MLE-agent
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MLSysOps
🤖 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 s...
MLE-Agent: Your intelligent companion for seamless AI engineering and research.
:love_letter: Fathers' love for Kaia :love_letter:
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 and Papers with Code 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
0.3.0with huge refactoring, many integrations, etc (v0.3.0) - :rocket: 07/11/2024: Release the
0.2.0with 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
pip install mle-agent -U
# or from source
git clone [email protected]:MLSysOps/MLE-agent.git
pip install -e .
Usage
mle new <project name>
And a project directory will be created under the current path, you need to start the project under the project directory.
cd <project name>
mle start
You can also start an interactive chat in the terminal under the project directory:
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 file if you want to contribute.
Support and Community
- Discord community. If you have any questions, please ask in the Discord community.
Star History
License
Check MIT License file for more information.
MLSysOps