Open-source AI lead generation tools on GitHub

Search for ai lead generation github and you get hundreds of repos promising free leads: Google Maps scrapers, LLM enrichment scripts, autonomous prospecting agents. The code is free. Running it every week, fixing it when it breaks, and turning its output into emails that land is not.

This guide sorts the kinds of projects you will find, counts the costs most READMEs skip, and gives you a simple test for deciding whether to build your own pipeline or pay a few dollars for a hosted one.

What people mean by AI lead generation on GitHub

When people search for open source AI lead generation, they usually want one of three things:

• A free list builder. Code that pulls businesses from a directory or map and saves them to a CSV.
• An AI layer on top. A script that reads each company's website and uses a language model to pull out the owner's name, services, or a personal first line.
• A full autonomous loop. An agent that finds prospects, researches them, writes emails, and sends them with little human input.

Most repos do one of these well and gesture at the others. A project described as an AI lead generation agent is often a scraper plus a prompt plus a sending function, each written quickly and tested on the author's own laptop.

That is not a criticism. It just means you are getting a starting point, not a product.

The main types of open-source projects

Once you browse past the star counts, almost every github lead generation tool falls into one of four groups.

Scrapers

These collect raw listings. The most common is the google maps scraper github search turns up, usually built on Playwright or Selenium to drive a real browser, scroll results, and click into each listing. Others target business directories, review sites, or social platforms with requests plus an HTML parser.

What you get: business name, address, phone, website, rating, review count, category. What you usually do not get: an email address or a person's name.

Enrichers

An llm lead enrichment script takes a list of websites, downloads the homepage, contact page and about page, then either runs regular expressions to find emails or sends the page text to a model with a prompt like: find the owner's name and any contact email.

These are the most useful repos for most people, and the most dangerous. A model asked to find an owner name will sometimes produce a plausible one that is not on the page at all.

AI agents

Agent projects chain tools together. You give a goal such as find 50 dental clinics in Austin that need a new website, and the agent searches, scrapes, scores and drafts. They are fun demos. In practice they are slow, expensive per lead because every step is a model call, and hard to make repeat the same result twice.

Outreach scripts

These read a CSV and send emails through SMTP or a mail API, sometimes with AI-written first lines. Few handle follow-ups that stop when someone replies, unsubscribe requests, bounce processing, or spreading volume across several mailboxes.

What a working pipeline needs beyond the repo

A self-hosted lead generation setup that runs every week needs more than the code. Here is the full list of parts, whether the README mentions them or not:

1. A machine to run it. Your laptop works for a test. Weekly jobs need a server or a scheduled cloud function, plus a headless browser that does not run out of memory.
2. Proxies. Map and directory sites throttle or block repeated requests from one IP. Serious scraping at volume usually means rotating residential proxies, which are billed by bandwidth.
3. CAPTCHA handling. Either a solving service or a slower scrape that avoids triggering them.
4. An LLM API key. Every page you send to a model costs tokens. Cheap per lead, but it adds up and it is a bill you have to watch.
5. Email verification. Scraped and guessed addresses bounce. Checking mailboxes yourself over SMTP is unreliable from most cloud servers, and catch-all domains accept everything anyway. Most people end up paying a verifier. Read why this matters in our guide to verifying your list before sending.
6. Storage and deduplication. A database or at least a master CSV, so next week's scrape does not re-email last week's leads.
7. Sending infrastructure. Domains, mailboxes, SPF, DKIM and DMARC records, warm-up, daily limits, follow-up logic, unsubscribe handling, and a place to read replies.

The repo is often only items 1 and part of 4. The rest is your job.

The real monthly cost of a self-hosted setup

Here is a worked example. Every number below is a sample assumption, not a quote. Prices for proxies, models and verifiers vary a lot by provider, so swap in your own.

Goal: 2,000 new local business leads a month, with emails checked before sending.

The cash line looks reasonable. The time line is what sinks most self-hosted projects. Five hours a month is optimistic in a month when a target site changes its layout, and the first month of setup can take far longer.

Now the hosted comparison. In GetClient, a lead costs 1 credit and verifying an email costs 1 credit. 2,000 leads plus 2,000 verifications is 4,000 credits, which fits inside the $19 plan (8,000 credits). A smaller run of 1,500 verified leads fits the $9 plan with 3,000 credits. Enrichment from each website (emails, owner name, phone numbers, social profiles) is included in the lead.

Sending costs need a fair note. Mailboxes and domains cost the same whichever route you take, because you pay your email provider either way. The difference is the software: a hosted platform that lets you connect your own Gmail, Outlook or SMTP mailboxes and send unlimited campaigns and follow-ups saves you from writing sequence logic, reply detection and unsubscribe handling yourself. For a fuller breakdown of every line item, see how to work out the real cost of your cold email stack.

Where open-source projects usually break and who has to fix them

With a hosted tool, someone else is paged when a source changes. With a GitHub project, that someone is you, or a maintainer who may have moved on.

