Programming Guide

API Stability

In general, any method or class prefixed with an underscore (like _method or _ClassName) is private, and the API may change at any time. You SHOULD NOT use these. Any method in an interface class (which all begin with I, like IAnInterface) are stable, public APIs and will maintain backwards-compatibility between releases.

There is one exception to this at the moment: the hidden- / onion- services APIs are NOT yet considered stable, and may still change somewhat.

Any APIs that will go away will first be deprecated for at least one major release before being removed.

There are also some attributes which don’t have underscores but really should; these will get “deprecated” via an @property decorator so your code will still work.

High Level Overview

Interacting with Tor via txtorcon should involve only calling methods of the Tor class.

You get an instance of Tor in one of two ways:

  • call txtorcon.connect() or;

  • call txtorcon.launch()

Once you’ve got a Tor instance you can use it to gain access to (or create) instances of the other interesting classes; see “A Tor Instance” below for various use-cases.

Note that for historical reasons (namely: Tor is a relatively new class) there are many other functions and classes exported from txtorcon but you shouldn’t need to instantiate these directly. If something is missing from this top-level class, please get in touch (file a bug, chat on IRC, etc) because it’s probably a missing feature.

A Tor Instance

You will need a connection to a Tor instance for txtorcon to control. This can be either an already-running Tor that you’re authorized to connect to, or a Tor instance that has been freshly launched by txtorcon.

We abstract “a Tor instance” behind the Tor class, which provides a very high-level API for all the other things you might want to do:

The actual control-protocol connection to tor is abstracted behind TorControlProtocol. This can usually be ignored by most users, but can be useful to issue protocol commands directly, listen to raw events, etc.

In general, txtorcon tries to never look at Tor’s version and instead queries required information directly via the control-protocol (there is only one exception to this). So the names of configuration values and events may change (or, more typically, expand) depending on what version of Tor you’re connected to.

Connecting to a Running Tor

Tor can listen for control connections on TCP ports or UNIX sockets. See “Tor Configuration” for information on how to configure Tor to work with txtorcon. By default, “COOKIE” authentication is used; only if that is not available do we try password authentication.

To connect, use txtorcon.connect() which returns a Deferred that will fire with a Tor instance. If you need access to the TorControlProtocol instance, it’s available via the .protocol property (there is always exactly one of these per Tor instance). Similarly, the current configuration is available via .get_config (which returns a Deferred firing a TorConfig). You can change the configuration by updating attributes on this class but it won’t take effect until you call

Launching a New Tor

It’s also possible to launch your own Tor instance. txtorcon keeps a “global” tor available for use by e.g. the .global_tor endpoint factory functions (like TCPHiddenServiceEndpoint.global_tor()). You can access it via get_global_tor_instance(). There is exactly zero or one of these per Python process that uses txtorcon.

To explicitly launch your own Tor instance, use launch(). You can pass a couple of minimal options (data_directory being recommended). If you need to set other Tor options, use .config to retrieve the TorConfig instance associated with this tor and change configuration afterwards.

Setting data_directory gives your Tor instance a place to cache its state information which includes the current “consensus” document. If you don’t set it, txtorcon creates a temporary directory (which is deleted when this Tor instance exits). Startup time is drammatically improved if Tor already has a recent consensus, so when integrating with Tor by launching your own client it’s highly recommended to specify a data_directory somewhere sensible (e.g. ~/.config/your_program_name/ is a popular choice on Linux). See the Tor manual under the DataDirectory option for more information.

Tor itself will create a missing data_directory with the correct permissions and Tor will also chdir into its DataDirectory when running. For these reasons, txtorcon doesn’t try to create the data_directory nor do any chdir-ing, and neither should you.

A Note On Style

Most of txtorcon tends towards “attribute-style access”. The guiding principle is that “mere data” that is immediately available will be an attribute, whereas things that “take work” or are async (and thus return Deferred s) will be functions. For example, Router.get_location() is a method because it potentially has to ask Tor for the country, whereas Router.hex_id is a plain attribute because it’s always available.

