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

> Source: <https://github.com/AryanDinakaran/EpochGo>
> Published: 2026-10-12 10:20:08+00:00

**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.