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AgentOS is a command line interface and python developer API for building, running, and sharing flexible learning agents.
This project consists of two major pieces: the Python Component System (PCS) and AgentOS.
PCS is an open source Python API, command line interface, and public web registry for building, running, and sharing Python programs. The goals of PCS are to:
Make Python program execution reproducible.
Transparently manage Python virtual environments while providing a Python API
for pip
and virtualenv
.
Simplify experiment tracking and code sharing.
PCS does this by allowing you to explicitly specify dependencies and arguments
for your program and then providing a thin runtime (currently based on MLflow <https://mlflow.org>
_) to automatically instrument your program's execution.
PCS is compatible with most frameworks that are used to build machine learning
and reinforcement learning systems.
AgentOS is a set of libraries built on top of the Python Component System that make it easy to build, run, and share agents that use Reinforcement Learning (RL) to solve tasks.
Key features of AgentOS:
Easy to use Agent API for developing and running new agents.
A public repository <https://aos-web.herokuapp.com/#TODO>
_ of popular RL
environments and agents, and runs of those agents in those environments
that can be reproduced with a single line of code.
Example learning agents from different disciplines and research areas are
available in the
example_agents <https://github.com/agentos-project/agentos/tree/master/example_agents>
_
directory of the project source code.
Ask questions or report bugs in PCS and AgentOS in
GitHub Issues <https://github.com/agentos-project/agentos/issues>
_
or on the
dev Discord <https://discord.gg/hUSezsejp3>
_.
Find the AgentOS source code on Github <https://github.com/agentos-project/agentos>
_.
.. image:: https://github.com/agentos-project/agentos/workflows/Tests%20on%20master/badge.svg :target: https://github.com/agentos-project/agentos/actions :alt: Test Status Indicator
The Python Component System and AgentOS are alpha software; APIs and overall architecture are likely to change significantly over time. They are licensed under the Apache License, Version 2.0.
See the agentos.org quickstarts <https://agentos.org/latest/quickstart>
_.
For detailed documentation see the agentos.org docs <https://agentos.org/latest>
_.
AgentOS uses GitHub Issues <https://github.com/agentos-project/agentos/issues>
_ to track development
work. Submit any bug reports or feature requests to this issues tracker.
For significant feature work (more than a couple dev days or something that
fundamentally changes internal or external interfaces), we run a design process
to solicit feedback from other collaborators. Read more about this process
in the Proposing Features
_ section.
To contribute to AgentOS, the general workflow is as follows:
Sync with the core development team via the
issue tracker <https://github.com/agentos-project/agentos/issues>
_
so we can avoid unnecessary or duplicated work.
Fork the AgentOS repo.
Complete your feature work on a branch in your forked repo. Ensure all checks and tests pass.
Issue a pull request from your forked repo into the central AgentOS repo. Assign a core developer to review.
Address any comments and the core developer will merge once the PR looks good.
For new features and other big chunks of work, AgentOS uses a design process centered around design proposals, discussions, and design docs. The goal of the process is to:
...before development begins.
If you'd like to propose a feature, please follow the procedure found in the
design_docs README <documentation/design_docs/README.rst>
_. You can also
browse existing design docs in the folder to get a feel for the general
content and style.
To install agentos from source (e.g., to play with the example_agents), run the following::
git clone https://github.com/agentos-project/agentos.git pip install -e agentos # you may want to do this in a virtualenv or conda env.
To run tests, first install the requirements (note, this script installs the Python requirements into the currently active virtual environment)::
cd agentos # the project root, not the nested agentos/agentos dir python install_requirements.py
Then run the tests::
pytest
Also, we use Github Actions to run tests with every commit
and pull request (see the test workflow <https://github.com/agentos-project/agentos/blob/master/.github/workflows/run-tests.yml>
_)
If you want to the CLI to interact with a local development server, define the
environment variable (or create a .env
file) USE_LOCAL_SERVER=True
.
To run website tests::
python install_requirements.py cd web # the web directory contained in project root python manage.py test
Note that some tests (e.g., see web/registry/tests/test_integration.py
)
test functionality for interacting with github repositories by fetching code
from https://github.com/agentos-project/agentos. Where possible, in order to
make it easy to have those tests run against code in a github repo that you can
change during development without disrupting other PRs, the test code uses
global variables defined in tests/utils.py
to decide which github
repo to use when testing.
