The LLDB Debugger

Continuous Integration

The following LLVM buildbots build and test LLDB trunk:

Building LLDB

Building LLDB on Windows

Required Dependencies

  • Visual Studio 2015 or greater
  • Windows SDK 8.0 or higher. In general it is best to use the latest available version.
  • Python 3.5 or higher or higher. Earlier versions of Python can be made to work by compiling your own distribution from source, but this workflow is unsupported and you are own your own.
  • Ninja build tool (strongly recommended)
  • GnuWin32
  • SWIG for Windows (version 3+)

Optional Dependencies

  • Python Tools for Visual Studio. If you plan to debug test failures or even write new tests at all, PTVS is an indispensable debugging extension to VS that enables full editing and debugging support for Python (including mixed native/managed debugging)


This section describes how to set up your system and install the required dependencies such that they can be found when needed during the build process. The steps outlined here only need to be performed once.

  1. Install Visual Studio and the Windows SDK.

  2. Install GnuWin32, making sure <GnuWin32 install dir>\bin is added to your PATH environment variable.

  3. Install SWIG for Windows, making sure <SWIG install dir> is added to your PATH environment variable.

Building LLDB

Any command prompt from which you build LLDB should have a valid Visual Studio environment setup. This means you should run vcvarsall.bat or open an appropriate Visual Studio Command Prompt corresponding to the version you wish to use.

Finally, when you are ready to build LLDB, generate CMake with the following command line:

cmake -G Ninja <cmake variables> <path to root of llvm src tree>

and run ninja to build LLDB. Information about running the LLDB test suite can be found on the test page.

Following is a description of some of the most important CMake variables which you are likely to encounter. A variable FOO is set by adding -DFOO=value to the CMake command line.

  • LLDB_TEST_DEBUG_TEST_CRASHES (Default=0): If set to 1, will cause Windows to generate a crash dialog whenever lldb.exe or the python extension module crashes while running the test suite. If set to 0, LLDB will silently crash. Setting to 1 allows a developer to attach a JIT debugger at the time of a crash, rather than having to reproduce a failure or use a crash dump.
  • PYTHON_HOME (Required): Path to the folder where the Python distribution is installed. For example, C:\Python35
  • LLDB_RELOCATABLE_PYTHON (Default=0): When this is 0, LLDB will bind statically to the location specified in the PYTHON_HOME CMake variable, ignoring any value of PYTHONHOME set in the environment. This is most useful for developers who simply want to run LLDB after they build it. If you wish to move a build of LLDB to a different machine where Python will be in a different location, setting LLDB_RELOCATABLE_PYTHON to 1 will cause Python to use its default mechanism for finding the python installation at runtime (looking for installed Pythons, or using the PYTHONHOME environment variable if it is specified).
  • LLDB_TEST_COMPILER: The test suite needs to be able to find a copy of clang.exe that it can use to compile inferior programs. Note that MSVC is not supported here, it must be a path to a clang executable. Note that using a release clang.exe is strongly recommended here, as it will make the test suite run much faster. This can be a path to any recent clang.exe, including one you built yourself.
Sample command line:
cmake -G Ninja -DLLDB_TEST_DEBUG_TEST_CRASHES=1 -DPYTHON_HOME=C:\Python35 -DLLDB_TEST_COMPILER=d:\src\llvmbuild\ninja_release\bin\clang.exe ..\..\llvm

Working with both Ninja and MSVC

Compiling with ninja is both faster and simpler than compiling with MSVC, but chances are you still want to debug LLDB with MSVC (at least until we can debug LLDB on Windows with LLDB!). One solution to this is to run cmake twice and generate the output into two different folders. One for compiling (the ninja folder), and one for editing / browsing / debugging (the MSVC folder).

To do this, simply run `cmake -G Ninja <arguments>` from one folder, and `cmake -G "Visual Studio 14 2015" <arguments>` in another folder. Then you can open the .sln file in Visual Studio, set lldb as the startup project, and use F5 to run it. You need only edit the project settings to set the executable and the working directory to point to binaries inside of the ninja tree.

Building LLDB on Mac OS X

Building on Mac OS X is as easy as downloading the code and building the Xcode project or workspace:


  • XCode 4.3 or newer requires the "Command Line Tools" component (XCode->Preferences->Downloads->Components).
  • Mac OS X Lion or newer requires installing Swig.

Building LLDB

  • Download the lldb sources.
  • Follow the code signing instructions in lldb/docs/code-signing.txt
  • In Xcode 3.x: lldb/lldb.xcodeproj, select the lldb-tool target, and build.
  • In Xcode 4.x: lldb/lldb.xcworkspace, select the lldb-tool scheme, and build.

Building LLDB on Linux, FreeBSD and NetBSD

This document describes the steps needed to compile LLDB on most Linux systems, FreeBSD and NetBSD.


