[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72446":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":36,"discoverSource":37},72446,"plexe","plexe-ai\u002Fplexe","plexe-ai","✨ Build a machine learning model from a prompt","https:\u002F\u002Fplexe.ai",null,"Python",2583,257,30,13,0,2,17,61.93,"Apache License 2.0",false,"main",true,[25,26,27,28,29,30,31,32],"agentic-ai","agents","ai","machine-learning","ml","mlengineering","mlops","multiagent","2026-06-12 04:01:05","\u003Cdiv align=\"center\">\n\n# plexe ✨\n\n[![PyPI version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fplexe.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fplexe\u002F)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1300920499886358529?logo=discord&logoColor=white)](https:\u002F\u002Fdiscord.gg\u002FSefZDepGMv)\n\n\u003Cimg src=\"resources\u002Fbacked-by-yc.png\" alt=\"backed-by-yc\" width=\"20%\">\n\n\nBuild machine learning models using natural language.\n\n[Quickstart](#1-quickstart) |\n[Features](#2-features) |\n[Installation](#3-installation) |\n[Documentation](#4-documentation)\n\n\u003Cbr>\n\n**plexe** lets you create machine learning models by describing them in plain language. Simply explain what you want,\nprovide a dataset, and the AI-powered system builds a fully functional model through an automated agentic approach.\nAlso available as a [managed cloud service](https:\u002F\u002Fplexe.ai).\n\n\u003Cbr>\n\nWatch the demo on YouTube:\n[![Building an ML model with Plexe](resources\u002Fdemo-thumbnail.png)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bUwCSglhcXY)\n\u003C\u002Fdiv>\n\n## 1. Quickstart\n\n### Installation\n```bash\npip install plexe\nexport OPENAI_API_KEY=\u003Cyour-key>\nexport ANTHROPIC_API_KEY=\u003Cyour-key>\n```\n\n### Using plexe\n\nProvide a tabular dataset (Parquet, CSV, ORC, or Avro) and a natural language intent:\n\n```bash\npython -m plexe.main \\\n    --train-dataset-uri data.parquet \\\n    --intent \"predict whether a passenger was transported\" \\\n    --max-iterations 5\n```\n\n```python\nfrom plexe.main import main\nfrom pathlib import Path\n\nbest_solution, metrics, report = main(\n    intent=\"predict whether a passenger was transported\",\n    data_refs=[\"train.parquet\"],\n    max_iterations=5,\n    work_dir=Path(\".\u002Fworkdir\"),\n)\nprint(f\"Performance: {best_solution.performance:.4f}\")\n```\n\n## 2. Features\n\n### 2.1. 🤖 Multi-Agent Architecture\nThe system uses 14 specialized AI agents across a 6-phase workflow to:\n- Analyze your data and identify the ML task\n- Select the right evaluation metric\n- Search for the best model through hypothesis-driven iteration\n- Evaluate model performance and robustness\n- Package the model for deployment\n\n### 2.2. 🎯 Automated Model Building\nBuild complete models with a single call. Plexe supports **XGBoost**, **CatBoost**, **LightGBM**, **Keras**, and **PyTorch** for tabular data:\n\n```python\nbest_solution, metrics, report = main(\n    intent=\"predict house prices based on property features\",\n    data_refs=[\"housing.parquet\"],\n    max_iterations=10,                    # Search iterations\n    allowed_model_types=[\"xgboost\"],      # Or let plexe choose\n    enable_final_evaluation=True,         # Evaluate on held-out test set\n)\n```\n\nRun `python -m plexe.main --help` for all CLI options.\n\nThe output is a self-contained model package at `work_dir\u002Fmodel\u002F` (also archived as `model.tar.gz`).\nThe package has no dependency on `plexe` — build the model with plexe, deploy it anywhere:\n\n```\nmodel\u002F\n├── artifacts\u002F          # Trained model + feature pipeline (pickle)\n├── src\u002F                # Inference predictor, pipeline code, training template\n├── schemas\u002F            # Input\u002Foutput JSON schemas\n├── config\u002F             # Hyperparameters\n├── evaluation\u002F         # Metrics and detailed analysis reports\n├── model.yaml          # Model metadata\n└── README.md           # Usage instructions with example code\n```\n\n### 2.3. 🐳 Batteries-Included Docker Images\nRun plexe with everything pre-configured — PySpark, Java, and all dependencies included.