Tracking and Changing Tor’s Configuration

Instances of the TorConfig class represent the current, live state of a running Tor. There is a bit of attribute-magic to make it possible to simply get and set things easily:

tor = launch(..)
print("SOCKS ports: {}".format(tor.config.SOCKSPort))

Only when .save() is called are any SETCONF commands issued – and then, all configuration values are sent in a single command. All TorConfig instances subscribe to configuration updates from Tor, so “live state” includes actions by any other controllers that may be connected.

For some configuration items, the order they’re sent to Tor matters. Sometimes, if you change one config item, you have to set a series of related items. TorConfig handles these cases for you – you just manipulate the configuration, and wait for .save() ‘s Deferred to fire and the running Tor’s configuration is updated.

Note there is a tiny window during which the state may appear slightly inconsistent if you have multiple TorConfig instances: after Tor has acknowledged a SETCONF command, but before a separate TorConfig instance has gotten all the CONF_CHANGED events (because they’re hung up in the networking stack for some reason). This shouldn’t concern most users. (I’m not even 100% sure this is possible; it may be that Tor doesn’t send the OK until after all the CONF_CHANGED events). In normal use, there should only be a single TorConfig instance for every Tor instance so this shouldn’t affect you unless you’ve created your own TorConfig.

Since TorConfig conforms to the Iterator protocol, you can easily find all the config-options that Tor supports:

tor = launch(..)
for config_key in tor.config:
    print("{} has value: {}".format(config_key, getattr(tor.config.config_key)))

These come from interrogating Tor using GETINFO config/names and so represent the configuration options of the current connected Tor process. If the value “isn’t set” (i.e. is the default), the value from Tor will be .DEFAULT_VALUE.

When you set values into TorConfig, they are parsed according to control-spec for the different types given to the values, via information from GETINFO config/names. So, for example, setting .SOCKSPort to a "quux" won’t work. Of course, it would also fail the whole SETCONF command if txtorcon happens to allow some values that Tor doesn’t. Unfortunately, for any item that’s a list, Tor doesn’t tell us anything about each element so they’re all strings. This means we can’t pre-validate them and so some things may not fail until you call .save().

Monitor and Change Tor’s State

Instances of TorState prepresent a live, interactive version of all the relays/routers (Router instances), all circuits (Circuit instances) and streams (Stream instances) active in the underlying Tor instance.

As the TorState instance has subscribed to various events from Tor, the “live” state represents an “as up-to-date as possible” view. This includes all other controlers, Tor Browser, etcetera that might be interacting with your Tor client.

A Tor instance doesn’t have a TorState instance by default (it can take a few hundred milliseconds to set up) and so one is created via the asynchronous method Tor.get_state().


If you need to be absolutely sure there’s nothing stuck in networking buffers and that your instance is “definitely up-to-date” you can issue a do-nothing command to Tor via TorControlProtocol.queue_command() (e.g. yield queue_command("GETINFO version")). Most users shouldn’t have to worry about this edge-case. In any case, there could be a new update that Tor decides to issue at any moment.

You can modify the state of Tor in a few simple ways. For example, you can call Stream.close() or Circuit.close() to cause a stream or circuit to be closed. You can wait for a circuit to become usable with Circuit.when_built().

For a lot of the read-only state, you can simply access interesting attributes. The relays through which a circuit traverses are in Circuit.path (a list of Router instances), Circuit.streams contains a list of Stream instances, .state and .purpose are strings. .time_created returns a datetime instance. There are also some convenience functions like Circuit.age().

For sending streams over a particular circuit, Circuit.stream_via() returns an IStreamClientEndpoint implementation that will cause a subsequent .connect() on it to go via the given circuit in Tor. A similar method (Circuit.web_agent()) exists for Web requests.

Listening for certain events to happen can be done by implementing the interfaces interface.IStreamListener and interface.ICircuitListener. You can request notifications on a Tor-wide basis with TorState.add_circuit_listener() or TorState.add_stream_listener(). If you are just interested in a single circuit, you can call Circuit.listen() directly on a Circuit instance.

