The present repository contains the source code of the Datadog Agent version 7 and version 6. Please refer to the Agent user documentation for information about differences between Agent v5, Agent v6 and Agent v7. Additionally, we provide a list of prepackaged binaries for an easy install process here
Note: the source code of Datadog Agent v5 is located in the dd-agent repository.
The general documentation of the project, including instructions for installation and development, is located under the docs directory of the present repo.
To build the Agent you need:
-
Go 1.22 or later. You'll also need to set your
$GOPATH
and have$GOPATH/bin
in your path. - Python 3.11+ along with development libraries for tooling. You will also need Python 2.7 if you are building the Agent with Python 2 support.
- Python dependencies. You may install these with
pip install -r requirements.txt
This will also pull in Invoke if not yet installed. - CMake version 3.12 or later and a C++ compiler
Note: you may want to use a python virtual environment to avoid polluting your
system-wide python environment with the agent build/dev dependencies. You can
create a virtual environment using virtualenv
and then use the invoke agent.build
parameters --python-home-3=<venv_path>
to use the virtual environment's
interpreter and libraries. By default, this environment is only used for dev dependencies
listed in requirements.txt
.
Note: You may have previously installed invoke
via brew on MacOS, or pip
in
any other platform. We recommend you use the version pinned in the requirements
file for a smooth development/build experience.
Note: You can enable auto completion for invoke tasks. Use the command below to add the appropriate line to your .zshrc
file.
echo "source <(inv --print-completion-script zsh)" >> ~/.zshrc
Builds and tests are orchestrated with invoke
, type invoke --list
on a shell
to see the available tasks.
To start working on the Agent, you can build the main
branch:
-
Checkout the repo:
git clone https://github.com/DataDog/datadog-agent.git $GOPATH/src/github.com/DataDog/datadog-agent
. -
cd into the project folder:
cd $GOPATH/src/github.com/DataDog/datadog-agent
. -
Install go tools:
invoke install-tools
(if you have a timeout error, you might need to prepend theGOPROXY=https://proxy.golang.org,https://goproxy.io,direct
env var to the command). -
Create a development
datadog.yaml
configuration file indev/dist/datadog.yaml
, containing a valid API key:api_key: <API_KEY>
. You can either start with an empty one or use the full one generated by the Agent build from Step 5 (located incmd/agent/dist/datadog.yaml
after the build finishes). -
Build the agent with
invoke agent.build --build-exclude=systemd
.You can specify a custom Python location for the agent (useful when using virtualenvs):
invoke agent.build \ --python-home-3=$GOPATH/src/github.com/DataDog/datadog-agent/venv3
Running
invoke agent.build
:- Discards any changes done in
bin/agent/dist
. - Builds the Agent and writes the binary to
bin/agent/agent
. - Copies files from
dev/dist
tobin/agent/dist
. Seehttps://github.com/DataDog/datadog-agent/blob/main/dev/dist/README.md
for more information.
If you built an older version of the agent, you may have the error
make: *** No targets specified and no makefile found. Stop.
. To solve the issue, you should removeCMakeCache.txt
fromrtloader
folder withrm rtloader/CMakeCache.txt
.Please note that the trace agent needs to be built and run separately.
- Discards any changes done in
Please refer to the Agent Developer Guide for more details. For instructions on setting up a windows dev environment, refer to Windows Dev Env.
Run unit tests using invoke test
.
invoke test --targets=./pkg/aggregator
You can also use invoke linter.go
to run just the go linters.
invoke linter.go
When testing code that depends on rtloader, build and install it first.
invoke rtloader.make && invoke rtloader.install
invoke test --targets=./pkg/collector/python
You can run the agent with:
./bin/agent/agent run -c bin/agent/dist/datadog.yaml
The file bin/agent/dist/datadog.yaml
is copied from dev/dist/datadog.yaml
by invoke agent.build
and must contain a valid api key.
In order to run a JMX based check locally, you must have:
- A copy of a JMXFetch
jar
copied todev/dist/jmx/jmxfetch.jar
-
java
available on your$PATH
For detailed instructions, see JMX checks
You'll find information and help on how to contribute code to this project under
the docs/dev
directory of the present repo.
The Datadog agent user space components are licensed under the Apache License, Version 2.0. The BPF code is licensed under the General Public License, Version 2.0.