Autonomous Lead Generation & Outreach – Top 30 Open-Source Projects

Community Article Published February 27, 2025

Automation of money is nice. I am quite interested in this. Who is not? So here I share my results with you. Here is an extensive search for the best autonomous AI lead generation and outreach systems that meet your needs. I'll focus on fully automated solutions that:

Scrape and extract leads from multiple sources (websites, LinkedIn, email, databases, etc.)
Automate personalized messaging across multiple channels (email, LinkedIn, SMS, WhatsApp, etc.)
Maintain lead interaction, follow-ups, and correspondence without manual intervention
Use AI to craft customized messages based on the product, service, or objective
Prioritize simplicity, requiring the fewest dependencies or repositories
Provide deployment-ready scripts and configurations

I'll identify 30 top repositories with their stats (stars, last commit date, activity level) and provide launch scripts, an analysis of the best approaches, and a prompt to set up each system optimally.

Overview: We identified 30 open-source projects enabling end-to-end lead generation and automated outreach. These include AI-driven sales agents, multi-channel marketing platforms, web scrapers for lead data, and automation tools. Below we rank the top repositories, detailing their automation level, lead extraction methods, messaging capabilities, community stats, dependencies, and deployment readiness. We then compare different approaches (scrapers vs. full-stack platforms vs. AI agents, etc.), provide deployment guidance, and recommend an optimal solution. Finally, we include sample AI prompts to enhance outreach efficacy.

