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.
- 🔬 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
- 🔐 Secure Authentication - JWT-based authentication with user profiles and multi-team support
- 👥 Multi-User Support - Collaborative workspace with organization and team management
- 🔌 Easy Integration - Simple Python API with async/await support
- Organization - Top-level entity for grouping teams and users
- Team - Collaborative workspace for organizing experiments and runs
- User - Individual account with secure authentication and team memberships
- Experiment - Logical grouping of runs with shared purpose, organized by labels
- Run - Individual execution instance with configuration and metrics
# From PyPI
pip install alphatrion
# Or from source
git clone https://github.com/inftyai/alphatrion.git && cd alphatrion
source start.sh# Start PostgreSQL, ClickHouse, and Registry
cp .env.example .env
make up
# Wait for services to be ready, then run migrations
make migrate
# Initialize your organization, team, and user account
alphatrion initOptional Tools:
- pgAdmin:
http://localhost:8081(alphatrion@inftyai.com / alphatr1on) - Registry UI:
http://localhost:80 - Grafana:
http://localhost:3000(admin / admin) - LLM metrics dashboard - Prometheus:
http://localhost:9090- Metrics explorer
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 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()# Start backend server (terminal 1)
alphatrion server
# Launch dashboard (terminal 2)
alphatrion dashboardAccess the dashboard at http://127.0.0.1:5173 and log in with your email and password to explore experiments, visualize metrics, and analyze traces.
AlphaTrion automatically captures distributed tracing data for all LLM calls, including latency, token usage, and span relationships.
make down- Architecture: Diagrams
- Dashboard: Setup Guide | CLI Reference | Architecture
- Development: Contributing Guide
- Claude Code Integration: Hooks Setup
We welcome contributions! Check out our development guide to get started.