LLDB relies on many of the technologies developed by the larger LLVM project. In particular, it requires both Clang and LLVM itself in order to build. Due to this tight integration the Getting Started guides for both of these projects come as prerequisite reading:

Supported compilers for building LLDB on Linux include:

  • Clang 3.2
  • GCC 4.6.2 (later versions should work as well)

It is recommended to use libstdc++ 4.6 (or higher) to build LLDB on Linux, but using libc++ is also known to work.

On FreeBSD the base system Clang and libc++ may be used to build LLDB, or the GCC port or package.

On NetBSD the base system GCC and libstdc++ are used to build LLDB, Clang/LLVM and libc++ should also work.

In addition to any dependencies required by LLVM and Clang, LLDB needs a few development packages that may also need to be installed depending on your system. The current list of dependencies are:

So for example, on a Fedora system one might run:

> yum install libedit-devel libxml2-devel ncurses-devel python-devel swig

On a Debian or Ubuntu system one might run:

> sudo apt-get install build-essential subversion swig python2.7-dev libedit-dev libncurses5-dev


> sudo apt-get build-dep lldb-3.3 # or lldb-3.4

On FreeBSD one might run:

> pkg install swig python

On NetBSD one might run:

> pkgin install swig python27 cmake ninja-build

If you wish to build the optional reference documentation, additional dependencies are required:

  • Graphviz (for the 'dot' tool).
  • doxygen (only if you wish to build the C++ API reference)
  • epydoc (only if you wish to build the Python API reference)

To install the prerequisites for building the documentation (on Debian/Ubuntu) do:

> sudo apt-get install doxygen graphviz
> sudo pip install epydoc # or install package python-epydoc

Building LLDB

We first need to checkout the source trees into the appropriate locations. Both Clang and LLDB build as subprojects of LLVM. This means we will be checking out the source for both Clang and LLDB into the tools subdirectory of LLVM. We will be setting up a directory hierarchy looking something like this:

                  `-- tools
                      +-- clang
                      `-- lldb

For reference, we will call the root of the LLVM project tree $llvm, and the roots of the Clang and LLDB source trees $clang and $lldb respectively.

Change to the directory where you want to do development work and checkout LLVM:

> svn co llvm

Now switch to LLVM’s tools subdirectory and checkout both Clang and LLDB:

> cd $llvm/tools
> svn co clang
> svn co lldb

In general, building the LLDB trunk revision requires trunk revisions of both LLVM and Clang.

It is highly recommended that you build the system out of tree. Create a second build directory and configure the LLVM project tree to your specifications as outlined in LLVM’s Getting Started Guide. A typical build procedure might be:

> cd $llvm/..
> mkdir build
> cd build

To build with CMake

Using CMake is documented on the Building LLVM with CMake page. Building LLDB is possible using one of the following generators:

  • Ninja
  • Unix Makefiles

Using CMake + Ninja

Ninja is the fastest way to build LLDB! In order to use ninja, you need to have recent versions of CMake and ninja on your system. To build using ninja:

> cmake ../llvm -G Ninja
> ninja lldb
> ninja check-lldb

If you want to debug the lldb that you're building -- that is, build it with debug info enabled -- pass two additional arguments to cmake before running ninja:


Using CMake + Unix Makefiles

If you do not have Ninja, you can still use CMake to generate Unix Makefiles that build LLDB:

> cmake ..
> make
> make check-lldb

Building API reference documentation

LLDB exposes a C++ as well as a Python API. To build the reference documentation for these two APIs, ensure you have the required dependencies installed, and build the lldb-python-doc and lldb-cpp-doc CMake targets.

The output HTML reference documentation can be found in <build-dir>/tools/lldb/docs/.

Additional Notes

LLDB has a Python scripting capability and supplies its own Python module named lldb. If a script is run inside the command line lldb application, the Python module is made available automatically. However, if a script is to be run by a Python interpreter outside the command line application, the PYTHONPATH environment variable can be used to let the Python interpreter find the lldb module.

Current stable NetBSD release doesn't ship with libpanel(3), therefore it's required to disable curses(3) support with the -DLLDB_DISABLE_CURSES:BOOL=TRUE option. To make sure check if /usr/include/panel.h exists in your system.

The correct path can be obtained by invoking the command line lldb tool with the -P flag:

> export PYTHONPATH=`$llvm/build/Debug+Asserts/bin/lldb -P`

If you used a different build directory or made a release build, you may need to adjust the above to suit your needs. To test that the lldb Python module is built correctly and is available to the default Python interpreter, run:

> python -c 'import lldb'

Cross-compiling LLDB

In order to debug remote targets running different architectures than your host, you will need to compile LLDB (or at least the server component) for the target. While the easiest solution is to just compile it locally on the target, this is often not feasible, and in these cases you will need to cross-compile LLDB on your host.