Top 30 Open-Source Repositories for Lead Gen & Outreach

SalesGPT (filip-michalsky/SalesGPT) – AI Sales Agent (Voice, Email, SMS, WhatsApp). A context-aware sales agent built on LLMs that can engage leads across voice calls, emails and texting (SMS, WhatsApp, WeChat, etc.) (GitHub - filip-michalsky/SalesGPT: Context-aware AI Sales Agent to automate sales outreach.). It maintains conversation context to handle different stages of a sales funnel. Automation: Fully autonomous once started – it understands conversation stages and uses product knowledge bases to reduce AI hallucinations. Lead Extraction: Not a scraper; assumes leads are provided or incoming. Messaging: Uses GPT-4 for dialogue; can integrate with phone/SMS APIs for voice or text outreach. Stats: 2.2k stars on GitHub (GitHub - filip-michalsky/SalesGPT: Context-aware AI Sales Agent to automate sales outreach.); actively updated. Dependencies: Python (LangChain, GPT APIs, telephony APIs). Deployment: Docker-compose provided (GitHub - filip-michalsky/SalesGPT: Context-aware AI Sales Agent to automate sales outreach.) for quick setup. Pros: Highly conversational AI, multi-channel support (GitHub - filip-michalsky/SalesGPT: Context-aware AI Sales Agent to automate sales outreach.). Cons: Not designed to find new leads; focuses on engaging existing contacts.
Dittofeed (dittofeed/dittofeed) – Omni-Channel Customer Engagement Platform. An open-source alternative to Customer.io/Braze for automated marketing journeys. Supports email, SMS, mobile push, WhatsApp, Slack and more (GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ). Automation: High – GUI to design workflows (“journeys”) that trigger messages across channels based on user events. Minimal ongoing input after setup. Lead Extraction: Not a scraper, but segments and imports customer data via API or ETL. Messaging: Templates (HTML/MJML), personalization with user attributes, and scheduling. Stats: New project (Y Combinator W23) – 200 stars. Dependencies: Node/TypeScript app with Postgres, Redis; integrates with SendGrid, Twilio, etc. Deployment: Docker-based install and even one-click Render deploy ([GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ](https://github.com/dittofeed/dittofeed#::text=Check%20out%20our%20walkthrough%20video,documentation%20can%20be%20found%20here)). Pros: Full-stack multi-channel automation, low-code segment and template builders (GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ) (GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ). Cons: Young project (fast evolving, smaller community).
theHarvester (laramies/theHarvester) – OSINT Contact Harvester. A long-standing reconnaissance tool to gather emails, names, and domains from multiple public sources (Intel 471 | Attack Surface Documentation). Automation: High – run it with a company/domain name, it auto-queries search engines, breach databases, etc., yielding emails and employee names. Minimal user input beyond target query. Lead Extraction: Uses ~~20+ search modules (Google, Bing, LinkedIn (via Bing), etc.) to find emails and names ([Intel 471 | Attack Surface Documentation](https://intel471.com/attack-surface-documentation#:~~:text=SpiderFoot%20is%20a%20reconnaissance%20tool,they%20relate%20to%20each%20other)). Messaging: None – this tool is focused on lead data collection; outputs results for use in outreach campaigns. Stats: ~~12k GitHub stars ([GitHub - laramies/theHarvester: E-mails, subdomains and names Harvester - OSINT](https://github.com/laramies/theHarvester#:~~:text=12,Tags%20%20%20Activity)); widely used in security and lead-gen alike. Dependencies: Python; optional API keys for certain sources (e.g. Hunter.io). Deployment: Simple CLI tool; has a Docker image for ease. Pros: Broad multi-source scraping (public web, certificates, etc.), no coding needed (Intel 471 | Attack Surface Documentation). Cons: No built-in outreach or CRM integration (data must be exported to email tools).
Omkarcloud Google-Maps-Scraper – Google Maps Lead Scraper with UI. A popular tool (1.4k⭐) to extract local business info from Google Maps (GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ). Automation: High – provides a UI where you enter queries (business type & location) and it scrapes results automatically in bulk. Minimal input after initial query. Lead Extraction: Scrapes names, addresses, phones, websites, ratings, and can even pull emails from websites (GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ). It supports batch queries and compiles results in minutes. Messaging: None built-in, but data (including emails when found) can feed into email outreach tools. Stats: 1.4k stars, actively maintained with cross-platform binaries (Windows/Mac/Linux) (GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ) (GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ). Dependencies: Electron/Node app (one-click installers available). Deployment: Not code-focused – use pre-built executable or run via Node; no complex setup. Pros: Easy UI, fast scraping (120 results in 2 minutes) ([GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ](https://github.com/omkarcloud/google-maps-scraper#::text=2%EF%B8%8F%E2%83%A3%20Now%2C%20Press%20the%20Run,search%20results%20within%202%20minutes)) (GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ). Cons: Limited to Google Maps; relies on Google’s interface (subject to UI changes or Google blocking).
Gosom Google-Maps-Scraper – Google Maps Scraper (Go-based CLI/Web). Another open-source scraper (1.2k⭐) that extracts similar data (business name, address, phone, website, coordinates, etc.) (GitHub - gosom/google-maps-scraper: scrape data data from Google Maps. Extracts data such as the name, address, phone number, website URL, rating, reviews number, latitude and longitude, reviews,email and more for each place). Automation: High – offers both command-line usage and a web UI for bulk queries. Supports solving reCAPTCHAs via external services (GitHub - gosom/google-maps-scraper: scrape data data from Google Maps. Extracts data such as the name, address, phone number, website URL, rating, reviews number, latitude and longitude, reviews,email and more for each place). Lead Extraction: Focused on Google Maps results; can use keywords or specific URLs. It can also retrieve emails by visiting each business’s website (if accessible). Messaging: No messaging, data output to CSV/JSON for later use. Stats: 1.2k stars ([GitHub - gosom/google-maps-scraper: scrape data data from Google Maps. Extracts data such as the name, address, phone number, website URL, rating, reviews number, latitude and longitude, reviews,email and more for each place](https://github.com/gosom/google-maps-scraper#::text=1,Tags%20%20%20Activity)); written in Go for performance and portability. Dependencies: Go binary (standalone). Deployment: Very easy – single binary or Docker; includes a web server for browser-based operation. Pros: Fast and scalable (compiled language), includes anti-CAPTCHA and Proxy support (GitHub - gosom/google-maps-scraper: scrape data data from Google Maps. Extracts data such as the name, address, phone number, website URL, rating, reviews number, latitude and longitude, reviews,email and more for each place). Cons: Channel-limited (Google Maps only), requires combining with email tools for outreach.
Kaymen99 AI Sales Outreach (LangGraph Agents) – Automated Lead Research & Outreach Reports. A Python project integrating LangChain agents to research leads and generate personalized outreach content (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)) (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)). Automation: High – once configured with a CRM and OpenAI API, it will fetch new leads, scrape information, analyze pain points, and prepare custom outreach materials with minimal user intervention (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)) (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)). Lead Extraction: Pulls leads from connected CRMs (HubSpot, Airtable, Google Sheets) and then scrapes each lead’s LinkedIn profile, company website, news, and social media to gather context (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)) (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)). Messaging: Generates personalized email drafts and outreach reports addressing each lead’s specific challenges (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)). It does not send the emails itself, but provides the content and updates the CRM with notes. Stats: 66 stars (niche but innovative) ([GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)](https://github.com/kaymen99/sales-outreach-automation-langgraph#::text=,Star%2066)); last commit mid-2023. Dependencies: Python (LangChain, Playwright/Selenium for web scraping, OpenAI API). Deployment: Docker and .env example included; requires API keys and browser automation setup. Pros: Deep insight generation (company news, digital presence analysis) leading to highly tailored pitches (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)) (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)). Cons: Does not automate the actual sending of outreach – user must email or import the generated content. Setup is somewhat complex (multiple APIs, headless browser for LinkedIn).
Madi-S Lead-Generation – Bulk Lead Generation Scripts (Python). A collection of Python scripts and techniques to generate leads at scale (GitHub - Madi-S/Lead-Generation: Python script, which empowers people with no programming background to generate robust leads on a mass scale. This repo will be compiled of various versatile techniques used in lead generation.). Automation: Medium-High – aims to empower non-programmers to gather leads via various methods (web scraping, email pattern guessing, etc.) using simple configurations. Lead Extraction: Multi-source – e.g., could scrape websites for contact info, use search engine queries, or integrate with APIs. (The project description suggests versatility, though specific modules include LinkedIn scraping, email validation, etc.) Messaging: Not the main focus; primarily about data collection. May include sample email templates or integration with an email sender (if combined with its PyPI package). Stats: 140 stars ([GitHub - Madi-S/Lead-Generation: Python script, which empowers people with no programming background to generate robust leads on a mass scale. This repo will be compiled of various versatile techniques used in lead generation.](https://github.com/Madi-S/Lead-Generation#::text=,Star%20140)); actively maintained through 2024 (v1.0.1 in Sep 2024) (GitHub - Madi-S/Lead-Generation: Python script, which empowers people with no programming background to generate robust leads on a mass scale. This repo will be compiled of various versatile techniques used in lead generation.). Dependencies: Python (requests, BeautifulSoup, Playwright/Chromedriver). Deployment: Provided as a pip package (py-lead-generation) (GitHub - Madi-S/Lead-Generation: Python script, which empowers people with no programming background to generate robust leads on a mass scale. This repo will be compiled of various versatile techniques used in lead generation.) for easy install; run via command line or Jupyter notebooks. Pros: Broad “toolkit” approach – multiple scraping and parsing strategies in one project. Good for experimentation across sources. Cons: Requires some assembly – user might need to choose and configure techniques. Lacks an integrated outreach component (focuses on finding leads, not contacting them).