You can instead use methods (which also function as decorators) such as TorState.on_circuit_launched() or TorState.on_stream_closed() to add listeners for single events.

The Tor relays are abstracted with Router instances. Again, these have read-only attributes for interesting information, e.g.: id_hex, ip, flags (a list of strings), bandwidth, policy, etc. Note that all information in these objects is from “microdescriptors”. If you’re doing a long-running iteration over relays, it may be important to remember that the collection of routers can change every hour (when a new “consensus” from the Directory Authorities is published) which may change the underlying collection (e.g. TorState.routers_by_hash) over which you’re iterating.

Here’s a simple sketch that traverses all circuits printing their router IDs, and closing each stream and circuit afterwards:

from twisted.internet.task import react
from twisted.internet.defer import inlineCallbacks
from twisted.internet.endpoints import UNIXClientEndpoint

import txtorcon

def main(reactor):
    Close all open streams and circuits in the Tor we connect to
    control_ep = UNIXClientEndpoint(reactor, '/var/run/tor/control')
    tor = yield txtorcon.connect(reactor, control_ep)
    state = yield tor.create_state()
    print("Closing all circuits:")
    for circuit in list(state.circuits.values()):
        path = '->'.join(map(lambda r: r.id_hex, circuit.path))
        print("Circuit {} through {}".format(, path))
        for stream in circuit.streams:
            print("  Stream {} to {}".format(, stream.target_host))
            yield stream.close()
            print("  closed")
        yield circuit.close()
    yield tor.quit()

Making Connections Over Tor


Tor exposes a SOCKS5 interface to make client-type connections over the network. There are also a couple of custom extensions Tor provides to do DNS resolution over a Tor circuit (txtorcon supports these, too).

All client-side interactions are via instances that implement IStreamClientEndpoint. There are several factory functions used to create suitable instances.

The recommended API is to acquire a Tor instance (see “A Tor Instance”) and then call Tor.create_client_endpoint(). To do DNS lookups (or reverse lookups) via a Tor circuit, use Tor.dns_resolve() and Tor.dns_resolve_ptr().

A common use-case is to download a Web resource; you can do so via Twisted’s built-in twisted.web.client package, or using the friendlier treq library. In both cases, you need a twisted.web.client.Agent instance which you can acquire with Tor.web_agent() or Circuit.web_agent(). The latter is used to make the request over a specific circuit. Usually, txtorcon will simply use one of the available SOCKS ports configured in the Tor it is connected to – if you care which one, you can specify it as the optional _socks_endpoint= argument (this starts with an underscore on purpose as it’s not recommended for “public” use and its semantics might change in the future).


Tor supports SOCKS over Unix sockets. So does txtorcon. To take advantage of this, simply pass a valid SocksPort value for unix sockets (e.g. unix:/tmp/foo/socks) as the _socks_endpoint argument to either web_agent() call. If this doesn’t already exist in the underlying Tor, it will be added. Tor has particular requirements for the directory in which the socket file is (0700). We don’t have a way (yet?) to auto-discover if the Tor we’re connected to can support Unix sockets so the default is to use TCP.

You can also use Twisted’s clientFromString API as txtorcon registers a tor: plugin. This also implies that any Twisted-using program that supports configuring endpoint strings gets Tor support “for free”. For example, passing a string like tor:fjblvrw2jrxnhtg67qpbzi45r7ofojaoo3orzykesly2j3c2m3htapid.onion:80 to clientFromString will return an endpoint that will connect to txtorcon’s onion-service website. Note that these endpoints will use the “global to txtorcon” Tor instance (available from get_global_tor()). Thus, if you want to control which tor instance your circuit goes over, this is not a suitable API.

There are also lower-level APIs to create TorClientEndpoint instances directly if you have a TorConfig instance. These very APIs are used by the Tor object mentioned above. If you have a use-case that requires using this API, I’d be curious to learn why the Tor methods are un-suitable (as those are the suggested API).