\nA `Makefile` is provided for common workflows:\n\n```bash\nmake build          # Build the Docker image\nmake test-quick     # Fast sanity check (~1 iteration)\nmake run-titanic    # Run on Spaceship Titanic dataset\n```\n\nOr run directly:\n\n```bash\ndocker run --rm \\\n    -e OPENAI_API_KEY=$OPENAI_API_KEY \\\n    -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \\\n    -v $(pwd)\u002Fdata:\u002Fdata -v $(pwd)\u002Fworkdir:\u002Fworkdir \\\n    plexe:py3.12 python -m plexe.main \\\n        --train-dataset-uri \u002Fdata\u002Fdataset.parquet \\\n        --intent \"predict customer churn\" \\\n        --work-dir \u002Fworkdir \\\n        --spark-mode local\n```\n\nA `config.yaml` in the project root is automatically mounted. A Databricks Connect image\nis also available: `docker build --target databricks .`\n\n### 2.4. ⚙️ YAML Configuration\nCustomize LLM routing, search parameters, Spark settings, and more via a config file:\n\n```yaml\n# config.yaml\nmax_search_iterations: 5\nallowed_model_types: [xgboost, catboost]\nspark_driver_memory: \"4g\"\nhypothesiser_llm: \"openai\u002Fgpt-5-mini\"\nfeature_processor_llm: \"anthropic\u002Fclaude-sonnet-4-5-20250929\"\n```\n\n```bash\nCONFIG_FILE=config.yaml python -m plexe.main ...\n```\n\nSee [`config.yaml.template`](config.yaml.template) for all available options.\n\n### 2.5. 🌐 Multi-Provider LLM Support\nPlexe uses LLMs via [LiteLLM](https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fproviders), so you can use any supported provider:\n\n```yaml\n# Route different agents to different providers\nhypothesiser_llm: \"openai\u002Fgpt-5-mini\"\nfeature_processor_llm: \"anthropic\u002Fclaude-sonnet-4-5-20250929\"\nmodel_definer_llm: \"ollama\u002Fllama3\"\n```\n\n> [!NOTE]\n> Plexe *should* work with most LiteLLM providers, but we actively test only with `openai\u002F*` and `anthropic\u002F*`\n> models. If you encounter issues with other providers, please let us know.\n\n### 2.6. 📊 Experiment Dashboard\nVisualize experiment results, search trees, and evaluation reports with the built-in Streamlit dashboard:\n\n```bash\npython -m plexe.viz --work-dir .\u002Fworkdir\n```\n\n### 2.7. 🔌 Extensibility\nConnect plexe to custom storage, tracking, and deployment infrastructure via the `WorkflowIntegration` interface:\n\n```python\nmain(intent=\"...\", data_refs=[...], integration=MyCustomIntegration())\n```\n\nSee [`plexe\u002Fintegrations\u002Fbase.py`](plexe\u002Fintegrations\u002Fbase.py) for the full interface.\n\n## 3. Installation\n\n### 3.1. Installation Options\n```bash\npip install plexe                    # Core (XGBoost, Keras, scikit-learn)\n```\n\nYou can add optional dependencies either by framework or by task grouping:\n- Framework extras: `catboost`, `lightgbm`, `pytorch`\n- Task extras: `tabular` (CatBoost + LightGBM), `vision` (PyTorch)\n- Platform extras: `pyspark`, `aws`\n\nExamples:\n```bash\npip install \"plexe[tabular,pyspark]\"   # tabular stack + local PySpark\npip install \"plexe[pytorch,aws]\"       # explicit framework + S3 support\n```\n\nRequires Python >= 3.10, \u003C 3.13.\n\n### 3.2. API Keys\n```bash\nexport OPENAI_API_KEY=\u003Cyour-key>\nexport ANTHROPIC_API_KEY=\u003Cyour-key>\n```\nSee [LiteLLM providers](https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fproviders) for all supported providers.\n\n## 4. Documentation\nFor full documentation, visit [docs.plexe.ai](https:\u002F\u002Fdocs.plexe.ai).\n\n## 5. Contributing\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. Join our [Discord](https:\u002F\u002Fdiscord.gg\u002FSefZDepGMv) to connect with the team.\n\n## 6. License\n[Apache-2.0 License](LICENSE)\n\n## 7. Citation\nIf you use Plexe in your research, please cite it as follows:\n\n```bibtex\n@software{plexe2025,\n  author = {De Bernardi, Marcello AND Dubey, Vaibhav},\n  title = {Plexe: Build machine learning models using natural language.},\n  year = {2025},\n  publisher = {GitHub},\n  howpublished = {\\url{https:\u002F\u002Fgithub.com\u002Fplexe-ai\u002Fplexe}},\n}\n```\n","plexe 是一个通过自然语言描述来构建机器学习模型的工具。其核心功能包括利用多代理架构自动完成从数据理解、模型选择到性能评估的全流程，支持XGBoost、CatBoost、LightGBM等多种主流算法。用户只需提供数据集和简单的意图说明，如“预测乘客是否被运送”，系统即可自动生成并优化相应的模型。此外，plexe还提供了打包服务，使得生成的模型可以独立部署。此项目特别适合那些希望快速试验不同机器学习方案但缺乏深厚编程或数据科学背景的用户，同时也为经验丰富的开发者提供了高效的自动化建模选项。","2026-06-11 03:42:06","high_star"]