JoshiAyush InB (LinkedIn Automation) – Automate LinkedIn Networking. An open-source tool to automate LinkedIn tasks: send connection requests, message contacts, endorse skills, etc. (GitHub - joshiayush/inb: Automate the world of LinkedIn!). Automation: High – once logged in (uses LinkedIn’s unofficial Voyager API), it can run sequences of actions without manual input. Great for scaling outreach on LinkedIn. Lead Extraction: It can scrape profile data of search results (names, titles, etc.) via the LinkedIn API, effectively building lead lists from Sales Navigator searches. Messaging: Yes – can auto-send connection invites and follow-up messages (with templating for personalization). Does not use AI for content, but you can define message templates. Stats: ~~87 stars ([GitHub - joshiayush/inb: Automate the world of LinkedIn!](https://github.com/joshiayush/inb#:~~:text=)); actively developed (800+ commits) (GitHub - joshiayush/inb: Automate the world of LinkedIn!). Dependencies: Python; no official API key needed (uses your LinkedIn cookies/session). Deployment: Dockerfile and manage.sh provided (GitHub - joshiayush/inb: Automate the world of LinkedIn!) (GitHub - joshiayush/inb: Automate the world of LinkedIn!) – setup requires a LinkedIn account login. Pros: Eliminates tedious LinkedIn actions – “connect with 100s of prospects” automatically (GitHub - joshiayush/inb: Automate the world of LinkedIn!). Supports multi-account management. Cons: Limited to LinkedIn platform; risk of account restrictions if overused. No built-in email/SMS integration (it’s LinkedIn-specific).
LeadBrowser (Geolavor/leads-generator-app) – AI-Powered Prospect Finder. A self-hosted tool (in development) aiming to find B2B prospects in real-time, touted as an open alternative to Hunter.io/Snov.io (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io) (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io). Automation: High – the vision is a “browser-like” interface where you input a search phrase and it live-searches the web for companies and contacts. Little user work aside from entering queries. Lead Extraction: Multiple sources – LinkedIn search (employees by role) without plugins, “Live search” crawling websites by keywords, plus a curated database marketplace (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io) (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io). It also uses AI to classify businesses and calculate a “contact risk score” (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io) (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io). Messaging: None yet (focused on data gathering). Stats: Still early – currently 0 stars (20% of code withheld pending research) (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io), but demo available. Dependencies: PHP (Laravel) backend with browser automation. Deployment: Docker-compose dev available but not fully open-sourced; project expected to fully release in the next year (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io). Pros: Ambitious scope – real-time web crawling, AI-based filtering, and integrated data validation (DNS info, email verification) (GitHub - Geolavor/leads-generator-app: Prospects AI browser. Unique AI tool to extract (prospects) contact details from all over the Internet, from all over the world in the real time. Better alternative to Hunter.io and Snov.io). Cons: Not fully released (as of now), so community adoption is minimal. Lacks outreach features.
Wikkiee LeadGenPy – Google Maps to Email Outreach Pipeline. A Python script that not only scrapes Google Maps for businesses but also auto-generates personalized emails using ChatGPT and sends them out (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email). Automation: High – a single tool performs end-to-end lead generation: search for businesses, extract their info, compose an email, and send it. User just chooses mode (0=exit, 1=extract, 4=generate emails, 5=full auto) via CLI prompts (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email). Lead Extraction: Uses Selenium to query Google Maps for a given business type & location, looping through results. For each place it collects name, address, phone, Google Maps URL, and attempts to find an email (via the place’s website) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email). Stores leads in CSV/JSON. Messaging: Yes – in “Mode 4/5”, it calls the OpenAI API to craft a personalized email for each lead (using the scraped data as context), then automatically sends the email to the extracted address (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email). Stats: 11 stars; created in 2023. Dependencies: Python (Selenium, BeautifulSoup, openai library) – needs ChromeDriver and API keys (.env for OpenAI, Google Sheets, email SMTP) ([GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email](https://github.com/Wikkiee/LeadGenPy#::text=3)) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email). Deployment: Local script – configure .env, install requirements, run main.py. Pros: Highly autonomous – can go from “zero to outreach” with one command (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email) (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email). Good for quick local lead campaigns. Cons: Relies on web scraping which can be brittle (Google changes or IP blocks). Limited email personalization (uses a single GPT prompt for all leads). Not suited for large-scale campaigns (single-thread Selenium).
PaulleDemon Email-Automation – Cold Email Campaign Manager. A web-based tool to send personalized cold emails and automated follow-ups on schedule (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). Automation: High – after you create templates and upload contacts, it will send emails and follow-ups per your schedule without manual sending. Supports conditional logic in templates. Lead Extraction: None (expects you import a list of leads or type them in). Messaging: Yes – dynamic templates with variables and IF/ELSE logic (Jinja2 syntax) for custom content (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). You can schedule multi-step drip sequences (e.g. 1st email, then auto follow-up 5 days later if no reply) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). It deliberately warns against spamming and suggests using proper mail servers (not Gmail) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). Stats: ~~67 stars ([GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool](https://github.com/PaulleDemon/Email-automation#:~~:text=67%20stars%20%20%2019,Tags%20%20%20Activity)); web app built with Django. Dependencies: Python (Django, Celery), uses SMTP to send emails (works with any email provider). Deployment: Docker/Heroku configs included (Procfile, vercel.json) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). After deploy, define templates and schedule via the UI. Pros: Open-source alternative to tools like Mailshake – you control your data. Follow-up logic and scheduling built-in (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). Cons: Single-channel (email only). No AI content generation – emails are semi-personalized via merge fields, but content relies on your templates.
Parcelvoy Platform – Open-Source Multi-Channel Marketing Automation. A self-hosted customer messaging tool for data-driven campaigns across email, SMS, push notifications, etc. (GitHub - parcelvoy/platform: Parcelvoy: Open source multi-channel marketing automation platform. Send data-driven emails, sms, push notifications and more!). Automation: High – supports event-triggered messages, batch campaigns, and user segmentation. Once configured, it runs continuously. Lead Extraction: Assumes you have/contact lists (no scraping). It focuses on messaging leads imported into it. It can ingest data via API to build segments. Messaging: Yes – send templated messages on multiple channels from one place. For example, it can send an email and an SMS as part of one campaign. It’s comparable to Braze or Iterable in functionality. Stats: 300 stars ([GitHub - parcelvoy/platform: Parcelvoy: Open source multi-channel marketing automation platform. Send data-driven emails, sms, push notifications and more!](https://github.com/parcelvoy/platform#::text=,52)); project is a full webapp (Rails/Node stack). Dependencies: Ruby on Rails backend, uses PostgreSQL and Redis; has React front-end. Deployment: Docker images available; or deploy on a server with standard Rails setup. Pros: Multi-channel out-of-the-box (GitHub - parcelvoy/platform: Parcelvoy: Open source multi-channel marketing automation platform. Send data-driven emails, sms, push notifications and more!) (email, SMS, web push). Good for lead nurturing once you have leads – can automate sequences across channels. Cons: Does not fetch new leads – you feed it contacts. Setup is heavier (requires webapp deployment). Smaller community than older projects like Mautic.
n8n.io (Workflow Automation) – Low-Code Automation for Lead Workflows. n8n is a general open-source workflow automation tool (similar to Zapier) with many nodes for APIs, allowing you to chain lead generation tasks visually. Automation: High – you can design a workflow once (e.g. “scrape website -> enrich data -> send email”) and n8n will run it on a schedule or trigger with no further input. Lead Extraction: Via integration nodes – e.g. it has an HTTP node for web scraping or dedicated nodes for services. There are community templates like one that enriches company info using GPT-3 (Automate LinkedIn Outreach with Notion and OpenAI | n8n workflow template), or a LinkedIn outreach workflow using Notion + OpenAI. Messaging: Yes – nodes exist for SMTP email, Slack, WhatsApp (Twilio), etc. You can incorporate an OpenAI node to generate content and then an Email node to send it – all in one automated flow. Stats: 30k+ stars; very active community. Dependencies: Node.js; provides a Docker container. Deployment: One-line Docker or use n8n cloud. Many ready “templates” for marketing (e.g. GPT-3 company enrichment workflow (Automate LinkedIn Outreach with Notion and OpenAI | n8n workflow template)). Pros: Extremely flexible – you can integrate multiple tools (scrapers, CRM APIs, email senders) without coding. There are even pre-built workflows for sales outreach and lead enrichment (Automate LinkedIn Outreach with Notion and OpenAI | n8n workflow template). Cons: Not specialized to lead-gen, so you must design the workflow (learning curve). Also requires self-hosting a server or using their cloud for always-on automation.
Apify Scrapers (Actors) – Library of Web Scrapers for Leads. Apify’s open-source actors cover scraping many sources like LinkedIn, Yellow Pages, Yelp, etc., which you can self-host (GitHub - cermak-petr/actor-yellowpages-scraper: Apify actor for scraping information from Yellow Pages listings based on search term and location or a list of URLs.). For example, Petr Cermak’s YellowPages actor will search YellowPages by keyword+location and output business details (GitHub - cermak-petr/actor-yellowpages-scraper: Apify actor for scraping information from Yellow Pages listings based on search term and location or a list of URLs.). Automation: High – these actors run with minimal input (just search queries or URLs). Lead Extraction: Wide range depending on actor – LinkedIn Company/People scrapers (to get profiles), Yellow Pages (local businesses), firmographic databases, etc. They often leverage headless Chrome to navigate sites and parse contact info. Messaging: None – they focus on data. However, you can chain Apify scrapers with an email-sending tool. Stats: Many actors are on GitHub with moderate stars (e.g. Yellow Pages scraper ~30 commits) and Apify SDK itself is well-known. Dependencies: Node.js (Apify SDK); each actor may require API keys or proxies for large volume. Deployment: Can run on Apify Cloud or via Docker locally (each actor usually has a Dockerfile). Pros: Huge variety – effectively an open-source toolbox to scrape almost any lead source (LinkedIn profiles, Google Maps (as above), GitHub contributors, etc.). Maintained by experts and battle-tested for web changes. Cons: Each actor is separate – you might need to orchestrate multiple to cover all sources. Some scraping may require paid proxies or solving CAPTCHAs for reliability.
