ado — accelerated discovery orchestrator

ado provides tools for executing computational experiment campaigns. Coding Agents can use ado to autonomously formulate, run, and analyze your experiments.

Why ado?

• Declarative Experiment Campaigns: Provides flexible, cross-domain schemas for defining experiment campaigns. A valid campaign definition has strong execution guarantees.
• Simplifies Execution : Handles the complex setup and distributed plumbing of scale-out campaign execution.
• Persistent Storage: Automatically captures campaign definitions and measurements in local or shared SQL databases.
• Adapt to Any Domain: Its flexible plugin model allows extending these core capabilities to your specific research field.
• Empowers Agent-Driven Research: The verifiable schemas, executable APIs, and persistent storage enable coding agents to treat experimentation as a coding problem.

At its core

ado is built on three concepts:

Concept	Role
Discovery Space	Defines what to measure (Entities), how to measure them (Experiments) and where to store results.
Operation	You explore or analyse a Discovery Space using Operations. You can select from different Operators to perform different types of Operations.
Sample Store	Stores the results of measurements, and enables Operations to transparently reuse existing results (memoization).

In ado the research loop involves defining a Discovery Space, exploring it with an Operation, analyzing the results with additional Operations, and repeating.

You can create your own Experiments to use in a Discovery Space. ado provides many advanced exploration and analysis Operators you can use, or you can define your own.

Try It Out

The following toy example runs a small experiment campaign that samples combinations of mass and volume, computes density at each point, and stores the results.

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

pip install ado-core

Clone the repository and install the density example package:

git clone https://github.com/IBM/ado.git
cd ado
pip install -e examples/density_example/

Run the experiment campaign:

ado create space -f examples/density_example/space.yaml
ado create operation -f examples/density_example/operation.yaml --use-latest space

Once the operation finishes, inspect the collected measurements:

ado show measurements operation --use-latest

For a deeper walkthrough, see the density example tutorial.

ado 🤝 agents

• 🔍 Self-describing resources: experiments and operators declare their required properties, so an agent can discover what's available and what's needed without parsing code.
• ✅ Campaigns as Verifiable Code: ado's structured, verifiable schemas for expressing research intent enable a tight generate → validate → fix → run cycle. This reduces hallucinations and the need for free-form code.
• 📦 Durable Long-Term Memory: ados structured databases enable agents to understand what has been done, and to access the relevant data, beyond their context window.
• 🔗 Full provenance: every result is annotated with resource relationships and plugin versions, so an agent always knows where data came from and how to reproduce it.
• 🤖 Bundled agent skills: skills guide agents through end-to-end discovery workflows — from formulating a problem to analysing results.

Use Cases

Here are some examples of what the team has built with ado:

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

Contributing

Contributions are welcome — new actuators, operators, bug fixes, and documentation improvements. To set up a development environment, run the test suite, or understand code style and commit conventions, see CONTRIBUTING.md, DEVELOPING.md and tests/README.md.

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).