If you make changes to code that is fetched from github for use by tests, then please follow this process for your PR:
TESTING_GITHUB_REPO_URL
and/or
TESTING_BRANCH_NAME
global variables in tests/utils.py
to point to a version of your PR branch that you've pushed to
github. We recommend commenting out the default "prod" values of these
variables so that you can uncomment them in the next step when the PR
is approved for merge.test_prod
branch of the agentos-project
account https://github.com/agentos-project/agentos.git
. And then update
the variables in tests/utils.py
(you should be able to just uncomment
the lines you commented out in step 1 above, and delete the lines you added).The documentation source is in the documentation
directory and written in
ReStructuredText <https://docutils.sourceforge.io/rst.html>
. The docs are
built using Sphinx <https://www.sphinx-doc.org>
. To build the docs, first
install the dev requirements (note, this script will install requirements into
the currently active Python virtual environment)::
python install_requirements.py
Then use the build script::
python scripts/build_docs.py
Use the --help
flag to learn more about other optional flags that
build_docs.py
takes, including --release
(for publishing the docs) and
--watch
(for auto-recompiling the docs whenever doc source files are
changed).
Notice that the build file puts the compiled docs into docs/<version_num>
where version_num
comes from pcs/version.py
.
Or you can build the docs manually (e.g., to control where output goes)::
sphinx-build documentation outdir # Or use sphinx-autobuild.
agentos.org <https://agentos.org>
_ is a github.io website where the AgentOS
docs are hosted. To publish updated docs to agentos.org, checkout the
website
branch and build the docs per the instructions above, then create a
PR against the agentos-dev/website
branch. Once committed, those changes
will become live at agentos.org automatically.
Assuming you have local branches tracking both the master
and website
branches, and all changes to the documentation source files have all been
committed in the master
branch, the workflow to publish updated docs to
agentos.org might look similar to::
git checkout website
git merge master
python scripts/build_docs.py --release -a # The -a is a sphinx-build
flag.
git add docs
git commit -m "push updated docs to website for version X.Y.Z"
git push
The main project README.rst
is built via the script
python scripts/build_readme.py
, which re-uses sections of
documentation. This avoids duplication of efforts and lowers the chances
that a developer will forget to update one or the either of the README or
the docs.
To update README.rst
, first familiarize yourself with its build script
scripts/build_readme.py
. There you can see which sections of
documentation are included in README.rst
, plus some text that is manually
inserted directly into README.rst
(e.g., the footer).
Here are the steps for releasing AgentOS:
#. Build and check the distribution artifacts for the release by running::
python install_requirements.py
python setup.py sdist --formats=gztar,zip bdist_wheel
twine check dist/*
This will create a wheel file <https://wheel.readthedocs.io/en/stable/>
_
as well as tar.gz and zip source distribution files, and catch any blockers
that PyPI would raise at upload time. Fix any errors before proceeding.
#. Create a release pull request (PR) that:
pcs/version.py
.#. Wait till the PR gets LGTMs from all other committers, then merge it.
#. Build and publish the docs for the new version, which involves creating a
pull request against website
branch. This is required for all releases,
even if the docs have not changed, since the docs are versioned. When you
run the build_docs.py
script, you will use the --release
flag
(see Building Docs
_ & Publishing Docs to agentos.org
_ for more details).
#. Create another follow-on PR that bumps version number to be X.Y.Z-alpha
which reflects that work going forward will be part of the next release
(we use semantic versioning <https://semver.org>
_).
#. Push the release to PyPI (see Pushing Releases to PyPI
_).
#. Create a github release <https://github.com/agentos-project/agentos/releases>
_ and upload the
tar.gz and zip source code distribution files. This will create a git tag.
For the tag name, use "vX.Y.Z" (e.g. v0.1.0).
We make AgentOS available in PyPI <https://pypi.org/project/agentos/>
. To
push a release to PyPI, you can approximately follow these python.org instructions <https://packaging.python.org/tutorials/packaging-projects/>
,
which will probably look something like::
python install_requirements.py rm -rf dist python setup.py sdist --formats=gztar bdist_wheel twine check dist/* twine upload dist/*
This README was compiled from the project documentation via:
python scripts/build_readme.py
.
FAQs
AgentOS is a command line interface and python developer API for building, running, and sharing flexible learning agents.
We found that agentos demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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