Rasa – Open-Source Conversational AI Framework. Rasa provides the infrastructure to build AI chatbots that can converse on channels like web chat, WhatsApp (via Twilio), SMS, Telegram, etc. (RASA - Messaging channels - Twilio - DEV Community). In a lead-gen context, you could use Rasa to create a bot that handles lead qualification dialogues or responds to inbound queries 24/7. Automation: High – once trained, the bot can handle conversations autonomously. It uses machine learning to understand intents and entities, and a dialogue manager to decide responses. Lead Extraction: Not from web sources, but it can collect lead info from users in a conversation (e.g. ask for name, email). It’s not a scraper; it’s for conversation automation. Messaging: Yes – connects to many messaging channels out-of-the-box (e.g. Twilio for WhatsApp/SMS, Slack, Facebook Messenger) (RASA - Messaging channels - Twilio - DEV Community). Rasa’s connectors allow deploying one bot across multiple platforms (RASA - Messaging channels - Twilio - DEV Community). Stats: 16k+ stars; very active enterprise-backed project. Dependencies: Python; requires training data (intents, example phrases). Deployment: Runs on-premise; can use Docker images. Integrating a new channel is as simple as adding credentials (for Twilio, etc.) and running the connector (RASA - Messaging channels - Twilio - DEV Community) (RASA - Messaging channels - Twilio - DEV Community). Pros: Lets you automate 1-to-1 lead conversations in a natural way. Pre-built connectors for popular channels make multi-channel bot deployment straightforward (RASA - Messaging channels - Twilio - DEV Community). Highly customizable AI behavior. Cons: Significant effort to design and train a good bot. Not focused on outbound outreach (better for inbound or chat follow-ups), so it pairs well with other tools rather than replacing email campaigns.
ColdContactXLSX – Automated Email Outreach for Job Leads. An open-source tool originally made for job seekers, but applicable to sales: it generates possible email addresses for contacts and sends outreach emails using templates (GitHub - aasthas2022/ColdContactXLSX: ColdContactXLSX automates personalized cold email outreach for job seekers, saving time and effort in reaching out to recruiters by generating potential email addresses and providing customizable templates.). Automation: Medium – it automates email delivery but still requires the user to supply names/company or a list of prospects (in XLSX). It then guesses email addresses (e.g. [email protected]) and sends a personalized email to each. Lead Extraction: Does not find leads on its own; it helps reach out once you have names/companies. It can generate common email permutations and even verify them via SMTP. Messaging: Yes – you can define an email template with placeholders. The script fills those (e.g. recipient name, role) and sends via an email server. No AI content, but personalization via fields. Stats: 18 stars ([GitHub - aasthas2022/ColdContactXLSX: ColdContactXLSX automates personalized cold email outreach for job seekers, saving time and effort in reaching out to recruiters by generating potential email addresses and providing customizable templates.](https://github.com/aasthas2022/ColdContactXLSX#::text=,Star%2018)). Dependencies: Python; uses smtplib, openpyxl for Excel, etc. Deployment: Run as a Python script; provide your SMTP creds and an Excel of contacts. Pros: Good for automating a personalized email blast to a list of leads you’ve collected (like a DIY mail merge) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool). It even helps in finding the correct email if you have a name+company by trying variations. Cons: Very narrow scope (email outreach only; tailored to job hunting scenario). Lacks web UI – fairly technical to configure (requires editing script or input files).
YCombinator Companies Scraper – Startup Lead Scraper. An example of a specialized scraper: this free Python tool pulls the list of startups from ycombinator.com/companies and extracts details like founder names and social links (Automate Lead Generation: YCombinator.com/Companies Scraper (Free Python Tool) : r/LeadGeneration). Automation: High – run it and it goes through all YC companies automatically. Lead Extraction: Collects company name, founder(s) details, Twitter, LinkedIn profiles, etc., outputting to a structured Excel (Automate Lead Generation: YCombinator.com/Companies Scraper (Free Python Tool) : r/LeadGeneration). Great for building a list of tech startup leads (for B2B sales or recruitment). Messaging: None built-in, but the data (especially founders’ LinkedIn/Twitter) can feed into your outreach system. Stats: New project from 2023 (shared on Reddit); modest stars. Dependencies: Python (Selenium for web browsing). Deployment: Run locally with ChromeDriver. Pros: Demonstrates how targeted scrapers can yield high-quality niche lead lists (in this case, tech founders). Easy to customize for similar directories. Cons: Limited to YC directory; not a general solution. Requires maintenance if YC site format changes.
LinkBuddy (Connect-Request-AI) – AI-Generated LinkedIn Connection Notes. A Chrome extension that uses GPT-3 to auto-write personalized connection request messages on LinkedIn (GitHub - dhanushtheijas08/connect-request-ai: LinkBuddy is a Chrome extension that uses OpenAI's GPT-3 to generate connect request notes for LinkedIn profiles. This extension is designed to save time for users who frequently send out connection requests on LinkedIn. The extension is free and open-source.). Automation: Medium – it doesn’t actively send requests on its own, but when you use it, it generates the note text for you. This reduces manual effort in outreach personalization. Lead Extraction: None – you choose profiles as usual. Messaging: Yes – it crafts the “Add Note” message using profile data (likely scraping the person’s profile for context) and GPT. This can save time for sales reps trying to tailor each invite. Stats: 6 stars (new project) ([GitHub - dhanushtheijas08/connect-request-ai: LinkBuddy is a Chrome extension that uses OpenAI's GPT-3 to generate connect request notes for LinkedIn profiles. This extension is designed to save time for users who frequently send out connection requests on LinkedIn. The extension is free and open-source.](https://github.com/dhanushtheijas08/connect-request-ai#::text=,Star%206)). Dependencies: JavaScript (Chrome extension) – requires an OpenAI API key. Deployment: Install as extension in Chrome; configure API key. Pros: Niche but useful – automates message personalization at the top of the funnel (initial LinkedIn touchpoint) using AI. Cons: Only covers the connection note, not ongoing conversation. Relies on LinkedIn’s UI (subject to break if LinkedIn changes DOM).
SpiderFoot – OSINT Automation Platform. (Related to theHarvester, but much more expansive.) SpiderFoot is an open-source OSINT tool with a web UI that automates querying 100+ data sources to gather intel on a target (Intel 471 | Attack Surface Documentation). Automation: High – you input a domain, email, or name, and it runs dozens of modules automatically. Lead Extraction: If you input a company domain, SpiderFoot will find associated emails, employee names, social media accounts, breaches, etc. (Intel 471 | Attack Surface Documentation). It’s often used in security, but those outputs (email addresses, names) are essentially leads. Messaging: None (not its purpose). Stats: 8k stars; very mature project. Dependencies: Python; optional web server mode. Deployment: Can run as a desktop UI app or in headless mode with a web dashboard. Docker available. Pros: Very powerful data gathering – combines WHOIS, DNS, breach data, social profiles, and more in one scan ([Intel 471 | Attack Surface Documentation](https://intel471.com/attack-surface-documentation#::text=SpiderFoot%20is%20a%20reconnaissance%20tool,they%20relate%20to%20each%20other)). For example, it might discover emails from data leaks that you can then target (carefully). Cons: OSINT focus means it may surface a lot of technical data (IPs, hosts) not all relevant to sales. Requires interpretation to extract useful leads. No direct outreach functions.
Tiledesk – Open-Source Live Chat and Chatbot Platform. Tiledesk lets you embed live chat on your website and integrates AI chatbots to automate FAQs and lead capture (Tiledesk · GitHub). It also connects to messaging apps (WhatsApp, Facebook Messenger) to unify chats. Automation: Medium-High – it can deploy AI agents that greet website visitors, ask qualifying questions, and collect contact info without human intervention (Tiledesk · GitHub). Agents can be configured with dialogues to schedule demos or handoff to human agents when needed. Lead Extraction: It captures leads inbound (through chat). Not a scraper; it helps convert your website traffic or social inbox inquiries into leads. Messaging: Multichannel – web widget, WhatsApp, Telegram, FB, etc., all through one dashboard. Supports bots that work across these channels (Tiledesk · GitHub). Stats: 500 stars on GitHub; active development. Dependencies: Node.js/Angular; uses MongoDB. Deployment: Docker or manual setup; also offered as cloud. Pros: Great for website lead generation – an AI chatbot can qualify visitors 24/7. Supports hand-off to live agents seamlessly. Open-core MIT-licensed ([Tiledesk · GitHub](https://github.com/tiledesk#::text=Tiledesk%3A%20open,automate%20workflows)). Cons: Focused on inbound/chat – not for outbound prospecting. To use effectively, you need traffic or an audience to engage with the bot.
Auto-GPT – Autonomous GPT-4 Agent. While not specific to leads, Auto-GPT demonstrated how an AI agent can autonomously perform multi-step research tasks. Users have successfully prompted it to generate leads. For example, one user asked Auto-GPT to find contacts for Italian citizenship assistance, and it broke the goal into sub-tasks: searching the web for businesses, extracting names, emails, phones, and compiling a report (I Just Used AutoGPT To Go And Generate Leads For My Upcoming Project | by Nikolas Kraljevic | Startup Stash). Automation: Very High – you give it a high-level goal, and it iterates on its own, creating and executing tasks (web searches, reading pages, saving info) without further user input. Lead Extraction: Can utilize its web browsing and scraping abilities to find lead information online (with the right prompts). It’s essentially an AI orchestrator that can control a browser. Messaging: Limited – it can draft emails or messages if asked, but out-of-the-box it won’t send them (no built-in email integration without adding a plugin). Stats: 136k+ stars (one of the most popular AI repos of 2023). Dependencies: Python; needs OpenAI API and optionally browser automation. Deployment: Run locally in CLI. Pros: Flexibility – can be pointed to any research task. In lead gen, it can comb through unstructured web info to find unconventional leads (e.g. niche forums or sites listing contacts) (I Just Used AutoGPT To Go And Generate Leads For My Upcoming Project | by Nikolas Kraljevic | Startup Stash). Cons: Unpredictable – it may get off-track or consume a lot of API credits. Often requires iterative prompt tweaking to get useful output. Not specialized for CRM or outreach (experimental for now).
Chatwoot – Open-Source Omnichannel Inbox. Chatwoot is a customer engagement/helpdesk platform that consolidates conversations from email, live chat, Facebook, Twitter, WhatsApp, SMS, and more into one interface (GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ). While traditionally for support, sales teams use it to manage outreach responses and initial inbound queries. Automation: Medium – it doesn’t automate sending (it’s agent-driven), but it can automate assignment of conversations, provide canned replies, and integrate bots for initial responses. Lead Extraction: Not applicable – it’s for managing interactions with known leads or visitors (you’ll see their contact info once they message). Messaging: Yes – it supports responding on all channels from one dashboard (GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ) (GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ). For example, you could email a lead and when they reply, it appears in Chatwoot for you to continue the convo. It also has an API to initiate outbound messages (or you use it with your website chat widget proactively). Stats: 15k stars ([GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ](https://github.com/chatwoot/chatwoot#::text=Chatwoot)); robust community. Dependencies: Ruby on Rails, Postgres, Redis. Deployment: Docker or Heroku one-click. Pros: Excellent for maintaining correspondence in one place – no more siloed LinkedIn inbox vs. email vs. WhatsApp, etc. (GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ). It even has basic CRM features (contact profiles, notes) (GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ). Cons: Not an outreach sequencer – you still need to initiate contacts elsewhere. Best used in tandem with other tools (e.g. use Chatwoot as the central inbox for replies generated by your campaigns).
