sherlock

Pros of EagleEye

• Focuses on facial recognition and image analysis
• Provides more in-depth information about a person's online presence through image searches
• Offers visualization of connections between found profiles

Cons of EagleEye

• Less actively maintained compared to Sherlock
• Requires more setup and dependencies
• Limited to image-based searches, potentially missing text-based profiles

Code Comparison

Sherlock (Python):

def sherlock(username):
    for site in sites:
        url = site['url'].format(username)
        response = requests.get(url)
        if response.status_code == 200:
            print(f"Found on {site['name']}")

EagleEye (Python):

def eagleeye(image_path):
    face_encoding = face_recognition.face_encodings(image)[0]
    for social_media in social_media_sites:
        matches = face_recognition.compare_faces(social_media_encodings, face_encoding)
        if True in matches:
            print(f"Match found on {social_media}")

While Sherlock focuses on username-based searches across multiple platforms, EagleEye specializes in facial recognition and image analysis. Sherlock is more actively maintained and easier to set up, but EagleEye provides deeper insights through image-based searches. The code snippets illustrate their different approaches: Sherlock checks for username availability, while EagleEye compares facial features across social media platforms.

Pros of maigret

• Supports more sites (2000+) compared to Sherlock's ~300 sites
• Includes additional features like parsing and analyzing collected data
• Offers more flexible search options, including custom site support

Cons of maigret

• May have a steeper learning curve due to additional features
• Potentially slower execution time due to the larger number of sites checked
• Less frequent updates compared to Sherlock

Code comparison

Sherlock:

def sherlock(username, site_data, timeout=60):
    results = {}
    for site in site_data:
        results[site] = check_username(username, site_data[site], timeout)
    return results

maigret:

async def maigret(username, sites, timeout=60, parse=True):
    results = {}
    for site in sites:
        results[site] = await check_username(username, site, timeout)
        if parse and results[site]:
            results[site]['parsed'] = parse_info(results[site]['raw'])
    return results

The main differences in the code snippets are:

1. maigret uses async/await for potentially improved performance
2. maigret includes an optional parsing step for collected data
3. Sherlock's implementation is simpler and more straightforward

Pros of holehe

• Focuses specifically on email address verification across various websites
• Provides more detailed information about each account found, including registration date and last connection
• Generally faster execution due to its specialized focus

Cons of holehe

• Limited to email-based searches, unlike Sherlock's broader username-based approach
• Supports fewer platforms compared to Sherlock's extensive list of supported sites
• Less actively maintained, with fewer recent updates and contributions

Code Comparison

Sherlock:

async def sherlock(username, site_data, query_notify):
    for site in site_data:
        await sherlock_site(username, site, query_notify)

holehe:

def holehe(email):
    for website in websites:
        result = website(email)
        if result:
            print_result(result)

Both projects use similar approaches, iterating through a list of supported websites and checking for user presence. However, Sherlock's implementation is asynchronous, potentially offering better performance for large-scale searches.

Pros of social-analyzer

• Offers a web-based GUI in addition to CLI, making it more user-friendly for non-technical users
• Provides more detailed information about profiles, including profile picture analysis and metadata extraction
• Supports a wider range of social media platforms and websites (300+)

Cons of social-analyzer

• Slower processing time due to more comprehensive analysis
• May require additional dependencies and setup compared to Sherlock's simpler installation process
• Less frequent updates and potentially lower community support

Code Comparison

Sherlock:

def sherlock(username, site_data, timeout=60):
    results = {}
    for site in site_data:
        url = site['url'].format(username)
        response = requests.get(url, timeout=timeout)
        if response.status_code == 200:
            results[site['name']] = url
    return results

social-analyzer:

def find_username_normal(req):
    for site in sites:
        url = site['url'].format(req['string'])
        response = requests.get(url, headers=headers, timeout=10)
        if site['check_text'] in response.text:
            req['found'].append({'name': site['name'], 'url': url})
    return req

Both projects use similar approaches for username searching, but social-analyzer includes additional features for more comprehensive analysis and data extraction.

Pros of linkedin2username

• Focused specifically on LinkedIn, providing more targeted and in-depth results for this platform
• Generates potential email addresses based on common patterns, enhancing the utility for professional networking
• Lightweight and easy to use, with minimal dependencies

Cons of linkedin2username

• Limited to a single platform (LinkedIn), whereas Sherlock searches across multiple social media sites
• Requires LinkedIn authentication, which may be a barrier for some users
• Less actively maintained, with fewer recent updates compared to Sherlock

Code Comparison

linkedin2username:

def get_company_employees(self, company):
    employees = []
    for page in range(1, 101):
        results = self.search_linkedin(company, page)
        if not results:
            break
        employees.extend(results)
    return employees

Sherlock:

def sherlock(username, site_data, query_notify, tor, unique_tor, proxy=None):
    results = {}
    for social_network, net_info in site_data.items():
        results[social_network] = {"url_main": net_info.get("urlMain")}
        url = net_info["url"].format(username)
        results[social_network]["url"] = url
        results[social_network]["exists"] = "yes"
        results[social_network]["http_status"] = net_info.get("errorType", "")

Pros of GHunt

• Focuses specifically on Google accounts, providing more in-depth information
• Offers advanced features like Google Photos metadata extraction
• Includes a graphical user interface for easier use

Cons of GHunt

• Limited to Google accounts, while Sherlock covers multiple platforms
• Requires more setup and dependencies
• May have potential legal and ethical concerns due to its depth of information gathering

Code Comparison

Sherlock:

def sherlock(username):
    for site in sites:
        url = site['url'].format(username)
        response = requests.get(url)
        if response.status_code == 200:
            print(f"Found on {site['name']}")

GHunt:

def ghunt(email):
    gaia_id = get_gaia_id(email)
    if gaia_id:
        profile = get_google_profile(gaia_id)
        photos = get_google_photos(gaia_id)
        print(f"Profile: {profile}")
        print(f"Photos: {photos}")

Both projects aim to gather information about individuals online, but they differ in scope and approach. Sherlock is a broader tool that searches for usernames across multiple platforms, while GHunt focuses exclusively on Google accounts, providing more detailed information within that ecosystem. Sherlock's code is simpler and more straightforward, iterating through a list of sites to check for username presence. GHunt's code involves more complex operations, such as retrieving Google-specific IDs and fetching detailed profile and photo information.