This is the repository for the accelerated discovery orchestrator
(ado).
ado is a Python platform for designing experiment campaigns and
executing them at scale. Built to address recurring challenges in research
software development, ado empowers distributed teams of researchers and
engineers to seamlessly collaborate, execute experiments, and share data.
You can extend ado across different domains through its lightweight plugin model-often as simple as decorating a Python function. By integrating your methodology, you gain powerful 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.
- 💻 CLI: Our human-centric CLI follows best practices
- 🤝 Projects: Allow distributed groups of users to collaborate and share data
- 🔌 Extendable: Easily add new experiments, optimizers or other tools.
- ⚙️ Scalable: We use ray as our execution engine allowing experiments and tools to easily scale
- ♻️ Automatic data-reuse: Avoid repeating work with
transparent reuse of experiment results.
adointernal protocols ensure this happens only when it makes sense - 🔗 Provenance: As you work, the relationship between the data you create and operations you perform are automatically tracked
- 🔎 Optimization and sampling: Out-of-the-box, leverage powerful
optimization methods via
raytuneor use our flexible in built 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.
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 models creation
A basic installation of ado only requires a recent Python version (3.10 to
3.13). This will allow you to run
many of our examples
and explore ado features.
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
- Multi-Node Execution (Optional): To support multi-node or scaling 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
To install you can execute the following (we recommend you set up a virtual environment)
git clone https://github.com/IBM/ado.git
cd ado
pip install .
Alternate instructions to install ado can be found here:
https://ibm.github.io/ado/getting-started/install/
Instructions for developing ado are available in DEVELOPING.
To run unit-tests read tests/README.md.
This video shows listing actuators and getting the details of an experiment.
Check demo for more videos.
step1_trimmed.mp4
For an overview of the design and architecture of ado, see
our Journal of Open Source Software paper.
If ado has been helpful 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 like APA.
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).