ado-core 2.0.0

ado is a unified platform for executing computational experiments at scale and analysing their results. It can be easily extended with new experiments or new analysis tools. It allows distributed teams of researchers and engineers to collaborate on projects, execute experiments, and share data.

ado — accelerated discovery orchestrator

ado is a Python platform for designing experiment campaigns and executing them at scale. It enables distributed teams of researchers and engineers to collaborate, execute experiments, and share data.

You can extend ado across different domains through its plugin model — often as simple as decorating a Python function. By integrating your methodology, you gain cross-cutting capabilities — such as parallel execution, data provenance, and a unified CLI — alongside a structured foundation that allows AI coding agents to autonomously formulate and run your experiments.

• 🧑‍💻 Using ado assumes familiarity with command line tools.
• 🛠️ Developing ado requires knowledge of Python.

Key Features

• 💻 CLI: Our human-centric CLI follows best practices
• 🤝 Projects: Allow distributed groups of users to collaborate and share data
• 🔌 Extendable: Easily add new experiments or optimizers and other tools
• ⚙️ Scalable: We use Ray as our execution engine, allowing experiments and tools to scale easily
• ♻️ Automatic data-reuse: Avoid repeating work with transparent reuse of experiment results; ado's internal protocols ensure this happens only when it makes sense
• 🔗 Provenance: Relationships between data and operations are automatically tracked. The versions of ado-core and every plugin used to create a resource are also recorded, keeping results reproducible and debuggable
• 🔎 Optimization and sampling: Out-of-the-box, leverage powerful optimization methods via Ray Tune or use our flexible built-in sampler
• 🤖 Coding agents: Supercharge your workflow. ado's typed resources and bundled skills enable AI assistants to autonomously formulate, validate, and run experiments. Learn more

Foundation Model Experimentation

We have developed ado plugins providing advanced capabilities for performance testing of foundation models:

• ⏱️ Fine-tuning performance benchmarking
• ⏱️ Inference performance benchmarking (using vLLM bench or guidellm)
• 🔮 Predictive performance model creation

Quick Start

Install ado from PyPI (a virtual environment is recommended). For complete instructions see the install guide:

pip install ado-core

Then try:

ado get contexts

You will see a context named local — a local sandbox you can use straight away. To see the built-in operators:

ado get operators

Next, follow the short random-walk tutorial for a hands-on introduction to how ado works.

Requirements

A basic installation of ado only requires a recent Python version (3.10 to 3.14). This will allow you to run many of our examples and explore ado features.

Additional Requirements

Some advanced features have additional requirements:

• Distributed Projects (Optional): To support projects with multiple users you will need a remote, accessible MySQL database. See here for more details
• Multi-Node Execution (Optional): To support multi-node or scaled execution you may need a multi-node RayCluster. See here for more details

In addition, ado plugins may have additional requirements for executing realistic experiments. For example:

• Fine-Tuning Benchmarking: Requires a RayCluster with GPUs
• vLLM Performance Benchmarking: Requires an OpenShift cluster with GPUs

Install from Source

To install ado from source (for development or to track the latest changes):

git clone https://github.com/IBM/ado.git
cd ado
pip install .

Detailed installation instructions are available at https://ibm.github.io/ado/getting-started/install/.

Contributing

To set up a development environment, run the test suite, or understand code style and commit conventions, see DEVELOPING.md and tests/README.md.

Example

This video shows listing actuators and getting the details of an experiment.

Check the demo page for more videos.

Citation

For an overview of the design and architecture of ado, see our Journal of Open Source Software paper.

If ado has been useful in your research, please cite us using:

@article{Johnston_ado_a_Python_2026,
author = {Johnston, Michael A. and Pomponio, Alessandro},
doi = {10.21105/joss.10304},
journal = {Journal of Open Source Software},
month = may,
number = {121},
pages = {10304},
title = {{ado: a Python framework for computational experimentation and benchmarking}},
url = {https://joss.theoj.org/papers/10.21105/joss.10304},
volume = {11},
year = {2026}
}

You can also click "Cite this repository" in the GitHub sidebar for alternative formats such as APA.

Acknowledgement

This project is partially funded by the European Union through the Smart Networks and Services Joint Undertaking (SNS JU) under grant agreement No. 101192750 (Project 6G-DALI).