You should expect these APIs to raise SOCKS5 errors, which can all be handled by catching the socks.SocksError class. If you need to work with each specific error (corresponding to the RFC-specified SOCKS5 replies), see the “txtorcon.socks Module” for a list of them.

Onion (Hidden) Services

An “Onion Service” (also called a “Hidden Service”) refers to a feature of Tor allowing servers (e.g. a Web site) to be availble via Tor. These bring additional security properties such as:

  • hiding the server’s network location;

  • providing end-to-end encryption;

  • self-certifying domain-names;

  • NAT penetration (connections to Tor network are client-like);

  • or offering authentication.

For details of how this works, please read Tor’s documentation on Hidden Services.

For more background, the RiseUp Onion service best-practices guide is a good read as well.

In the newest Tor versions, Onion services have been upgraded (”Proposition 279”) and these are known as “version 3” services. The prior / legacy is “version 2”. In txtorcon, the default version is usually 3 but in some older APIs the default has to remain “2” if unspecified (for backwards-compatibility). You should thus usually pass version=3 (and if you expect to connect to an older Tor release, pass version=2).


In some places there will be “Hidden” in a classname; these are typically for backwards-compatilibity reasons. “Onion service” is the preferred name.

From an API perspective, here are the parts we care about:

  • each service has a secret, private key (with a corresponding public part);

    • these keys can be on disk (in the “hidden service directory”);

    • or, they can be “ephemeral” (only in memory);

  • the “host name” is a hash of the public-key (e.g. fjblvrw2jrxnhtg67qpbzi45r7ofojaoo3orzykesly2j3c2m3htapid.onion);

  • a “Descriptor” (which tells clients how to connect) must be published (to a “Hidden Service Directory”, or HSDir);

  • a service has a list of port-mappings (public -> local):
    • e.g. "80" says you can contact the service publically on port 80, which Tor will redirect to a daemon running locally on port 5432;

    • note that “Descriptors” only show the public port

  • services can be “authenticated”, which means they have a list of client names for which Tor creates associated keys (.auth_token).

  • Tor has two flavours of service authentication: basic and stealth – there’s no API-level difference, but the .hostname is unique for each client in the stealth case.

To summarize the above in a table format, here are the possible types of Onion Service interfaces classes you may interact with.

Keys on disk

Keys in memory

no authentication



basic/stealth authentication



txtorcon.IFilesystemOnionService is a subclass of txtorcon.IOnionService and the concrete objects will be different for on-disk versus in-memory keys; depend on the methods in the interfaces (only).

Note that it’s up to you to save the private keys of ephemeral services if you want to re-launch them later; the “ephemeral” refers to the fact that Tor doesn’t persist the private keys – when Tor shuts down, they’re gone and there will never be a service at the same URI again (unless you saved the key).

Onion Services Endpoints API

No matter which kind of service you need, you interact via Twisted’s IStreamServerEndpoint interface. There are various txtorcon methods (see “Creating Onion Endpoints”) which return some instance implementing that interface. These instances will also implement txtorcon.IProgressProvider – which is a hook to register listeners which get updates about Tor’s launching progress (if we started a new Tor) and Descriptor uploading.

Fundamentally, “authenticated” services are different from non-authenticated services because they have a list of clients. Services on-disk are “slightly” different because the user may need to know the “hidden service dir” that contains the private keys. However, there is a single endpoint which takes enough options to produce any kind of onion service. The service instance you retrieve after the .listen() call will, however, be different and implement one of the interfaces in the table above. Those are:

The .listen() method of the endpoint will return an instance implementing IListeningPort. This will have a .onion_service property that gives you an instance implementing one of the above interfaces.

txtorcon.IOnionService and its subclass txtorcon.IFilesystemOnionService correspond to a non-authenticated services, while txtorcon.IAuthenticatedOnionClients is authenticated. The latter manages a collection of instances by (arbitrary) client names, where each of these instances implements txtorcon.IOnionClient. Note that the .auth_token member is secret, private data which you need to give to one client; this information goes in the client’s Tor configuration as HidServAuth onion-address auth-cookie [service-name]. See the Tor manual for more information.