Cross-compilation is often a daunting task and has a lot of quirks which depend on the exact host and target architectures, so it is not possible to give a universal guide which will work on all platforms. However, here we try to provide an overview of the cross-compilation process along with the main things you should look out for.

First, you will need a working toolchain which is capable of producing binaries for the target architecture. Since you already have a checkout of clang and lldb, you can compile a host version of clang in a separate folder and use that. Alternatively you can use system clang or even cross-gcc if your distribution provides such packages (e.g., g++-aarch64-linux-gnu on Ubuntu).

Next, you will need a copy of the required target headers and libraries on your host. The libraries can be usually obtained by copying from the target machine, however the headers are often not found there, especially in case of embedded platforms. In this case, you will need to obtain them from another source, either a cross-package if one is available, or cross-compiling the respective library from source. Fortunately the list of LLDB dependencies is not big and if you are only interested in the server component, you can reduce this even further by passing the appropriate cmake options, such as:


In this case you, will often not need anything other than the standard C and C++ libraries.

Once all of the dependencies are in place, it's just a matter of configuring the build system with the locations and arguments of all the necessary tools. The most important cmake options here are:

Set to 1 to enable cross-compilation.
Affects the cmake search path when looking for libraries. You may need to set this to your architecture triple if you do not specify all your include and library paths explicitly.
C and C++ compilers for the target architecture
The flags for the C and C++ target compilers. You may need to specify the exact target cpu and abi besides the include paths for the target headers.
The flags to be passed to the linker. Usually just a list of library search paths referencing the target libraries.
Paths to llvm-tblgen and clang-tblgen for the host architecture. If you already have built clang for the host, you can point these variables to the executables in your build directory. If not, you will need to build the llvm-tblgen and clang-tblgen host targets at least.
The triple of the system that lldb (or lldb-server) will run on. Not setting this (or setting it incorrectly) can cause a lot of issues with remote debugging as a lot of the choices lldb makes depend on the triple reported by the remote platform.

You can of course also specify the usual cmake options like CMAKE_BUILD_TYPE, etc.

Example 1: Cross-compiling for linux arm64 on Ubuntu host

Ubuntu already provides the packages necessary to cross-compile LLDB for arm64. It is sufficient to install packages gcc-aarch64-linux-gnu, g++-aarch64-linux-gnu, binutils-aarch64-linux-gnu. Then it is possible to prepare the cmake build with the following parameters:

-DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
-DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \
-DLLVM_HOST_TRIPLE=aarch64-unknown-linux-gnu \
-DLLVM_TABLEGEN=<path-to-host>/bin/llvm-tblgen \
-DCLANG_TABLEGEN=<path-to-host>/bin/clang-tblgen \

An alternative (and recommended) way to compile LLDB is with clang. Unfortunately, clang is not able to find all the include paths necessary for a successful cross-compile, so we need to help it with a couple of CFLAGS options. In my case it was sufficient to add the following arguments to CMAKE_C_FLAGS and CMAKE_CXX_FLAGS (in addition to changing CMAKE_C(XX)_COMPILER to point to clang compilers):

-target aarch64-linux-gnu \
-I /usr/aarch64-linux-gnu/include/c++/4.8.2/aarch64-linux-gnu \
-I /usr/aarch64-linux-gnu/include

If you wanted to build a full version of LLDB and avoid passing -DLLDB_DISABLE_PYTHON and other options, you would need to obtain the target versions of the respective libraries. The easiest way to achieve this is to use the qemu-debootstrap utility, which can prepare a system image using qemu and chroot to simulate the target environment. Then you can install the necessary packages in this environment (python-dev, libedit-dev, etc.) and point your compiler to use them using the correct -I and -L arguments.

Example 2: Cross-compiling for Android on Linux

In the case of Android, the toolchain and all required headers and libraries are available in the Android NDK.

The NDK also contains a cmake toolchain file, which makes configuring the build much simpler. The compiler, include and library paths will be configured by the toolchain file and all you need to do is to select the architecture (ANDROID_ABI) and platform level (ANDROID_PLATFORM, should be at least 21). You will also need to set ANDROID_ALLOW_UNDEFINED_SYMBOLS=On, as the toolchain file defaults to "no undefined symbols in shared libraries", which is not compatible with some llvm libraries. The first version of NDK which supports this approach is r14.

For example, the following arguments are sufficient to configure an android arm64 build:

-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_HOME/build/cmake/android.toolchain.cmake \
-DANDROID_ABI=arm64-v8a \
-DLLVM_HOST_TRIPLE=aarch64-unknown-linux-android \

Note that currently only lldb-server is functional on android. The lldb client is not supported and unlikely to work.