MLE Agent
Open-source agent research, data, and evaluation for ml engineering, research assistant, code rag.
What does this agent do, and when should you use it?
The repository describes MLE Agent as: π€ MLE-Agent: Your intelligent companion for seamless AI engineering and research. π Integrate with arxiv and paper with code to provide better code/research plans π§° OpenAI, Anthropic, Gemini, Ollama, etc supported. :fireworks: Code RAG. This profile is a source-based catalog entry; an independent FARS review is still pending.
π€ MLE-Agent: Your intelligent companion for seamless AI engineering and research. π Integrate with arxiv and paper with code to provide better code/research plans π§° OpenAI, Anthropic, Gemini, Ollama, etc supported. :fireworks: Code RAG.
- Run a documented research, analysis, or evaluation workflow.
- Compare agent behavior with reproducible evidence.
- Adapt its datasets, environments, or analysis components.
What are this agent's strengths and limitations?
- Public source and README are available for inspection.
- Focused on ml engineering, research assistant, code rag.
- Setup, model-provider support, and maturity must be confirmed against the current release.
- No independent FARS score has been assigned yet.
How do you install or deploy this agent?
Follow the current installation instructions in the [repository README](https://github.com/MLSysOps/MLE-agent#readme). Requirements and provider setup vary by release.
How do you use this agent?
Start with the examples and quickstart in the [repository documentation](https://github.com/MLSysOps/MLE-agent#readme), then test the workflow with limited permissions and non-sensitive data.