Show HN: EpochGo – Local-first multi-agent AutoML on Ollama ($0 API costs)

Epochlypse Research released EpochGo, an open-source local-first AutoML platform that runs three autonomous AI agents on Ollama with zero API keys and $0 cloud costs. The system uses CrewAI with the recommended qwen2.5-coder:7b model (~4.7 GB RAM) to generate Scikit-Learn preprocessing pipelines, benchmark 7 machine learning algorithms (XGBoost, LightGBM, CatBoost, Random Forest, Extra Trees, Gradient Boosting, and Logistic or Ridge Regression), and synthesize a FastAPI serving script, all via a single Docker Compose command on 16 GB RAM hardware. EpochGo targets developers who want private, offline data science automation without cloud dependencies.

Autonomous, Local-First Multi-Agent AutoML Platform by Epochlypse Research https://epochlypse.com/ • Powered by CrewAI, Ollama, and Scikit-Learn / XGBoost / LightGBM / CatBoost Zero API keys. Zero cloud costs. 100% private and offline on your local machine. EpochGo is an open-source, local-first AutoML software system developed by Epochlypse Research . Simply git clone the repository and run Docker. You get a localised link in your browser http://localhost:8000 , where you can create projects, drop in raw CSV datasets, and let a coordinated team of three autonomous AI agents do all the data science: 1. 🧹 Agent 1: Data Preprocessor & Feature Architect - Automatically detects patterns, missing values, extreme outliers IQR , categorical encodings, scaling, and high-dimensional multicollinearity PCA . - Generates and executes a standalone Scikit-Learn pipeline preprocessing.py . - Produces a detailed before-vs-after comparison report preprocessing.md . - Saves serialized transformer preprocessor.joblib and cleaned dataset cleaned data.csv . 2. 🏆 Agent 2: Machine Learning Benchmark & Tuning Specialist - Evaluates 7 baseline machine learning algorithms without hyperparameter bias: - Classification : XGBoost, LightGBM, CatBoost, Random Forest, Extra Trees, Gradient Boosting, Logistic Regression. - Regression : XGBoost, LightGBM, CatBoost, Random Forest, Extra Trees, Gradient Boosting, Ridge Regression. - Evaluates baseline accuracy/F1/R²/RMSE and selects the Top 3 contenders . - Runs high-efficiency hyperparameter optimization RandomizedSearchCV . - Generates reproducible training script model building.py and comprehensive Markdown report model building.md . - Serializes the champion model to model.joblib . 3. Evaluates 4. 🚀 Agent 3: MLOps & Model Serving Engineer - Synthesizes a production-ready, standalone FastAPI microservice script serve.py with Pydantic type validation matching your dataset schema. - Enables an interactive, in-browser Prediction Playground to test predictions and probabilities in real time. - Generates ready-to-use curl and Python API snippets. 5. Synthesizes a production-ready, standalone FastAPI microservice script - Docker & Docker Compose installed. - 16 GB RAM recommended Apple Silicon M1/M2/M3/M4/M5 Mac, or Windows/Linux with 16GB RAM . git clone https://github.com/AryanDinakaran/EpochGo.git cd EpochGo docker compose up --build Navigate to: http://localhost:8000 That's it EpochGo will automatically connect to Ollama, ensure the coding model is loaded, and start the web dashboard. | Metric | Target Specification | |---|---| | Recommended Model | qwen2.5-coder:7b | | Why This Model? | World-class code synthesis, scikit-learn mastery, and precise Markdown report writing. | | VRAM / RAM Footprint | ~4.7 GB RAM | | Target Hardware | 16 GB RAM Mac Apple Silicon M-Series or PC | | Available Headroom | Leaves 10 GB RAM free for parallel XGBoost / CatBoost / LightGBM model training | | Low-Resource Fallback | qwen2.5-coder:1.5b or qwen2.5-coder:3b for 8GB environments | Tip Already have Ollama installed on your Mac? You can save disk space and run native Metal GPU acceleration by pointing EpochGo to your existing Ollama instance: OLLAMA BASE URL=http://host.docker.internal:11434 docker compose up php flowchart TD A "Raw CSV