WhatsApp-Web.js – WhatsApp Automation Library. A popular Node.js library that lets you control WhatsApp Web for automation. Using it, developers can send WhatsApp messages, read chats, etc., programmatically (What Are the Open-Source WhatsApp Integration APIs). Automation: High (as a library) – you can script it to send a series of messages to a list of numbers (after scanning your QR code for auth). Lead Extraction: Not for finding leads, but for reaching leads on WhatsApp. For example, integrate this with a scraper that gathered phone numbers to blast an introductory message (opt-in recommended!). Messaging: Yes – text, images, and file sending are supported. Many growth hackers use this to automate WhatsApp outreach (within WhatsApp’s policy limits). Stats: Very active project on npm (7k+ weekly downloads). Dependencies: Node.js; needs an instance of Chrome to run WhatsApp Web. Deployment: Include the npm package and write JS scripts. Pros: Enables multi-contact messaging on WhatsApp with an open API (WhatsApp’s official API is restrictive and paid). Has active community and support for message status updates, group messaging, etc. (What Are the Open-Source WhatsApp Integration APIs). Cons: Requires technical use; risk of number ban if messages are unsolicited or too frequent.
Baileys (WhatsApp Baileys API) – Lightweight WhatsApp Bot Library. Another open-source WhatsApp API that doesn’t require running an actual browser. Baileys connects directly via WhatsApp Web socket, allowing sending/receiving messages and managing chats via code (What Are the Open-Source WhatsApp Integration APIs). Automation: High – ideal for creating a WhatsApp chatbot or bulk messenger. You can automate sequences of messages or respond to triggers. Lead Extraction: Not applicable (for messaging). Messaging: Yes – supports text, media, buttons, etc. Many WhatsApp self-hosted bots (for customer service or drip campaigns) are built on Baileys. Stats: 3.5k+ stars; very active. Dependencies: Node.js. Deployment: Use as a library in a Node app. Pros: Versatile – handle encrypted messages, join groups, manage contacts. Favored for WhatsApp bot development due to reliability (What Are the Open-Source WhatsApp Integration APIs). Cons: Like other unofficial WhatsApp APIs, it works by mimicking a phone – you must keep the session alive. Non-technical to set up.
OpenWA/WA-Automate – Comprehensive WhatsApp Automation. OpenWA is a project around a powerful Node.js library (@open-wa/wa-automate) that makes building WhatsApp bots easier. It has advanced features like message scheduling, message templates, and even a visual flow builder. Automation: High – you can script complex workflows on WhatsApp (e.g., send an intro, wait 2 days, send follow-up, etc.). Lead Extraction: N/A. Messaging: Yes – full WhatsApp functionality. Supports bulk messaging with ease (caution on WhatsApp spam rules). Stats: ~2k stars; maintained actively. Dependencies: Node; requires Chrome instance or can run headless. Deployment: Node scripts or use their CLI tool. Pros: Developer-friendly with extensive docs. Good for integrating WhatsApp into your outreach multi-channel mix (e.g., send WhatsApp follow-ups to leads who don’t respond to email). Cons: Same WhatsApp automation caveats (unofficial, possible account bans if abused).
VICIdial – Open-Source Contact Center Dialer. VICIdial is the most popular open-source predictive dialer for call centers, used by thousands of companies (VICIdial.com). For outbound sales, VICIdial can automate cold-calling campaigns. Automation: High – it can auto-dial through lead lists, detect voicemail vs. human pick-up, and either play a message or connect to a sales rep. Agents can also get a “preview” of lead info before the call is dialed (VICIDIAL Open Source Contact Center Suite). Lead Extraction: None, you import lead phone lists (CSV). Some companies integrate it with web scraping by feeding scraped phone numbers into VICIdial. Messaging: Phone/Voice – it handles outbound calls and can also manage inbound calls, plus send follow-up emails or SMS via integrations. Stats: 14,000+ installations globally (VICIdial.com); very mature since 2003. Dependencies: Runs on top of Asterisk PBX (Linux). Deployment: More involved – requires a server (or use VICIdial’s ISO image for easy install). Many use hosted VICIdial. Pros: Efficient telephone outreach – maximizes agents’ talk time by automating dialing and call routing. Supports call recordings, scripts, and even website chat in the same interface (VICIdial.com). Cons: Not AI-driven (though you can add voicebots). It’s specialized for phone leads – not useful for email/LinkedIn. Setup can be technical (telecom knowledge needed).
Socioboard InBoard (LinkedIn InBoardPro) – Desktop LinkedIn Automation Suite. An older open-source project (from Socioboard) providing a Windows app that could manage multiple LinkedIn accounts and automate many actions (GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.). Automation: Medium-High – features included auto-connecting by keywords, auto-posting updates, group management, profile scraping by URL, etc. (GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.) (GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.). It essentially tried to “do everything you can do on LinkedIn” automatically (GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.). Lead Extraction: Yes – it had a “LinkedIn search” feature to fetch people or company info by name, and an advanced search scraper similar to Sales Navigator (GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.). Messaging: It supported sending connection requests and messages in bulk (with spintax for variation). Also could auto-endorse and auto-follow. Stats: 24 stars (project appears inactive) ([GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.](https://github.com/socioboard/inboard#::text=24%20stars%20%20%2016,Tags%20%20%20Activity)). Dependencies: .NET/C# application. Deployment: Download and run on PC (no recent updates for compatibility). Pros: Ambitious all-in-one LinkedIn automation (connection growth, content, data export) from a single interface (GitHub - socioboard/inboard: World's first free and open source desktop based Linkedin marketing, management and analytics software.). Useful for lead gen, recruiting, or personal branding. Cons: Outdated – LinkedIn’s UI and policies have changed since its last commit, so some features may not work or risk account safety. Newer tools (like InB above) have largely superseded it.
Mautic – Open-Source Marketing Automation. Mautic is a mature platform for email marketing, lead nurturing, and CRM integration (GitHub - mautic/mautic: Mautic: Open Source Marketing Automation Software.). Automation: High – it allows you to set up drip email campaigns, scoring rules (e.g. increase lead score if they click a link), and trigger actions (send email, add to campaign) based on lead behavior. All runs automatically once configured. Lead Extraction: Through landing pages and forms – Mautic provides embeddable forms to capture leads on your site, and it can also track anonymous visitors and turn them into identified leads when they fill a form. But it does not scrape external sources. Messaging: Yes – mainly email, plus SMS (via plugins) and social DMs (limited). You can create email templates with personalization and schedule blasts or drip sequences. It also has a robust segmentation engine to target specific groups. Stats: ~~5.9k stars; huge user base (200k+ organizations) ([GitHub - mautic/mautic: Mautic: Open Source Marketing Automation Software.](https://github.com/mautic/mautic#:~~:text=About%20Mautic)). Dependencies: PHP (Symfony framework); MySQL. Deployment: PHP app – can self-host on LAMP stack or use community Docker images. Pros: Full-featured lead management – contacts, segments, campaigns, scoring, analytics, all in one UI (GitHub - mautic/mautic: Mautic: Open Source Marketing Automation Software.). It integrates with CRMs like SuiteCRM and Salesforce to pull leads or push updates. Cons: Primarily focused on email/web marketing; not much for LinkedIn/WhatsApp channels. Requires server resources and maintenance (comparable to hosting a CRM).
Krayin CRM – Laravel CRM with Marketing Features. Krayin is an open-source CRM that emphasizes opportunity management and omni-channel communications. It includes email integration, live chat, phone call logging, and even social media monitoring for lead generation (Open Source CRM Software | Laravel CRM - Krayin). Automation: Medium – it’s more a toolset than an automation engine. It has “automation features” for tasks like data deduplication and some workflow triggers, but not as advanced as Mautic’s campaign automation. Lead Extraction: Via “Social Media Integration”, it can do basic social monitoring – possibly pulling contacts who interact on social or allowing you to manually import those as leads (Open Source CRM Software | Laravel CRM - Krayin). It’s a CRM, so it centralizes lead info rather than discovering new leads in the wild. Messaging: Yes – you can send emails from the CRM (email templates and campaigns module) (Open Source CRM Software | Laravel CRM - Krayin), handle inbound emails, and respond to chats or calls (with proper integrations) (Open Source CRM Software | Laravel CRM - Krayin). Stats: Newer project by developers of Bagisto; not widely known yet. Dependencies: PHP (Laravel), MySQL. Deployment: Self-host on LAMP stack. Pros: Modern UI and the benefit of a CRM (pipeline tracking, activities, etc.) combined with multi-channel communication support in one system (Open Source CRM Software | Laravel CRM - Krayin) (Open Source CRM Software | Laravel CRM - Krayin). Could be a one-stop for a small business to manage and contact leads. Cons: Still maturing; automation capabilities are relatively light (no advanced drip logic). Suited for teams that want an open-source CRM with some marketing capabilities, rather than hardcore growth hacking.
AgentGPT / BabyAGI frameworks – Autonomous Agent Platforms. These experimental platforms allow you to configure an AI “agent” with a goal (like “find potential clients in X industry and contact them”) and then they recursively create and execute tasks to achieve it (babyAGI | Mukund Mohan) (babyAGI | Mukund Mohan). For instance, AgentGPT (a browser-based version of AutoGPT) could be tasked with generating and qualifying leads, and it would strategize steps to do so (babyAGI | Mukund Mohan). Automation: Very High – they aim for long-term autonomy in completing objectives. Lead Extraction: Potentially yes – an agent can decide to search for leads, use an API or scraping tool, compile a list, etc., all on its own. One could integrate tools like theHarvester or Google search APIs as “skills” for the agent. Messaging: In theory, an agent could draft outreach messages and even use an email-sending plugin to reach out (AgentGPT is extensible with plugins) (babyAGI | Mukund Mohan). This blurs into AI doing the entire outreach process end-to-end, though this is cutting-edge and not reliable at scale yet. Stats: BabyAGI (the minimal task-driven agent) 13k stars; AgentGPT ~8k stars. Dependencies: Python (for BabyAGI) or JavaScript (AgentGPT web). Deployment: These are more frameworks – one runs them and gives instructions via prompts. Pros: Visionary approach – a single AI could handle everything from researching a lead list to writing emails to scheduling meetings, theoretically. ([babyAGI | Mukund Mohan](https://mukundmohan.blog/tag/babyagi/#::text=,to%20create%20personalized%20learning%20experiences)) This is being actively explored; some early users have seen it complete lead-gen tasks autonomously. Cons: Not battle-tested for production – agents often get stuck or produce suboptimal results. You sacrifice control and reliability. It’s more of a glimpse into the future than a ready solution for today’s sales team (at least without a human in the loop to vet the agent’s output).

