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

Autonomous, Local-First Multi-Agent AutoML Platform

by Epochlypse Research • 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:

🧹 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 ).

🏆 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

🚀 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.

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>/:

EpochGo includes two pre-configured datasets in sample_data/ for immediate testing:

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.

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:

git clone https://github.com/AryanDinakaran/EpochGo.git
cd EpochGo

python3 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

ollama serve
ollama pull qwen2.5-coder:7b

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
  • ✉️ Email: founder@epochlypse.com
  • 💼 LinkedIn: Aryan Dinakaran
  • 📸 Instagram: @aryandinakaran
  • 🐙 GitHub: @AryanDinakaran

• 🌐 Website:

MIT License. Free for personal, commercial, and research use.