Upload & Project Name" -- B "Agent 1: Data Preprocessor" subgraph Agent1 "🧹 Agent 1: Data Preprocessing" B -- B1 "Data Profiling & Pattern Analysis" B1 -- B2 "Imputation, Outlier Clipping, Encoding & Scaling" B2 -- B3 "Synthesize & Execute preprocessing.py" B3 -- B4 "Generate preprocessing.md & preprocessor.joblib" end B4 -- C "Agent 2: Model Builder" subgraph Agent2 "🏆 Agent 2: Model Building & Tuning" C -- C1 "7 Baseline Algorithms Benchmark" C1 -- C2 "Select Top 3 Best Performers" C2 -- C3 "RandomizedSearchCV Hyperparameter Tuning" C3 -- C4 "Save Champion model.joblib" C4 -- C5 "Generate model building.py & model building.md" end C5 -- D "Agent 3: Model Serving" subgraph Agent3 "🚀 Agent 3: Deployment & Serving" D -- D1 "Extract Pydantic Input Schema" D1 -- D2 "Synthesize Standalone serve.py FastAPI " D2 -- D3 "Activate In-Browser Inference Playground" end Every project you create stores complete, reproducible artifacts inside projects/<project id / : | File | Purpose | |---|---| | raw data.csv | Original uploaded dataset | | cleaned data.csv | Transformed dataset after missing values, outliers, scaling & encoding | | preprocessor.joblib | Scikit-Learn transformer pipeline for downstream inference | | preprocessing.py | Standalone, reproducible Python script for data preparation | | preprocessing.md | Comprehensive Markdown report profiling before-and-after distributions | | model.joblib | Trained champion machine learning model artifact | | model building.py | Standalone, reproducible model training and evaluation script | | model building.md | Benchmark leaderboard 7 models , tuning stats, and feature importances | | serve.py | Production FastAPI microservice ready to deploy anywhere | | metadata.json | Project status, metric scoreboard, and feature schema | EpochGo includes two pre-configured datasets in sample data/ for immediate testing: 1. Customer Churn sample data/customer churn.csv : - Task : Binary Classification churn : 0 or 1 - Features: age , tenure months , monthly charges , total charges , contract , internet service , etc. - Includes real-world missing values and outliers to demonstrate agent cleaning. 2. House Prices sample data/house prices.csv : - Task : Regression price - Features: square feet , bedrooms , bathrooms , year built , neighborhood , condition , etc. If you prefer to run EpochGo directly on your host machine: 1. Clone repository git clone https://github.com/AryanDinakaran/EpochGo.git cd EpochGo 2. Create virtual environment python3 -m venv venv source venv/bin/activate 3. Install dependencies pip install -r requirements.txt 4. Make sure Ollama is running ollama serve ollama pull qwen2.5-coder:7b 5. Start EpochGo uvicorn epoch go.main:app --host 0.0.0.0 --port 8000 --reload - GET /api/health - Check service status and Ollama model connectivity. - GET /api/projects - List all projects and execution states. - POST /api/projects - Upload a CSV, name the project, and trigger the CrewAI pipeline. - GET /api/projects/{id} - Retrieve project metadata, status, and benchmark leaderboard. - GET /api/projects/{id}/files/{filename} - Retrieve generated Markdown reports and Python scripts. - GET /api/projects/{id}/download/{filename} - Download .joblib models or .py scripts. - POST /api/projects/{id}/predict - Run real-time predictions against the project's champion model. - WS /ws/projects/{id} - Real-time WebSocket stream for agent reasoning logs. - Lead Architect : Aryan Dinakaran - Mission : Local-first, private, reproducible multi-agent systems for high-leverage data science & machine learning engineering. - Connect & Collaborate : - 🌐 Website: epochlypse.com https://epochlypse.com/ - ✉️ Email: founder@epochlypse.com mailto:founder@epochlypse.com - 💼 LinkedIn: Aryan Dinakaran https://www.linkedin.com/in/aryan-dinakaran-462277230/ - 📸 Instagram: @aryandinakaran https://www.instagram.com/aryandinakaran/ - 🐙 GitHub: @AryanDinakaran https://github.com/AryanDinakaran - 🌐 Website: MIT License. Free for personal, commercial, and research use.