Comparing Approaches: Scrapers vs. Full-Stack vs. AI Agents

Lead Data Extraction vs. Outreach Automation: Some tools focus only on collecting leads (e.g. Google Maps scrapers, theHarvester, SpiderFoot) while others focus on messaging (email automation tools, dialers, chatbot frameworks). Integrated platforms (Mautic, Parcelvoy, Dittofeed) assume you already have lead info and help you nurture those leads via multi-channel campaigns. In contrast, scrapers find new contacts but don’t engage them. A few projects try to bridge this gap (LeadGenPy scraping then emailing (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email), or AutoGPT agents that do research and can draft emails).

Degree of Autonomy: Projects also differ in how “hands-off” they are after initial setup. Full marketing automation suites and CRM systems may require significant initial configuration – writing email content, setting up segments or triggers – but then run with minimal user intervention. Pure scrapers might need new queries each time but can fetch results quickly. AI agent frameworks (AutoGPT, etc.) promise to figure out tasks on the fly, but often need supervision or prompt tweaking. Overall, email campaign tools and dialers are very autonomous in execution (sending follow-ups or dialing without user action), whereas scraping tools output data that then needs to be acted on (either by a human or by feeding into another automation).

Multi-Channel Outreach vs. Single-Channel: Multi-channel platforms like Parcelvoy, Dittofeed, Mautic (with plugins), Chatwoot (for response management) let you orchestrate email, SMS, push, and more in one place. This increases reach but also complexity (you might need API keys for Twilio, Mail servers, etc.). Single-channel tools (e.g. an email sender or a LinkedIn bot) are simpler and often have fewer dependencies, but you might need to combine several to cover all desired channels. Using multiple single-channel tools in parallel can be powerful but requires integration – for example, manually importing leads scraped from LinkedIn into an email tool, or using n8n to glue a scraper output to WhatsApp API calls. Full-stack solutions handle integration internally (e.g. Dittofeed provides a journey builder where one step could be email and the next an SMS).