Creating Onion Endpoints

The easiest-to-use API are methods of Tor, which allow you to create IStreamServerEndpoint instances for the various Onion Service types. For all service types there is a single endpoint that you create: TCPHiddenServiceEndpoint. Thus, you are advised to use a factory-method to create the instance

It’s also possible to use Twisted’s serverFromString API with the onion: prefix. (Thus, any program supporting endpoint strings for configuration can use Tor Onion Services with no code changes).

Each of the four main classes of onion service has a corresponding factory method (while these get nearly to Java lengths, these are at least explicit):

  • Tor.create_onion_endpoint(): ephemeral service

  • Tor.create_authenticated_onion_endpoint(): ephemeral service with authentication

  • Tor.create_filesystem_onion_endpoint(): on-disk service

  • Tor.create_authenticated_filesystem_onion_endpoint(): on-disk service with authentication

Factors to consider when deciding whether to use “authenticated” service or not:

  • if you want anyone with e.g. the URL http://fjblvrw2jrxnhtg67qpbzi45r7ofojaoo3orzykesly2j3c2m3htapid.onion to be able to put it in Tor Browser Bundle and see a Web site, you do not want authentication;

  • if you want only people with the URL and a secret authentication token to see the Web site, you want basic authentication (these support many more clients than stealth auth);

  • if you don’t even want anyone to be able to decrypt the descriptor without a unique URL and a secret authentication token, you want stealth authentication (a lot less scalable; for only “a few” clients – less than 16 in latest Tor).

Non-Authenticated Services

You can create non-authenticated with Tor.create_onion_service() (for an ephemeral service) or Tor.create_filesystem_onion_service() (for an on-disk service).

If you don’t want to manage launching or connecting to Tor yourself (and thus a Tor instance), you can use one of the three factory-functions in TCPHiddenServiceEndpoint, which all return a new endpoint instance:

  • TCPHiddenServiceEndpoint.global_tor(): uses a Tor instance launched at most once in this Python process (the underlying Tor instance for this is available via get_global_tor() if you need to make manual configuration adjustments);

  • TCPHiddenServiceEndpoint.system_tor(): connects to the control-protocol endpoint you provide (a good choice on Debian would be UNIXClientEndpoint('/var/run/tor/control'));

  • TCPHiddenServiceEndpoint.private_tor(): causes a fresh, private instance of Tor to be launched for this service alone. This uses a tempdir (honoring $TMP) which is deleted upon reactor shutdown or loss of the control connection.

Note that nothing actually “happens” until you call .listen() on the IStreamServerEndpoint at which point Tor will possibly be launched, the Onion Service created, and the descriptor published.

Authenticated Services

Authenticated services take an instance of AuthBasic or AuthStealth. You may use the factory methods on Tor: Tor.create_authenticated_onion_service() (for an ephemeral service) or Tor.create_authenticated_filesystem_onion_service() (for an on-disk service).

You may also use one of the three @classmethod-s on TCPHiddenServiceEndpoint (and passing an auth= kwarg):

  • TCPHiddenServiceEndpoint.global_tor()

  • TCPHiddenServiceEndpoint.system_tor()

  • TCPHiddenServiceEndpoint.private_tor()

Onion Service Configuration

If you just want to “look at” the configuration of existing onion services, they are avaialble via TorConfig and the .HiddenServices or .EphemeralHiddenServices attributes.

These presents a “flattened” version of any authenticated services, so that each element in the list of .HiddenServices is itself at least a txtorcon.IOnionService (it may also implement other interfaces, but every one will implement IOnionService).

You can still set any settable attributes on these objects, and Tor’s configuration for them will be updated when you call with an important exception: “ephemeral” services cannot be updated after they’re created.

Note that it’s possible for other controllers to create ephemeral services that Tor doesn’t allow your controller to enumerate.

