Open, modular framework to build and optimize GenAI applications

AlphaTrion is an open-source framework for building and optimizing GenAI applications. Track experiments, monitor performance, analyze model usage, and manage artifacts—all through an intuitive dashboard. Named after the oldest and wisest Transformer.

Currently in active development.

Features

• 🔬 Experiment Tracking - Organize and manage ML experiments with hierarchical teams, experiments, and runs
• 📊 Performance Monitoring - Track metrics, visualize trends, and monitor experiment status in real-time
• 🔍 Distributed Tracing - Automatic OpenTelemetry integration for LLM calls with detailed span analysis
• 💰 Token Usage Analytics - Monitor daily token consumption across input/output with historical trends
• 🤖 Model Distribution - Analyze request patterns and usage across different AI models
• 📦 Artifact Management - Store and version execution results, checkpoints, and model outputs
• 🎯 Interactive Dashboard - Modern web UI for exploring experiments, metrics, and traces
• 🔌 Easy Integration - Simple Python API with async/await support

Core Concepts

• Team - Top-level organizational unit for user collaboration
• Experiment - Logical grouping of runs with shared purpose, organized by labels
• Run - Individual execution instance with configuration and metrics

Quick Start

1. Installation

# From PyPI
pip install alphatrion

# Or from source
git clone https://github.com/inftyai/alphatrion.git && cd alphatrion
source start.sh

2. Setup Infrastructure

# Start PostgreSQL, ClickHouse, and Registry
cp .env.example .env
make up

# Wait for services to be ready, then run migrations
make migrate

# Initialize your team and user
alphatrion init  # Use -h for custom options

Save the generated user ID — you'll need it to track experiments.

Optional Tools:

• pgAdmin: http://localhost:8081 ([email protected] / alphatr1on)
• Registry UI: http://localhost:80

3. Track Your First Experiment

import alphatrion as alpha
from alphatrion import experiment

# Initialize with your user ID
alpha.init(user_id="<your_user_id>")

async def my_task():
    # Your ML code here
    await alpha.log_metrics({"accuracy": 0.95, "loss": 0.12})

async with experiment.CraftExperiment.start(name="my_experiment") as exp:
    task = exp.run(my_task)
    await task.wait()

4. Launch Dashboard

# Start backend server (terminal 1)
alphatrion server

# Launch dashboard (terminal 2)
alphatrion dashboard

Access the dashboard at http://127.0.0.1:5173 to explore experiments, visualize metrics, and analyze traces.

5. View Traces

AlphaTrion automatically captures distributed tracing data for all LLM calls, including latency, token usage, and span relationships.

6. Other APIs

• log_params: Track hyperparameters and configuration settings
• log_metrics: Record performance metrics and visualize trends
• log_artifacts: Store and manage files, checkpoints, and model outputs

Cleanup

make down

Documentation

• Dashboard: Setup Guide | CLI Reference | Architecture
• Development: Contributing Guide

Contributing

We welcome contributions! Check out our development guide to get started.