AI Personalization: An emerging difference is the use of AI for content and conversation. Traditional outreach tools rely on user-written templates and rules. New AI-driven projects (SalesGPT, Kaymen99’s LangGraph agents, LinkBuddy for notes, GPT in n8n workflows) add intelligence to personalize messages or hold dialogues. The pros of AI-driven messaging are higher personalization and the ability to maintain a conversation context (as SalesGPT does, acting like a human rep) (GitHub - filip-michalsky/SalesGPT: Context-aware AI Sales Agent to automate sales outreach.). This can increase reply rates and handle leads in real-time. The cons include risk of AI errors or off-brand messages if not carefully controlled, and dependency on external AI APIs (with cost and data considerations). Pure rule-based systems (like traditional mail merge or drip sequences) give you more predictable output but less dynamism. A hybrid approach can work well: e.g. using AI to draft initial outreach, then using a classic sequencer for follow-ups, and an AI chatbot to handle responses – essentially mixing these tools in the pipeline.

Community & Support: Mature projects like Mautic or VICIdial have large communities, which means more tutorials, bug fixes, and possibly third-party plugins (for example, Mautic has many integrations contributed by the community). Newer projects (Dittofeed, LeadBrowser) may not have that, but they offer modern tech stacks and fresh approaches. If stability and support are paramount, leaning on proven projects (even if they might require more manual effort to achieve AI capabilities) could be safer. On the other hand, for a competitive edge, experimenting with newer AI-driven tools might yield higher efficiency once the kinks are worked out.

Minimal Dependencies: Simpler scrapers and scripts tend to have fewer moving parts (often just requiring Python or Node and some keys) – they can be quicker to get running. Full platforms often require databases, web servers, etc. If one’s goal is a quick proof-of-concept or a small-scale campaign, a lightweight approach (like running a Google Maps scraper and then a small email script) might be preferable to deploying a heavy CRM. But for long-term maintainability, an integrated platform can reduce the patchwork of scripts – it centralizes data and operations. The trade-off is the setup overhead.

In summary, specialized scrapers excel at building lead lists from various sources (Pros: fresh data, complete control of what to scrape; Cons: need to manually combine with outreach, potential maintenance when sites update) (GitHub - gosom/google-maps-scraper: scrape data data from Google Maps. Extracts data such as the name, address, phone number, website URL, rating, reviews number, latitude and longitude, reviews,email and more for each place) (GitHub - cermak-petr/actor-yellowpages-scraper: Apify actor for scraping information from Yellow Pages listings based on search term and location or a list of URLs.). Full-stack outreach systems excel at managing and contacting leads across channels (Pros: automation of messaging and tracking; Cons: they rely on input data – “fuel” for these engines has to come from somewhere). AI messaging bots and agents add cutting-edge personalization and autonomy (Pros: can mimic human touch at scale; Cons: unpredictability and newness). The best solution often combines elements: for example, use scrapers to get raw leads, feed them into a marketing automation tool for sequences, and use AI to personalize content or handle replies.

Deployment & Configuration Considerations

Deploying these tools ranges from running a simple script on your laptop to setting up complex server infrastructure:

One-click / Docker-friendly: Many projects provide Docker images or easy installers. For instance, SalesGPT includes a docker-compose.yml for quick start (GitHub - filip-michalsky/SalesGPT: Context-aware AI Sales Agent to automate sales outreach.); Dittofeed offers both Docker and a public demo (GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ); Mautic can be launched via official Docker containers. If you prefer not to manage dependencies individually, choose a project with container support or a SaaS option (where available). Parcelvoy, Chatwoot, Rasa, and Mautic all have documented Docker setups.
APIs and Keys: Nearly all AI or multi-channel systems require configuration of API keys – e.g. OpenAI API for any GPT usage, Twilio for SMS/WhatsApp, SMTP credentials for email, LinkedIn session cookies for LinkedIn bots, etc. Deployment isn’t just running code; it’s also adding these credentials to config files or env vars. The research above noted which integrations are needed: PaulleDemon’s email tool needs your SMTP (and advises against Gmail) (GitHub - PaulleDemon/Email-automation: open-source cold email outreach tool), LeadGenPy needs OpenAI and an email account configured (GitHub - Wikkiee/LeadGenPy: LeadGenPy is a tool for lead generation that extracts Google Maps business details using Selenium, processes the data, stores it in a database, and dynamically generates personalized messages using the ChatGPT API, which are then sent to the lead's email), Rasa needs Twilio credentials for WhatsApp/SMS connectors (RASA - Messaging channels - Twilio - DEV Community), etc. Ensuring these are set up correctly is crucial. Many projects provide a sample .env or settings file – start by filling those out.
Resource requirements: Simpler scrapers (Omkar’s, gosom’s, theHarvester) can run on a typical PC or small VM. Large platforms (Mautic, Vicidial) might need a dedicated server or cloud instance with sufficient RAM/CPU, especially when handling thousands of leads. Consider using managed databases or services if you’re not comfortable managing those components yourself. For example, hooking Mautic to Amazon SES for email sending (to avoid deliverability issues), or using a cloud-hosted browser like Apify for heavy scraping (to avoid IP bans on your local network).
Security and Compliance: When deploying systems that send emails or messages autonomously, ensure you comply with spam regulations (CAN-SPAM, GDPR). E.g., Mautic and SuiteCRM have features for unsubscribe links and managing opt-outs – use them. If scraping personal data via tools like theHarvester or SpiderFoot, be mindful of data privacy laws and the terms of service of sources (LinkedIn explicitly forbids scraping; users often still do it, but it carries risk). Running these tools on your own server gives you control but also responsibility for data handling.
Integration and Webhooks: Deployment also involves integration into your workflow. Many platforms support webhooks or APIs to connect with each other. For instance, you might deploy n8n alongside Mautic and use n8n’s webhook to trigger whenever Mautic captures a new lead, then n8n calls a scraper to enrich that lead’s data (e.g. fetch their social media info) and pushes it back. Designing these interactions requires configuration on both ends. Fortunately, open systems have APIs – e.g., Mautic’s API or Chatwoot’s API – which you can call from automation scripts to keep everything in sync.
Monitoring: Once deployed, set up basic monitoring/logging. Email sending tools should be monitored for bounce rates and deliverability (use inbox placement tests and monitor logs for errors). Scrapers should be monitored for IP blocks or errors (most scrapers log if Google starts requiring a CAPTCHA, etc.). Many Dockerized setups come with logs accessible via docker logs or a built-in UI (Chatwoot has an admin panel for errors, for example). Proactively checking these ensures your “autonomous” system truly runs without intervention.

In essence, choose a deployment strategy that matches your team’s skill set: non-developers might lean towards GUI-based tools or those with hosted options (Chatwoot offers a cloud, n8n has a cloud service, etc.), whereas developers can self-host and even customize the code. The good news is all the listed projects have documentation and communities to assist with setup, and being open-source, you can debug or tweak them as needed.

Best Approach Recommendation

After evaluating all these solutions, the recommended approach is a hybrid stack leveraging strengths of multiple tools: Use specialized scrapers to build targeted lead lists, then feed those leads into a multi-channel automation platform enhanced with AI for personalization. This combines data breadth with outreach depth.

