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Mobile browser feature detection using multiple backends

Project description


mobile.sniffer is Python framework for abstracting mobile browser detection and feature database access.

When rendering web pages for mobile phones one must deal with varying handset features: different screen sizes and shapes, different supported file formats, different sets of web browser features. Also, the fact that you know the user is browsing on a mobile phone is most critical for building successful mobile web user experience.

mobile.sniffer provides two phase mobile phone detection (a.k.a sniffing)

  • mobile detection - this simply detects whether a browser is a mobile phone based or not. This is done in mobile/sniffer/ module. This is useful to redirect to your visitors from a web site to a mobile site if they are using a mobile phone to arrive on your web site.

  • mobile handset feature extraction - the handset database is looked for a mobile web browser user agent match. Since there might be version changes, local varieties, etc. in user agent strings, heurestics are applied to the string matching. If a database entry is found, with certain match accuracy, it’s records like device screen width and height are made available to the web server so that it can tailor HTML, image and video output suitable for this particular mobile phone.

Mobile detection can be done with a fast regular expression match. Mobile handset feature extraction always requires a some sort of database of mobile phone entries and mobile.sniffer framework provides abstraction of these databases.


  • Easily plug-in mobile redirects to your Python based web sites

  • Able to source data from multiple sniffing backends leading better handset coverage

  • Automatically download, parse and cache complex RDF based WAP profiles

  • Very convenient Python API designed by professionals

  • Open source

  • Unit test coverage

The code is Django, WSGI and Zope/Plone compatible.

Supported sniffing backends

  • Wurfl

  • ApexVertex. Commercially available from mFabrik.

  • DeviceAtlas. Commercially available.

  • WAP profiles. User agents post a link to their WAP profile data, which is an XML file and maintained by the handset manufacturer. (note: as WAP is deprecating protocol these are not supported on newer smartphones)


mobile.sniffer is distributed as Python egg in PyPi repository.

You can install it using standard Python egg installation methods

  • easy_install

  • pip

  • buildout


You might need to install additional libraries depending on what handset database you use

Usage examples

There is no single standard to name properties queried from the handset database. For legacy reasons, we use DeviceAtlas database column names (keys) and then map them to database-dependent keys.

Redirection example

detect_mobile_browser(user_agent) will return True of False whether the HTTP request was made by a mobile phone.


from mobile.sniffer.detect import  detect_mobile_browser
from mobile.sniffer.utilities import get_user_agent

# Get HTTP_USER_AGENT from HTTP request object
ua = get_user_agent(self.request)
if ua:
    # Apply reg
    if detect_mobile_browser(ua):
        # Redirect the visitor from a web site to a mobile site
        # A regular web site visitor
    # User agent header is missing from HTTP request
    return False

Feature extraction example

This example will work out of the box with the included pywurlf database.


    from mobile.sniffer.wurlf.sniffer import WurlfSniffer

    # Wrapper sniffer instance
    # All start-up delay goes on this line
    sniffer = WurlfSniffer()
except ImportError, e:
    import traceback
    logger.error("Could not import Wurlf sniffer... add pywurfl and python-Lehvenstein to buildout.cfg eggs section")
    sniffer = None

def sniff_request(request):
    @param request: Request can be Django, WSGI or Zope HTTPRequest object

    if not sniffer:
        # We failed to initialize Wurfl
        return None

    user_agent = sniffer.sniff(request)

    if user_agent == None:
        # No match in the handset database,
        return None
        return user_agent # mobile.sniffer.wurlf.sniffer.UserAgent object

def web_or_mobile(request)
        ua = sniff_request(request)

        # How certain we must be about UA
        # match to make decisions
        # float 0...1, the actual value is UA search algorithm specific
        # We use JaroWinkler as the default algorithm
        certainty_threshold = 0.7

        if ua.get("is_wireless_device") and ua.getCertainty() > certainty_threshold:
                # Mobile code
                # Webby code

Match-making process for Wurfl

Since Wurfl is the default backend the process of finding UA record is explained more carefully

  • Wurlf database is usually loaded during the start-up (slow operation) - it is possible to make this to use lazy initialization pattern

  • The search algorithm is initialized with certain match threshold - all matches below this threshold will be ignored. The default search algorithm is JaroWinkler from Lehvenstein Python package.

  • When the user agent is searched

    • Take in HTTP request User-Agent header

    • Go through all entries in database

    • Match this entry against incoming User-Agent using the search algorithm

      • First search pass is doing using exact string matches (no algorithm involved). In this case exposed certainty will be 1.1.

      • If there was no match in the first pass, do the second pass using the search algorithm

    • If match is found and threshold is exceed return this user agent record

      • User agent record is retrofitted with the information how accurate the match was (ua.getCertainty() method exposes this)

Chained example

Use all available handset information sources to accurately get device data. Matching is done on property level - if one data source lacks the property information the next data source is tried. Finally if the handset is unknown, but it publishes WAP profile information, the profile is downloaded and analyzed and saved for further requests.


from mobile.sniffer.chain import ChainedSniffer
from mobile.sniffer.apexvertex.sniffer import ApexVertexSniffer
from mobile.sniffer.wapprofile.sniffer import WAPProfileSniffer
from mobile.sniffer.deviceatlas.sniffer import DeviceAtlasSniffer

# Create all supported sniffers
da = DeviceAtlasSniffer(da_api_file)
apex = ApexVertexSniffer()
wap = WAPProfileSniffer()

# Preferred order of sniffers
sniffer = ChainedSniffer([apex, da, wap])

ua = sniffer.sniff(request) # Sniff HTTP_USER_AGENT, HTTP_PROFILE and many other fields
property = ua.get("usableDisplayWidth") # This will look up data from all the databases in the chain

Automatic database installers

Proprietary handset databases do not publicly distribute their APIs or data. mobile.sniffer deals with the problem by automatic installation wrappers. Also, these handset database APIs are not open source compatible which makes it further difficult to use them in open source projects. Instead of manually download and set up bunch of files each time you deploy your code on a new server, just make call to one magical Python function which will take care of all of this for you.

Source code and issue tracking

The project is hosted at Google Code project repository.

Commercial support and development

This package is licenced under open source GPL 2 license.

Commercial CMS and mobile development support options are available from Web and Mobiel web site.

Our top class Python developers are ready to help you with any software development needs.


mFabrik Research Oy - Python and Plone professionals for hire.



  • Updated Wurfl db [miohtama]


  • It’s spellt LeveNshtein - why this guy would have just be called John Doe [miohtama]


  • It’s spellt Leveshtein [miohtama]


  • Depend on Levehstein [miohtama]


  • Major product rework [miohtama]


  • Updated README to describe detection and redirects [miohtama]


  • Initial release

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