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
Stars: 980
MLE-Agent is an intelligent companion designed for machine learning engineers and researchers. It features autonomous baseline creation, integration with Arxiv and Papers with Code, smart debugging, file system organization, comprehensive tools integration, and an interactive CLI chat interface for seamless AI engineering and research workflows.
README:
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
- 🚀 07/25/2024: Release the
0.3.0
with huge refactoring, many integrations, etc (v0.3.0) - 🚀 07/11/2024: Release the
0.2.0
with multiple agents interaction (v0.2.0) - 👨🍼 07/03/2024: Kaia is born
- 🚀 06/01/2024: Release the first rule-based version of MLE agent (v0.1.0)
pip install mle-agent -U
# or from source
git clone [email protected]:MLSysOps/MLE-agent.git
pip install -e .
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
The following is a list of the tasks we plan to do, welcome to propose something new!
🔨 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
⭐ More LLMs and Serving Tools
- [x] Ollama LLama3
- [x] OpenAI GPTs
- [x] Anthropic Claude 3.5 Sonnet
💖 Better user experience
- [x] CLI Application
- [ ] Web UI
- [ ] Discord
🧩 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
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.
- Discord community. If you have any questions, please ask in the Discord community.
Check MIT License file for more information.
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