder", "companyName": "GrowthPilot SaaS", "industry": "SaaS", "location": "United States", "linkedinProfileUrl": "https://www.linkedin.com/in/example", "companyUrl": "https://www.linkedin.com/company/example", "website": "https://example.com", "companyDescription": "B2B SaaS platform for sales teams", "recentPostText": "We are working on improving inbound growth this quarter.", "painPoint": "", "offerOrService": "SEO growth strategy" } ], "csvText": "", "offerOrService": "SEO growth strategy", "targetAudience": "B2B SaaS founders", "messageGoal": "book_call", "messageStyle": "consultative", "tone": "friendly", "language": "en", "includeEmailVersion": true, "includeObjectionHandling": true, "includeSequence": true, "includeLeadScoring": true, "maxLeads": 25, "exportFormat": "dataset" } ```` # Actor output Schema ## `results` (type: `string`): No description ## `report` (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 = { "leads": [ { "name": "Alex Morgan", "jobTitle": "Founder", "companyName": "GrowthPilot SaaS", "industry": "SaaS", "location": "United States", "linkedinProfileUrl": "https://www.linkedin.com/in/example", "companyUrl": "https://www.linkedin.com/company/example", "website": "https://example.com", "companyDescription": "B2B SaaS platform for sales teams", "recentPostText": "We are working on improving inbound growth this quarter.", "painPoint": "", "offerOrService": "SEO growth strategy" } ], "offerOrService": "SEO growth strategy", "targetAudience": "B2B SaaS founders" }; // Run the Actor and wait for it to finish const run = await client.actor("shahabuddin38/linkedin-lead-ai-outreach-message-generator").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": [{ "name": "Alex Morgan", "jobTitle": "Founder", "companyName": "GrowthPilot SaaS", "industry": "SaaS", "location": "United States", "linkedinProfileUrl": "https://www.linkedin.com/in/example", "companyUrl": "https://www.linkedin.com/company/example", "website": "https://example.com", "companyDescription": "B2B SaaS platform for sales teams", "recentPostText": "We are working on improving inbound growth this quarter.", "painPoint": "", "offerOrService": "SEO growth strategy", }], "offerOrService": "SEO growth strategy", "targetAudience": "B2B SaaS founders", } # Run the Actor and wait for it to finish run = client.actor("shahabuddin38/linkedin-lead-ai-outreach-message-generator").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": [ { "name": "Alex Morgan", "jobTitle": "Founder", "companyName": "GrowthPilot SaaS", "industry": "SaaS", "location": "United States", "linkedinProfileUrl": "https://www.linkedin.com/in/example", "companyUrl": "https://www.linkedin.com/company/example", "website": "https://example.com", "companyDescription": "B2B SaaS platform for sales teams", "recentPostText": "We are working on improving inbound growth this quarter.", "painPoint": "", "offerOrService": "SEO growth strategy" } ], "offerOrService": "SEO growth strategy", "targetAudience": "B2B SaaS founders" }' | apify call shahabuddin38/linkedin-lead-ai-outreach-message-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shahabuddin38/linkedin-lead-ai-outreach-message-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Lead AI Outreach Message Generator", "description": "Generate personalized LinkedIn outreach messages, follow-ups, and email versions from lead data. Score prospects, detect pain points, and create reply-focused sequences that feel human and relevant. No scraping or automation, just smarter outreach to connect and convert better leads.", "version": "0.1", "x-build-id": "FeBpG1umZf3qXftC4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shahabuddin38~linkedin-lead-ai-outreach-message-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shahabuddin38-linkedin-lead-ai-outreach-message-generator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shahabuddin38~linkedin-lead-ai-outreach-message-generator/runs": { "post": { "operationId": "runs-sync-shahabuddin38-linkedin-lead-ai-outreach-message-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shahabuddin38~linkedin-lead-ai-outreach-message-generator/run-sync": { "post": { "operationId": "run-sync-shahabuddin38-linkedin-lead-ai-outreach-message-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "offerOrService" ], "properties": { "leads": { "title": "Leads", "type": "array", "description": "Lead objects with name, job title, company, LinkedIn URL, website, industry, and pain point." }, "csvText": { "title": "Paste CSV Leads", "type": "string", "description": "Optional pasted CSV text with columns like name, jobTitle, companyName, industry, location, website, linkedinProfileUrl.", "default": "" }, "offerOrService": { "title": "Your Offer / Service", "type": "string", "description": "What you want to sell or pitch." }, "targetAudience": { "title": "Target Audience", "type": "string", "description": "Who you want to contact." }, "messageGoal": { "title": "Outreach Goal", "enum": [ "book_call", "free_audit", "start_conversation", "send_resource", "hire_candidate", "partnership" ], "type": "string", "description": "What action the outreach should aim to create.", "default": "book_call" }, "messageStyle": { "title": "Message Style", "enum": [ "soft", "direct", "consultative", "founder_style", "agency_style", "recruiter_style" ], "type": "string", "description": "The messaging style to use for the generated outreach.", "default": "consultative" }, "tone": { "title": "Tone", "enum": [ "friendly", "professional", "casual", "confident", "warm" ], "type": "string", "description": "Tone of voice for the generated messages.", "default": "friendly" }, "language": { "title": "Language", "enum": [ "en", "es", "fr", "de", "it", "pt", "ur", "hi", "ar" ], "type": "string", "description": "Language for message generation.", "default": "en" }, "includeEmailVersion": { "title": "Include Email Version", "type": "boolean", "description": "Whether to include an email subject and email version for each lead.", "default": true }, "includeObjectionHandling": { "title": "Include Objection Handling", "type": "boolean", "description": "Whether to generate common objections and suggested replies.", "default": true }, "includeSequence": { "title": "Include Follow-up Sequence", "type": "boolean", "description": "Whether to generate the 4-step outreach sequence.", "default": true }, "includeLeadScoring": { "title": "Include Lead Scoring", "type": "boolean", "description": "Whether to include lead qualification and reply scoring in the output.", "default": true }, "maxLeads": { "title": "Maximum Leads", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of leads to process and return.", "default": 25 }, "exportFormat": { "title": "Export Format", "enum": [ "dataset", "json", "csv-ready" ], "type": "string", "description": "How to save the generated results in storage.", "default": "dataset" } } }, "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", 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0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```