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https://github.com/MLSysOps/MLE-agent
🤖 MLE-Agent: Your intelligent companion for seamless AI engineering and research. 🔍 Integrate with arxiv and paper with code to provide better code/research plans 🧰 OpenAI, Anthropic, Ollama, etc supported. :fireworks: Code RAG
https://github.com/MLSysOps/MLE-agent
agent ai llm ml mle mlops
Last synced: 2 months ago
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🤖 MLE-Agent: Your intelligent companion for seamless AI engineering and research. 🔍 Integrate with arxiv and paper with code to provide better code/research plans 🧰 OpenAI, Anthropic, Ollama, etc supported. :fireworks: Code RAG
- Host: GitHub
- URL: https://github.com/MLSysOps/MLE-agent
- Owner: MLSysOps
- License: mit
- Created: 2024-04-16T23:49:21.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T02:54:59.000Z (2 months ago)
- Last Synced: 2024-09-07T04:05:39.590Z (2 months ago)
- Topics: agent, ai, llm, ml, mle, mlops
- Language: Python
- Homepage: https://repx.app/
- Size: 1.81 MB
- Stars: 955
- Watchers: 9
- Forks: 39
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
MLE-Agent: Your intelligent companion for seamless AI engineering and research.
:love_letter: Fathers' love for Kaia :love_letter:
[](https://pepy.tech/project/mle-agent)
## Overview
MLE-Agent is designed as a pairing LLM agent for machine learning engineers and researchers. It is featured by:
- 🤖 Autonomous Baseline Creation: Automatically builds ML/AI baselines.
- 🔍 [Arxiv](https://arxiv.org/) and [Papers with Code](https://paperswithcode.com/) Integration: Access best practices and state-of-the-art methods.
- 🐛 Smart Debugging: Ensures high-quality code through automatic debugger-coder interactions.
- 📂 File System Integration: Organizes your project structure efficiently.
- 🧰 Comprehensive Tools Integration: Includes AI/ML functions and MLOps tools for a seamless workflow.
- ☕ Interactive CLI Chat: Enhances your projects with an easy-to-use chat interface.
https://github.com/user-attachments/assets/dac7be90-c662-4d0d-8d3a-2bc4df9cffb9
## Milestones
- :rocket: 07/25/2024: Release the `0.3.0` with huge refactoring, many integrations, etc (v0.3.0)
- :rocket: 07/11/2024: Release the `0.2.0` with multiple agents interaction (v0.2.0)
- 👨🍼 **07/03/2024: Kaia is born**
- :rocket: 06/01/2024: Release the first rule-based version of MLE agent (v0.1.0)
## Get started
### Installation
```bash
pip install mle-agent -U
# or from source
git clone [email protected]:MLSysOps/MLE-agent.git
pip install -e .
```
### Usage
```bash
mle new
```
And a project directory will be created under the current path, you need to start the project under the project directory.
```bash
cd
mle start
```
You can also start an interactive chat in the terminal under the project directory:
```bash
mle chat
```
## Roadmap
The following is a list of the tasks we plan to do, welcome to propose something new!
:hammer: General Features
- [x] Understand users' requirements to create an end-to-end AI project
- [x] Suggest the SOTA data science solutions by using the web search
- [x] Plan the ML engineering tasks with human interaction
- [x] Execute the code on the local machine/cloud, debug and fix the errors
- [x] Leverage the built-in functions to complete ML engineering tasks
- [x] Interactive chat: A human-in-the-loop mode to help improve the existing ML projects
- [ ] Kaggle mode: to finish a Kaggle task without humans
- [ ] Summary and reflect the whole ML/AI pipeline
- [ ] Integration with Cloud data and testing and debugging platforms
- [x] Local RAG support to make personal ML/AI coding assistant
- [ ] Function zoo: generate AI/ML functions and save them for future usage
:star: More LLMs and Serving Tools
- [x] Ollama LLama3
- [x] OpenAI GPTs
- [x] Anthropic Claude 3.5 Sonnet
:sparkling_heart: Better user experience
- [x] CLI Application
- [ ] Web UI
- [ ] Discord
:jigsaw: Functions and Integrations
- [x] Local file system
- [x] Local code exectutor
- [x] Arxiv.org search
- [x] Papers with Code search
- [x] General keyword search
- [ ] Hugging Face
- [ ] SkyPilot cloud deployment
- [ ] Snowflake data
- [ ] AWS S3 data
- [ ] Databricks data catalog
- [ ] Wandb experiment monitoring
- [ ] MLflow management
- [ ] DBT data transform
## Contributing
We welcome contributions from the community. We are looking for contributors to help us with the following tasks:
- Benchmark and Evaluate the agent
- Add more features to the agent
- Improve the documentation
- Write tests
Please check the [CONTRIBUTING.md](CONTRIBUTING.md) file if you want to contribute.
## Support and Community
- [Discord community](https://discord.gg/SgxBpENGRG). If you have any questions, please ask in the Discord community.
## Star History
[](https://star-history.com/#MLSysOps/MLE-agent&Date)
## License
Check [MIT License](LICENSE) file for more information.