Custom Circuits

Tor provides a way to let controllers like txtorcon decide which streams go on which circuits. Since your Tor client will then be acting differently from a “normal” Tor client, it may become easier to de-anonymize you.

High Level

With that in mind, you may still decide to attach streams to circuits. Most often, this means you simply want to make a client connection over a particluar circuit. The recommended API uses Circuit.stream_via() for arbitrary protocols or Circuit.web_agent() as a convenience for Web connections. The latter can be used via Twisted’s Web client or via treq (a “requests”-like library for Twisted).

See the following examples:

Note that these APIs mimic Tor.stream_via() and Tor.web_agent() except they use a particular Circuit.

Low Level

Under the hood of these calls, txtorcon provides a low-level interface directly over top of Tor’s circuit-attachment API.

This works by:

  • setting __LeaveStreamsUnattached 1 in the Tor’s configuration

  • listening for STREAM events

  • telling Tor (via ATTACHSTREAM) what circuit to put each new stream on

  • (we can also choose to tell Tor “attach this one however you normally would”)

This is an asynchronous API (i.e. Tor isn’t “asking us” for each stream) so arbitrary work can be done on a per-stream basis before telling Tor which circuit to use. There are two limitations though:

  • Tor doesn’t play nicely with multiple controllers playing the role of attaching circuits. Generally, there’s not a good way to know if there’s another controller trying to attach streams, but basically the first one to answer “wins”.

  • Tor doesn’t currently allow controllers to attach circuits destined for onion-services (even if the circuit is actually suitable and goes to the correct Introduction Point).

In order to do custom stream -> circuit mapping, you call TorState.set_attacher() with an object implementing interface.IStreamAttacher. Then every time a new stream is detected, txtorcon will call interface.IStreamAttacher.attach_stream() with the Stream instance and a list of all available circuits. You make an appropriate return.

There can be either no attacher at all or a single attacher object. You can “un-set” an attacher by calling set_attacher(None) (in which case __LeaveStreamsUnattached will be set back to 0). If you really do need multiple attachers, you can use the utility class attacher.PriorityAttacher which acts as the “top level” one (so you add your multiple attachers to it).

Be aware that txtorcon internally uses this API itself if you’ve ever called the “high level” API (Circuit.stream_via() or Circuit.web_agent()) and so it is an error to set a new attacher if there is already an existing attacher.

Building Your Own Circuits

To re-iterate the warning above, making your own circuits differently from how Tor normally does runs a high risk of de-anonymizing you. That said, you can build custom circuits using txtorcon.

Building a Single Circuit

If your use-case needs just a single circuit, it is probably easiest to call TorState.build_circuit(). This methods takes a list of Router instances, which you can get from the TorState instance by using one of the attributes:

  • .all_routers

  • .routers

  • .routers_by_name or

  • .routers_by_hash

The last three are all dicts. For relays that have the Guard flag, you can access the dicts .guards (for all of them) or .entry_guards (for just the entry guards configured on this Tor client).

If you don’t actually care which relays are used, but simply want a fresh circuit, you can call TorState.build_circuit() without any arguments at all which asks Tor to build a new circuit in the way it normally would (i.e. respecting your guard nodes etc).

There is also build_timeout_circuit() as a convenience method if you wish the attempt to time out after a while.

Building Many Circuits


This API doesn’t exist yet; this is documenting what may become a new API in a future version of txtorcon. Please get in touch if you want this now.

If you would like to build many circuits, you’ll want an instance that implements txtorcon.ICircuitBuilder (which is usually simply an instance of CircuitBuilder). Instances of this class can be created by calling one of the factory functions like circuit_builder_fixed_exit().

XXX what about a “config object” idea, e.g. could have keys:

  • guard_selection: one of entry_only (use one of the current entry guards) or random_guard (use any relay with the Guard flag, selected by XXX).

  • middle_selection: one of uniform (selected randomly from all relays), weighted (selected randomly, but weighted by consensus weight – basically same way as Tor would select).