Common failure points:

• Selector changes. Scrapers depend on class names and page structure. When a site ships a redesign, the script returns empty fields or crashes, often silently.
• Result caps. Map searches stop returning results after a limit per query, so naive scrapers miss most of a city. Our explainer on why Google Maps stops at about 120 results covers the workaround of splitting searches by area and keyword.
• Blocking. A run that worked on 50 listings gets blocked at 500. Proxies help but do not guarantee anything.
• Dependency drift. Browser automation libraries, browser versions and Python packages update. A repo pinned to old versions may not install cleanly six months later.
• Model changes. Hosted LLMs get deprecated or change behavior. A prompt that returned clean JSON may start adding commentary that breaks your parser.
• Abandoned code. Many lead tools on GitHub were weekend projects. Before you depend on one, check the date of the last commit, how many open issues mention it no longer works, and whether active forks have taken over.

A quick rule: if the last meaningful commit is older than the last redesign of the site it scrapes, assume it is broken until you prove otherwise.

Data quality checks to run before you trust a GitHub scraper's output

An open source lead scraper can return a full CSV that is still mostly wrong. Run these checks on the first batch before you email anyone.

1. Hand-check a sample. Pick 30 random rows and open each business's real listing and website. Count how many fields match. If more than a few are wrong, stop and fix the scraper.
2. Look for invented names. For every owner name your LLM step produced, confirm the name actually appears on a page it read. Make your script save the source URL and the exact snippet it pulled the name from. No snippet, no name.
3. Match email domains to websites. An address at a different domain from the business website may belong to a web designer, a franchise head office or a directory.
4. Remove duplicates. The same business often appears under several categories or searches. Dedupe on website domain and phone number, not just name.
5. Flag closed businesses. Listings marked permanently or temporarily closed should go.
6. Separate role addresses. info@ and contact@ are fine for some offers, but keep them in their own segment so you can write for a shared inbox.
7. Verify every address. Treat a bounce rate above a couple of percent as a list problem, not bad luck.
8. Check encoding. Accented names and non-English characters often break in CSV exports, which then breaks your merge tags.

If you skip step 2, you will eventually send an email opening with Hi Michael to a business owned by someone called Priya. That one mistake costs more trust than any amount of personalization earns.

Legal and platform terms risks of running your own scraper

This section is general information, not legal advice. Talk to a lawyer if you are unsure about your situation.

When you run your own scraper, the risk sits with you:

• Terms of service. Many sites prohibit automated collection in their terms. Breaking them can lead to blocked IPs, closed accounts, or in some cases legal letters.
• Logged-in scraping. Scraping pages that require your account is riskier than public pages, because you agreed to the terms when you signed up and the platform can ban that account.
• Personal data. Owner names and personal email addresses are personal data under laws like GDPR. You need a lawful reason to process them and a way to honor removal requests.
• Sending rules. CAN-SPAM in the US requires a real sender identity, a physical address, and a working opt-out. Other countries are stricter.
• Storage. A CSV of personal data on an unsecured server is a liability.

For a practical run-through, read is web scraping legal before you schedule your first job.

When self-hosting makes sense and when a hosted tool is cheaper

Use this test. Self-hosting is usually worth it if you can answer yes to at least three:

• The source you need is not covered by any affordable tool.
• You already run scraping or data infrastructure and know how to keep it alive.
• Your volume is high enough that per-lead pricing would cost you more than your monthly maintenance time.
• Your time is cheap this month, or you are building skills you will sell.
• The pipeline is part of a product you are building for clients.

A hosted tool is usually cheaper if any of these are true:

• You need fewer than tens of thousands of leads a month.
• Your billable hour is worth more than $25.
• You need verification and sending, not just a CSV.
• A broken scraper in the middle of a client campaign would cost you the client.

For most freelancers and small agencies, the math lands on the hosted side. A $9 or $19 plan costs less than one hour of fixing a broken scraper. That is the plain reason budget tools exist.

A middle path: hosted lead data pulled into your own code

You do not have to choose between writing everything and clicking buttons. If you like code, keep the parts that are fun and valuable, such as scoring, segmentation and AI-written first lines, and rent the fragile parts.

GetClient collects live leads from Google Maps, the Facebook Ad Library, Yelp, Yellow Pages, YouTube, TrustMRR, Skool and Skool Discovery, enriches them from each website, and verifies emails. You can pull those lists into your own Python or JavaScript through a REST API, or let an AI assistant query them through an MCP server. Our guide to using a lead generation API shows the pattern, and what an MCP server is explains the assistant side.

A simple hybrid setup looks like this:

1. Request leads for a niche and city through the API.
2. Run your own ai lead generation python script to score each lead, for example by review count, rating, or whether the site mentions online booking.
3. Use your LLM to draft a first line from the enriched data, with the source field saved next to it.
4. Send the top segment from your own connected mailboxes, or push the list to GoHighLevel or Instantly if that is where your team works.

You keep control of the logic. Someone else deals with proxies, selector changes and verification.

Next steps

1. Write down how many verified leads you need per month and what your hour is worth.
2. Fill in the cost table above with real quotes for proxies, models and verification.
3. If you still want to self-host, pick one repo, check its last commit and open issues, and run the eight data quality checks on a 30-row sample before scaling.
4. If the hosted side wins, start with the 100 free credits on GetClient and compare the output to your test batch side by side.

Either way, judge by verified leads that reply, not by stars on a repo.