Why not one tool? Currently, no single open-source project perfectly does “end-to-end lead gen + outreach” at scale. A combination is most efficient:

Lead Acquisition: Employ scrapers for your key sources (e.g. LinkedIn for B2B, Google Maps or Yellow Pages for local businesses, etc.). For instance, run the Google Maps Scraper to get a list of local companies in your niche along with contacts (GitHub - omkarcloud/google-maps-scraper: HOLA HOLA HOLA ! ENJOY OUR GOOGLE MAPS SCRAPER TO EFFORTLESSLY EXTRACT DATA SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, REVIEWS, WEBSITES, AND RATINGS FROM GOOGLE MAPS WITH EASE! ), and the theHarvester or SpiderFoot to grab any publicly listed emails for those domains (Intel 471 | Attack Surface Documentation). This yields a high-quality initial lead database.
Lead Enrichment: Then, use an AI tool to enrich this data. For example, an n8n workflow or a Python script with OpenAI API can take a company name from the scraper and fetch a summary or find a likely decision-maker via a quick web search. This is where something like AutoGPT (with a controlled prompt) or AgentGPT could automate research on each lead – e.g. finding talking points like recent news about that company (I Just Used AutoGPT To Go And Generate Leads For My Upcoming Project | by Nikolas Kraljevic | Startup Stash). Alternatively, use a service like Dittofeed’s segmentation and integration to import additional attributes (since Dittofeed can ingest from Segment or other sources) (GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ) (GitHub - dittofeed/dittofeed: Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more ).
Outreach Execution: Import the enriched leads into a robust outreach platform. For a code-free setup, Mautic is a solid choice for email-centric campaigns – you can design a drip campaign (intro email, wait 3 days, follow-up email, etc.) to run automatically for every new lead added (GitHub - mautic/mautic: Mautic: Open Source Marketing Automation Software.). Mautic can also score leads based on engagement (opens/clicks), helping you prioritize follow-ups. If you need LinkedIn and SMS touchpoints as well, a combination of Mautic for email, plus using InB or LinkBuddy for LinkedIn and a WhatsApp Web.js script for WhatsApp messages, could cover those channels. This requires coordinating timing – e.g., day 1 send LinkedIn invite (via InB), day 2 send email (via Mautic), day 5 send WhatsApp message. You might coordinate this via an automation tool like n8n to trigger each action in sequence across systems.
AI Personalization: Use AI in the content. For example, you can integrate OpenAI into Mautic by generating email text outside and inserting it via Mautic’s API, but a simpler method: take advantage of SalesGPT or Kaymen99’s LangGraph agent to generate a first-touch email draft for each lead (highlighting that lead’s specific pain point using the research) (GitHub - kaymen99/sales-outreach-automation-langgraph: Automate lead research, qualification, and outreach with AI agents and Langgraph, creating personalized messaging and connecting with your CRMs (HubSpot, Airtable, Google Sheets)). Then use that content in the campaign. If you have many leads, doing this manually is hard – instead, an automated script can loop through leads and call GPT for each (with a prompt like “Write a short email to ${lead_name} of ${company}, referencing ${company_recent_news} and offering help with ${pain_point}”). This yields a highly personalized email for each lead, which you can then send via the automation platform. For replies, deploy a Rasa chatbot or SalesGPT on channels like email or WhatsApp to handle common questions automatically, and escalate hot leads to you or your sales team. This way, initial engagement is fast and hands-free, but human takeover happens for high-value conversations.
Unified Tracking: Use an inbox tool like Chatwoot to gather all responses in one place (GitHub - chatwoot/chatwoot: Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. ). While Mautic will track email metrics, Chatwoot can consolidate when a lead responds via email or WhatsApp. Chatwoot (or even just a unified Gmail with filters) ensures you don’t miss responses coming from different channels.
CRM Loop: As leads convert or respond, log them in a CRM (could be something like Krayin or SuiteCRM since they are open-source). Mautic can sync to CRMs, or n8n can create CRM contacts when leads engage. This closes the loop, so future follow-ups or sales pipelines are managed.

This best-of-breed approach is feasible entirely with open-source components and minimal recurring costs (mostly for API usage like OpenAI and maybe proxy services for scrapers). It provides efficiency (lots of automation), simplicity in chunks, and strong performance through personalization. Efficiency comes from scraping and contacting volumes of leads automatically, simplicity from using each tool for what it does best (rather than forcing one tool to do unnatural tasks), and performance from tailoring messages with AI and hitting multiple channels.

If a single solution is preferred, Dittofeed is promising as an all-in-one for outreach (covering channels and scheduling) and could incorporate scraping via its API in the future – but as of now, it’s the combination of tools that yields the best results.

Sample AI Prompts for Outreach Optimization

To fully leverage AI in your lead generation and outreach, here are some prompt examples you can use with GPT-4 or similar models. These can be applied in tools like SalesGPT, or via API calls in your custom scripts, to personalize messages and strategize:

Lead Research Prompt: *“Summarize the business of {Company Name} and identify 2 potential challenges they might be facing in {Your Solution Domain}. Provide the summary and challenges to be used in a sales email.”* – (Use this to generate talking points for each lead, which you can plug into your email template)
Cold Email Draft Prompt: *“You are a sales rep reaching out to {Lead Name}, the {Lead Title} at {Company}. Draft a friendly, 3-paragraph email introducing how {Your Product} can help with {Pain Point from Research}, referencing {a recent accomplishment or news about Company} to show you did your homework. End with an invitation to chat, and keep the tone consultative and not too pushy.”* – (This yields a highly tailored email using the lead’s context) (babyAGI | Mukund Mohan) (babyAGI | Mukund Mohan).
Follow-Up Message Prompt: *“Write a brief follow-up message to {Lead Name} if they haven’t responded to my first email. Remind them of the key benefit for {Company} (e.g. {Save 30% time in process X}) and share one relevant success story or result. Maintain a polite tone and encourage them to ask any questions.”* – (Use a variant of this for second or third touch emails, to keep follow-ups fresh and value-focused.)
LinkedIn Connection Note Prompt: *“Generate a 300-character LinkedIn connection note for {Lead Name}, mentioning that we both work in {industry} and referring to their post about {topic}. Be personable and avoid sounding like a template.”* – (This can feed into LinkBuddy or used manually) (GitHub - dhanushtheijas08/connect-request-ai: LinkBuddy is a Chrome extension that uses OpenAI's GPT-3 to generate connect request notes for LinkedIn profiles. This extension is designed to save time for users who frequently send out connection requests on LinkedIn. The extension is free and open-source.).
Objection Handling Prompt: *“Lead says they are not interested because {common objection, e.g. ‘we use a competitor’}. As a sales assistant AI, draft a courteous reply that acknowledges their current solution but briefly points out one differentiator of ours, and offers resources or a trial for when they reconsider.”* – (Have GPT craft responses to common objections or negative replies, to maintain professionalism and possibly reopen dialogue).
Qualification Questions Prompt (for chatbot): “You are an AI sales assistant on our website chat. Greet the visitor and ask two simple questions to understand their needs (e.g., ‘What’s the biggest challenge you’re looking to solve?’). If they answer, respond with a tailored value proposition of our product and offer to schedule a call.” – (Use in Rasa or Tiledesk bot to qualify web leads automatically).

These prompts can be adjusted per industry and tool. The key is to inject specifics about the lead (name, company, context) and to direct the AI to be concise and relevant. By integrating such prompts into your workflow – whether via an API call before sending an email, or in a chatbot – you significantly enhance the personalization of your outreach at scale. This human-like touch, powered by AI, combined with the automation of the aforementioned open-source systems, will yield an efficient, scalable, and effective lead generation and outreach engine for your needs. (babyAGI | Mukund Mohan) (I Just Used AutoGPT To Go And Generate Leads For My Upcoming Project | by Nikolas Kraljevic | Startup Stash)

Hope you enjoyed the post! Email me if you need any custom AI solution or training (or want to share any thoughts) at [email protected]

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