https://github.com/aibuildai/AI-Build-AI
AIBuildAI Inc
https://github.com/aibuildai/AI-Build-AI
Last synced: about 1 month ago
JSON representation
AIBuildAI Inc
- Host: GitHub
- URL: https://github.com/aibuildai/AI-Build-AI
- Owner: aibuildai
- License: mit
- Created: 2026-03-15T18:20:49.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-06-08T02:56:21.000Z (about 2 months ago)
- Last Synced: 2026-06-08T04:24:03.117Z (about 2 months ago)
- Language: Python
- Homepage: https://www.aibuildai.io
- Size: 41.3 MB
- Stars: 284
- Watchers: 2
- Forks: 38
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-agentic-machine-learning - AIBuildAI - Build-AI?style=flat-square) | (AutoML Agents)
README
AIBuildAI – An AI agent that automatically builds AI models
🏆 #1 on OpenAI MLE-Bench
---
## News
- **[5/26/2026]** The **AIBuildAI Agent 2.0** version is made available to [Pro users](https://www.aibuildai.io/#products).
- **[5/1/2026]** In the [TGS Salt Identification Challenge](https://www.kaggle.com/competitions/tgs-salt-identification-challenge) hosted by Kaggle, the model automatically developed by our AIBuildAI Agent ranked in the top 5.7%. Among 3,219 teams composed of human experts, this performance reaches the level of top-tier human AI experts. Model and code developed by the Agent: [tasks/tgs-salt-identification-challenge](https://github.com/aibuildai/AI-Build-AI/tree/main/tasks/tgs-salt-identification-challenge).
- **[4/27/2026]** Excited to announce **AIBuildAI Agent 2.0**! It has once again achieved #1 on OpenAI's MLE-Bench, reaching a score of 70.7% and substantially outperforming the agents ranked 2nd through 5th. Compared to version 1.0, Agent 2.0 introduces several technical advancements, which we will detail in an upcoming technical report. The 2.0 version will be available to Pro users soon.
- **[4/24/2026]** In a [heart disease prediction competition](https://www.kaggle.com/competitions/playground-series-s6e2/overview) hosted by Kaggle, the model automatically developed by our AIBuildAI Agent ranked in the top 6.6%. Among 4,370 teams composed of human experts, this performance reaches the level of top-tier human AI experts. Model and code developed by the agent: [tasks/playground-series-s6e2](https://github.com/aibuildai/AI-Build-AI/tree/main/tasks/playground-series-s6e2).
---
https://github.com/user-attachments/assets/b6043d39-43df-464a-8e25-d24006ba99c8
---
## Introduction
AIBuildAI is an AI agent that automatically builds AI models. Given a task, it runs an agent loop that analyzes the problem, designs models, writes code to implement them, trains them, tunes hyperparameters, evaluates model performance, and iteratively improves the models. By automating the model development workflow, AIBuildAI reduces much of the manual effort required to build AI models.
---
## Current Results
On OpenAI [MLE-Bench](https://github.com/openai/mle-bench), AIBuildAI ranked #1, demonstrating strong performance on real-world AI model building tasks.
---
## Quick Start
### Installation
AIBuildAI requires a **Linux x86_64** machine.
#### Free version
```bash
curl -L -O https://github.com/aibuildai/AI-Build-AI/releases/latest/download/aibuildai-linux-x86_64-v0.1.1.tar.gz
tar -xzf aibuildai-linux-x86_64-v0.1.1.tar.gz
cd aibuildai-linux-x86_64-v0.1.1
./install.sh
```
#### Pro version (AIBuildAI 2.0)
The Pro version delivers stronger performance than the Free version (see the [4/27/2026 News](#news) entry).
1. **Register and subscribe.** Create an account at [accounts.aibuildai.io/sign-up](https://accounts.aibuildai.io/sign-up). After signing in, go to the **Billing** section and switch to the **Pro plan** ($50 / month). Once payment is processed, your account will show an active Pro subscription.
2. **Download and install AIBuildAI 2.0.**
```bash
curl -L -o aibuildai.tar.gz \
https://github.com/aibuildai/AI-Build-AI/releases/download/v2.0.0/aibuildai-linux-x86_64-v2.0.0.tar.gz
tar -xzf aibuildai.tar.gz
cd aibuildai-linux-x86_64-v2.0.0
./install.sh
```
3. **Log in.** After installation, run:
```bash
aibuildai login
```
This prints a link you can open in your browser to grant permission with your Pro account. To verify your login status, run:
```bash
aibuildai whoami
```
The output should show an active Pro plan.
### Set up credentials
```bash
export ANTHROPIC_API_KEY=your-api-key
```
### Run
**Example task:** Predict the enzyme class of a protein from its amino acid sequence ([Yu et al., *Science* 2023](https://www.science.org/doi/10.1126/science.adf2465)).
```bash
git clone https://github.com/aibuildai/AI-Build-AI.git && cd AI-Build-AI
aibuildai --task-name protein-ec-prediction \
--data-dir data/protein-ec-prediction \
--playground-dir /path/to/playground \
--model claude-opus-4-6 \
--max-agent-calls 8 \
--run-budget-minutes 60 \
--num-candidates 3 \
--instruction "$(cat tasks/protein-ec-prediction.md)" \
--pipeline-budget-minutes 90 \
--no-form
```
AIBuildAI takes two key inputs: `--data-dir`, the path to the training data for the task, and `--instruction`, a natural-language description of the AI task to solve.
**Important:**
Run the command directly in your terminal. Do not wrap the command in a `.sh` or `.bash` script. Running it through a script may cause the TUI (Text User Interface) to crash.
### Results
#### Output directory
After a run completes, the output directory usually looks like (structure may slightly vary by task):
```
├── candidate_1/ candidate_2/ candidate_3/ # Auto-generated training scripts and model checkpoints
├── checkpoint.pth # Best model checkpoint
├── inference.py # Standalone inference script for the final model
├── submission.csv # Test predictions (if test inputs are provided)
└── progress.pdf # Visual progress report
```
The main outputs of an AIBuildAI run are the model checkpoints and the script `inference.py`, which runs predictions with the final model on any data.
#### Evaluation
In the example protein-ec-prediction task, we provide unlabeled test data in the data folder, so AIBuildAI also generates a predicted-label file `submission.csv`. To evaluate the predictions against ground-truth labels:
```bash
python scripts/eval_protein_ec.py \
--labels data/labels/protein-ec-prediction.csv \
--submission /path/to/playground/code/protein-ec-prediction/timestamp/submission.csv
```
### Other tasks
We provide additional task markdowns in the `tasks/` folder. You can also write your own task markdown and point `--data-dir` to your own dataset.
---
### Command line options
To see all available options, run:
```bash
aibuildai -h
```
### Interactive form mode
Alternatively, you can run AIBuildAI using the interactive form interface by running without `--no-form`:
```bash
aibuildai
```
This will launch a TUI (Text User Interface) where you can fill in the required parameters interactively.
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## Citation
```bibtex
@article{zhang2026aibuildai,
title={AIBuildAI: An AI Agent for Automatically Building AI Models},
author={Ruiyi Zhang and Peijia Qin and Qi Cao and Li Zhang and Pengtao Xie},
year={2026},
journal={arXiv},
url={https://arxiv.org/abs/2604.14455}
}
```