Aideml
AIDE: AI-Driven Exploration in the Space of Code. The machine Learning engineering agent that automates AI R&D.
Install / Use
/learn @WecoAI/AidemlREADME
What Is AIDE ML?
AIDE ML is the open‑source “reference build” of the AIDE algorithm, a tree‑search agent that autonomously drafts, debugs and benchmarks code until a user‑defined metric is maximised (or minimised). It ships as a research‑friendly Python package with batteries‑included utilities (CLI, visualisation, config presets) so that academics and engineer‑researchers can replicate the paper, test new ideas, or prototyping ML pipelines.
| Layer | Description | Where to find it |
| --- | --- | --- |
| AIDE algorithm | LLM‑guided agentic tree search in the space of code. | Described in our paper. |
| AIDE ML repo (this repo) | Lean implementation for experimentation & extension. | pip install aideml |
| Weco product | The platform generalizes AIDE's capabilities to broader code optimization scenarios, providing experiment tracking and enhanced user control. | weco.ai |
Who should use it?
- Agent‑architecture researchers – swap in new search heuristics, evaluators or LLM back‑ends.
- ML Practitioners – quickly build a high performance ML pipelines given a dataset.
Key Capabilities
- Natural‑language task specification Point the agent at a dataset and describe goal + metric in plain English. No YAML grids or bespoke wrappers.
aide data_dir=… goal="Predict churn" eval="AUROC" - Iterative agentic tree search Each python script becomes a node in a solution tree; LLM‑generated patches spawn children; metric feedback prunes and guides the search. OpenAI’s MLE‑Bench (75 Kaggle comps) found the tree‑search of AIDE wins 4 × more medals than the best linear agent (OpenHands).
- HTML visualiser – inspect the full solution tree and code attached to each node.
- Streamlit UI – prototype ML solution .
- Model‑neutral plumbing – OpenAI, Anthropic, Gemini, or any local LLM that speaks the OpenAI API.
Featured Research built on/with AIDE
| Institution | Paper / Project Name | Links | |-------------|----------------------|-------| | OpenAI | MLE-bench: Evaluating Machine-Learning Agents on Machine-Learning Engineering | Paper, GitHub | | METR | RE-Bench: Evaluating frontier AI R&D capabilities of language-model agents against human experts | Paper, GitHub | | Sakana AI | The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search | Paper, GitHub | | Meta | The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements | Paper, GitHub | | Meta | AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench | Paper, GitHub | | SJTU | ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning | Paper, GitHub |
Know another public project that cites or forks AIDE?
Open a PR and add it to the table!
How to Use AIDE ML
Quick Start
# 1 Install
pip install -U aideml
# 2 Set an LLM key
export OPENAI_API_KEY=<your‑key> # https://platform.openai.com/api-keys
# 3 Run an optimisation
aide data_dir="example_tasks/house_prices" \
goal="Predict the sales price for each house" \
eval="RMSE between log‑prices"
After the run finishes you’ll find:
logs/<id>/best_solution.py– best code foundlogs/<id>/tree_plot.html– click to inspect the solution tree
Web UI
pip install -U aideml # adds streamlit
cd aide/webui
streamlit run app.py
Use the sidebar to paste your API key, upload data, set Goal & Metric, then press Run AIDE.
The UI shows live logs, the solution tree, and the best code.
Advanced CLI Options
# Choose a different coding model and run 50 steps
aide agent.code.model="claude-4-sonnet" \
agent.steps=50 \
data_dir=… goal=… eval=…
Common flags
| Flag | Purpose | Default |
| --- | --- | --- |
| agent.code.model | LLM used to write code | gpt-4-turbo |
| agent.steps | Improvement iterations | 20 |
| agent.search.num_drafts | Drafts per step | 5 |
Use AIDE ML Inside Python
import aide
import logging
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
aide_logger = logging.getLogger("aide")
aide_logger.setLevel(logging.INFO)
print("Starting experiment...")
exp = aide.Experiment(
data_dir="example_tasks/bitcoin_price", # replace this with your own directory
goal="Build a time series forecasting model for bitcoin close price.", # replace with your own goal description
eval="RMSLE" # replace with your own evaluation metric
)
best_solution = exp.run(steps=2)
print(f"Best solution has validation metric: {best_solution.valid_metric}")
print(f"Best solution code: {best_solution.code}")
print("Experiment finished.")
if __name__ == '__main__':
main()
Power‑User Extras
Local LLM (Ollama example)
export OPENAI_BASE_URL="http://localhost:11434/v1"
aide agent.code.model="qwen2.5" data_dir=… goal=… eval=…
Note: evaluator defaults to gpt‑4o.
Fully local (code + evaluator — no external calls)
export OPENAI_BASE_URL="http://localhost:11434/v1"
aide agent.code.model="qwen2.5" agent.feedback.model="qwen2.5" data_dir=… goal=… eval=…
Tip: Expect some performance drop with fully local models.
Docker
docker build -t aide .
docker run -it --rm \
-v "${LOGS_DIR:-$(pwd)/logs}:/app/logs" \
-v "${WORKSPACE_BASE:-$(pwd)/workspaces}:/app/workspaces" \
-v "$(pwd)/aide/example_tasks:/app/data" \
-e OPENAI_API_KEY="your-actual-api-key" \
aide data_dir=/app/data/house_prices goal="Predict price" eval="RMSE"
Development install
git clone https://github.com/WecoAI/aideml.git
cd aideml && pip install -e .
Citation
If you use AIDE in your work, please cite the following paper:
@article{aide2025,
title={AIDE: AI-Driven Exploration in the Space of Code},
author={Zhengyao Jiang and Dominik Schmidt and Dhruv Srikanth and Dixing Xu and Ian Kaplan and Deniss Jacenko and Yuxiang Wu},
year={2025},
eprint={2502.13138},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2502.13138},
}
